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208 results on '"Jerome Mars"'

1. The nocturnal life of the great scallops (Pecten maximus, L.): First description of their natural daily valve opening cycle

Author: Elie Retailleau, Arthur Chauvaud, Gaetan Richard, Delphine Mathias, Laurent Chauvaud, Sarah Reynaud, Jerome Mars, and Sylvain Chauvaud
Subjects: Medicine, Science
Abstract: Valvometry techniques used to monitor bivalve gaping activity have elucidated numerous relationships with environmental fluctuations, along with biological rhythms ranging from sub-daily to seasonal. Thus, a precise understanding of the natural activity of bivalves (i.e., not exposed to stressful environmental variations) is necessary as a baseline for detecting abnormal behaviors (deviations). This knowledge is also needed to reliably interpret observations of bivalve gaping behavior and associated biological processes (e.g., respiration, nutrition) acquired over time-limited periods. With this in mind, we investigated the natural daily gaping activity of the great scallop (Pecten maximus) by continuously monitoring 35 individuals in several individual tanks and in situ (Bay of Saint-Brieuc, Brittany, France) using fully autonomous Hall effect sensors. Our results revealed a circadian cycle (τ = 24.0h) in scallop gaping activity. Despite significant inter-individual variability in mean opening and cycle amplitude, almost all individuals (87.5%) exhibited nocturnal activity, with valves more open at night than during the day. A shift in light regime in the tanks triggered an instantaneous change in opening pattern, indicating that light levels strongly determine scallop activity. Based on the opening status of scallops, we also identified several gaping behaviors deviating from the regular daily pattern (lack of rhythmicity, high daytime opening), potentially reflecting physiological weakness. While further long-term studies are required to fully understand the natural activity of scallops, these findings pave the way for studies focused on the scallop response to external factors and introduce further research into the detection of abnormal behaviors. Coupling observations of diel valve gaping cycles with other daily variations in organismal and environmental parameters could help explain mechanisms driving the growth patterns of scallops observed in their shell striations. From a technical perspective, our field-based monitoring demonstrates the suitability of autonomous valvometry sensors for studying mobile subtidal bivalve activity in remote offshore environments.
Published: 2023

2. Hydrophone Array Optimization, Conception, and Validation for Localization of Acoustic Sources in Deep-Sea Mining

Author: Arthur Finez, Simon Bouley, Florent Fayet, Barbara Nicolas, Jerome Mars, Valentin Baron, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Imagerie Ultrasonore, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), MicrodB, OSEAN S.A.S [Le Pradet], Osean, Nicolas, Barbara, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Subjects: Beamforming, Ground truth, source localization, Hydrophone, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, 010505 oceanography, Computer science, Mechanical Engineering, Acoustics, 020206 networking & telecommunications, Ocean Engineering, 02 engineering and technology, 01 natural sciences, deep-sea mining, Deep sea mining, underwater acoustics, Noise, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, 14. Life underwater, Electrical and Electronic Engineering, Underwater, Underwater acoustics, Image resolution, 0105 earth and related environmental sciences
Abstract: International audience; As the mining of deep-sea natural deposits is becoming cost competitive compared to similar land-based mining, companies have started to dig into the seabeds to collect minerals. However, the acoustic contribution of this activity in the surrounding environment can be significant. To predict the impact of such noise, the starting point is to localize and quantify the sources that create it. In this study, a 3-D prototype acoustic array to perform this localization and quantification is designed, built, and deployed at sea for validation of its localization capacities. The design method performs a two-step study to define the array shape and select the hydrophone arrangement over it, under harsh constraints. Each step relies on two metrics to rank the candidates: the maximum sidelobe level, and the spatial resolution. These are computed on conventional beamforming maps for simulated sources that represent excavation machines on the ground. The shape is first determined to be the one that yields steady maximum sidelobe value levels over frequency. Second, the hydrophone arrangement that achieves the lowest maximum sidelobe level while limiting the spatial resolution is selected. This leads to a tip down conical array with 21 hydrophones, of about 3 m in height and diameter, and this is manufactured and used during an experimental campaign in the Mediterranean Sea. The experimental localization maps show strong agreement between the estimated source position and its ground truth. A more detailed comparison between simulated and real performances confirms accurate array conception and realization. Thus, this design procedure provides an efficient underwater acoustic array for monitoring deep-sea mining, the localization capacities of which are validated in a real-life setting.
Published: 2021
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3. Multiplane deconvolution in underwater acoustics: Simultaneous estimations of source level and position

Author: Barbara Nicolas, Maxime Polichetti, Valentin Baron, Jerome Mars, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), MicrodB, Imagerie Ultrasonore, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), ANR-10-LABX-0060,CeLyA,Lyon Acoustics Centre(2010), ANR-16-IDEX-0005,IDEXLYON,IDEXLYON(2016), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Nicolas, Barbara, Lyon Acoustics Centre - - CeLyA2010 - ANR-10-LABX-0060 - LABX - VALID, and IDEXLYON - - IDEXLYON2016 - ANR-16-IDEX-0005 - IDEX - VALID
Subjects: Pulmonary and Respiratory Medicine, Beamforming, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, Plane (geometry), Acoustics, Signal Processing, Computer-Assisted, Source level, 01 natural sciences, 010305 fluids & plasmas, Noise, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Position (vector), 0103 physical sciences, Pediatrics, Perinatology and Child Health, Computer Simulation, 14. Life underwater, Deconvolution, Underwater, Underwater acoustics, 010301 acoustics, ComputingMilieux_MISCELLANEOUS
Abstract: International audience; In many acoustic imaging applications, conventional beamforming (CBF) cannot provide both accurate position and source level estimates simultaneously. Also, the CBF acoustic maps suffer from many artifacts due to the spreading of large point-spread-functions. An original CLEAN deconvolution procedure, including an additional plane containing out-of-plane interfering sources, is proposed here to achieve simultaneous localization, source level estimation, and de-noising. The approach is illustrated using experimental data mimicking a challenging deep-sea mining configuration: an underwater acoustic source of interest is located 700 m below the sea surface, tens of meters from a 3 m-length array, with boat noise as the disturbing source.
Published: 2021
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4. A machine-learning approach for automatic classification of volcanic seismicity at La Soufrière Volcano, Guadeloupe

Author: Jean-Christophe Komorowski, Jean-Bernard de Chabalier, Alexis Falcin, Roberto Moretti, G. Ucciani, Arnaud Lemarchand, Jean-Philippe Métaxian, Jerome Mars, Céline Dessert, François Beauducel, Jean-Marie Saurel, Eléonore Stutzmann, Marielle Malfante, Arnaud Burtin, Institut de Physique du Globe de Paris (IPGP (UMR_7154)), Institut national des sciences de l'Univers (INSU - CNRS)-Université de La Réunion (UR)-Institut de Physique du Globe de Paris (IPG Paris)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Institut des Sciences de la Terre (ISTerre), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel-Université Grenoble Alpes (UGA), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Observatoire Volcanologique et Sismologique de Guadeloupe (OVSG), Institut de Physique du Globe de Paris (IPG Paris), Département d'Architectures, Conception et Logiciels Embarqués-LIST (DACLE-LIST), Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut de Recherche pour le Développement (IRD), Institut de Physique du Globe de Paris (IPGP), Institut national des sciences de l'Univers (INSU - CNRS)-IPG PARIS-Université de La Réunion (UR)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Institut de Physique du Globe de Paris, Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Falcin, A., Metaxian, J. -P., Mars, J., Stutzmann, E., Komorowski, J. -C., Moretti, R., Malfante, M., Beauducel, F., Saurel, J. -M., Dessert, C., Burtin, A., Ucciani, G., de Chabalier, J. -B., and Lemarchand, A.
Subjects: 010504 meteorology & atmospheric sciences, Seismology Volcano La Soufrière Classification Machine Learning, Class (philosophy), 010502 geochemistry & geophysics, 01 natural sciences, Machine Learning, Geochemistry and Petrology, Cepstrum, Feature (machine learning), [SDU.STU.VO]Sciences of the Universe [physics]/Earth Sciences/Volcanology, Entropy (energy dispersal), Volcano, Seismology, 0105 earth and related environmental sciences, Event (probability theory), business.industry, Pattern recognition, Classification, La Soufrière, Random forest, Data set, Geophysics, Modal, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Geology
Abstract: International audience; The classification of seismo-volcanic signals is performed manually at La Soufrière Volcano, which is time consuming and can be biased by subjectivity of the operator. We propose here a machine-learning-based model for classification of these signals, to handle large datasets and provide objective and reproducible results. To describe the properties of the signals, we used 104 statistical, entropy, and shape descriptor features computed from the time waveform, the spectrum, and the cepstrum. First, we trained a random forest classifier with a dataset provided by the Observatoire Volcanologique et Sismologique de Guadeloupe that consisted of 845 labeled events that were recorded from 2013 to 2018: 542 volcano-tectonic (VT); 217 Nested; and 86 long period (LP). We obtained an overalll accuracy of 72%. We determined that the VT class includes a variety of signals that cover the VT, Nested and LP classes. After visual inspection of the waveforms and spectral characteristics of the data set, we introduced two new classes: Hybrid and Tornillo. A new random forest classifier was trained with this new information, and we obtained a much better overall accuracy of 82%. The model is very good for recognition of all event classes, except Hybrid events (67% accuracy, 70% precision). Hybrid events are often considered to be a mix of VT and LP events. This can be explained by the nature of this class and the physical processes that include both fracturing and resonating components with different modal frequencies. By analyzing the feature weights and by training a model with the most important features, we show that a subset of the 14 best features is sufficient to obtain a performance that is close to that of the model with the whole feature set. However, these best features are different from the 13 best features obtained for another volcano in Peru, with only one feature common to both sets of best features. Therefore, the model is not universal and it must be trained for each volcano, or it is too specific to the one station used here.
Published: 2021
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5. Detecting abnormal cell behaviors from dry mass time series

Author: Romain Bailly, Marielle Malfante, Cédric Allier, Chiara Paviolo, Lamya Ghenim, Kiran Padmanabhan, Sabine Bardin, and Jérôme Mars
Subjects: Medicine, Science
Abstract: Abstract The prediction of pathological changes on single cell behaviour is a challenging task for deep learning models. Indeed, in self-supervised learning methods, no prior labels are used for the training and all of the information for event predictions are extracted from the data themselves. We present here a novel self-supervised learning model for the detection of anomalies in a given cell population, StArDusTS. Cells are monitored over time, and analysed to extract time-series of dry mass values. We assessed its performances on different cell lines, showing a precision of 96% in the automatic detection of anomalies. Additionally, anomaly detection was also associated with cell measurement errors inherent to the acquisition or analysis pipelines, leading to an improvement of the upstream methods for feature extraction. Our results pave the way to novel architectures for the continuous monitoring of cell cultures in applied research or bioproduction applications, and for the prediction of pathological cellular changes.
Published: 2024
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6. Sismo -volcano classification in real time based on Empirical Mode Decomposition (EMD) and Machine Learning

Author: Adolfo Inza, Pablo Eduardo Espinoza Lara, Carlos Alexandre, Jean Philippe Metaxian, Jerome Mars, Marielle Malfante, Mauro Dalla Mura, Instituto Geofísico del Perú (IGP), Universidade Federal do Ceará = Federal University of Ceará (UFC), Institut de Recherche pour le Développement (IRD), Institut de Physique du Globe de Paris (IPGP), Institut national des sciences de l'Univers (INSU - CNRS)-IPG PARIS-Université de La Réunion (UR)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Département Systèmes et Circuits Intégrés Numériques (DSCIN), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut de Physique du Globe de Paris (IPGP (UMR_7154)), Institut national des sciences de l'Univers (INSU - CNRS)-Université de La Réunion (UR)-Institut de Physique du Globe de Paris (IPG Paris)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), and Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA))
Subjects: [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph], [SDU.STU.VO]Sciences of the Universe [physics]/Earth Sciences/Volcanology, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2020

