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1. Short echo time dual-frequency MR Elastography with Optimal Control RF pulses

Author

Pilar Sango-Solanas, Kevin Tse Ve Koon, Eric Van Reeth, Helene Ratiney, Fabien Millioz, Cyrielle Caussy, and Olivier Beuf

Subjects
Medicine, Science
Abstract

Abstract Magnetic Resonance Elastography (MRE) quantifies the mechanical properties of tissues, typically applying motion encoding gradients (MEG). Multifrequency results allow better characterizations of tissues using data usually acquired through sequential monofrequency experiments. High frequencies are difficult to reach due to slew rate limitations and low frequencies induce long TEs, yielding magnitude images with low SNR. We propose a novel strategy to perform simultaneous multifrequency MRE in the absence of MEGs: using RF pulses designed via the Optimal Control (OC) theory. Such pulses control the spatial distribution of the MRI magnetization phase so that the resulting transverse magnetization reproduces the phase pattern of an MRE acquisition. The pulse is applied with a constant gradient during the multifrequency mechanical excitation to simultaneously achieve slice selection and motion encoding. The phase offset sampling strategy can be adapted according to the excitation frequencies to reduce the acquisition time. Phantom experiments were run to compare the classical monofrequency MRE to the OC based dual-frequency MRE method and showed excellent agreement between the reconstructed shear storage modulus G′. Our method could be applied to simultaneously acquire low and high frequency components, which are difficult to encode with the classical MEG MRE strategy.

Published
2022
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2. Hearing in Noise: The Importance of Coding Strategies—Normal-Hearing Subjects and Cochlear Implant Users

Author

Pierre-Antoine Cucis, Christian Berger-Vachon, Ruben Hermann, Fabien Millioz, Eric Truy, and Stéphane Gallego

Subjects
cochlear implant, coding strategy, Fixed-Channel, Channel-Picking, vocoder simulation, normal-hearing, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

Two schemes are mainly used for coding sounds in cochlear implants: Fixed-Channel and Channel-Picking. This study aims to determine the speech audiometry scores in noise of people using either type of sound coding scheme. Twenty normal-hearing and 45 cochlear implant subjects participated in this experiment. Both populations were tested by using dissyllabic words mixed with cocktail-party noise. A cochlear implant simulator was used to test the normal-hearing subjects. This simulator separated the sound into 20 spectral channels and the eight most energetic were selected to simulate the Channel-Picking strategy. For normal-hearing subjects, we noticed higher scores with the Fixed-Channel strategy than with the Channel-Picking strategy in the mid-range signal-to-noise ratios (0 to +6 dB). For cochlear implant users, no differences were found between the two coding schemes but we could see a slight advantage for the Fixed-Channel strategies over the Channel-Picking strategies. For both populations, a difference was observed for the signal-to-noise ratios at 50% of the maximum recognition plateau in favour of the Fixed-Channel strategy. To conclude, in the most common signal-to-noise ratio conditions, a Fixed-Channel coding strategy may lead to better recognition percentages than a Channel-Picking strategy. Further studies are indicated to confirm this.

Published
2019
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8. Complex Convolutional Neural Networks for Ultrafast Ultrasound Imaging Reconstruction From In-Phase/Quadrature Signal

Author

Damien Garcia, Jingfeng Lu, Sebastien Salles, Denis Friboulet, Fabien Millioz, and Dong Ye

Subjects
Acoustics and Ultrasonics, Computer science, business.industry, Image quality, Deep learning, Q code, Frame rate, Convolutional neural network, Signal, Image Processing, Computer-Assisted, Computer vision, Neural Networks, Computer, Radio frequency, Artificial intelligence, Electrical and Electronic Engineering, business, Instrumentation, In-phase and quadrature components, Ultrasonography
Abstract

Ultrafast ultrasound imaging remains an active area of interest in the ultrasound community due to its ultra-high frame rates. Recently, a wide variety of studies based on deep learning have sought to improve ultrafast ultrasound imaging. Most of these approaches have been performed on radio frequency (RF) signals. However, in-phase/quadrature (I/Q) digital beamformers are now widely used as low-cost strategies. In this work, we used complex convolutional neural networks for reconstruction of ultrasound images from I/Q signals. We recently described a convolutional neural network architecture called ID-Net, which exploited an inception layer designed for reconstruction of RF diverging-wave ultrasound images. In the present study, we derive the complex equivalent of this network; i.e., the Complex-valued Inception for Diverging-wave Network (CID-Net) that operates on I/Q data. We provide experimental evidence that CID-Net provides the same image quality as that obtained from RF-trained convolutional neural networks; i.e., using only three I/Q images, the CID-Net produces high-quality images that can compete with those obtained by coherently compounding 31 RF images. Moreover, we show that CID-Net outperforms the straightforward architecture that consists of processing the real and imaginary parts of the I/Q signal separately, which thereby indicates the importance of consistently processing the I/Q signals using a network that exploits the complex nature of such signals.

Published
2022

17. Time Undersampled Acquisition for Multidimensional Sparse Signals With Application to Magnetic Resonance Spectroscopic Imaging

Author

Rémy Prost, Fabien Millioz, Hélène Ratiney, Magalie Viallon, Jabrane Karkouri, RMN et optique : De la mesure au biomarqueur, 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), Siemens Healthcare, Modeling & analysis for medical imaging and Diagnosis (MYRIAD), 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), and Rayet, Béatrice

Subjects
Interleaving, Computer science, Irregular sampling, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, sensor placement, 02 engineering and technology, sample selection, spiral spectroscopic imaging, Signal, symbols.namesake, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Electrical and Electronic Engineering, Sparse matrix, Time evolution, Magnetic resonance spectroscopic imaging, 020206 networking & telecommunications, undersampling, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Fourier transform, Undersampling, Signal Processing, symbols, least-square, magnetic resonance spectroscopic imaging, A priori and a posteriori, Algorithm
Abstract

International audience; This paper deals with acceleration of multidimensional signal acquisition. The signal is assumed to have multiple discrete spatial dimensions where each point is time varying. The one-dimensional Fourier transform of the time evolution of each point is assumed to have an a priori known bounded support. The Fourier transform of the spatial domain is divided into several partitions, each of which can be sequentially acquired over time. We propose a method for undersampling the time dimension that enables interleaving of the samples hence speeding up signal acquisition. This method, applied to realistic simulated magnetic resonance spectroscopic imaging data (MRSI), leads to a reduction in the acquisition time by a factor of three.

