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International audience; Despite predictions of the paperless office, global demand for printing and writing paper remains strong, and paper appears to be here to stay for some time. Not only firms, but also governments, libraries, and archives are in possession of large collections of legacy documents that still must be sorted and scanned. In this study, terahertz-based techniques are demonstrated to address several routine tasks related to the automated paper handling of unsorted legacy documents. Specifically, we demonstrate terahertz-based counting of the number of sheets in unconsolidated paper stacks, as well as locating stapled documents buried in paper stacks.

This paper presents an ultrasound transceiver application-specific integrated circuit (ASIC) directly integrated with an array of 12 × 80 piezoelectric transducer elements to enable next-generation ultrasound probes for 3D carotid artery imaging. The ASIC, implemented in a 0.18 µm high-voltage Bipolar-CMOS-DMOS (HV BCD) process, adopted a programmable switch matrix that allowed selected transducer elements in each row to be connected to a transmit and receive channel of an imaging system. This made the probe operate like an electronically translatable linear array, allowing large-aperture matrix arrays to be interfaced with a manageable number of system channels. This paper presents a second-generation ASIC that employed an improved switch design to minimize clock feedthrough and charge-injection effects of high-voltage metal–oxide–semiconductor field-effect transistors (HV MOSFETs), which in the first-generation ASIC caused parasitic transmissions and associated imaging artifacts. The proposed switch controller, implemented with cascaded non-overlapping clock generators, generated control signals with improved timing to mitigate the effects of these non-idealities. Both simulation results and electrical measurements showed a 20 dB reduction of the switching artifacts. In addition, an acoustic pulse-echo measurement successfully demonstrated a 20 dB reduction of imaging artifacts.

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long–Short Term Memory (BiLSTM) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which were used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors to estimate vehicle unsprung mass relative velocity.

Two important tasks in many e-commerce applications are identity verification of the user accessing the system and determining the level of rights that the user has for accessing and manipulating system’s resources. The performance of these tasks is directly dependent on the certainty of establishing the identity of the user. The main research focus of this paper is user identity verification approach based on voice recognition techniques. The paper presents research results connected to the usage of open-source speaker recognition technologies in e-commerce applications with an emphasis on evaluating the performance of the algorithms they use. Four open-source speaker recognition solutions (SPEAR, MARF, ALIZE, and HTK) have been evaluated in cases of mismatched conditions during training and recognition phases. In practice, mismatched conditions are influenced by various lengths of spoken sentences, different types of recording devices, and the usage of different languages in training and recognition phases. All tests conducted in this research were performed in laboratory conditions using the specially designed framework for multimodal biometrics. The obtained results show consistency with the findings of recent research which proves that i-vectors and solutions based on probabilistic linear discriminant analysis (PLDA) continue to be the dominant speaker recognition approaches for text-independent tasks.

Generative adversarial network (GAN) has been regarded as a promising solution to many machine learning problems, and it comprises of a generator and discriminator, determining patterns and anomalies in the input data. However, GANs have several common failure modes. Typically, a mode collapse occurs when a GAN fails to fit the set optimizations and leads to several instabilities in the generative model, diminishing the capability to generate new content regardless of the dataset. In this paper, we study conditional limiter solutions for mode collapse for the Intrusion Detection System (IDS) Control Flow GAN (ICF-GAN) model. Specifically, the ICF-GAN’s mode collapse instances are limited by a mini-batch method that significantly improves the model accuracy. Performance evaluation is conducted using numerical results obtained from experiments.

Species identification is a critical factor for obtaining accurate forest inventories. This paper compares the same method of tree species identification (at the individual crown level) across three different types of airborne laser scanning systems (ALS): two linear lidar systems (monospectral and multispectral) and one single-photon lidar (SPL) system to ascertain whether current individual tree crown (ITC) species classification methods are applicable across all sensors. SPL is a new type of sensor that promises comparable point densities from higher flight altitudes, thereby increasing lidar coverage. Initial results indicate that the methods are indeed applicable across all of the three sensor types with broadly similar overall accuracies (Hardwood/Softwood, 83–90%; 12 species, 46–54%; 4 species, 68–79%), with SPL being slightly lower in all cases. The additional intensity features that are provided by multispectral ALS appear to be more beneficial to overall accuracy than the higher point density of SPL. We also demonstrate the potential contribution of lidar time-series data in improving classification accuracy (Hardwood/Softwood, 91%; 12 species, 58%; 4 species, 84%). Possible causes for lower SPL accuracy are (a) differences in the nature of the intensity features and (b) differences in first and second return distributions between the two linear systems and SPL. We also show that segmentation (and field-identified training crowns deriving from segmentation) that is performed on an initial dataset can be used on subsequent datasets with similar overall accuracy. To our knowledge, this is the first study to compare these three types of ALS systems for species identification at the individual tree level.