7. Shells of the bivalve Astarte moerchi give new evidence of a strong pelagic-benthic coupling shift occurring since the late 1970s in the North Water polynya

Author: Simon Bélanger, Aurélie Jolivet, Anne-Hélène Olivier, André L. Martel, Jerome Mars, Frédéric Olivier, Blandine Gaillard, Silvain Gerber, Søren Rysgaard, Julien Pettré, Laurent Chauvaud, Réjean Tremblay, Dany Dumont, Julien Thébault, Tarik Meziane, Michel Gosselin, Philippe Archambault, Biologie des Organismes et Ecosystèmes Aquatiques (BOREA), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA), Université du Québec à Rimouski (UQAR), Laboratoire des Sciences de l'Environnement Marin (LEMAR) (LEMAR), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes [2020-....] (UGA [2020-....])-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology [2020-....] (Grenoble INP [2020-....]), Université Grenoble Alpes [2020-....] (UGA [2020-....]), Université Laval [Québec] (ULaval), Laboratoire Mouvement Sport Santé (M2S), École normale supérieure - Cachan (ENS Cachan)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Institut de Recherche pour le Développement (IRD)-Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2020-....] (UGA [2020-....])-Institut polytechnique de Grenoble - Grenoble Institute of Technology [2020-....] (Grenoble INP [2020-....]), Muséum national d'Histoire naturelle (MNHN), Université des Antilles (UA), Institut des Sciences de la MER de Rimouski (ISMER), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Departement de Biologie, Chimie et Géographie, TBM environnement, Canadian Museum of Nature (CANADA), Greenland Climate Research Centre, Greenland Institute of Natural Resources (GINR), Centre for Earth Observation Science [Winnipeg], University of Manitoba [Winnipeg], Arctic Research Centre [Aarhus] (ARC), Aarhus University [Aarhus], Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Université de Rennes (UR)-École normale supérieure - Rennes (ENS Rennes)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), GIPSA-Services (GIPSA-Services), Takuvik International Research Laboratory, Université Laval [Québec] (ULaval)-Centre National de la Recherche Scientifique (CNRS), ArcticNet (Network of Centres of Excellence of Canada), Canadian Healthy Oceans Network (CHONe), Natural Sciences and Engineering Research Council of Canada (NSERC) Natural Sciences and Engineering Research Council of Canada, Quebec-Ocean (Fonds de recherche du Quebec - Nature et technologies), MNHN through a 3-year visiting professorship at ISMER-UQAR, Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Sorbonne Université (SU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU), Greenland Institute for Natural Resources (GINR), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), and Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)
Subjects: 0106 biological sciences, match, 010504 meteorology & atmospheric sciences, bivalve growth, acl, General Mathematics, [SDV]Life Sciences [q-bio], General Physics and Astronomy, Climate change, match/mismatch hypothesis, 01 natural sciences, Arctic, Sclerochronology, Sea ice, mismatch hypothesis, 14. Life underwater, 0105 earth and related environmental sciences, geography, geography.geographical_feature_category, 010604 marine biology & hydrobiology, General Engineering, Pelagic zone, The arctic, Oceanography, climate change, Coupling (computer programming), 13. Climate action, Benthic zone, pelagic-benthic coupling, [SDE]Environmental Sciences, sclerochronology, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Geology
Abstract: Climate changes in the Arctic may weaken the currently tight pelagic-benthic coupling. In response to decreasing sea ice cover, arctic marine systems are expected to shift from a ‘sea-ice algae–benthos' to a ‘phytoplankton-zooplankton’ dominance. We used mollusc shells as bioarchives and fatty acid trophic markers to estimate the effects of the reduction of sea ice cover on the food exported to the seafloor. Bathyal bivalve Astarte moerchi living at 600 m depth in northern Baffin Bay reveals a clear shift in growth variations and Ba/Ca ratios since the late 1970s, which we relate to a change in food availability. Tissue fatty acid compositions show that this species feeds mainly on microalgae exported from the euphotic zone to the seabed. We, therefore, suggest that changes in pelagic-benthic coupling are likely due either to local changes in sea ice dynamics, mediated through bottom-up regulation exerted by sea ice on phytoplankton production, or to a mismatch between phytoplankton bloom and zooplankton grazing due to phenological change. Both possibilities allow a more regular and increased transfer of food to the seabed. This article is part of the theme issue ‘The changing Arctic Ocean: consequences for biological communities, biogeochemical processes and ecosystem functioning'.
Published: 2020
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8. Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD

Author: Jerome Mars, Pablo Eduardo Espinoza Lara, Carlos Alexandre Rolim Fernandes, Marielle Malfante, Jean-Philippe Metaxian, Mauro Dalla Mura, Adolfo Inza, Universidade Federal do Ceará = Federal University of Ceará (UFC), Instituto Geofísico del Perú (IGP), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Institut de Recherche pour le Développement (IRD), Institut de Physique du Globe de Paris (IPGP), Institut national des sciences de l'Univers (INSU - CNRS)-IPG PARIS-Université de La Réunion (UR)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut de Physique du Globe de Paris (IPGP (UMR_7154)), and Institut national des sciences de l'Univers (INSU - CNRS)-Université de La Réunion (UR)-Institut de Physique du Globe de Paris (IPG Paris)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)
Subjects: Atmospheric Science, Artificial intelligence, Computer science, Geophysics. Cosmic physics, Cepstral analysis, 02 engineering and technology, Deconvolution, 010502 geochemistry & geophysics, computer.software_genre, 01 natural sciences, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Cepstrum, 0202 electrical engineering, electronic engineering, information engineering, Preprocessor, Empirical mode decomposition, time domain analysis, purl.org/pe-repo/ocde/ford#1.05.07 [http], Ocean engineering, Principal component analysis, Volcanoes, Curse of dimensionality, Communication channel, Feature vector, Time domain analysis, Explosions, deconvolution, Machine learning, spectral domain analysis, Hilbert–Huang transform, Seismic signal processing, purl.org/pe-repo/ocde/ford#1.05.00 [http], Databases, Computers in Earth Sciences, empirical mode decomposition, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, TC1501-1800, 0105 earth and related environmental sciences, Support vector machines, Sensors, business.industry, QC801-809, 020206 networking & telecommunications, cepstral analysis, Support vector machine, Spectral domain analysis, business, computer
Abstract: International audience; This article proposes the design of an automatic classifier using the empirical mode decomposition (EMD) along with machine learning techniques for identifying the five most important types of events of the Ubinas volcano, the most active volcano in Peru. The proposed method uses attributes from temporal, spectral and cepstral domains, extracted from the EMD of the signals, as well as a set of pre-processing and instrument correction techniques. Due to the fact that multichannel sensors are currently being installed in seismic networks worldwide, the proposed approach uses a multichannel sensor to perform the classification, contrary to the usual approach of the literature of using a single channel. The presented method is scalable to use data from multiple stations with one or more channels. The principal component analysis (PCA) method is applied to reduce the dimensionality of the feature vector and the supervised classification is carried out by means of several machine learning algorithms, the support vector machine (SVM) providing the best results. The presented investigation was tested with a large database that has a considerable number of explosion events, measured at the Ubinas volcano, located in Arequipa, Peru. The proposed classification system achieved a success rate of more than 90%.
Published: 2020
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9. Automatic classification of seismo-volcanic signals at La Soufrière of Guadeloupe

Author: Eléonore Stutzmann, Jean-Philippe Métaxian, Jerome Mars, Roberto Moretti, Jean-Christophe Komorowski, and Alexis Falcin
Subjects: geography, geography.geographical_feature_category, Volcano, Geology, Seismology
Abstract: Seismic activity at La Soufrière volcano of Guadeloupe is composed of various transient signals, which are classified manually by the Observatoire Volcanologique et Sismologique de Guadeloupe (OVSG-IPGP) considering waveforms recorded at several stations. Although five main types of signals are recognized in the data analysis by the observatory (Moretti et al., 2020), only three main classes readily distinguishable on seismic traces during the daily analytical protocol have been catalogued: Volcano-Tectonic events, Long-Period events and Nested events, each related to a distinct physical process.Automatic classification of seismo-volcanic signals of La Soufrière was performed by using an architecture based on supervised learning, available at github.com/malfante/AAA. Seismic waveforms are transformed into a large set of features (34 features for each representation domain) computed from three representation domain of the signal (time, frequency, quefrency). The resulting vectors of features are then used for the modeling. We are using the Random Forest Classifier algorithm from the scikit-learn library.At first, we trained the model with the dataset given by the OVSG consisting of 845 available labeled events (542 VT, 217 nested and 86 LP) recorded in the period 2013-2018. We obtained an average classification rate of 72 %. We determined that the VT class includes a variety of signals covering the LP, Nested and VT classes. Reviewing in details the waveforms and the spectral characteristics of the signals belonging to the 3 classes we then introduced Hybrid events and also defined a monochromatic class (so-called Tornillo) of LP signals, thus matching the full description of signals provided in Moretti et al. (2020).Then, using the new information, a new model was trained with 5 classes and tested. We obtained a much better classification average rate of 84 %. The classification is excellent for Nested events (93 % of accuracy and precision) and Tornillo events (93% of accuracy and precision). The classification of VT events (90% accuracy, 89% precision) and LP events (86% accuracy, 82% precision) were also very good. The most difficult class to recognize is the Hybrid class (64 % accuracy, 69 % precision). Hybrid events are often mixed with VT and LP events. This may be explained by the nature of this class and the physical process that includes both a fracturing and a resonating component with different modal frequencies.Machine learning is a powerful tool to handle large datasets. From a dataset built manually, the processing we applied allowed to obtain a reliable automatic classification by refining class definitions. This has important implications for observatory data processing during unrest and eruptive activity.
Published: 2020
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10. Simulation study of an X-ray diffraction system for breast tumordetection

Author: Loick Verger, C. Paulus, Olivier Michel, Fanny Marticke, G. Montemont, Jerome Mars, GIPSA - Communication Information and Complex Systems (GIPSA-CICS), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), and CEA_LETI
Subjects: Diffraction, Nuclear and High Energy Physics, medicine.medical_specialty, Photon, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [SDV.CAN]Life Sciences [q-bio]/Cancer, [PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex], Breast phantom simulation, Spectral line, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Depth imaging, Breast cancer, 0302 clinical medicine, medicine, Medical physics, Instrumentation, Physics, System optimization, Scattering, Radiation dose, X-ray scattering, medicine.disease, 030220 oncology & carcinogenesis, X-ray crystallography, Reconstruction, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Energy (signal processing), Biomedical engineering
Abstract: X-ray diffraction (XRD) is a powerful technique used to determine the molecular structure of biological tissues. In breast tissues for example, the scattering signatures of dense fibroglandular tissue and carcinoma have been shown to be significantly different. In this study, XRD was used as a second control level when conventional mammography results were unclear, for instance because of overly high breast density. A system optimized for this issue, called multifocal XRD, was developed combining energy dispersive spectral information at different scattering angles. This system allows depth-imaging in one go but needs an x,y-direction scan to image the region conventional mammography identified as suspect. The scan-time for about 10 cm3 with an incident flux of about 4 . 8 ⋅ 1 0 7 photons per second would be around 2 s. For this study, breast phantoms with and without cancerous nodule were simulated to assess the separation power of the method and to determine the radiation dose required to obtain nearly ideal separation. For tumors situated in the center of the breast, the required dose was only about 0.3 mGy, even for breasts with high density. The tumor position was shown to have a low impact on detectability provided it remained in a zone where the system was sufficiently sensitive. The influence of incident spectrum maximum energy was also studied. The required dose remained very low with any of the incident spectra tested. Finally, an image slice was reconstructed in the x-direction and showed that the system can detect the presence of a small tumor (4 mm). Hence, XRD is a very promising tool to reduce the number of unnecessary invasive breast biopsies.
Published: 2017
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11. Surface perturbation inverted from angle variations of eigenbeams in an ultrasonic waveguide