Published
2021

18. Harmonic wideband simultaneous dual‐frequency MR Elastography

Author

Hélène Ratiney, Kevin Tse Ve Koon, Fabien Millioz, Cyrielle Caussy, Olivier Beuf, Pilar Sango Solanas, RMN et optique : De la mesure au biomarqueur, 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), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Cardiovasculaire, métabolisme, diabétologie et nutrition (CarMeN), Université de Lyon-Université de Lyon-Hospices Civils de Lyon (HCL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ANR-11-INBS-0006,FLI,France Life Imaging(2011), ANR-11-IDEX-0007,Avenir L.S.E.,PROJET AVENIR LYON SAINT-ETIENNE(2011), ANR-10-LABX-0064,Imust,Institut for Multiscale Science and Technology : from Fundamental Physics and Chemistry to Engineering in New Material and Processes and Ecotechnologies(2010), Sango Solanas, Pilar, Infrastructures - France Life Imaging - - FLI2011 - ANR-11-INBS-0006 - INBS - VALID, PROJET AVENIR LYON SAINT-ETIENNE - - Avenir L.S.E.2011 - ANR-11-IDEX-0007 - IDEX - VALID, Institut for Multiscale Science and Technology : from Fundamental Physics and Chemistry to Engineering in New Material and Processes and Ecotechnologies - - Imust2010 - ANR-10-LABX-0064 - LABX - VALID, 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), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Shear waves, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Acoustics, [SDV]Life Sciences [q-bio], Phase (waves), Signal-To-Noise Ratio, Imaging phantom, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Motion, 0302 clinical medicine, Aliasing, Image Processing, Computer-Assisted, Humans, Radiology, Nuclear Medicine and imaging, Wideband, Spectroscopy, Physics, MR elastography, Phantoms, Imaging, Viscosity, multifrequency, phase offset undersampling, Magnetic Resonance Imaging, Elasticity, Magnetic resonance elastography, Vibration, [SDV] Life Sciences [q-bio], [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Harmonic, Molecular Medicine, Elasticity Imaging Techniques, 030217 neurology & neurosurgery
Abstract

International audience; Magnetic resonance elastography (MRE) is used to non‐invasively quantify viscoelastic properties of tissues based on the measurement of propagation characteristics of shear waves. Because some of these viscoelastic parameters show a frequency dependence, multifrequency analysis allows us to measure the wave propagation dispersion, leading to a better characterization of tissue properties. Conventionally, motion encoding gradients (MEGs) oscillating at the same frequency as the mechanical excitation encode motion. Hence, multifrequency data is usually obtained by sequentially repeating monochromatic wave excitations experiments at different frequencies. The result is that the total acquisition time is multiplied by a factor corresponding to the number of repetitions of monofrequency experiments, which is a major limitation of multifrequency MRE. In order to make it more accessible, a novel single‐shot harmonic wideband dual‐frequency MRE method is proposed. Two superposed shear waves of different frequencies are simultaneously generated and propagate in a sample. Trapezoidal oscillating MEGs are used to encode mechanical vibrations having frequencies that are an odd multiple of the MEG frequency. The number of phase offsets is optimized to reduce the acquisition time. For this purpose, a sampling method not respecting the Shannon theorem is used to produce a controlled temporal aliasing that allows us to encode both frequencies without any additional examination time. Phantom experiments were run to compare conventional monofrequency MRE with the single‐shot dual‐frequency MRE method and showed excellent agreement between the reconstructed shear storage moduli G′. In addition, dual‐frequency MRE yielded an increased signal‐to‐noise ratio compared with conventional monofrequency MRE acquisitions when encoding the high frequency component. The novel proposed multifrequency MRE method could be applied to simultaneously acquire more than two frequency components, reducing examination time. Further studies are needed to confirm its applicability in preclinical and clinical models.

Published
2021

19. Complex Convolutional Neural Networks for Fast Diverging Wave Imaging

Author

Dong Ye, Jingfeng Lu, Sebastien Salles, Fabien Millioz, Damien Garcia, Denis Friboulet, IEEE, Friboulet, Denis, Modeling & analysis for medical imaging and Diagnosis (MYRIAD), 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), Metislab, LIA CNRS Harbin Institute of Technology, and Imagerie Ultrasonore

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Computer science, Image quality, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Iterative reconstruction, 01 natural sciences, Convolutional neural network, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0103 physical sciences, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, 010301 acoustics, ComputingMilieux_MISCELLANEOUS, business.industry, Deep learning, Ultrasound, Frame rate, Quadrature (mathematics), [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Ultrasound imaging, Artificial intelligence, Radio frequency, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Ultrashort pulse, Algorithm
Abstract

International audience; Single diverging wave (DW) imaging produces ultrasound (US) images at high frame rate (ultrafast) but of low quality. Conventional high-quality DW imaging relies on the coherent compounding of multiple consecutive steered emissions, which in turn reduces the gain in frame rate. Reconstructing high-quality US images for ultrafast imaging using deep learning techniques has recently raised a growing interest in the US community. We recently described a convolutional neural network (CNN) architecture called ID-Net, which exploited an inception layer devoted to the reconstruction of DW ultrasound images using radio frequency (RF) data. We derive in this work the complex equivalent of this network, i.e., the complex inception for DW network (CID-Net), operating on in-phase/quadrature (I/Q) data. We experimentally demonstrate that the CID-Net yields the same image quality as that obtained from the RF-trained CNN, i.e., using only three I/Q images, the CID-Net yields high-quality images competing with those obtained by coherently compounding 31 RF images