The rapid urbanization process brings consequences to urban environments, such poor air quality and the urban heat island issues. Due to these effects, environmental monitoring is gaining attention with the aim of identifying local risks and improving cities’ liveability and resilience. However, these environments are very heterogeneous, and high-spatial-resolution data are needed to identify the intra-urban variations of physical parameters. Recently, wearable sensing techniques have been used to perform microscale monitoring, but they usually focus on one environmental physics domain. This paper presents a new wearable system developed to monitor key multidomain parameters related to the air quality, thermal, and visual domains, on a hyperlocal scale from a pedestrian’s perspective. The system consisted of a set of sensors connected to a control unit settled on a backpack and could be connected via Wi-Fi to any portable equipment. The device was prototyped to guarantee the easy sensors maintenance, and a user-friendly dashboard facilitated a real-time monitoring overview. Several tests were conducted to confirm the reliability of the sensors. The new device will allow comprehensive environmental monitoring and multidomain comfort investigations to be carried out, which can support urban planners to face the negative effects of urbanization and to crowd data sourcing in smart cities.

The main objective of a Cooperative Multiple-Input Multiple-Output (CMIMO) system is to improve network throughput and network coverage and save energy. By grouping wireless devices as virtual multi-antenna nodes, it can thus simulate the functions of multi-antenna systems. A Space-Time Block Code (STBC) was proposed to utilize the spatial diversity of MIMO systems to improve the diversity gain and coding gain. In this paper, we proposed a cooperative strategy based on STBC and CMIMO, which is referred to as Space-Time Block Coded Cooperative Multiple-Input Multiple-Output (STBC-CMIMO) to inherit the advantages from both STBC and CMIMO. The theoretical performance analysis for the proposed STBC-CMIMO is presented. The performance advantages of the STBC-CMIMO are also shown by simulations. In the simulations, it is demonstrated that STBC-CMIMO can obtain significant performance compared with the existing CMIMO system.

Unlike the fixed power grid cooperative networks, which are mainly based on the reception reliability parameter while choosing the best relay, the wireless-powered cooperative communication network (WPCCN) and in addition to the reception reliability the transmission requirement consideration is important for relay selection schemes. Hence, enabling efficient transmission techniques that address high attenuation of radio frequency (RF) signals according to the distance without increasing the total transmission power is an open issue worth studying. In this relation, a multiantennas power beacon (PB) that assists wireless-powered cooperative communication network (PB-WPCCN) is studied in this paper. The communication between source and destination is achieved with the aid of multiple relays, where both the source and the multiple relays need to harvest energy from the PB in the first place to enable their transmission functionalities. A novel relay selection scheme is proposed, named as two-round relay selection (2-RRS), where a group of relays that successfully decode the source information is selected in the first round selection. In the second round, the optimal relay is selected to forward the recorded information to the destination. The proposed 2-RRS scheme is compared with two existing relay selection schemes, i.e., partial relay selection (PRS) and opportunistic relay selection (ORS). The analytical closed-form expressions of outage probability and average system throughput are derived and validated by numerical simulation. The comparison results between different relay selection schemes show: (I) The superiority of the proposed 2-RRS scheme as it achieves around 17% better throughput compared to the conventional ORS scheme and 40% better than the PRS scheme, particularly when PB transmit power is 10 dB, (II) The proposed 2-RRS scheme guarantees the lowest outage probability, especially when the PB is equipped with multiantennas and performs beamforming technique, (III) The optimal localisation of the PB between the source and N relays depends on the adopted relay selection scheme, (IV) The exhaustive search of the maximum system throughput value shows that the proposed 2-RRS scheme required shorter energy harvesting time compared to other schemes. The increase in energy harvesting time and number of relays do not necessarily reflect positively on the system throughput performance, hence tradeoffs should be taken into consideration.