Author: Jerome Mars, Barbara Nicolas, Tobias van Baarsel, Philippe Roux, Nicolas, Barbara, Institut des Sciences de la Terre (ISTerre), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel-Université Grenoble Alpes (UGA), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Imagerie Ultrasonore, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Grenoble Alpes (UGA)-Université Gustave Eiffel-Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and ANR-16-IDEX-0005,IDEXLYON,IDEXLYON(2016)
Subjects: Physics, Acoustics and Ultrasonics, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Acoustics, Observable, 01 natural sciences, 03 medical and health sciences, 0302 clinical medicine, Amplitude, Transducer, Arts and Humanities (miscellaneous), [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Surface wave, 0103 physical sciences, Ultrasonic sensor, Tomography, 030223 otorhinolaryngology, 010301 acoustics, Ocean acoustic tomography, Multipath propagation
Abstract: International audience; Ocean acoustic tomography is traditionally performed using the travel-time variations of an acoustic path between a source and a receiver. In the context of shallow-water tomography and multipath propagation, the different acoustic paths can be correctly identified if the source and the receiver are arrays of transducers. Here, a double-beamforming algorithm can be applied to extract a collection of eigenbeams from the raw acoustic dataset. In this study, four observables can be measured for each eigenbeam: the travel-time, the amplitude, and the emitting and receiving angles. In this study, the sensitivity kernel (SK) formulation is used to establish a quantitative relation between a perturbation of the surface of an ultrasonic waveguide and the emitting and receiving angles of each eigenbeam. This theoretical relation is experimentally demonstrated using a forward model experiment designed to measure the SK. The SK formulation is then used in a second experiment to quantitatively and dynamically image the propagation of a surface wave traveling across the surface of the waveguide. The inversion results show that the quality of the joint inversion of the emitting and receiving angles is higher than previous results based on amplitude or travel-time observables.
Published: 2020
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12. Improving Localization of Deep Inclusions in Time-Resolved Diffuse Optical Tomography

Author: Lionel Herve, David Orive-Miguel, Jerome Mars, Laurent Condat, Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and GIPSA - Architecture, Géométrie, Perception, Images, Gestes (GIPSA-AGPIG)
Subjects: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, diffusion approximation, 01 natural sciences, 010309 optics, Set (abstract data type), 03 medical and health sciences, symbols.namesake, 0302 clinical medicine, datatypes, 0103 physical sciences, General Materials Science, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], Absorption (electromagnetic radiation), Instrumentation, Fluid Flow and Transfer Processes, Physics, [PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics], Process Chemistry and Technology, General Engineering, Process (computing), Inverse problem, Heavy traffic approximation, Diffuse optical imaging, Computer Science Applications, Computational physics, Fourier transform, Attenuation coefficient, symbols, inverse problem, diffuse optical tomography, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery
Abstract: International audience; Time-resolved diffuse optical tomography is a technique used to recover the optical properties of an unknown diffusive medium by solving an ill-posed inverse problem. In time-domain, reconstructions based on datatypes are used for their computational efficiency. In practice, most used datatypes are temporal windows and Fourier transform. Nevertheless, neither theoretical nor numerical studies assessing different datatypes have been clearly expressed. In this paper, we propose an overview and a new process to compute efficiently a long set of temporal windows in order to perform diffuse optical tomography. We did a theoretical comparison of these large set of temporal windows. We also did simulations in a reflectance geometry with a spherical inclusion at different depths. The results are presented in terms of inclusion localization and its absorption coefficient recovery. We show that (1) the new windows computed with the developed method improve inclusion localization for inclusions at deep layers, (2) inclusion absorption quantification is improved at all depths and, (3) in some cases these windows can be equivalent to frequency based reconstruction at GHz order.
Published: 2019
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13. Dynamic imaging of a capillary-gravity wave in shallow water using amplitude variations of eigenbeams

Author: Michel Arrigoni, Tobias van Baarsel, Julien Bonnel, Philippe Roux, Barbara Nicolas, Steven Kerampran, Jerome Mars, Institut des Sciences de la Terre (ISTerre), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Woods Hole Oceanographic Institution (WHOI), Institut de Recherche Dupuy de Lôme (IRDL), Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Centre National de la Recherche Scientifique (CNRS), Imagerie Ultrasonore, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Centre National de la Recherche Scientifique (CNRS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), ANR-16-IDEX-0005,IDEXLYON,IDEXLYON(2016), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut national des sciences de l'Univers (INSU - CNRS)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université Joseph Fourier - Grenoble 1 (UJF), SIGMAPHY (GIPSA-SIGMAPHY), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-GIPSA Pôle Sciences des Données (GIPSA-PSD), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Brest (UBO)-Université de Bretagne Sud (UBS), Mars, Jerome, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Diffraction, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, [SPI.ACOU] Engineering Sciences [physics]/Acoustics [physics.class-ph], Materials science, Acoustics and Ultrasonics, [SDU.OCEAN] Sciences of the Universe [physics]/Ocean, Atmosphere, Acoustics, Dynamic imaging, Waves and shallow water, Amplitude, Arts and Humanities (miscellaneous), Surface wave, Ultrasonic sensor, Gravity wave, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Blast wave, Physics::Atmospheric and Oceanic Physics, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; Dynamic acoustic imaging of a surface wave propagating at an air-water interface is a complex task that is investigated here at the laboratory scale through an ultrasonic experiment in a shallow water waveguide. Using a double beamforming algorithm between two source-receiver arrays, the authors isolate and identify each multi-reverberated eigenbeam that interacts with the air-water and bottom interfaces. The waveguide transfer matrix is recorded 100 times per second while a low-amplitude gravity wave is generated by laser-induced breakdown at the middle of the waveguide, just above the water surface. The controlled, and therefore repeatable, breakdown results in a blast wave that interacts with the air-water interface, which creates ripples at the surface that propagate in both directions. The amplitude perturbations of each ultrasonic eigenbeam are measured during the propagation of the gravity-capillary wave. Inversion of the surface deformation is performed from the amplitude variations of the eigenbeams using a diffraction-based sensitivity kernel approach. The accurate ultrasonic imaging of the displacement of the air-water interface is compared to simultaneous measurements with an optical camera, which provides independent validation.
Published: 2019
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14. Transmitted Sequence Influence to Sonar Target Detection using Compressive Sensing

Author: Jerome Mars, Jitendra Singh Sewada, Matt Geen, Milos Dakovic, Isidora Stankovic, Cornel Ioana, University of Montenegro (UCG), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and ITER Systems [Annecy]
Subjects: Computer science, Signal reconstruction, business.industry, Matched filter, 020207 software engineering, 02 engineering and technology, Sonar, Range (mathematics), Compressed sensing, Transmission (telecommunications), [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, Chirp, Computer vision, Detection theory, Artificial intelligence, business, ComputingMilieux_MISCELLANEOUS
Abstract: In this paper, the detection of targets in real sonar signals by transmitting various sequences is considered. In sonar systems, only the basic forms of signals were considered for transmission. The challenge of target detection under real data will be analyzed by transmitting two sequences, Alltop and Bjorck. Also, the goal is to detect and reconstruct targets by using a simple yet effective compressive sensing algorithm. Then the amplitude and the range will be calculated. The results of will be compared to the two traditionally used chirp signal, and also to the matched filter as one of the most important tools for signal detection.
Published: 2019
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15. Drone localization and identification using an acoustic array and supervised learning

Author: Jerome Mars, Matthieu Muschinowski, Valentin Baron, Barbara Nicolas, Simon Bouley, MicrodB, Imagerie Ultrasonore, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and GIPSA-Services (GIPSA-Services)
Subjects: Acoustic array processing, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], Support Vector Machine, Computer science, business.industry, Supervised learning, Direction of arrival, 020206 networking & telecommunications, 02 engineering and technology, Drone, law.invention, Machine Learning, Support vector machine, Drone identification, Noise, Identification (information), Signal-to-noise ratio, law, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, Radar, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; Drones are well-known threats both in military and civil environments. Identifying them accurately and localizing their trajectory is an issue that more and more methods are trying to solve. Several modalities can be used to make it such as radar, optics, radio-frequency communications and acoustics. Nevertheless radar suffers from a lack of reflected signal for small targets, optical techniques can be very difficult to set in natural environments with small targets, and self-flying drones can avoid radio detection. Consequently, this paper deals with the remaining acoustic modality and aims to localize an acoustic source, then to identify it as a drone or a noise using array measurements and a supervised learning method. The acoustic array allows to determine the source direction of arrival and a spatial filtering is performed to improve the signal to noise ratio. A focused signal is then obtained and used for characterizing the source. The performances obtained to identify this source as a drone or not are compared for two different learning models. The first one uses two classes drone and noise with a classic Support Vector Machine model while the second one is based on an One Class Support Vector Machine algorithm where only the drone class is learned. A database is generated with 7001 observations of drone flights and 3818 observations of noise recordings within a controlled environment where signals are played one at a time, given that an observation is a sequence of 0.2 s of signal. Results of localization show an average error concerning the elevation angle bounded to 3.7 • whereas identification results on this database give 99.5 % and 95.6 % accuracies for the two classes approach and the one class approach, respectively. It is shown that this high accuracy is reached thanks to the intrinsic separability of the created data obtained by the different features that have been chosen to compute.
Published: 2019
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16. Time-resolved diffuse optical tomography: a novel method to compute datatypes allows better absorption quantification

Author: Jerome Mars, David Orive-Miguel, Pierre Jallon, Laurent Condat, Lionel Herve, Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Laboratoire Imagerie et Systèmes d'Acquisition (LISA), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Architecture, Géométrie, Perception, Images, Gestes (GIPSA-AGPIG), Chaire Radio Flexible Alcatel-Lucent/Supélec (Chaire Radio Flexible), and Ecole Supérieure d'Electricité - SUPELEC (FRANCE)-Alcatel-Lucent
Subjects: Materials science, business.industry, 01 natural sciences, Diffuse optical imaging, 030218 nuclear medicine & medical imaging, 010309 optics, 03 medical and health sciences, 0302 clinical medicine, Optics, 0103 physical sciences, Absorption (electromagnetic radiation), business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2019
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17. Interferometric Measurements with Wideband Signal Processing Techniques

Author: Matt Geen, Jerome Mars, Jitendra Singh Sewada, Cornel Ioana, ITER Systems [Annecy], GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and Mars, Jerome
Subjects: Matched Filters, Phase Differential Bathymetry Sonar, 02 engineering and technology, 01 natural sciences, Sonar, Wideband signals, Signal-to-noise ratio, 0202 electrical engineering, electronic engineering, information engineering, Bathymetry, 14. Life underwater, Wideband, Sonar resolution, Interferometer, Decorrelation, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Remote sensing, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], [SPI.ACOU] Engineering Sciences [physics]/Acoustics [physics.class-ph], Matched filter, 010401 analytical chemistry, 020206 networking & telecommunications, Bathymetric sonar, [SPI.TRON] Engineering Sciences [physics]/Electronics, 0104 chemical sciences, [SPI.TRON]Engineering Sciences [physics]/Electronics, Noise, Pulse compression, Measurement uncertainty, PDBS, Swath bathymetry, Bottom estimation, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Geology
Abstract: International audience; Bathymetric sonar systems are widely used nowadays in seafloor mapping and exploration. The differential phase technique, also known as interferometry, is widely used in these bathymetry measurement sonars, but the assessment of measurement uncertainty and data density are not completely understood and explored. This research paper concerns the description of measurement uncertainties due to different noise sources in interferometric sonars and a wideband signal processing approach to solve them. Interferometers are mostly used in shallow waters, e.g. lakes, inland waterways, rivers, reservoirs and coastal areas, due to having wider swaths compared to conventional multibeam echosounders or beamformers. This paper focuses on getting high-resolution bathymetry with minimum measurement uncertainty. It considers the effect of various acoustic noise sources, and neglects external error sources, e.g. INS, GNSS, sound velocity profiles. Wideband pulses are now used to overcome the range-resolution trade off problem of CW pulses. We have done an assessment of wideband signal processing techniques to reduce the bathymetry uncertainties caused by different noise sources. ITER Systems provided us with a Bathyswath-2 bathymetry system and essential surveying tools. Mathematical noise models are compared with data recorded in the field.
Published: 2019