Published
2020

20. Reconstruction for Diverging-Wave Imaging Using Deep Convolutional Neural Networks

Author

Denis Friboulet, Jingfeng Lu, Sebastien Salles, Wanyu Liu, Fabien Millioz, Damien Garcia, Modeling & analysis for medical imaging and Diagnosis (MYRIAD), 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), Imagerie Ultrasonore, Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Friboulet, Denis, 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
Signal Processing (eess.SP), [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Acoustics and Ultrasonics, Image quality, Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computation, Concatenation, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Iterative reconstruction, 01 natural sciences, Convolutional neural network, 030218 nuclear medicine & medical imaging, Convolution, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0103 physical sciences, FOS: Electrical engineering, electronic engineering, information engineering, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Electrical and Electronic Engineering, Electrical Engineering and Systems Science - Signal Processing, 010301 acoustics, Instrumentation, ComputingMilieux_MISCELLANEOUS, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, business.industry, Image and Video Processing (eess.IV), Pattern recognition, Electrical Engineering and Systems Science - Image and Video Processing, Frame rate, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Contrast ratio, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

In recent years, diverging wave (DW) ultrasound imaging has become a very promising methodology for cardiovascular imaging due to its high temporal resolution. However, if they are limited in number, DW transmits provide lower image quality compared with classical focused schemes. A conventional reconstruction approach consists in summing series of ultrasound signals coherently, at the expense of frame rate, data volume, and computation time. To deal with this limitation, we propose a convolutional neural network (CNN) architecture, Inception for DW Network (IDNet), for high-quality reconstruction of DW ultrasound images using a small number of transmissions. In order to cope with the specificities induced by the sectorial geometry associated with DW imaging, we adopted the inception model composed of the concatenation of multiscale convolution kernels. Incorporating inception modules aims at capturing different image features with multiscale receptive fields. A mapping between low-quality images and corresponding high-quality compounded reconstruction was learned by training the network using in vitro and in vivo samples. The performance of the proposed approach was evaluated in terms of contrast ratio (CR), contrast-to-noise ratio (CNR), and lateral resolution (LR), and compared with standard compounding method and conventional CNN methods. The results demonstrated that our method could produce high-quality images using only 3 DWs, yielding an image quality equivalent to that obtained with compounding of 31 DWs and outperforming more conventional CNN architectures in terms of complexity, inference time, and image quality.

Published
2019

21. Fast Diverging Wave Imaging Using Deep-Learning-Based Compounding

Author

Damien Garcia, Denis Friboulet, Sebastien Salles, Jingfeng Lu, and Fabien Millioz

Subjects
business.industry, Computer science, Image quality, Deep learning, Ultrasound, Resolution (electron density), Iterative reconstruction, Frame rate, 01 natural sciences, Convolutional neural network, 030218 nuclear medicine & medical imaging, Convolution, 03 medical and health sciences, 0302 clinical medicine, 0103 physical sciences, Ultrasound imaging, Computer vision, Artificial intelligence, business, 010301 acoustics, Ultrashort pulse
Abstract

Diverging wave (DW) ultrasound imaging has become a very promising methodology for ultrafast cardiovascular imaging due to its high temporal resolution. However, if they are limited in number, DW transmits alter image quality compared with classical focused schemes. A conventional reconstruction approach consists in summing successive RF images coherently, at the expense of the frame rate. To deal with this limitation, we propose in this work a convolutional neural network (CNN) architecture for high-quality reconstruction of DW ultrasound images using a small number of transmissions. We experimentally demonstrate that the proposed method produces high-quality images using only three DWs, yielding an image quality equivalent to the one obtained with standard compounding of 31 DWs in terms of contrast and resolution.

Published
2019

22. Discrimination between emboli and artifacts for outpatient transcranial Doppler ultrasound data

Author

Philippe Delachartre, Marilys Almar, Blaise Kevin Guepie, Fabien Millioz, Bruno Sciolla, 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), Images et Modèles, and Atys Medical

Subjects
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Ultrasonography, Doppler, Transcranial, Speech recognition, Embolism, Biomedical Engineering, Likelihood, 02 engineering and technology, Transcranial, Time–frequency approach, Background noise, 03 medical and health sciences, 0302 clinical medicine, Outpatients, 0202 electrical engineering, electronic engineering, information engineering, Humans, Medicine, Artifacts rejection, Computer vision, Doppler Ultrasound, Emboli detection, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], Spectral, kurtosis, business.industry, Removal procedure, Ultrasound, Human physiology, Blood flow, Computer Science Applications, Transcranial Doppler, Cerebrovascular Circulation, 020201 artificial intelligence & image processing, Artificial intelligence, Artifacts, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithms, 030217 neurology & neurosurgery, circulatory and respiratory physiology
Abstract

International audience; This paper addresses the detection of emboli in transcranial Doppler ultrasound data acquired from an original portable device. The challenge is the removal of several artifacts (motion and voice) intrinsically related to long-duration (up to 1 h 40 mn per patient) outpatient signals monitoring from this device, as well as high intensities due to the stochastic nature of blood flow. This paper proposes an adapted removal procedure. This firstly consists of reducing the background noise and detecting the blood flow in the time–frequency domain using a likelihood method for contour detection. Then, a hierarchical extraction of features from magnitude and bounding boxes is achieved for the discrimination of emboli and artifacts. After processing of the long-duration outpatient signals, the number of artifacts predicted as emboli is considerably reduced (by 92% for some parameter values) between the first and the last step of our algorithm.

Published
2017

23. Time samples selection in spiral acquisition for sparse magnetic resonance spectroscopic imaging

Author

Hélène Ratiney, Magalie Viallon, Rémy Prost, Jabrane Karkouri, Fabien Millioz, RMN et optique : De la mesure au biomarqueur, 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), Images et Modèles, Department of Radiology, University Hospital of Geneva, Centre de Recherche et d'Application en Traitement de l'Image et du Signal (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)-École Supérieure Chimie Physique Électronique de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), 5 - RMN et optique : De la mesure aux biomarqueurs, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé ( CREATIS ), 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-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 ), and 2 - Images et Modèles

Subjects
Computer science, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, 02 engineering and technology, Iterative reconstruction, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, spiral spectroscopic imaging, computer.software_genre, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Magnetic resonance spectroscopic imaging, 0302 clinical medicine, Nuclear magnetic resonance, under-sampling, Sampling (signal processing), Voxel, 0202 electrical engineering, electronic engineering, information engineering, medicine, Image resolution, Spiral, [ SDV.IB.IMA ] Life Sciences [q-bio]/Bioengineering/Imaging, ComputingMilieux_MISCELLANEOUS, compressed sensing, medicine.diagnostic_test, business.industry, k-space, 020206 networking & telecommunications, Pattern recognition, Magnetic resonance imaging, Artificial intelligence, business, computer
Abstract