Target detection in hyperspectral imagery (HSI) aims at extracting target components of interest from hundreds of narrow contiguous spectral bands, where the prior target information plays a vital role. However, the limitation of the previous methods is that only single-layer detection is carried out, which is not sufficient to discriminate the target parts from complex background spectra accurately. In this paper, we introduce a hierarchical structure to the traditional algorithm matched filter (MF). Because of the advantages of MF in target separation performance, that is, the background components are suppressed while preserving the targets, the detection result of MF is used to further suppress the background components in a cyclic iterative manner. In each iteration, the average output of the previous iteration is used as a suppression criterion to distinguish these pixels judged as backgrounds in the current iteration. To better stand out the target spectra from the background clutter, HSI spectral input and the given target spectrum are whitened and then used to construct the MF in the current iteration. Finally, we provide the corresponding proofs for the convergence of the output and suppression criterion. Experimental results on three classical hyperspectral datasets confirm that the proposed method performs better than some traditional and recently proposed methods.

Accurate and fast path calculation is essential for applications such as vehicle navigation systems and transportation network routing. Although many shortest path algorithms for restricted search areas have been developed in the past ten years to speed up the efficiency of path query, the performance including the practicability still needs to be improved. To settle this problem, this paper proposes a new method of calculating statistical parameters based on a unidirectional road network model that is more in line with the real world and a path planning algorithm for dynamically restricted search areas that constructs virtual boundaries at a lower confidence level. We conducted a detailed experiment on the proposed algorithm with the real road network in Zhengzhou. As the experiment shows, compared with the existing algorithms, the proposed algorithm improves the search performance significantly in the condition of optimal path under the premise of ensuring the optimal path solution.

The measurement and analysis of the arterial pulse wave provides information about the state of vascular health. When measuring blood pressure according to Riva-Rocci, the systolic and diastolic blood pressure is measured non-invasively with an inflatable pressure cuff on the upper arm. Today&rsquo, s blood pressure monitors analyze the pulse wave in reference to the rising or falling cuff pressure. With the help of additional pulse wave analysis, one can determine the pulse rate and the heart rate variability. In this paper, we investigated the concept, the construction, and the limitations of ultrawideband (UWB) radar and continuous wave (CW) radar, which provide continuous and non-invasive pulse wave measurements. We integrated the sensors into a complete measurement system. We measured the pulse wave of the cuff pressure, the radar sensor (both UWB and CW), the optical sensor, and ultrasonic Doppler as a reference. We discussed the results and the sensor characteristics. The main conclusion was that the resolution of the pulse radar was too low, even with a maximum bandwidth of 10 GHz, to measure pulse waves reliably. The continuous wave radar provides promising results for a phantom if adjusted properly with phase shifts and frequency. In the future, we intend to develop a CW radar solution with frequency adaption.

In this paper, we propose AirSign, a novel user authentication technology to provide users with more convenient, intuitive, and secure ways of interacting with smartphones in daily settings. AirSign leverages both acoustic and motion sensors for user authentication by signing signatures in the air through smartphones without requiring any special hardware. This technology actively transmits inaudible acoustic signals from the earpiece speaker, receives echoes back through both built-in microphones to &ldquo, illuminate&rdquo, signature and hand geometry, and authenticates users according to the unique features extracted from echoes and motion sensors. To evaluate our system, we collected registered, genuine, and forged signatures from 30 participants, and by applying AirSign on the above dataset, we were able to successfully distinguish between genuine and forged signatures with a 97.1% F-score while requesting only seven signatures during the registration phase.

This paper addresses the Multi-Athlete Tracking (MAT) problem, which plays a crucial role in sports video analysis. There exist specific challenges in MAT, e.g., athletes share a high similarity in appearance and frequently occlude with each other, making existing approaches not applicable for this task. To address this problem, we propose a novel online multiple athlete tracking approach which make use of long-term temporal pose dynamics for better distinguishing different athletes. Firstly, we design a Pose-based Triple Stream Network (PTSN) based on Long Short-Term Memory (LSTM) networks, capable of modeling long-term temporal pose dynamics of athletes, including pose-based appearance, motion and athletes&rsquo, interaction clues. Secondly, we propose a multi-state online matching algorithm based on bipartite graph matching and similarity scores produced by PTSN. It is robust to noisy detections and occlusions due to the reliable transitions of multiple detection states. We evaluate our method on the APIDIS, NCAA Basketball and VolleyTrack databases, and the experiment results demonstrate its effectiveness.