18. Conception of an underwater prototype array: from simulation design to mechanical assessment

Author: Barbara Nicolas, Arthur Finez, Valentin Baron, Joffrey Jaumont, Jerome Mars, Florent Fayet, OSEAN S.A.S [Le Pradet], Osean, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Imagerie Ultrasonore, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), and MicrodB
Subjects: Acoustic array, 010505 oceanography, Computer science, Computation, Acoustic source localization, 01 natural sciences, Stability (probability), Deep sea mining, Noise, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Software deployment, deep sea mining, 14. Life underwater, acoustic array, Underwater, mechanical assessment, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Marine engineering
Abstract: As natural resources are increasingly difficult to find on the surface of the earth, mining industries start to dig in deep waters to find new deposits. The production of noise in the surrounding environment is a direct consequence of this activity and it must be monitored to predict its impact on the nearby marine life. This is the goal of Abysound research project in which a monitoring system is conceived and used to localize and quantify deep underwater acoustic sources and propagate the sound they emit to create noise impact maps. This study focuses on the conception of the acoustic array which enables the localization of the sources. Given the harsh conditions of utilization, it must be ensured that from the design phase to the real deployment all the array parts are mechanically stable and they allow to perform acoustic computations accurately. After a presentation of the array shape and the mechanical constraints given as an input of the construction, the stability of the chosen structure is assessed. Firstly numerical simulations are performed to validate the stability. Then a deployment test is conducted in shallow waters and it shows both the mechanical stability of the array during a real launching and the effectiveness of the array to perform acoustic source localization thanks to an immersed source producing noise during the experiment.
Published: 2019

19. High resolution source localization in underwater acoustics for deep sea mining monitoring

Author: Valentin Baron, Barbara Nicolas, Arthur Finez, Jerome Mars, Gia-Thuy Pham, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), MicrodB, Imagerie Ultrasonore, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Subjects: 0106 biological sciences, Beamforming, 010604 marine biology & hydrobiology, 010603 evolutionary biology, 01 natural sciences, [PHYS.MECA.ACOU]Physics [physics]/Mechanics [physics]/Acoustics [physics.class-ph], Power (physics), Deep sea mining, Waves and shallow water, 14. Life underwater, Underwater, Wideband, Underwater acoustics, ComputingMilieux_MISCELLANEOUS, Geology, Seabed, Remote sensing
Abstract: The objective is to establish the feasibility of an underwater system for estimating acoustic noise generated by seafloor mining in deep water and to evaluate its impact on marine life. We show in this article, several steps: the first is the design of the array, the second concerns validation of the performance (localization and source power estimation) from real data recorded in situ –shallow water. We propose several algorithms as conventional beamforming and MUSIC algorithms in 3D (x,y,z).
Published: 2019
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20. ABYSOUND, an end to end system for noise impact measurement of deep sea mining production tools

Author: Rania Bassila, Valentin Baron, Marie-Edith Bouhier, Hervé Glotin, Lucie Somaglino, Florent Fayet, Theo Bertet, Jerome Mars, Paul Best, Paul Cristini, Claire Noel, and Barbara Nicolas
Subjects: 0106 biological sciences, Estimation, Service (systems architecture), Noise measurement, Computer science, 010604 marine biology & hydrobiology, 010603 evolutionary biology, 01 natural sciences, Deep sea mining, Noise, Submarine pipeline, Environmental impact assessment, Seabed, Marine engineering
Abstract: The project’s main objective is to establish the feasibility of a new service and an associated system aimed to estimate the noise radiated into the water by the equipment and systems deployed on the ground during deep offshore sea floor operations. Based on this estimation, the main purpose of the project will be to assess the acoustic environmental impact on marine fauna. It will also make it possible to assess the acoustic disturbance generated by the devices or systems deployed on the ground on the operation of the sub-robots monitoring and implement adaptive strategies to prevent malfunctions of the latter. The project involves technological developments and system validation through deep sea experiments.
Published: 2019
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21. Quantification of Submillimeter Displacements by Distributed Optical Fiber Sensors

Author: Edouard Buchoud, Sylvain Blairon, Alexandre Girard, Jerome Mars, Valeriu Vrabie, Jean-Marie Henault, and Guy d'Urso
Subjects: Optical fiber cable, geography, geography.geographical_feature_category, Optical fiber, Scattering, Sinkhole, Acoustics, 020208 electrical & electronic engineering, 0211 other engineering and technologies, 02 engineering and technology, Signature (logic), Light scattering, Displacement (vector), Physics::Geophysics, law.invention, law, 0202 electrical engineering, electronic engineering, information engineering, Vertical displacement, Electrical and Electronic Engineering, Instrumentation, Geology, 021101 geological & geomatics engineering, Remote sensing
Abstract: The estimation of sinkhole-induced ground displacement is an important issue for monitoring soil structures. Distributed optical fiber sensors (DOFSs) composed of an interrogator based on scattering effects in an optical fiber cable sensing element can be used to assess ground displacement. These sensors provide longitudinal strain measurements of the soil structure. This paper proposes a methodology that enables the estimation of displacement fields in the soil structure when a sinkhole appears. It also exposes an experiment that was carried out to create an artificial sinkhole instrumented by optical fiber sensors. This is the first time that those sensors are used to provide submillimeter vertical displacements. The first step of the methodology is to model the ground displacement under 2-D conditions. The longitudinal strain measured by a DOFS can thus be linked to the displacement of the structure. This model is described by the following parameters: 1) the spatial extent of the displacement signature; 2) a coefficient that depends on the interface between the optical fiber cable and the soil; 3) the depth of the sinkhole; and 4) the maximal vertical displacement. The second step consists of the estimation of each parameter independently. The spatial extension is given by fitting the measured strain signature with the empirical model. The depth of the sinkhole can be determined by the measurement of the spatial extension of the ground-displacement profile at several observation depths in the structure. Finally, the vertical maximal displacement is furnished with high precision.
Published: 2016
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22. Automatic fish sounds classification

Author: Mauro Dalla Mura, Marielle Malfante, Jerome Mars, Cedric Gervaise, Mars, Jerome, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Chaire CHORUS - Fondation Grenoble-INP, Institut Polytechnique de Grenoble - Grenoble Institute of Technology, DGA/MRIS, and Labex OSUG2020
Subjects: Passive acoustic monitoring, Acoustics and Ultrasonics, Computer science, Feature vector, 02 engineering and technology, 01 natural sciences, Set (abstract data type), Machine Learning, 03 medical and health sciences, Arts and Humanities (miscellaneous), Discriminative model, 0103 physical sciences, Cepstrum, 0202 electrical engineering, electronic engineering, information engineering, Animals, 14. Life underwater, Underwater, 010301 acoustics, 030304 developmental biology, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], 0303 health sciences, [SDE.IE]Environmental Sciences/Environmental Engineering, business.industry, Fishes, 020206 networking & telecommunications, Pattern recognition, Acoustics, Sound, %22">Fish , State (computer science), Artificial intelligence, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Vocalization, Animal, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract: The work presented in this paper focuses on the use of acoustic systems for passive acoustic monitoring of ocean vitality for fish populations. Specifically, it focuses on the use of acoustic systems for passive acoustic monitoring of ocean vitality for fish populations. To this end, various indicators can be used to monitor marine areas such as both the geographical and temporal evolution of fish populations. A discriminative model is built using supervised machine learning (random-forest and support-vector machines). Each acquisition is represented in a feature space, in which the patterns belonging to different semantic classes are as separable as possible. The set of features proposed for describing the acquisitions come from an extensive state of the art in various domains in which classification of acoustic signals is performed, including speech, music, and environmental acoustics. Furthermore, this study proposes to extract features from three representations of the data (time, frequency, and cepstral domains). The proposed classification scheme is tested on real fish sounds recorded on several areas, and achieves 96.9% correct classification compared to 72.5% when using reference state of the art features as descriptors. The classification scheme is also validated on continuous underwater recordings, thereby illustrating that it can be used to both detect and classify fish sounds in operational scenarios.
Published: 2018
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23. Use of deep features for the automatic classification of fish sounds

Author: Cedric Gervaise, Mauro Dalla Mura, Marielle Malfante, Omar Mohammed, Jerome Mars, SIGMAPHY ( GIPSA-SIGMAPHY ), Département Images et Signal ( GIPSA-DIS ), Grenoble Images Parole Signal Automatique ( GIPSA-lab ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Grenoble Images Parole Signal Automatique ( GIPSA-lab ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ), CRISSP ( GIPSA-CRISSP ), Département Parole et Cognition ( GIPSA-DPC ), Chaire CHORUS - Fondation Grenoble-INP, Institut Polytechnique de Grenoble - Grenoble Institute of Technology, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), and Mars, Jerome
Subjects: Computer science, Automatic processing, 02 engineering and technology, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, Convolutional neural network, [ SDE.IE ] Environmental Sciences/Environmental Engineering, 0202 electrical engineering, electronic engineering, information engineering, 14. Life underwater, Underwater, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Signal processing, [ SDE.BE ] Environmental Sciences/Biodiversity and Ecology, [SDE.IE]Environmental Sciences/Environmental Engineering, business.industry, 020206 networking & telecommunications, Pattern recognition, [SDE.BE] Environmental Sciences/Biodiversity and Ecology, Feature (computer vision), %22">Fish , 020201 artificial intelligence & image processing, [SDE.IE] Environmental Sciences/Environmental Engineering, Artificial intelligence, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Focus (optics), business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; — The work presented in this paper focuses on the environmental monitoring of underwater areas using acoustic signals. In particular, we propose to compare the effectiveness of various feature sets used to represent the underwater acoustic data for the automatic processing of fish sounds We focus on the detection and classification tasks. Specifically, we compare the use of features issued from signal processing presented and validated in [15], [16] to the use of features obtained through deep convolutional neural networks. Experimental results show that the use of signal processing features outperform the deep features in terms of classification accuracy.
Published: 2018