International audience; Magnetic resonance spectroscopic imaging (MRSI) has multiple interests in clinical practice, and especially for brain disease diagnosis. However, it faces quite long acquisition time in practice which limits their use in a clinical environment. Fast MRI acquisitions can help to reduce this acquisition time and intensify their clinical use. In this work, a new fast Magnetic Resonance Spectroscopic image acquisition method is introduced and evaluated based on a k-t space spiral sampling. The time-domain subsampling, below the Shannon-Nyquist rate, is allowed on the hypothesis of a sparse spectrum with an a priori known support. Then the nonzero components of the spectrum of each voxel are recovered using an over-determined least square problem. When data are noise-free the recovered spectrum is exact. In the real-world noisy scenario the error in the recovered spectrum highly depends on the acquired samples. We reduce this error to an acceptable level by selecting irregularly the samples using the Sequential Backward Selection algorithm. A realistic simulated irregular spiral acquisition proves the feasibility of the proposed approach.

Published
2017

24. Accelerating Plane Wave Imaging through Deep Learning-Based Reconstruction: An Experimental Study

Author

Maxime Gasse, Fabien Millioz, Emmanuel Roux, Hervé Liebgott, Denis Friboulet, Images et Modèles, 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), Institut National des Sciences Appliquées (INSA)-Université de Lyon-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), Institut National des Sciences Appliquées (INSA)-Université de Lyon-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), Department of Information Engineering [Firenze], Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Imagerie Ultrasonore, ANR-11-LABX-0063,PRIMES,Physique, Radiobiologie, Imagerie Médicale et Simulation(2011), 2 - Images et Modèles, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé ( CREATIS ), 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-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 ), Università degli Studi di Firenze [Firenze], 3 - Imagerie Ultrasonore, and ANR-11-IDEX-0007-02/11-LABX-0063,PRIMES,Physique, Radiobiologie, Imagerie Médicale et Simulation ( 2011 )

Subjects
[SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], [ SPI.ACOU ] Engineering Sciences [physics]/Acoustics [physics.class-ph], [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [ SDV.IB.IMA ] Life Sciences [q-bio]/Bioengineering/Imaging
Abstract

International audience; Background, Motivation and ObjectiveObtaining ultrafast images using steered plane wave (PW) imaging remains a challenge due to the trade-off between image quality and frame rate. PW imaging indeed relies on compounding in order to preserve a good image quality, usually using multiple successive emissions, which in turn yields a decrease of the frame rate. As opposed to this classicalapproach, we propose a new strategy to reduce the number of emitted PWs. This is done using a deep learning technique, i.e. by training a convolutional neural network (CNN) to reconstruct high quality images using a small number of PW emissions (typically two).Statement of Contribution/MethodsThe training data was generated as follows. A standard linear array probe (ATL L7-4 38 mm, 128 elements, bandwidth 4-7 MHz, transmitted frequency 5.208 MHz) was interfaced with a Verasonics system. 31 PWs were emitted (angle spanning ±15° in one degree steps). For each PW emission, the received raw-data was then processed using the f-k migration method [Garcia et al. IEEE UFFC13] to produce the reconstructed RF image. These 31 images were then averaged to get the final compounded image used as a reference in the training process, while only two images corresponding to angles -10° and +10° were given as input to the CNN.A total of 2000 reference images were acquired, 1000 images from in-vivo tissues (carotid and thyroid), 500 images from a Gammex phantom (410 SCG), and 500 images from a water-based polyamide mixture with air bubbles. 1800 images were used for training, 100 for validation, and 100 for testing. The CNN employed in this study is a fully-convolutionalneural network, with 4 hidden layers and maxout activation units. Training is done via stochastic gradient descent and the minimized loss function is the mean absolute error (MAE) between the CNN-reconstructed and the reference RF image, plus a L2 regularization term.Results/DiscussionOur approach was evaluated by comparing the CNN-based reconstruction of the test images with the conventional compounding method (see Fig. 1). Preliminary results indicate that CNN-based reconstruction is a very promising approach, as the reconstructed images are visually very close to those obtained by standard compounding with 31 PWs, while implying the emission of only 2 PWs.

Published
2017

25. Notice of Removal: Accelerating plane wave imaging through deep learning-based reconstruction: An experimental study

Author

Emmanuel Roux, Herve Liebgott, Maxime Gasse, Fabien Millioz, and Denis Friboulet

Subjects
Artificial neural network, Image quality, Computer science, business.industry, Deep learning, Plane wave, Iterative reconstruction, Frame rate, 01 natural sciences, Convolutional neural network, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Quality (physics), Computer Science::Computer Vision and Pattern Recognition, 0103 physical sciences, Computer vision, Artificial intelligence, business, 010301 acoustics
Abstract

Obtaining ultrafast images using steered plane wave (PW) imaging remains a challenge due to the trade-off between image quality and frame rate. PW imaging indeed relies on compounding in order to preserve a good image quality, usually using multiple successive emissions, which in turn yields a decrease of the frame rate. As opposed to this classical approach, we propose a new strategy to reduce the number of emitted PWs. This is done using a deep learning technique, i.e. by training a convolutional neural network (CNN) to reconstruct high quality images using a small number of PW emissions (typically two).