A vehicular network is composed of an in-vehicle network (IVN) and Internet of Vehicles (IoV). IVN exchanges information among in-vehicle devices. IoV constructs Vehicle-to-X (V2X) networks outside vehicles and exchanges information among V2X elements. These days, in-vehicle devices that require high bandwidth is increased for autonomous driving services. Thus, the spread of data for vehicles is exploding. This kind of data is exchanged through IoV. Even if the Ethernet backbone of IVN carries a lot of data in the vehicle, the explosive increase in data from outside the vehicle can affect the backbone. That is, the transmission efficiency of the IVN backbone will be reduced due to excessive data traffic. In addition, when IVN data traffic is transmitted to IoV without considering IoV network conditions, the transmission efficiency of IoV is also reduced. Therefore, in this paper, we propose an IoV access gateway to controls the incoming data traffic to the IVN backbone and the outgoing data traffic to the IoV in the network environment where IVN and IoV are integrated. Computer simulations are used to evaluate the performance of the proposed system, and the proposed system shows better performance in the accumulated average transmission delay.

The superdirective beamformer, while attractive for processing broadband acoustic signals, often suffers from the problem of white noise amplification. So, its application requires well-designed acoustic arrays with sensors of extremely low self-noise level, which is difficult if not impossible to attain. In this paper, a new binaural superdirective beamformer is proposed, which is divided into two sub-beamformers. Based on studies and facts in psychoacoustics, these two filters are designed in such a way that they are orthogonal to each other to make the white noise components in the binaural beamforming outputs incoherent while maximizing the output interaural coherence of the diffuse noise, which is important for the brain to localize the sound source of interest. As a result, the signal of interest in the binaural superdirective beamformer&rsquo, s outputs is in phase but the white noise components in the outputs are random phase, so the human auditory system can better separate the acoustic signal of interest from white noise by listening to the outputs of the proposed approach. Experimental results show that the derived binaural superdirective beamformer is superior to its conventional monaural counterpart.

The existing lens correction methods deal with the distortion correction by one or more specific image distortion models. However, distortion determination may fail when an unsuitable model is used. So, methods based on the distortion model would have some drawbacks. A model-free lens distortion correction based on the phase analysis of fringe-patterns is proposed in this paper. Firstly, the mathematical relationship of the distortion displacement and the modulated phase of the sinusoidal fringe-pattern are established in theory. By the phase demodulation analysis of the fringe-pattern, the distortion displacement map can be determined point by point for the whole distorted image. So, the image correction is achieved according to the distortion displacement map by a model-free approach. Furthermore, the distortion center, which is important in obtaining an optimal result, is measured by the instantaneous frequency distribution according to the character of distortion automatically. Numerical simulation and experiments performed by a wide-angle lens are carried out to validate the method.

As an effective means of solving collision problems caused by the limited perspective on board, the cooperative roadside system is gaining popularity. To improve the vehicle detection abilities in such online safety systems, in this paper, we propose a novel multi-sensor multi-level enhanced convolutional network model, called multi-sensor multi-level enhanced convolutional network architecture (MME-YOLO), with consideration of hybrid realistic scene of scales, illumination, and occlusion. MME-YOLO consists of two tightly coupled structures, i.e., the enhanced inference head and the LiDAR-Image composite module. More specifically, the enhanced inference head preliminarily equips the network with stronger inference abilities for redundant visual cues by attention-guided feature selection blocks and anchor-based/anchor-free ensemble head. Furthermore, the LiDAR-Image composite module cascades the multi-level feature maps from the LiDAR subnet to the image subnet, which strengthens the generalization of the detector in complex scenarios. Compared with YOLOv3, the enhanced inference head achieves a 5.83% and 4.88% mAP improvement on visual dataset LVSH and UA-DETRAC, respectively. Integrated with the composite module, the overall architecture gains 91.63% mAP in the collected Road-side Dataset. Experiments show that even under the abnormal lightings and the inconsistent scales at evening rush hours, the proposed MME-YOLO maintains reliable recognition accuracy and robust detection performance.