24. Machine Learning for Volcano-Seismic Signals: Challenges and Perspectives

Author: Jean-Philippe Métaxian, Orlando Macedo, Jerome Mars, Adolfo Inza, Marielle Malfante, Mauro Dalla Mura, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Géophysique des volcans & géothermie, Institut des Sciences de la Terre (ISTerre), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Instituto Geofisico del Peru (IPG), Instituto Geofísico del Perú (IGP), Universidad Nacional de San Agustín (UNSA), DGA - MRIS, ANR10 LABX56, and ANR-10-LABX-0056,OSUG@2020,Innovative strategies for observing and modelling natural systems(2010)
Subjects: Volcanic hazards, Geospatial analysis, Support Vector Machine, 010504 meteorology & atmospheric sciences, Computer science, [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph], Big data, 010502 geochemistry & geophysics, computer.software_genre, 01 natural sciences, Machine Learning, Natural hazard, Environmental monitoring, [SDU.STU.VO]Sciences of the Universe [physics]/Earth Sciences/Volcanology, Data Mining, Electrical and Electronic Engineering, 0105 earth and related environmental sciences, Random Forest, business.industry, Applied Mathematics, Scale (chemistry), Automatic Statistical Classification, Feature Space, Signal Representation, Data science, Volcano-seismic signals, 13. Climate action, Index Terms, Signal Processing, Task analysis, business, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, LEAPS
Abstract: International audience; Environmental monitoring is a topic of increasing interest, especially concerning the matter of natural hazards prediction. Regarding volcanic unrest, effective methodologies along with innovative and operational tools are needed to monitor, mitigate and prevent risks related to volcanic hazards. In general, the current approaches for volcanoes monitoring are mainly based on the manual analysis of various parameters, including gas leaps, deformations measurements and seismic signals analysis. However, due to the large amount of data acquired by in situ sensors for long term monitoring, manual inspection is no longer a viable option. As in many Big Data situations, classic Machine Learning approaches are now considered to automatize the analysis of years of recorded signals, thereby enabling monitoring at a larger scale. This paper focuses on integrated and operational tools dedicated to the automatic analysis of volcano-seismic signals. Namely we review (i) tools for the optimal representation of volcano-seismic signals (feature space) and the available methods for volcano-seismic events (ii) detection and (iii) classification. We then propose an architecture for the automatic classification of volcano-seismic events. Our prediction system is tested on 6 years of recordings containing 109434 volcano-seismic events acquired from Ubinas volcano (the most active volcano in PerúPer´Perú). Our new proposed model is build using supervised machine learning algorithms (Support Vector Machine) and reaches 92.2% of correct classification over six classes. This prediction model is then used to fully analyze the 6 years of recorded signals.
Published: 2018
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25. Color Sparse Representations for Image Processing: Review, Models, and Prospects

Author: Quentin Barthélemy, Jerome Mars, Anthony Larue, Laboratoire d'analyse des données et d'intelligence des systèmes (LADIS), Département Métrologie Instrumentation & Information (DM2I), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), CEA LIST / LOAD, GIPSA-lab, Laboratoire Outils d'Analyse des Données (LOAD), Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)
Subjects: DECOMPOSITION, DICTIONARIES, Color histogram, quaternion, sparse coding, Color normalization, online learning, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Color balance, HYPERCOMPLEX, FOURIER-TRANSFORMS, Color gel, Computer vision, ALGORITHM, ComputingMethodologies_COMPUTERGRAPHICS, Mathematics, model, business.industry, Color image, LINEAR INVERSE PROBLEMS, COMPONENT ANALYSIS, color image, artificial intelligence, OVERCOMPLETE REPRESENTATIONS, Computer Graphics and Computer-Aided Design, Color quantization, machine learning, color filter, Colors of noise, Color filter array, Index Terms—Sparse representation, Artificial intelligence, dictionary learning, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, MATRIX-FACTORIZATION, Software
Abstract: International audience; Sparse representations have been extended to deal with color images composed of three channels. A review of dictionary-learning-based sparse representations for color images is made here, detailing the differences between the models, and comparing their results on real data and simulated data. These models are considered in a unifying framework that is based on the degrees of freedom of the linear filtering/transformation of the color channels. Moreover, this allows it to be shown that the scalar quaternionic linear model is equivalent to constrained matrix-based color filtering, which highlights the filtering implicitly applied through this model. Based on this reformulation, the new color filtering model is introduced, using unconstrained filters. In this model, spatial morphologies of color images are encoded by atoms, and colors are encoded by color filters. Color variability is no longer captured in increasing the dictionary size, but with color filters, this gives an efficient color representation.
Published: 2015
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26. Similarity matrix analysis and divergence measures for statistical detection of unknown deterministic signals hidden in additive noise

Author: Jerome Mars, Cedric Gervaise, O. Le Bot, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Chaire CHORUS - Fondation Grenoble-INP, Institut Polytechnique de Grenoble - Grenoble Institute of Technology, ENSTA-BRETAGNE, CHAIRE CHORUS Fondation Grenoble-INP, and ANR-12-ASTR-0021,MER CALME,Caractérisation Acoustique de Littoraux Marins et de leurs Ecosystèmes(2012)
Subjects: Physics, Physics::Instrumentation and Detectors, Noise (signal processing), Detector, General Physics and Astronomy, Filter (signal processing), Statistical power, symbols.namesake, Additive white Gaussian noise, symbols, Detection theory, Divergence (statistics), [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithm, Energy (signal processing)
Abstract: International audience; This Letter proposes an algorithm to detect an unknown deterministic signal hidden in additive white Gaussian noise. The detector is based on recurrence analysis. It compares the distribution of the similarity matrix coefficients of the measured signal with an analytic expression of the distribution expected in the noise-only case. This comparison is achieved using divergence measures. Performance analysis based on the receiver operating characteristics shows that the proposed detector outperforms the energy detector, giving a probability of detection 10% to 50% higher, and has a similar performance to that of a sub-optimal filter detector.
Published: 2015
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27. Machine Learning for Automatic Classification of Volcano-Seismic Signatures

Author: Jean-Philippe Métaxian, Mauro Dalla Mura, Marielle Malfante, Jerome Mars, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut des Sciences de la Terre (ISTerre), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), ANR10 LABX56, DGA/MRIS, and Mars, Jerome
Subjects: Computer science, business.industry, SIGNAL (programming language), Feature extraction, 020206 networking & telecommunications, Linear classifier, 02 engineering and technology, Machine learning, computer.software_genre, Task (project management), Support vector machine, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Transient (computer programming), Data mining, Artificial intelligence, Representation (mathematics), business, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; —The evaluation and prediction of volcanoes activities and associated risks is still a timely and open issue. The amount of volcano-seismic data acquired by recent monitoring stations is huge (e.g., several years of continuous recordings), thereby making machine learning absolutely necessary for their automatic analysis. The transient nature of the volcano-seismic signatures of interest further enforces the need of automatic detection and classification of such events. In this paper, we present a novel architecture for automatic classification of volcano-seismic events based on a comprehensive signal representation with a large feature set. To the best of our knowledge this is one of the first attempts to automatize the classification task of these signals. The proposed approach relies on supervised machine learning techniques to build a prediction model.
Published: 2018
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28. Wideband signals for Phase Differencing Sonar Systems

Author: Matt Geen, Jitendra Singh Sewada, Jerome Mars, Cornel Ioana, Mars, Jerome, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and ITER Systems [Annecy]
Subjects: 010504 meteorology & atmospheric sciences, Computer science, Acoustics, Active Sonar, 01 natural sciences, Sonar, Wideband signals, Narrowband, Astronomical interferometer, Angular resolution, 14. Life underwater, Wideband, Interferometric Sonar, 0105 earth and related environmental sciences, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], [SPI.ACOU] Engineering Sciences [physics]/Acoustics [physics.class-ph], Phase Differential Bathymetric Sonar, [SDE.IE]Environmental Sciences/Environmental Engineering, 010505 oceanography, Interferometry, Bathymetry, [SDE.IE] Environmental Sciences/Environmental Engineering, Marine mammals and sonar, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Frequency modulation, Matched Filtering
Abstract: International audience; This research work deals with the resolution problem in Phase Differencing Bathymetric Sonar Systems (PDBS), also called interferometric sonars ("Interferometers"). Wideband signals are compared with the narrowband continuous wave (CW) pulses to assess the relative merits of signals to improve the range and angle resolution. The idea is inspired by the marine mammals, who use complex wideband signals for navigation and target detection. A set of waveforms e.g. CW Pulses, LFM (Linear Frequency Modulated) Pulses, EFM (Exponential Frequency Modulation) Pulses were used with Bathyswath-1 transducers for the experiment. A comparative study was done to assess the set of signals for range and angular resolution of Interferometers. Wideband signal processing techniques are used to solve the problem of trade-off between range and resolution. This paper gives an introduction to interferometry sonars, the major problems with them and the wideband processing approach to improving them.
Published: 2018

29. Computational Performance of Time-Resolved Diffuse Optical Tomography in a Two-Layer Brain Model

Author: Sophie Morales, Lionel Herve, Laurent Condat, David Orive-Miguel, Jerome Mars, Jean-Marc Dinten, Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Architecture, Géométrie, Perception, Images, Gestes (GIPSA-AGPIG), and Mars, Jerome
Subjects: [PHYS.PHYS.PHYS-OPTICS] Physics [physics]/Physics [physics]/Optics [physics.optics], [PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics], Materials science, Flexibility (anatomy), Brain model, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, business.industry, Two layer, Layer thickness, Finite element method, Diffuse optical imaging, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Optics, medicine.anatomical_structure, Neuroimaging, OCIScodes: 170.6920, 110.6955, 110.0113, 030.5260, 100.3190, medicine, business, Absorption (electromagnetic radiation), [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; We evaluate Mellin-Laplace algorithm in a two-layer brain model with Monte-Carlo generated data. Results show accurate absorption estimation and the flexibility of the algorithm to detect each layer thickness.
Published: 2018
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30. Raypath Separation With a High-Resolution Algorithm in a Shallow-Water Waveguide

Author: Longyu Jiang, Jerome Mars, Philippe Roux, Nanjing Southeast University (SEU), Ondes et Structures, Institut des Sciences de la Terre (ISTerre), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and Nanjing Southeast University
Subjects: Beamforming, 010505 oceanography, Computer science, Mechanical Engineering, Acoustics, 020206 networking & telecommunications, Ocean Engineering, Context (language use), Multipath propagation, 02 engineering and technology, Index Terms Ocean acoustic tomography, 01 natural sciences, Signal, 0202 electrical engineering, electronic engineering, information engineering, Refraction (sound), Waveguide (acoustics), High resolution, 14. Life underwater, Electrical and Electronic Engineering, Shallow-water waveguide, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Ocean acoustic tomography, Smoothing, 0105 earth and related environmental sciences
Abstract: International audience; One intriguing property that is exploited by ocean acoustic tomography (OAT) is that acoustic signals travel in a multipath. As the first step of OAT, each ray path should be identified with a particular travel time. However, the set of multipath rays generated by an emitted signal is correlated or coherent, as they are produced by reflection and or by refraction in propagation. In this paper, a high-resolution method called smoothing-Multiple Signal Classification Active Large Band (MUSICAL) is presented in the context of shallow-water OAT for separating coherent or fully correlated raypaths in the direction of arrival (DOA)-temporal domain. The method is a combination of the MUSICAL and spatial-frequency smoothing processing. Furthermore, the performance of smoothing-MUSICAL is illustrated by experiments based on both synthetic data and real data. This algorithm largely improves separation performances and presents fewer artifacts compared to conventional beamforming. In particular , experimental results show that smoothing-MUSICAL is more robust than beamforming facing a noisy environment with moderate signal-to-noise ratio (SNR).
Published: 2018
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31. ALMA 2015: Sea Trial Of An Underwater Target Localization Technique Using Hausdorff Distance

Author: Xavier Cristol, Pedro Eugenio Martins de Magalhaes, Dominique Fattaccioli, Jerome Mars, Cornel Ioana, Mars, Jerome, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Thales Underwater Systems (Thalès) (TUS), DGA Techniques Navales, Direction générale de l'Armement (DGA), Thales underwater Systems, Thales Underwater Systems, SIGMAPHY ( GIPSA-SIGMAPHY ), Département Images et Signal ( GIPSA-DIS ), Grenoble Images Parole Signal Automatique ( GIPSA-lab ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Grenoble Images Parole Signal Automatique ( GIPSA-lab ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ), and Thales Underwater Systems (Thalès) ( TUS )
Subjects: [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], Hausdorff Distance Method, [SPI.ACOU] Engineering Sciences [physics]/Acoustics [physics.class-ph], Hydrophone, Computer science, [ SPI.ACOU ] Engineering Sciences [physics]/Acoustics [physics.class-ph], Model based signal processing, Sea trial, Inversion (meteorology), [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, Multilateration, Passive sonar, Linear array, Target localization, Hausdorff distance, Pattern recognition, 14. Life underwater, Underwater, Underwater localization inversion, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; — The fundamental and practical problem of passive localization in range and depth, of an acoustic underwater source is addressed, with application to an at-sea experiment. We propose and try a new matching method based on a metric called as Hausdorff distance as a cost-function to be minimized, in order to perform the localization inversion. The data set analyzed here was collected during the DGA campaign ALMA 2015, which took place in a shallow water environment of the southern coast of France. Acoustic data were measured over a 10m-high vertical linear array (VLA), composed of 64 hydrophones. The 2-D localization, in range and depth, is performed by matching the patterns of time difference of arrival (TDOA), between respectively observed and modeled sequences. Several variants of the Hausdorff Distance are applied, firstly separately in each single hydrophone, and then combined in order to improve the localization accuracy, reducing the ambiguity either is depth and in range. The performance is evaluated in terms of the localization accuracy of the proposed method, in a context of passive localization with a cooperative system considering a motionless target. Very satisfactory performance and accuracy are obtained.
Published: 2018