Published
2017

26. High-Quality Plane Wave Compounding using Convolutional Neural Networks

Author

Fabien Millioz, Maxime Gasse, Damien Garcia, Denis Friboulet, Emmanuel Roux, Herve Liebgott, Images et Modèles, 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), Imagerie Ultrasonore, ANR-11-LABX-0063,PRIMES,Physique, Radiobiologie, Imagerie Médicale et Simulation(2011), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), 2 - Images et Modèles, 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 ), 3 - Imagerie Ultrasonore, and ANR-11-IDEX-0007-02/11-LABX-0063,PRIMES,Physique, Radiobiologie, Imagerie Médicale et Simulation ( 2011 )

Subjects
Standards, Speedup, [ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing, Acoustics and Ultrasonics, Computer science, Plane wave, Iterative reconstruction, 01 natural sciences, Convolutional neural network, 030218 nuclear medicine & medical imaging, Imaging, 03 medical and health sciences, 0302 clinical medicine, Optics, Image resolution, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0103 physical sciences, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Electrical and Electronic Engineering, 010301 acoustics, Instrumentation, Artificial neural network, [ INFO.INFO-IM ] Computer Science [cs]/Medical Imaging, business.industry, Acoustics, Frame rate, Compounding, Image reconstruction, Frequency control, business, Algorithm, Neural networks
Abstract

International audience; Single plane wave (PW) imaging produces ultrasound (US) images of poor quality at high frame rates (ultrafast). High-quality PW imaging usually relies on the coherent compounding of several successive steered emissions (typically more than ten), which in turn results in a decreased frame rate. We propose a new strategy to reduce the number of emitted PWs by learning a compounding operation from data, i.e. by training a convolutional neural network (CNN) to reconstruct high quality images using a small number of transmissions. We present experimental evidence that this approach is promising, as we were able to produce high-quality images from only 3 PWs, competing in terms of contrast ratio and lateral resolution with the standard compounding of 31 PWs (10x speed-up factor).

Published
2017

27. Gaussian Noise Time-Varying Power Spectrum Estimation with Minimal Statistics

Author

Nadine Martin, Fabien Millioz, Julien Huillery, 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), Centre de recherche en neurosciences de Lyon (CRNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institute for Digital Communications (IDCOM), University of Edinburgh, GIPSA - Signal et Automatique pour la surveillance, le diagnostic et la biomécanique (GIPSA-SAIGA), Département Automatique (GIPSA-DA), 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)-Département Images et Signal (GIPSA-DIS), 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
Noise measurement, Noise (signal processing), Estimation theory, Estimator, White noise, Gradient noise, symbols.namesake, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Gaussian noise, Signal Processing, Statistics, symbols, Value noise, Electrical and Electronic Engineering, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Mathematics
Abstract

International audience; Given the spectrogram of an unknown signal embedded in a Gaussian noise, the Minimal Statistics Maximum Likelihood (MiniSMaL) estimator of the noise time-varying power spectrum is presented and a method to tune one of its parameter is studied. The objective of the minimal statistics approach is to separate the signal of interest from the noise in order to estimate properly the probabilistic properties of the latter. Considering an initial time-frequency estimation neighborhood, the strategy relies on the selection of a minimal subset containing the timefrequency coefficients with the smallest values. Estimators of the noise are then sought from this minimal subset. In this work the case of a spectrogram constructed from a finite-length discretetime noisy signal is presented. This study extends previous works on minimal statistics on two aspects: first, the Maximum Likelihood estimate of the noise is formulated according to a clear analysis of the probability distribution of the time-frequency coefficients. Second, the choice of an optimal minimal subset is investigated. The signal versus noise discrimination property of the spectral kurtosis is used to select a minimal subset which ensures a fair trade-off between the bias and the variance of the estimator. The resulting performances are discussed and compared with those of other methods through numerical simulations on synthetic signals. The use of the MiniSMaL estimator in a time-frequency detection procedure is finally illustrated on a realworld signal.

Published
2014

28. A low-complexity sub-Nyquist sampling system for wideband Radar ESM receivers

Author

Michael A. Lexa, Michael Davies, Mehrdad Yaghoobi, Fabien Millioz, 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), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Signal processing, signal sampling, electronic support measures, Computer science, radar receivers, wideband radiofrequency sampling, LoCoMC, Receivers, low-complexity multicoset sampling setting, law.invention, Sampling (signal processing), law, categₛt2i, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Nyquist–Shannon sampling theorem, Electronic Surveillance, Radar, Wideband, subNyquist sampling system, Delays, wideband radar ESM receivers, ComputingMilieux_MISCELLANEOUS, compressed sensing, Multiple signal classification, Wideband Radar, Sub-Nyquist Sampling, business.industry, Images et Modèles, reseauₐucun, Sampling (statistics), labₐucun, Compressive Sampling, radar signal processing, Transforms, Sampling system, Compressed sensing, Coherent sampling, Nyquist rate, Telecommunications, business, Algorithm
Abstract

The problem of efficient sampling of wideband Radar signals for Electronic Support Measures (ESM) is investigated in this paper. Wideband radio frequency sampling generally needs a sampling rate at least twice the maximum frequency of the signal, i.e. Nyquist rate, which is generally very high. However, when the signal is highly structured, like wideband Radar signals, we can use the fact that signals do not occupy the whole spectrum and instead, there exists a parsimonious structure in the time-frequency domain. Here, we use this fact and introduce a novel low complexity sampling system, which has a recovery guarantee, assuming that received RF signals follow a particular structure. The proposed technique is inspired by the compressive sampling of sparse signals and it uses a multi-coset sampling setting, however it does not involve a computationally expensive reconstruction step. We call this here Low-Complexity Multi-Coset (LoCoMC) sampling technique. Simulation results, show that the proposed sub-Nyquist sampling technique works well in simulated ES scenarios.

Published
2014

29. Sparse Detection in the Chirplet Transform: Application to FMCW Radar Signals

Author

Michael Davies, Fabien Millioz, Centre de recherche en neurosciences de Lyon (CRNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institute for Digital Communications (IDCOM), University of Edinburgh, IDCOM, School of Engineering [Edinburgh], University of Edinburgh-University of Edinburgh-Institute for Digital Communications (IDCOM), and European Project: 225913,EC:FP7:ICT,FP7-ICT-2007-C,SMALL(2009)

Subjects
[SPI.OTHER]Engineering Sciences [physics]/Other, Speech recognition, 02 engineering and technology, FMCW radar, symbols.namesake, 0202 electrical engineering, electronic engineering, information engineering, Chirp, LPI radar, Electrical and Electronic Engineering, Chirplet transform, Low probability of intercept radar, Mathematics, Estimation theory, business.industry, 020206 networking & telecommunications, Pattern recognition, chirplet transform, Time–frequency analysis, Continuous-wave radar, Fourier transform, Signal Processing, symbols, 020201 artificial intelligence & image processing, Artificial intelligence, business, parameter estimation, Frequency modulation
Abstract

International audience; This paper aims to detect and characterize a signal coming from frequency modulation continuous wave radars. The radar signals are made of piecewise linear frequency modulations. The maximum chirplet transform (MCT), a simplification of the chirplet transform is proposed. A detection of the relevant maximum chirplets is proposed based on iterative masking, an iterative detection followed by window subtraction that does not require the recomputation of the spectrum. This detection is designed to provide a sparse subset of maximum chirplet coefficients. The chirplets are then gathered into linear chirps whose starting time, length, and chirprate are estimated. These chirps are then gathered again back into the different frequency modulation continuous wave signals, ready to be classified. An illustration is provided on synthetic data.