Non-orthogonal multiple access schemes with grant free access have been recently highlighted as a prominent solution to meet the stringent requirements of massive machine-type communications (mMTCs). In particular, the multi-user shared access (MUSA) scheme has shown great potential to grant free access to the available resources. For the sake of simplicity, MUSA is generally conducted with the successive interference cancellation (SIC) receiver, which offers a low decoding complexity. However, this family of receivers requires sufficiently diversified received user powers in order to ensure the best performance and avoid the error propagation phenomenon. The power allocation has been considered as a complicated issue especially for a decentralized decision with a minimum signaling overhead. In this paper, we propose a novel algorithm for an autonomous power decision with a minimal overhead based on a tight approximation of the bit error probability (BEP) while considering the error propagation phenomenon. We investigate the efficiency of multi-armed bandit (MAB) approaches for this problem in two different reward scenarios: (i) in Scenario 1, each user reward only informs about whether its own packet was successfully transmitted or not, (ii) in Scenario 2, each user reward may carry information about the other interfering user packets. The performances of the proposed algorithm and the MAB techniques are compared in terms of the successful transmission rate. The simulation results prove that the MAB algorithms show a better performance in the second scenario compared to the first one. However, in both scenarios, the proposed algorithm outperforms the MAB techniques with a lower complexity at user equipment.

Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor signal, the mathematical treatment of the acquired data, and the validation of the correlation of the odour metric are key topics to control in order to ensure a robust and reliable measurement. The research presents and discusses the use of different pattern recognition and feature extraction techniques in the elaboration and effectiveness of the odour classification monitoring model (OCMM). The effect of the rise, intermediate, and peak period from the original response curve, in collaboration with Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) as a pattern recognition algorithm, were investigated. Laboratory analyses were performed with real odour samples collected in a complex industrial plant, using an advanced smart IOMS. The results demonstrate the influence of the choice of method on the quality of the OCMM produced. The peak period in combination with the Artificial Neural Network (ANN) highlighted the best combination on the basis of high classification rates. The paper provides information to develop a solution to optimize the performance of IOMS.

With the development of wireless rechargeable sensor networks (WRSNs ), security issues of WRSNs have attracted more attention from scholars around the world. In this paper, a novel epidemic model, SILS(Susceptible, Infected, Low-energy, Susceptible), considering the removal, charging and reinfection process of WRSNs is proposed. Subsequently, the local and global stabilities of disease-free and epidemic equilibrium points are analyzed and simulated after obtaining the basic reproductive number R0. Detailedly, the simulations further reveal the unique characteristics of SILS when it tends to being stable, and the relationship between the charging rate and R0. Furthermore, the attack-defense game between malware and WRSNs is constructed and the optimal strategies of both players are obtained. Consequently, in the case of R0&lt, 1 and R0&gt, 1, the validity of the optimal strategies is verified by comparing with the non-optimal control group in the evolution of sensor nodes and accumulated cost.

One of the challenging problems in robot navigation is efficient and safe planning in a highly dynamic environment, where the robot is required to understand pedestrian patterns in the environment, such as train station. The rapid movement of pedestrians makes the robot more difficult to solve the collision problem. In this paper, we propose a navigation probability map to solve the pedestrians&rsquo, rapid movement problem based on the influencer recognition model (IRM). The influencer recognition model (IRM) is a data-driven model to infer a distribution over possible causes of pedestrian&rsquo, s turning. With this model, we can obtain a navigation probability map by analyzing the changes in the effective pedestrian trajectory. Finally, we combined navigation probability map and artificial potential field (APF) method to propose a robot navigation method and verified it on our data-set, which is an unobstructed, overlooked pedestrians&rsquo, data-set collected by us.