32. Hausdorff distance: A new target localization using single hydrophone

Author: Pedro Martins de Magalhães, Xavier Cristol, Cornel Ioana, Jerome Mars, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Thales Underwater Systems (Thalès) (TUS), and Mars, Jerome
Subjects: [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], [SPI.ACOU] Engineering Sciences [physics]/Acoustics [physics.class-ph], [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract: This paper addresses the problem of source localization with only one sensor. A new metric called Hausdorff Distance (HD) for an optimal matching, is used for TDOAs best matching between simulations (TDOAs) and real data (TDOAr). To obtain the simulated impulse response, we drive an acoustic ray path propagation method. So we compare these simulated data with TDOA estimated after matched filtering considering a known transmitted signal. Two derivated techniques from HD are presented to find correct position, in range and depth, 1) the Maximum Hausdorff distance and 2) the Median Hausdorff distance. Results in terms of the localization, accuracy are shown in a real tank experiment., Cet article traite du problème de la localisation de source avec un seul capteur en acoustique sous marine. Une nouvelle mesure de distance appelée Hausdorff Distance (HD) est introduite pour comparer différents ensemble de TDOA (Time difference of Arrival) simulés (TDOAs) aux TDOA reçus (TDOAr). Cette méthode utilisée en traitement d'image pour mesurer le degré de similitude entre deux objets est adaptée et appliquée ici dans un contexte complètement nouveau. Deux extensions (la distance maximum de Hausdorff et la distance médiane de Hausdorff) sont présentées sur un cas réel pour trouver la position correcte, (en portée et en profondeur) d'une cible. Les résultats en termes de localisation, de précision sont montrés dans une expérience réelle en cuve.
Published: 2017

33. Instantaneous Frequency Law tracking using signal's representation in phase diagram domain

Author: Gabriel Vasile, Costin Vasile, Alexandru Serbanescu, Jerome Mars, Cornel Ioana, Angela Digulescu, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), MTA's electronic&computer research center, Académie Technique Militaire [Bucarest] (ATM), and Ioana, Cornel
Subjects: Radar tracker, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, 010308 nuclear & particles physics, Tracking (particle physics), 01 natural sciences, Signal, Instantaneous phase, Time–frequency analysis, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Control theory, Law, Frequency domain, 0103 physical sciences, Trajectory, 010306 general physics, Representation (mathematics), ComputingMilieux_MISCELLANEOUS, Mathematics
Abstract: Time-frequency analysis is naturally the way of characterization of non-stationary signals. One fundamental parameter of the any-non-stationary is the Instantaneous Frequency Law of its components. The Instantaneous Frequency Law (IFL) estimation is usually subject of tracking in time-frequency domain, especially in the case of complex time-frequency structures. Using the concept of phase diagram representation of a signal, related to the concept of nonlinear dynamic systems, we aim to track the Instantaneous Frequency Law of a received signal, providing a new method for the estimation of the frequency's evolution in time. The main advantage of this approach is the capability of phase diagram representation to highlight the signal's phase continuity, very useful for an accurate IFL tracking.
Published: 2017

34. Polar coastal soundscapes: Tridimensional mapping of benthic biophony and ice geophony with a compact sensor array

Author: Delphine Mathias, Aurélie Jolivet, Laurent Chauvaud, Cedric Gervaise, Julie Lossent, Jerome Mars, Chaire CHORUS - Fondation Grenoble-INP, Institut Polytechnique de Grenoble - Grenoble Institute of Technology, France Energies Marines [Brest], GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Laboratoire des Sciences de l'Environnement Marin (LEMAR) (LEMAR), Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Centre National de la Recherche Scientifique (CNRS)-Université de Brest (UBO), TBM environnement, Société d’Observation Multi-Modale de l’Environnement [Plouzané], Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Acoustical Society of America, ANR-15-ASTR-0025,GAAP,Glace Arctique par Acoustique Passive(2015), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Institut Universitaire Européen de la Mer (IUEM), and Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Passive acoustics, Acoustics and Ultrasonics, medicine.medical_treatment, Tridimensional maps, ACTI, Arctic, Arts and Humanities (miscellaneous), medicine, Ice pack, 14. Life underwater, Geophonic, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], geography, geography.geographical_feature_category, Biophonic, [SDE.IE]Environmental Sciences/Environmental Engineering, Biophony, Elevation, Arctic ice pack, Iceberg, Oceanography, Marine fauna, 13. Climate action, Benthic zone, Soundscape, Antarctic, Ice sheet, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Benthic fauna, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Geology, Seismology, Polar areas
Abstract: International audience; Polar areas show fast changes linked to global warming. The reduction of the ice pack and the melting of the ice sheet modify the conditions of living of marine fauna. We propose the simultaneous monitoring of the ice and benthic fauna using passive acoustics. Thanks to a compact sensor array of 4 hydrophones (2m*2m*2m), we detected, localized and mapped in three dimensions ({azimuth, elevation} or {x, y, z}) the biophonic and geophonic contributions made up of short and wideband pulses. Tridimensional maps of benthic biophony and ice geophony of Antarctic and Arctic 7 days-long recording sessions (2015, 2016) are built and analyzed over a surface of the order of 1 km2. Benthic invertebrates emit high energetic pulses with peak frequencies ranging from 2 to 55 kHz, most of them below 15 kHz. Geophony is structured into two parts. The ice sheet, located several kilometers or tens of kilometers away, creates a stable spatial distribution of low energetic pulses (representing the majority of pulses in the soundscape) modulated by the temporal variability. The movements of isolated icebergs or pack ice produce localized acoustic events identifiable by the high sound levels and the stable peak frequencies of the emitted pulses.
Published: 2017
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35. Performance evaluation of time-domain multispectral diffuse optical tomography in the reflection geometry

Author: Andrea Farina, Judy Zouaoui, Jean-Marc Dinten, Jacques Derouard, Laura Di Sieno, Laurent Condat, Antonio Pifferi, David Orive-Miguel, Alberto Dalla Mora, Lionel Herve, Jerome Mars, Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Dipartimento di Fisica, ICT Institute of Politecnico di Milano, CNR Istituto di Fotonica e Nanotecnologie [Milano] (IFN), Consiglio Nazionale delle Ricerche [Roma] (CNR), Laboratoire Interdisciplinaire de Physique [Saint Martin d’Hères] (LIPhy), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Architecture, Géométrie, Perception, Images, Gestes (GIPSA-AGPIG), Rayet, Béatrice, and National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR)
Subjects: Materials science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Multispectral image, Photodetector, Photon counting, 01 natural sciences, Image reconstruction techniques, 010309 optics, 03 medical and health sciences, Optics, 0103 physical sciences, Time domain, Tomography, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, 030304 developmental biology, 0303 health sciences, business.industry, Time-resolved imaging, Imaging through turbid media, Photodetectors, Diffuse optical imaging, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Reflection (physics), Gradation, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; To evaluated capabilities of multispectral TD-DOT systems in reflection geometry, we performed a measurement campaign on multimaterial composition phantoms. Results show correct composition gradation of inclusions but still lack absolute accuracy.
Published: 2017

36. 3D source location using overlapping multipath propagation

Author: Amélie Barazzutti, R Jorge Urbán, Aaron Thode, Jerome Mars, M. Esther Jimenez-Lopez, Kerri D. Seger, Pamela Martínez-Loustalot, Diana López-Arzate, Direction générale de l'Armement (DGA), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Scripps Institution of Oceanography (SIO), University of California [San Diego] (UC San Diego), University of California-University of California, School of Marine Science and Ocean Engineering, University of New Hampshire (UNH), Universidad Autónoma de Baja California Sur (UABCS), and DGA/MRIS
Subjects: 010302 applied physics, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], Passive acoustic monitoring, source localization, Speech recognition, warping, 01 natural sciences, underwater acoustics, Geography, 0103 physical sciences, Source localization, [SDE]Environmental Sciences, Image warping, Underwater acoustics, 010301 acoustics, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Multipath propagation, [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography
Abstract: International audience; — In passive acoustic monitoring, source localization using multipath propagation can be challenging whenever the source-receiver configuration leads to overlapping multipath arrival patterns. We propose a single-hydrophone model-based method with matched-impulse response (IR) processing that exploits unresolved multipath arrival patterns to locate the source in 3D, making the best use of the bathymetric information as well as the time differences of groups of arrivals. When only groups of paths are resolved, the identity of the paths constituting the groups remains unknown, preventing the use of classical localization methods. To overcome this critical limitation, we apply an approach based on the characterization of groups of paths to estimate the source location.
Published: 2017

37. Automatic Detection of the Number of Raypaths in a Shallow-Water Waveguide

Author: Jerome Mars, Longyu Jiang, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)
Subjects: shallow-water tomography, minimum description length (MDL), noise-whitening exponential fitting test (NWEFT), Ocean Engineering, 02 engineering and technology, Tracking (particle physics), 01 natural sciences, 010104 statistics & probability, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Statistics, 0202 electrical engineering, electronic engineering, information engineering, Waveguide (acoustics), 14. Life underwater, 0101 mathematics, Electrical and Electronic Engineering, Minimum description length, Mathematics, Mechanical Engineering, 020206 networking & telecommunications, Akaike information criterion (AIC), Exponential function, Waves and shallow water, Identification (information), Array signal processing, Tomography, Akaike information criterion, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithm
Abstract: International audience; Correct identification and tracking of stable raypaths are critical for shallow-water acoustic tomography. Separating raypaths using high-resolution methods has been presented to improve resolution ability based on the prior knowledge of the number of raypaths. It is clear that the precise knowledge of the number of raypaths largely determines the separation performance. Therefore, a noise-whitening exponential fitting test (NWEFT) using short-length samples is proposed in this paper to automatically detect the number of raypaths in a shallow-water waveguide. Two information-theoretic criteria are considered as comparative methods in terms of the capability of correct detection. Their performances are tested with simulation data and real data obtained from a small-scale experiment. The experimental results show that the NWEFT can provide satisfactory detection compared to the two classic information-theoretic criteria--the Akaike information criterion (AIC) and the minimum description length (MDL). MDL is asymptotically consistent while AIC overestimates even if analyzed asymptotically. Compared to these criteria, the proposed method is more suitable for short-length data.
Published: 2014
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38. Monitoring and early detection of internal erosion: Distributed sensing and processing