Published
2012

30. Measurement of instantaneous shaft speed by advanced vibration signal processing- application to wind turbine gearbox

Author

Walter Bartelmus, Radoslaw Zimroz, Nadine Martin, Tomasz Barszcz, Jacek Urbanek, Fabien Millioz, Wroclaw University of Science and Technology, Department of Mechanical Engineering and Robotics, AGH University of Science and Technology [Krakow, PL] (AGH UST), GIPSA - Signal et Automatique pour la surveillance, le diagnostic et la biomécanique (GIPSA-SAIGA), Département Automatique (GIPSA-DA), 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)-Département Images et Signal (GIPSA-DIS), 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 Martin, Nadine

Subjects
0209 industrial biotechnology, Signal processing, Wind power, business.industry, Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Condition monitoring, Rotational speed, Control engineering, 02 engineering and technology, Turbine, Instantaneous phase, Signal, Vibration, 020303 mechanical engineering & transports, 020901 industrial engineering & automation, 0203 mechanical engineering, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Control and Systems Engineering, business, Instrumentation, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Condition monitoring of machines working under non-stationary operations is a one of the most challenging problems in maintenance. Wind turbine is an example of such class of machines. As it was suggested by Bartelmus and Zimroz, one of effective approach may be to identify operating conditions and investigate their influence on used diagnostic features. Commonly used methods based on measurement electric current, rotational speed, power and other process variables requires additional equipment (sensors, acquisition cards) and software. It is proposed to use advanced signal processing techniques for instantaneous shaft speed recovery from vibration signal. It may be used instead of extra channels or in parallel as signal verification.

Published
2010

31. A procedure of vibration analysis from planetary gearbox under non-stationary cyclic operations by instantaneous frequency estimation in time-frequency domain

Author

Radoslaw Zimroz, Fabien Millioz, Nadine Martin, Wroclaw University of Science and Technology, GIPSA - Signal et Automatique pour la surveillance, le diagnostic et la biomécanique (GIPSA-SAIGA), Département Automatique (GIPSA-DA), 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)-Département Images et Signal (GIPSA-DIS), 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 Martin, Nadine

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Condition monitoring of gearboxes via vibration analysis is well-recognized approach in scientific literature and also in engineering practice. However, in many cases machine works under non-stationary operating conditions (load and speed variation), that often requires special signal processing and pattern recognition suitable for time varying systems. One of key problem is to identify variation of external load or speed. Measurement of current consumed by electric motor or instantaneous speed obtained by processing of tachometer signal, in many practical situations (industrial condition) may be difficult or impossible. In such case non-stationary load variation may be identified by extraction of information hidden in vibration signal. For example it may be extracted from amplitude or frequency demodulation. Unfortunately both approaches are difficult (or even impossible) for our machines due to complexity of design and wide range of load/speed variation. In order to avoid these constrains in this paper new approach will be proposed and exploited for complex, multistage gearboxes with planetary stage. To extract information related to cyclic load variation, an instantaneous speed obtained via time-frequency spectrogram will be used. Algorithms for Instantaneous Frequency (IF) estimation via T-F maps have been initially developed by Millioz and Martin. In this paper a novel procedure for Instantaneous Speed estimation (based on IF identification by mentioned automatic algorithm) will be proposed, next the procedure will be applied to vibration signals from planetary gearboxes.

Published
2010

32. Estimation of a white Gaussian noise in the Short Time Fourier Transform based on the spectral kurtosis of the minimal statistics: application to underwater noise

Author

Nadine Martin, Fabien Millioz, GIPSA - Signal et Automatique pour la surveillance, le diagnostic et la biomécanique (GIPSA-SAIGA), Département Automatique (GIPSA-DA), 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)-Département Images et Signal (GIPSA-DIS), 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), and Martin, Nadine

Subjects
[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Stochastic resonance, Computer science, 02 engineering and technology, 03 medical and health sciences, symbols.namesake, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Statistics, Phase noise, 0202 electrical engineering, electronic engineering, information engineering, Detection theory, Value noise, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Noise measurement, Detector, Short-time Fourier transform, Spectral density estimation, Estimator, 020206 networking & telecommunications, Salt-and-pepper noise, White noise, Speech processing, Noise floor, Time–frequency analysis, Gradient noise, Noise, Fourier transform, Additive white Gaussian noise, Gaussian noise, Colors of noise, symbols, Kurtosis, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery
Abstract

International audience; In this paper we present a noise level estimator using minimal values of the Short Time Fourier Transform of a signal embedded in a white Gaussian noise. The spectral kurtosis of the smallest values is used to estimate the variance of the noise without any a priori knowledge on the signal. This estimation is illustrated on both a synthetic and speech signal. A dolphin whistle detection in underwater noise is given as an application.