The emergence of the Edge computing paradigm has shifted data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructures. Therefore, data processing solutions must consider data locality to reduce the performance penalties from data transfers among remote data centres. Existing Big Data processing solutions provide limited support for handling data locality and are inefficient in processing small and frequent events specific to the Edge environments. This article proposes a novel architecture and a proof-of-concept implementation for software container-centric Big Data workflow orchestration that puts data locality at the forefront. The proposed solution considers the available data locality information, leverages long-lived containers to execute workflow steps, and handles the interaction with different data sources through containers. We compare the proposed solution with Argo Workflows and demonstrate a significant performance improvement in the execution speed for processing the same data units. Finally, we carry out experiments with the proposed solution under different configurations and analyze individual aspects affecting the performance of the overall solution. The work in this paper was partly funded by the EC H2020 project “DataCloud ” (grant number 101016835) and the NFR project “BigDataMine” (grant number 309691).

Icing is a hazard which is important for the aerospace industry and which has grown over the last few years. Developing sensors that can detect the existence not only of standard icing conditions with typically small droplet size, but also of Supercooled Large Droplet (SLD) conditions is one of the most important aims in order to minimize icing hazards in the near future. In the present paper a study of the Fiber Bragg Grating Sensors’ (FBGSs) performance as a flight icing detection system that predicts the conditions of an icing cloud is carried out. The test matrix was performed in the INTA Icing Wind Tunnel (IWT) with several icing conditions including SLD. Two optic fibers with 16 FBGS in total were integrated in the lower and upper surface of an airfoil to measure the temperature all over the chord. The results are compared with a Messinger heat and mass balance model and the measurements of the FBGS are used to predict the Liquid Water Content (LWC) and Ice Accretion Rate (IAR). Finally, the results are evaluated and a sensor assessment is made. A good correlation was observed between theoretical calculations and test results obtained with the FBGS in the IWT tests. FBGS proved to detect the beginning and end of ice accretion, LWC and IAR quickly and with good precision.

Chronic wounds, or wounds that are not healing properly, are a worldwide health problem that affect the global economy and population. Alongside with aging of the population, increasing obesity and diabetes patients, we can assume that costs of chronic wound healing will be even higher. Wound assessment should be fast and accurate in order to reduce the possible complications, and therefore shorten the wound healing process. Contact methods often used by medical experts have drawbacks that are easily overcome by non-contact methods like image analysis, where wound analysis is fully or partially automated. This paper describes an automatic wound recording system build upon 7 DoF robot arm with attached RGB-D camera and high precision 3D scanner. The developed system presents a novel NBV algorithm that utilizes surface-based approach based on surface point density and discontinuity detection. The system was evaluated on multiple wounds located on medical models as well as on real patents recorded in clinical medical center.

International audience; This paper addresses the two simultaneous leak diagnosis problem in pipelines based on a state vector reconstruction as a strategy to improve water shortages in large cities by only considering the availability of the flow rate and pressure head measurements at both ends of the pipeline. The proposed algorithm considers the parameters of both leaks as new state variables with constant dynamics, which results in an extended state representation. By applying a suitable persistent input, an invertible mapping in x can be obtained as a function of the input and output, including their time derivatives of the third-order. The state vector can then be reconstructed by means of an algebraic-like observer through the computation of time derivatives using a Numerical Differentiation with Annihilatorsconsidering its inherent noise rejection properties. Experimental results showed that leak parameters were reconstructed with accuracy using a test bed plant built at Cinvestav Guadalajara.

A technological system capable of automatically producing damage scenarios at an urban scale, as soon as an earthquake occurs, can help the decision-makers in planning the first post-disaster response, i.e., to prioritize the field activities for checking damage, making a building safe, and supporting rescue and recovery. This system can be even more useful when it works on densely populated areas, as well as on historic urban centers. In the paper, we propose a processing chain on a GIS platform to generate post-earthquake damage scenarios, which are based: (1) on the near real-time processing of the ground motion, that is recorded in different sites by MEMS accelerometric sensor network in order to take into account the local effects, and (2) the current structural characteristics of the built heritage, that can be managed through an information system from the local public administration authority. In the framework of the EU-funded H2020-ARCH project, the components of the system have been developed for the historic area of Camerino (Italy). Currently, some experimental fragility curves in the scientific literature, which are based on the damage observations after Italian earthquakes, are implemented in the platform. These curves allow relating the acceleration peaks obtained by the recordings of the ground motion with the probability to reach a certain damage level, depending on the structural typology. An operational test of the system was performed with reference to an ML3.3 earthquake that occurred 13 km south of Camerino. Acceleration peaks between 1.3 and 4.5 cm/s2 were recorded by the network, and probabilities lower than 35% for negligible damage (and then about 10% for moderate damage) were calculated for the historical buildings given this low-energy earthquake.