Author: Valeriu Vrabie, Amir A. Khan, Jerome Mars, Yves-Laurent Beck, Guy d'Urso, SEECS, National University of Sciences and Technology, Islamabad, Pakistan, School of Electrical Engineering and Computer Science [Islamabad Univ] (NUST - SEECS), National University of Sciences and Technology [Islamabad] (NUST)- National University of Sciences and Technology [Islamabad] (NUST), Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804 (CRESTIC), Université de Reims Champagne-Ardenne (URCA), EDF - Division Technique Générale (DTG), EDF (EDF), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), and EDF R&D EDF DTG
Subjects: Engineering, Optical fiber, Embankment dams, 0207 environmental engineering, Biophysics, early warning systems, 02 engineering and technology, 01 natural sciences, Temperature measurement, law.invention, leakage detection, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, law, Thermal, Source separation, Internal erosion, 020701 environmental engineering, Remote sensing, Signal processing, optical fiber sensors, business.industry, Mechanical Engineering, 010401 analytical chemistry, 6. Clean water, 0104 chemical sciences, Permeability (earth sciences), 13. Climate action, Heat transfer, surveillance, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; Early detection of leakages in hydraulic infrastructures is important to ensure their safety and security. Significant flow of water through the dike can be an indicator of internal erosion and results in a thermal anomaly. Temperature measurements are therefore capable of revealing information linked to leakage. Optical fiber-based distributed temperature sensors present an economically viable and reliable solution for recording spatio-temporal temperature data over long distances, with spatial and temperature resolutions of 1m and 0.05 C, respectively. The acquired data are influenced by several factors, among them water leakages, heat transfer through the above soil depth, seasonal thermal variations, and the geomechanical environment. Soil properties such as permeability alter the acquired signal locally. This article presents leakage detection methods based on signal processing of the raw temperature data from optical fiber sensors. The first approach based on source separation identifies leakages by separating them from the non-relevant information. The second approach presents a potential alarm system based on the analysis of daily temperature variations. Successful detection results for simulated as well as real experimental setups of Electricité de France are presented.
Published: 2014
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39. Decomposition and dictionary learning for 3D trajectories

Author: Quentin Barthélemy, Anthony Larue, Jerome Mars, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Laboratoire Outils d'Analyse des Données (LOAD), Département Métrologie Instrumentation & Information (DM2I), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, ANR ARTUS, Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA))
Subjects: Shift-invariant, 3D rotation invariant (3DRI), sparse coding, Computer science, online learning, Procrustes registration, [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Orthogonal matching pursuit, Electrical and Electronic Engineering, Invariant (mathematics), Linear combination, [STAT.AP]Statistics [stat]/Applications [stat.AP], Signal processing, K-SVD, business.industry, Dictionary learning, Pattern recognition, Sparse approximation, artificial intelligence, Matching pursuit, machine learning, Control and Systems Engineering, Signal Processing, 3D motion trajectory, Rotation invariant, Computer Vision and Pattern Recognition, Artificial intelligence, business, Neural coding, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Software
Abstract: International audience; A new model for describing a three-dimensional (3D) trajectory is proposed in this paper. The studied trajectory is viewed as a linear combination of rotatable 3D patterns. The resulting model is thus 3D rotation invariant (3DRI). Moreover, the temporal patterns are considered as shift-invariant. This paper is divided into two parts based on this model. On the one hand, the 3DRI decomposition estimates the active patterns, their coefficients, their rotations and their shift parameters. Based on sparse approximation, this is carried out by two non-convex optimizations: 3DRI matching pursuit (3DRI-MP) and 3DRI orthogonal matching pursuit (3DRI-OMP). On the other hand, a 3DRI learning method learns the characteristic patterns of a database through a 3DRI dictionary learning algorithm (3DRI-DLA). The proposed algorithms are first applied to simulation data to evaluate their performances and to compare them to other algorithms. Then, they are applied to real motion data of cued speech, to learn the 3D trajectory patterns characteristic of this gestural language.
Published: 2014
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40. About QLMS Derivations

Author: Quentin Barthélemy, Jerome Mars, Anthony Larue, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Laboratoire Outils d'Analyse des Données (LOAD), Département Métrologie Instrumentation & Information (DM2I), Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST)
Subjects: Discrete mathematics, Applied Mathematics, Quaternionic signal processing, 16. Peace & justice, Adaptive filter, Least mean squares filter, Operator (computer programming), Simulated data, adaptive filtering, Signal Processing, Applied mathematics, quaternionic least mean squares (QLMS), Electrical and Electronic Engineering, Quaternion, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA], Mathematics
Abstract: International audience; In this letter, a review of the quaternionic least mean squares (QLMS) algorithm is proposed. Three versions coming from three derivation ways exist: the original QLMS based on component wise gradients, the HR-QLMS based on a quaternion gradient operator and iQLMS based on an involutions-gradient. Noting and investigating the differences between the three QLMS formulations, we show that the original QLMS suffers from a mistake in the derivation calculus. Thus, we propose to derive rigorously the criterion following the first way, giving the correct version of QLMS. A comparison with the other QLMS versions validates these results on simulated data.
Published: 2014
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41. Automatic Classification of Volcano Seismic Signatures

Author: Jean-Philippe Métaxian, Adolfo Inza, Jerome Mars, Mauro Dalla Mura, Marielle Malfante, Orlando Macedo, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut des Sciences de la Terre (ISTerre), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Faculdad de Geologia, Geofisica y Minas, Universidad Nacional de San Agustin de Arequipa, Universidad Nacional de San Agustín (UNSA), Insituto Geofisico del Peru (IPG), OSUG2020-ANR10 LABX56, ANR-15-IDEX-02, DGA-MRIS, and Instituto Geofísico del Perú (IGP)
Subjects: 010504 meteorology & atmospheric sciences, Feature vector, automatic classification, 010502 geochemistry & geophysics, 01 natural sciences, Geochemistry and Petrology, [SDU.STU.VO]Sciences of the Universe [physics]/Earth Sciences/Volcanology, Earth and Planetary Sciences (miscellaneous), Extensive data, Preprocessor, 0105 earth and related environmental sciences, geography, geography.geographical_feature_category, volcano monitoring, Ubinas Volcano, volcano seismic signal, Random forest, Support vector machine, Tectonics, machine learning, Geophysics, Volcano, volcanic hazards, 13. Climate action, Space and Planetary Science, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Classifier (UML), Seismology, Geology
Abstract: International audience; The prediction of volcanic eruptions and the evaluation of associated risks remain a timely and unresolved issue. This paper presents a method to automatically classify seismic events linked to volcanic activity. As increased seismic activity is an indicator of volcanic unrest, automatic classification of volcano seismic events is of major interest for volcano monitoring. The proposed architecture is based on supervised classification, whereby a prediction model is built from an extensive data set of labeled observations. Relevant events should then be detected. Three steps are involved in the building of the prediction model: (i) signals preprocessing, (ii) representation of the signals in the feature space, and (iii) use of an automatic classifier to train the model. Our main contribution lies in the feature space where the seismic observations are represented by 102 features gathered from both acoustic and seismic fields. Ideally, observations are separable in the feature space, depending on their class. The architecture is tested on 109,609 seismic events that were recorded between June 2006 and September 2011 at Ubinas Volcano, Peru. Six main classes of signals are considered: long-period events, volcanic tremors, volcano tectonic events, explosions, hybrid events, and tornillos. Our model reaches 93.5% ± 0.50% accuracy, thereby validating the presented architecture and the features used. Furthermore, we illustrate the limited influence of the learning algorithm used (i.e., random forest and support vector machines) by showing that the results remain accurate regardless of the algorithm selected for the training stage. The model is then used to analyze 6 years of data.
Published: 2018
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42. Physics-Based Time-Frequency Representations for Underwater Acoustics: Power Class Utilization with Waveguide-Invariant Approximation

Author: Jerome Mars, Grégoire Le Touzé, Julien Bonnel, and Barbara Nicolas
Subjects: Computer science, Applied Mathematics, Acoustics, Scalar (physics), 020206 networking & telecommunications, Context (language use), 02 engineering and technology, 01 natural sciences, law.invention, Nonlinear system, law, 0103 physical sciences, Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Invariant (mathematics), Underwater acoustics, 010301 acoustics, Waveguide, Underwater acoustic communication, Group delay and phase delay
Abstract: Time-frequency (T-F) analysis of signals propagated in dispersive environments or systems is a challenging problem. When considering dispersive waveguides, propagation can be described by modal theory. Propagated signals are usually multicomponent, and the group delay of each mode (i.e., each component) is nonlinear and varies with the mode number. Consequently, existing T-F representations (TFRs) covariant to group delay shifts (GDSs) are not naturally adapted to this context. To overcome this issue, one solution is to approximate the propagation using simple models for which the dispersion properties do not vary with the mode number. If the chosen model is both simple and robust to uncertainties about the waveguide, it can be used to define adapted TFRs, such as the power-class with a suitable power coefficient. This article focuses on a context where this methodology can be applied: low-frequency acoustic propagation in shallow water. In this case, the global oceanic dispersion can be summarized using a single scalar called the waveguide invariant. This parameter can be used to approximate the group delay of each mode with a power law. Consequently, it is possible to use power-class TFRs with a -based power coefficient. Their practical use is demonstrated on two experimental data sets: a man-made implosion used for underwater geoacoustic inversion, and a right-whale impulsive vocalization that can be used to localize the animal.
Published: 2013
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43. Automated segmentation of linear time-frequency representations of marine-mammal sounds

Author: Cedric Gervaise, Cornel Ioana, Julien Huillery, Florian Dadouchi, Jerome Mars, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Chaire CHORUS - Fondation Grenoble-INP, Institut Polytechnique de Grenoble - Grenoble Institute of Technology, Modélisation Multiéchelle pour la Conception, Ampère (AMPERE), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ANR ASTRID, and DGA
Subjects: Sound Spectrography, Time Factors, Acoustics and Ultrasonics, Computer science, Common Dolphins, Oceans and Seas, Acoustics, Marine Biology, 02 engineering and technology, 01 natural sciences, Statistical power, Pattern Recognition, Automated, Background noise, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Arts and Humanities (miscellaneous), 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, False positive paradox, Animals, Segmentation, 14. Life underwater, Marine Mammals, 010301 acoustics, Chi-Square Distribution, business.industry, segmentation, Reproducibility of Results, Signal Processing, Computer-Assisted, 020206 networking & telecommunications, Pattern recognition, Density estimation, Theory Probability, Thresholding, Detection, Noise, Linear Models, Spectrogram, France, Artificial intelligence, Vocalization, Animal, business, Bioacoustics, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithms, Smoothing, Environmental Monitoring
Abstract: International audience; Many marine mammals produce highly nonlinear frequency modulations. Determining the time-frequency support of these sounds offers various applications, which include recognition, localization, and density estimation. This study introduces a low parameterized automated spectrogram segmentation method that is based on a theoretical probabilistic framework. In the first step, the background noise in the spectrogram is fitted with a Chi-squared distribution and thresholded using a Neyman-Pearson approach. In the second step, the number of false detections in time-frequency regions is modeled as a binomial distribution, and then through a Neyman-Pearson strategy, the time-frequency bins are gathered into regions of interest. The proposed method is validated on real data of large sequences of whistles from common dolphins, collected in the Bay of Biscay (France). The proposed method is also compared with two alternative approaches: the first is smoothing and thresholding of the spectrogram; the second is thresholding of the spectrogram followed by the use of morphological operators to gather the time-frequency bins and to remove false positives. This method is shown to increase the probability of detection for the same probability of false alarms
Published: 2013
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44. Hausdorff Distance Applied On Real Data Experiment For Underwater Localization

Author: Jerome Mars, Cornel Ioana, Pedro Eugenio Martins de Magalhaes, Xavier Cristol, Mars, Jerome, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Thales Underwater Systems (Thalès) (TUS), and Thales Underwater Systems
Subjects: business.industry, 020206 networking & telecommunications, 02 engineering and technology, Hausdorff Distance, Tracking (particle physics), Surface (topology), [SDU.STU.OC] Sciences of the Universe [physics]/Earth Sciences/Oceanography, Underwater source localization, Underwater Ray path propagation, Range (mathematics), Hausdorff distance, TDOAs correlation, Real signal, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Geoacoustic Inversion Underwater problem Matched Field Processing, Artificial intelligence, Underwater, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography, Mathematics, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; — This paper addresses the problem of localizing and tracking a surface or underwater vessel with the technique called as Hausdorff Distance. Two proposed approaches, based on TDOAs comparison, were used for 2-D localization, in range and depth, with one sensor only, and have been successfully applied to localize a motionless unknown target in a tank's experiment. Results in terms of the localization accuracy have been obtained with real signal and the performance of the proposed localization techniques have been demonstrated and confirmed by simulation with respect of signal-to-noise ratio and compared with the correlation techniques used nowadays for single hydrophones.
Published: 2017