Published
2010

33. Time-frequency and Time-Scale analysis of Barkhausen noise signals

Author

Linilson Rodrigues Padovese, Fabien Millioz, Nadine Martin, Department of Mechanical Engineering, LADIN, GIPSA - Signal et Automatique pour le Diagnostic et la Surveillance (GIPSA-SA-IGA), 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)-Département Automatique (GIPSA-DA), 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
Materials science, Barkhausen noise, Acoustics, Aerospace Engineering, Time-frequency representation, 02 engineering and technology, non-destructive test, symbols.namesake, Time–frequency representation, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Nondestructive testing, 0202 electrical engineering, electronic engineering, information engineering, Barkhausen effect, Time-scale representation, business.industry, Mechanical Engineering, Magnetic barkhausen noise, 020206 networking & telecommunications, 021001 nanoscience & nanotechnology, Microstructure, Ferromagnetic steel, Time–frequency analysis, Ferromagnetism, Capon spectral estimator, symbols, Spectrogram, Feature extraction, hardness analysis, 0210 nano-technology, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Carrying out information about the microstructure and stress behaviour of ferromagnetic steels, Magnetic Barkhausen Noise (MBN) has been used as a basis for effective Non Destructive Testing methods, opening new areas in industrial applications. One of the factors that determines the quality and reliability of the MBN analysis is the way information is extracted from the signal. Commonly, simple scalar parameters are used to characterize the information content, such as amplitude maxima and signal root mean square. This paper presents a new approach based on the time-frequency analysis. The experimental test case relates the use of MBN signals to characterize hardness gradients in a AISI4140 steel. To that purpose are assessed different time-frequency and time scale representations such as the spectrogram, the Wigner-Ville distribution, the Capongram, the ARgram obtained from an AutoRegressive model, the scalogram, and the Mellingram obtained from a Mellin transform. It is shown that, due to non-stationary characteristics of the MBN, time-frequency representations can provide a rich and new panorama of these signals. Extraction techniques of some time-frequency parameters are used to allow a diagnostic process. Comparison with results obtained by the classical method highlights the improvement on the diagnosis provided by the method proposed.

Published
2009

34. Estimation de la densité spectrale de puissance d'un bruit gaussien basée sur le kurtosis des statistiques minimales

Author

Fabien Millioz, Nadine Martin, Martin, Nadine, GIPSA - Signal et Automatique pour le Diagnostic et la Surveillance (GIPSA-SA-IGA), 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)-Département Automatique (GIPSA-DA), 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
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

National audience; Nous présentons dans ce papier un estimateur de la variance d'un bruit blanc gaussien à partir des valeurs minimales de la transformée de Fourier à court terme d'un signal composé d'un signal quelconque noyé dans un bruit à estimer. Le kurtosis des statistiques minimales dans le plan temps-fréquence est utilisé pour estimer la variance du bruit à partir de points ne contenant que du bruit.

Published
2009

35. On the Description of Spectrogram Probabilities with a Chi-Squared Law

Author

Nadine Martin, Fabien Millioz, Julien Huillery, GIPSA - Signal et Automatique pour le Diagnostic et la Surveillance (GIPSA-SA-IGA), 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)-Département Automatique (GIPSA-DA), 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
Chis-quared law, Kullback–Leibler divergence, Kullback-Leibler divergence, Probability density function, Time-Frequency statistical detection, 02 engineering and technology, 01 natural sciences, 010104 statistics & probability, symbols.namesake, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Statistics, 0202 electrical engineering, electronic engineering, information engineering, Spectrogram probability distribution, 0101 mathematics, Electrical and Electronic Engineering, Divergence (statistics), Gaussian process, Mathematics, Law of total probability, 020206 networking & telecommunications, Computer Science::Sound, Likelihood-ratio test, Law, Signal Processing, symbols, Probability distribution, Spectrogram, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

see also Acoustics, Speech, and Signal Processing, IEEE Transactions on; International audience; Given a correlated Gaussian signal, may a chi-squared law of probability always be used to describe a spectrogram coefficient distribution? If not, would a "chi-squared description" lead to an acceptable amount of error when detection problems are to be faced in the time-frequency domain? The two questions prompted the study reported in this paper. After deriving the probability distribution of spectrogram coefficients in the context of a non-centred Gaussian correlated signal, the Kullback-Leibler divergence is first used to evaluate to what extent the non-whiteness of the signal and the Fourier analysis window impact the probability distribution of the spectrogram. To complete the analysis, a detection task formulated as a binary hypothesis test is considered. We evaluate the error committed on the probability of false alarm when the likelihood ratio test is expressed with chi-squared laws. From these results, a chi-squared description of the spectrogram distribution appears accurate when the analysis window used to construct the spectrogram decreases to zero at its boundaries, regardless of the level of correlation contained in the signal. When other analysis windows are used, the length of the window and the correlation contained in the analysed signal impact the validity of the chi-squared description.

Published
2008

36. Reassignment Vector Field for Time-Frequency Segmentation

Author

Fabien Millioz, Nadine Martin, Martin, Nadine, GIPSA - Signal et Automatique pour le Diagnostic et la Surveillance (GIPSA-SA-IGA), 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)-Département Automatique (GIPSA-DA), 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
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; In this paper, we propose to use the phase information of the Short Time Fourier Transform (STFT) to improve a time-frequency segmentation based on the statistical features of the STFT, and proposed by the authors in 2006. If the resolution of the STFT is too low, close components may be segmented in a single pattern. The idea is to add phase information provided by the reassignment principle in order to determine if there are more than one component in a pattern instead of two. Reassignment, originally proposed by Kodera et al. in 1976, is a non-linear method which creates a new time-frequency representation by moving the spectrogram values away from their computation place. Reassignment focuses energy components by moving each time-frequency location to its group delay and instantaneous frequency that represent more accurately the component energy. The obtained reassignment vector field associated to a given spectrogram describes how time-frequency locations are reassigned. We propose to use the reassignment vector field not to modify the time-frequency representation, but to give information on the signal structure. We compute local reassignment vectors on patterns segmented by the method mentioned above. Given that spectrogram tends to spread the time-frequency patterns, whereas reassignment method moves back energy to a pattern's point, reassignment vectors aim at the pattern. That leads to a pattern's boundary information, which is used to determine how many components are embedded in a single segmented pattern. Moreover, this information describes the boundaries of frequency modulations as well as wide band signal, and extends the use of the reassignment principle to wide band signals. This principle is finally applied to the shaft's vibrations of a three phase AC induction engine, in order to separate the different harmonics embedded in a single pattern.