International audience; Soil moisture (SM) data are required at high spatio-temporal resolution—typically the crop field scale every 3–6 days—for agricultural and hydrological purposes. To provide such high-resolution SM data, many remote sensing methods have been developed from passive microwave, active microwave and thermal data. Despite the pros and cons of each technique in terms of spatio-temporal resolution and their sensitivity to perturbing factors such as vegetation cover, soil roughness and meteorological conditions, there is currently no synergistic approach that takes advantage of all relevant (passive, active microwave and thermal) remote sensing data. In this context, the objective of the paper is to develop a new algorithm that combines SMAP L-band passive microwave, MODIS/Landsat optical/thermal and Sentinel-1 C-band radar data to provide SM data at the field scale at the observation frequency of Sentinel-1. In practice, it is a three-step procedure in which: (1) the 36 km resolution SMAP SM data are disaggregated at 100 m resolution using MODIS/Landsat optical/thermal data on clear sky days, (2) the 100 m resolution disaggregated SM data set is used to calibrate a radar-based SM retrieval model and (3) the so-calibrated radar model is run at field scale on each Sentinel-1 overpass. The calibration approach also uses a vegetation descriptor as ancillary data that is derived either from optical (Sentinel-2) or radar (Sentinel-1) data. Two radar models (an empirical linear regression model and a non-linear semi-empirical formulation derived from the water cloud model) are tested using three vegetation descriptors (NDVI, polarization ratio (PR) and radar coherence (CO)) separately. Both models are applied over three experimental irrigated and rainfed wheat crop sites in central Morocco. The field-scale temporal correlation between predicted and in situ SM is in the range of 0.66–0.81 depending on the retrieval configuration. Based on this data set, the linear radar model using PR as a vegetation descriptor offers a relatively good compromise between precision and robustness all throughout the agricultural season with only three parameters to set. The proposed synergistical approach combining multi-resolution/multi-sensor SM-relevant data offers the advantage of not requiring in situ measurements for calibration.

Today’s IoT deployments are highly complex, heterogeneous and constantly changing. This poses severe security challenges such as limited end-to-end security support, lack of cross-platform cross-vertical security interoperability as well as the lack of security services that can be readily applied by security practitioners and third party developers. Overall, these require scalable, decentralized and intelligent IoT security mechanisms and services which are addressed by the SecureIoT project. This paper presents the definition, implementation and validation of a SecureIoT-enabled socially assisted robots (SAR) usage scenario. The aim of the SAR scenario is to integrate and validate the SecureIoT services in the scope of personalized healthcare and ambient assistive living (AAL) scenarios, involving the integration of two AAL platforms, namely QTrobot (QT) and CloudCare2U (CC2U). This includes risk assessment of communications security, predictive analysis of security risks, implementing access control policies to enhance the security of solution, and auditing of the solution against security, safety and privacy guidelines and regulations. Future perspectives include the extension of this security paradigm by securing the integration of healthcare platforms with IoT solutions, such as Healthentia with QTRobot, by means of a system product assurance process for cyber-security in healthcare applications, through the PANACEA toolkit.

In this paper, a novel approach for raindrop size distribution retrieval using dual-polarized microwave signals from low Earth orbit satellites is proposed. The feasibility of this approach is studied through modelling and simulating the retrieval system which includes multiple ground receivers equipped with signal-to-noise ratio estimators and a low Earth orbit satellite communicating with the receivers using both vertically and horizontally polarized signals. Our analysis suggests that the dual-polarized links offer the opportunity to estimate two independent raindrop size distribution parameters. To achieve that, the vertical and horizontal polarization attenuations need to be measured at low elevation angles where the difference between them is more distinct. Two synthetic rain fields are generated to test the performance of the retrieval. Simulation results suggest that the specific attenuations for both link types can be retrieved through a least-squares algorithm. They also confirm that the specific attenuation ratio of vertically to horizontally polarized signals can be used to retrieve the slope and intercept parameters of raindrop size distribution.