45. Enhancement of an Optical Fiber Sensor: Source Separation Based on Brillouin Spectrum

Author: Jerome Mars, Valeriu Vrabie, Sylvain Blairon, Alexandre Girard, Jean-Marie Henault, Edouard Buchoud, Guy d'Urso, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804 (CRESTIC), Université de Reims Champagne-Ardenne (URCA), EDF (EDF), EDF, SIGMAPHY, and EDF R&D
Subjects: Spectrum analyzer, Optical fiber, Rayleigh scattering measurements, General Computer Science, Source Separation, Physics::Optics, 02 engineering and technology, non-negative least-squares algorithm, Brillouin Spectrum, 01 natural sciences, law.invention, non-negative matrix factorization, Superposition principle, 020210 optoelectronics & photonics, Optics, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, law, Brillouin scattering, 0202 electrical engineering, electronic engineering, information engineering, General Materials Science, Physics, Optical fiber cable, business.industry, 010401 analytical chemistry, General Engineering, Distributed acoustic sensing, 0104 chemical sciences, Brillouin zone, Optical Fiber Sensor, Fiber optic sensor, lcsh:Electrical engineering. Electronics. Nuclear engineering, Brillouin optical time domain analyzer, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, lcsh:TK1-9971, structural-health monitoring
Abstract: International audience; Distributed optical fiber sensors have gained an increasingly prominent role in structural-health monitoring. These are composed of an optical fiber cable in which a light impulse is launched by an opto-electronic device. The scattered light is of interest in the spectral domain: the spontaneous Brillouin spectrum is centered on the Brillouin frequency, which is related to the local strain and temperature changes in the optical fiber. When coupled with an industrial Brillouin optical time-domain analyzer (B-OTDA), an optical fiber cable can provide distributed measurements of strain and/or temperature, with a spatial resolution over kilometers of 40 cm. This paper focuses on the functioning of a B-OTDA device, where we address the problem of the improvement of spatial resolution. We model a Brillouin spectrum measured within an integration base of 1 m as the superposition of the elementary spectra contained in the base. Then, the spectral distortion phenomenon can be mathematically explained: if the strain is not constant within the integration base, the Brillouin spectrum is composed of several elementary spectra that are centered on different local Brillouin frequencies. We propose a source separation methodology approach to decompose a measured Brillouin spectrum into its spectral components. The local Brillouin frequencies and amplitudes are related to a portion of the integration base where the strain is constant. A layout algorithm allows the estimation of a strain profile with new spatial resolution chosen by the user. Numerical tests enable the finding of the optimal parameters, which provides a reduction to 1 cm of the 40-cm spatial resolution of the B-OTDA device. These parameters are highlighted during a comparison with a reference strain profile acquired by a 5-cm-resolution Rayleigh scatter analyzer under controlled conditions. In comparison with the B-OTDA strain profile, our estimated strain profile has better accuracy, with centimeter spatial resolut ion.
Published: 2013
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46. Target-depth estimation in active sonar: Cramer-Rao bounds for a bilinear sound-speed profile

Author: Nicolas Josso, Alexis Mours, Jerome Mars, Cornel Ioana, Yves Doisy, Thales Underwater Systems, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and THALES UNDERWATER SYSTEMS
Subjects: Acoustics and Ultrasonics, Computer science, Acoustics, Bilinear interpolation, Context (language use), Active Sonar, Depth Estimation, 01 natural sciences, Sonar, 03 medical and health sciences, 0302 clinical medicine, Arts and Humanities (miscellaneous), 0103 physical sciences, Position Estimation, Range (statistics), Sound Speed Profile, 14. Life underwater, 030223 otorhinolaryngology, 010301 acoustics, Range Estimation, Estimator, Cramer-Rao Bounds, Ray-back Propagation, Experimental Tank, Marine mammals and sonar, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Cramér–Rao bound
Abstract: International audience; This paper develops a localization method to estimate the depth of a target in the context of active sonar, at long ranges. The target depth is tactical information for both strategy and classification purposes. The Cramer–Rao lower bounds for the target position as range and depth are derived for a bilinear profile. The influence of sonar parameters on the standard deviations of the target range and depth are studied. A localization method based on ray back-propagation with a probabilistic approach is then investigated. Monte-Carlo simulations applied to a summer Mediterranean sound-speed profile are performed to evaluate the efficiency of the estimator. This method is finally validated on data in an experimental tank.
Published: 2016
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47. Hausdorff distance optimization used for underwater passive source localization with real data experiment

Author: Pedro Martins de Magalhães, Jerome Mars, Cornel Ioana, Xavier Cristol, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Thales Underwater Systems (Thalès) (TUS), and Magalhaes, Pedro
Subjects: [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], [SPI.ACOU] Engineering Sciences [physics]/Acoustics [physics.class-ph], Hausdorff distance, Underwater passive source detection, sum of minimum difference, Underwater propagation, Ray path simulation, Hausdroff Distance, Sum of minimum distance), [SDU] Sciences of the Universe [physics], [SDU]Sciences of the Universe [physics], [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Source localization, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract: This paper addresses passive source localization using the TDOAs with comparison between simulations using the acoustic ray path propagation method and recorded signal in real experiment in a tank (from GIPSA-Lab, France), using only one sensor respectively for transmission and reception, not being possible to utilize the beamforming approach. Two new techniques are presented in order to find correct position, in range and depth, 1) the Hausdorff distance and 2) sum of minimum difference. Results in terms of the localization accuracy will be shown with respect of signal-to-noise ratio., Este trabalho descreve a localização passiva de alvos usando a comparação dos TDOAs entre os simulados no Matlab, usando o método de propagação acústica ray path e os sinais gravados em um experimento real no tanque do GIPSA-LAB, Franca, usando apenas um sensor na transmissão e outro na recepção, não sendo possível utilizar a técnica beamforming. Duas técnicas novas são apresentadas para achar a correta posição, em distancia e em profundidade, 1) Hausdorff Distance e 2) soma das mínimas diferenças. Resultados em termos de precisão na localização serão mostrados com os relação a signal-to-noise ratio.
Published: 2016

48. Target-depth estimation in active sonar

Author: Cornel Ioana, Alexis Mours, Nicolas Josso, Yves Doisy, Jerome Mars, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Thales Underwater Systems, and Mars, Jerome
Subjects: Acoustics and Ultrasonics, Computer science, Acoustics, Elevation, Probabilistic logic, Experimental data, Estimator, 01 natural sciences, 030507 speech-language pathology & audiology, 03 medical and health sciences, Noise, Identification (information), Arts and Humanities (miscellaneous), 0103 physical sciences, Statistics, Path (graph theory), 14. Life underwater, Marine mammals and sonar, 0305 other medical science, 010301 acoustics, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; In active sonar, the objectives are to detect, localize and classify an underwater target. Azimuth and range are often used in anti-submarine warfare to localize targets. The depth may also be used as the key tactical information for strategy purposes or as a good feature for target classification or discrimination. Two dimensional arrays as ank arrays, cylindrical arrays, and hullmounted arrays have access to elevation angles. Even linear towed arrays can give some information about the elevation using the different conical bearings measured when multipath propagation arises. In the context of long ranges and summer Mediterranean sound-speed profile (SSP), this paper presents a new target-depth estimation method, which uses elevation and arrival time measures from one sonar ping in a multipath environment. This method is based on ray back-propagation with a probabilistic approach. This localization algorithm minimizes the mean-squared error of elevation angles at the receiver and arrival times between a model and measures. This method is tested through Monte-Carlo simulations of classic active sonar scenarios and using experimental data from a real reduced-scaled tank. In active sonar, acoustic waves can take the same path on the way back or another path, so ray path combinations can occur. Our localization method discusses also about this ray identification, or how these combined acoustic paths were managed.
Published: 2016
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49. Neyman-Pearson detection of underwater bioacoustic signals

Author: Cedric Gervaise, Julien Huillery, Florian Dadouchi, Jerome Mars, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Ampère, Département Méthodes pour l'Ingénierie des Systèmes (MIS), Ampère (AMPERE), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Chaire CHORUS - Fondation Grenoble-INP, Institut Polytechnique de Grenoble - Grenoble Institute of Technology, ANR-12-ASTR-0021,MER CALME,Caractérisation Acoustique de Littoraux Marins et de leurs Ecosystèmes(2012), Mars, Jerome, Accompagnement spécifique des travaux de recherches et d’innovation Défense - Caractérisation Acoustique de Littoraux Marins et de leurs Ecosystèmes - - MER CALME2012 - ANR-12-ASTR-0021 - ASTRID - VALID, Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-École Centrale de Lyon (ECL), and Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Subjects: Noise power, Acoustics and Ultrasonics, Computer science, Bioacoustics, business.industry, Acoustics, Detector, Pattern recognition, Density estimation, 01 natural sciences, Binomial distribution, Background noise, 03 medical and health sciences, Noise, 0302 clinical medicine, Arts and Humanities (miscellaneous), 0103 physical sciences, Spectrogram, 14. Life underwater, Artificial intelligence, 030223 otorhinolaryngology, business, 010301 acoustics, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; The use of passive acoustic for the classification, localization, and density estimation of populations of marine mammals is a current area of interest. It is a cheap and an efficient alternative to visual surveys. However, the lack of an efficient automatic detector for unknown marine mammal calls greatly undermines the feasibility of those tasks, especially when dealing with species showing a great variability of calls. This study adds one more step toward the fully automatic detection of unknown bioacoustic signals in impulsive, non-stationary, and colored ocean noise. The detection procedure is a two-steps fully statistical method solely based on the knowledge of the background noise in the spectrogram. The first step models the noise power as a chi-squared distribution, which parameter is estimated. The signal is then detected using a Neyman-Pearson approach, providing a binary spectrogram that contains false and true detections. The second step removes a significant amount of false detections from the binary spectrogram. The time-frequency distribution of false detections is fitted with a correlated binomial distribution, which is used to discriminate patches of detections (signal) from uniformly distributed detections (false alarms). Examples showing the applicability of this method on several real underwater sounds are presented.
Published: 2016

50. Sparse Deconvolution for Moving-Source Localization

Author: Mai Quyen Pham, Jerome Mars, Barbara Nicolas, Benoit Oudompheng, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), MicrodB, Imagerie Ultrasonore, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), DGA MRIS, DGA / RAPID ARMADA, Mars, Jerome, Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Blind deconvolution, Beamforming, business.industry, Moving-source localization, 020206 networking & telecommunications, Pattern recognition, 02 engineering and technology, Sparse approximation, 01 natural sciences, Simulated data, Smoothed L1/L2 regularization, 0103 physical sciences, Source localization, 0202 electrical engineering, electronic engineering, information engineering, Beamforming deconvolution, Deconvolution, Smooth approximation, Artificial intelligence, business, 010301 acoustics, Sparse representation, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Mathematics, Acoustic signal processing
Abstract: International audience; In this paper, we propose a method for moving-source localization based on beamforming output and on sparse representation of the source positions. The goal of this method is to achieve spatial deconvolution of the beamforming, to provide accurate source localization for pass-by experiments. To perform this deconvolution, we use a smooth approximation of L1/L2 [1], which is well suited for the recovery of sparse signals. We validate this method on simulated data, and compare it to the DAMAS-MS method [2], one of the classical methods used in beamforming deconvolution.
Published: 2016
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