Published
2007

37. Short time Fourier transform probability distribution for time-frequency segmentation

Author

Julien Huillery, Fabien Millioz, Nadine Martin, Laboratoire des images et des signaux (LIS), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF), Millioz, Fabien, Francis CASTANIE, and Martin, Nadine

Subjects
[SPI.OTHER]Engineering Sciences [physics]/Other, short time Fourier transform, STFT law model, 02 engineering and technology, symbols.namesake, short-time fourier transform, modèle de loi dans la TFCT time-frequency representations, 0202 electrical engineering, electronic engineering, information engineering, Mathematics, estimation, business.industry, [SPI.OTHER] Engineering Sciences [physics]/Other, segmentation, transformée de Fourier à court terme, Short-time Fourier transform, Nonstationary signals, 020206 networking & telecommunications, Pattern recognition, White noise, Image segmentation, Time–frequency analysis, statistical distribution, Additive white Gaussian noise, Fourier transform, représentation temps-fréquence, Gaussian noise, Computer Science::Sound, Time-frequency, Computer Science::Computer Vision and Pattern Recognition, segmentation temps-fréquence, symbols, Spectrogram, 020201 artificial intelligence & image processing, Artificial intelligence, business, time-frequency segmentation
Abstract

Taking as signal model the sum of a non-stationnary deterministic part embedded in a white Gaussian noise, this paper presents the distribution of the coefficients of the Short Time Fourier Transform (STFT), which is used to determine the maximum likelihood estimator of the noise level. We then propose an automatic segmentation algorithm of the real and imaginary parts of the STFT based on statistical features, which is an alternative to the spectrogram segmentations considered as image segmentations. Examples of segmented time-frequency space are presented on a simulated signal and on a dolphin whistle.

Published
2006

38. On the Probability Distributions of Spectrogram Coefficients for Correlated Gaussian Process

Author

Nadine Martin, Fabien Millioz, M. Huillery, Laboratoire des images et des signaux (LIS), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF), Francis CASTANIE, Martin, Nadine, and Huillery, Julien

Subjects
[SPI.OTHER]Engineering Sciences [physics]/Other, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Probability density function, 02 engineering and technology, time-frequency, symbols.namesake, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, spectrogram, 0202 electrical engineering, electronic engineering, information engineering, Divergence (statistics), Gaussian process, approximation, chi-square distribution, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Mathematics, nonstationary signals, correlated noise, [SPI.OTHER] Engineering Sciences [physics]/Other, Mathematical analysis, Short-time Fourier transform, 020206 networking & telecommunications, Time–frequency analysis, Fourier transform, symbols, Probability distribution, Spectrogram, 020201 artificial intelligence & image processing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; This paper deals with the probability distribution of spectrogram coefficients related to a correlated centered Gaussian process. It is shown that the windowing operation and the presence of correlation between input samples may introduce heteroscedaticity and correlation between the real and imaginary parts of the Short Time Fourier Transform. The impact of this phenomenon on spectrogram distribution is evaluated in terms of deviation from the chi-square distribution. A numerical method is provided to calculate the probability density function of the spectrogram coefficients and deviation from the chi-square distribution is evaluated using the Kullback-Liebler divergence. This measure of deviation is used to control the validity of a chi-square approximation.

Published
2006

39. Time-Frequency Segmentation for Engine Speed Monitoring

Author

Fabien Millioz, Nadine Martin, Laboratoire des images et des signaux (LIS), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF), and Millioz, Fabien

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; This paper presents a segmentation algorithm of a Time-Frequency Representation, which automatically selects time-frequency patterns containing signal of interest. Considering a deterministic non-stationary signal embedded in a white Gaussian noise, we know that the real and imaginary parts of the Short Time Fourier Transform coefficients (STFT) have a Gaussian distribution. We already proposed an unsupervised segmentation based on the spectrogram, the squared modulus of the STFT, whose coefficients have a non-central chi-square distribution. In order to keep simple Gaussian distributions and the phase information, we consider here only real and imaginary part of the STFT. We first highlight the difference existing between the variance of these real and imaginary parts. The ratio of these variances is a function of the nature and length of the analysis window. Estimated on a cell around each time-frequency location, these variances are used to determine if a time-frequency location contains deterministic signal or noise only. Given that the noise variance is unknown, an iterative algorithm is proposed. In addition of the STFT's parameters, which are the size and shape of the analysis window, the overlap between two consecutive windows, and the amount of zero padding, three different parameters control the segmentation. The most important one is the kurtosis of the distribution, calculated on time-frequency coefficients supposed to contain noise only. It is used to define a stop criterion of the segmentation and permits the monitoring of the signal pattern segmentation. The influence of the other parameters on the segmentation result is also discussed. This tool is applied to monitor a three-phase AC induction motor on the test bench GOTIX of the laboratory. Sensors measure vibration, torque and phase tensions and intensities. Segmented patterns provide information about time evolution of the spectral energy, and permit the tracking of the engine speed.

40. Detection of the boundaries of time-frequency patterns with a reassignment-based method

Author

Fabien Millioz, Nadine Martin, Laboratoire des images et des signaux (LIS), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF), and Martin, Nadine

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, spectrogram, Time-frequency, segmentation, reassignment vector field, Boundary detection, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; A boundary detection of time-frequency patterns of the spectrogram based on the reassignment vector field is proposed. Given that the spectrogram tends to spread the time-frequency patterns, whereas reassignment moves the spread energy back to a pattern's point, reassignment vectors aim at the pattern. Looking for time-frequency areas with homogeneous reassignment vector angles leads to a boundary detection.

41. Réallocation du spectrogramme pour la détection de frontières de motifs temps-fréquence

Author

Fabien Millioz, Nadine Martin, GIPSA - Signal et Automatique pour le Diagnostic et la Surveillance (GIPSA-SA-IGA), 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)-Département Automatique (GIPSA-DA), 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), and Martin, Nadine

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, spectrogram, Time-frequency, segmentation, Computer Science::Social and Information Networks, Quantitative Biology::Genomics, reassignment, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Computer Science::Distributed, Parallel, and Cluster Computing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

National audience; In this paper, we propose a boundary detection of time-frequency patterns of the spectrogram, based on the reassignment method. The reassignment vector is used here not to modify the time-frequency representation, but only as a source of information about the spectral structure of the signal. This approach focused on the boundaries extends the use of the reassignment principle to wide band signals.

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