LOFAR Sparse Image Reconstruction

Context. The LOw Frequency ARray (LOFAR) radio telescope is a giant digital phased array interferometer with multiple antennas distributed in Europe. It provides discrete sets of Fourier components of the sky brightness. Recovering the original brightness distribution with aperture synthesis forms an inverse problem that can be solved by various deconvolution and minimization methods Aims. Recent papers have established a clear link between the discrete nature of radio interferometry measurement and the "compressed sensing" (CS) theory, which supports sparse reconstruction methods to form an image from the measured visibilities. Empowered by proximal theory, CS offers a sound framework for efficient global minimization and sparse data representation using fast algorithms. Combined with instrumental direction-dependent effects (DDE) in the scope of a real instrument, we developed and validated a new method based on this framework Methods. We implemented a sparse reconstruction method in the standard LOFAR imaging tool and compared the photometric and resolution performance of this new imager with that of CLEAN-based methods (CLEAN and MS-CLEAN) with simulated and real LOFAR data Results. We show that i) sparse reconstruction performs as well as CLEAN in recovering the flux of point sources; ii) performs much better on extended objects (the root mean square error is reduced by a factor of up to 10); and iii) provides a solution with an effective angular resolution 2-3 times better than the CLEAN images. Conclusions. Sparse recovery gives a correct photometry on high dynamic and wide-field images and improved realistic structures of extended sources (of simulated and real LOFAR datasets). This sparse reconstruction method is compatible with modern interferometric imagers that handle DDE corrections (A- and W-projections) required for current and future instruments such as LOFAR and SKAComment: Published in A&A, 19 pages, 9 figure

LOFAR sparse image reconstruction

International audienceContext. The LOw Frequency ARray (LOFAR) radio telescope is a giant digital phased array interferometer with multiple antennas distributed in Europe. It provides discrete sets of Fourier components of the sky brightness. Recovering the original brightness distribution with aperture synthesis forms an inverse problem that can be solved by various deconvolution and minimization methods. Aims. Recent papers have established a clear link between the discrete nature of radio interferometry measurement and the " compressed sensing " (CS) theory, which supports sparse reconstruction methods to form an image from the measured visibilities. Empowered by proximal theory, CS offers a sound framework for efficient global minimization and sparse data representation using fast algorithms. Combined with instrumental direction-dependent effects (DDE) in the scope of a real instrument, we developed and validated a new method based on this framework. Methods. We implemented a sparse reconstruction method in the standard LOFAR imaging tool and compared the photometric and resolution performance of this new imager with that of CLEAN-based methods (CLEAN and MS-CLEAN) with simulated and real LOFAR data. Results. We show that i) sparse reconstruction performs as well as CLEAN in recovering the flux of point sources; ii) performs much better on extended objects (the root mean square error is reduced by a factor of up to 10); and iii) provides a solution with an effective angular resolution 2−3 times better than the CLEAN images. Conclusions. Sparse recovery gives a correct photometry on high dynamic and wide-field images and improved realistic structures of extended sources (of simulated and real LOFAR datasets). This sparse reconstruction method is compatible with modern interferometric imagers that handle DDE corrections (A-and W-projections) required for current and future instruments such as LOFAR and SKA

LOFAR sparse image reconstruction

The LOw Frequency ARray (LOFAR) radio telescope is a giant digital phased array interferometer with multiple antennas distributed in Europe. It provides discrete sets of Fourier components of the sky brightness. Recovering the original brightness distribution with aperture synthesis forms an inverse problem that can be solved by various deconvolution and minimization methods Aims. Recent papers have established a clear link between the discrete nature of radio interferometry measurement and the "compressed sensing" (CS) theory, which supports sparse reconstruction methods to form an image from the measured visibilities. Empowered by proximal theory, CS offers a sound framework for efficient global minimization and sparse data representation using fast algorithms. Combined with instrumental direction-dependent effects (DDE) in the scope of a real instrument, we developed and validated a new method based on this framework Methods. We implemented a sparse reconstruction method in the standard LOFAR imaging tool and compared the photometric and resolution performance of this new imager with that of CLEAN-based methods (CLEAN and MS-CLEAN) with simulated and real LOFAR data Results. We show that i) sparse reconstruction performs as well as CLEAN in recovering the flux of point sources; ii) performs much better on extended objects (the root mean square error is reduced by a factor of up to 10); and iii) provides a solution with an effective angular resolution 2-3 times better than the CLEAN images. Conclusions. Sparse recovery gives a correct photometry on high dynamic and wide-field images and improved realistic structures of extended sources (of simulated and real LOFAR datasets). This sparse reconstruction method is compatible with modern interferometric imagers that handle DDE corrections (A- and W-projections) required for current and future instruments such as LOFAR and SK

Spatial encoding in primate hippocampus during free navigation.

The hippocampus comprises two neural signals-place cells and θ oscillations-that contribute to facets of spatial navigation. Although their complementary relationship has been well established in rodents, their respective contributions in the primate brain during free navigation remains unclear. Here, we recorded neural activity in the hippocampus of freely moving marmosets as they naturally explored a spatial environment to more explicitly investigate this issue. We report place cells in marmoset hippocampus during free navigation that exhibit remarkable parallels to analogous neurons in other mammalian species. Although θ oscillations were prevalent in the marmoset hippocampus, the patterns of activity were notably different than in other taxa. This local field potential oscillation occurred in short bouts (approximately .4 s)-rather than continuously-and was neither significantly modulated by locomotion nor consistently coupled to place-cell activity. These findings suggest that the relationship between place-cell activity and θ oscillations in primate hippocampus during free navigation differs substantially from rodents and paint an intriguing comparative picture regarding the neural basis of spatial navigation across mammals

Analog VLSI Circuits for Biosensors, Neural Signal Processing and Prosthetics

Stroke, spinal cord injury and neurodegenerative diseases such as ALS and Parkinson's debilitate their victims by suffocating, cleaving communication between, and/or poisoning entire populations of geographically correlated neurons. Although the damage associated with such injury or disease is typically irreversible, recent advances in implantable neural prosthetic devices offer hope for the restoration of lost sensory, cognitive and motor functions by remapping those functions onto healthy cortical regions. The research presented in this thesis is directed toward developing enabling technology for totally implantable neural prosthetics that could one day restore lost sensory, cognitive and motor function to the victims of debilitating neural injury or disease. There are three principal components to this work. First, novel integrated biosensors have been designed and implemented to transduce weak extra-cellular electrical potentials and optical signals from cells cultured directly on the surface of the sensor chips, as well as to manipulate cells on the surface of these chips. Second, a method of detecting and identifying stereotyped neural signals, or action potentials, has been mapped into silicon circuits which operate at very low power levels suitable for implantation. Third, as one small step towards the development of cognitive neural implants, a learning silicon synapse has been implemented and a neural network application demonstrated. The original contributions of this dissertation include: * A contact image sensor that adapts to background light intensity and can asynchronously detect statistically significant optical events in real-time; * Programmable electrode arrays for enhanced electrophysiological recording, for directing cellular growth, for site-specific in situ bio-functionalization, and for analyte and particulate collection; * Ultra-low power, programmable floating gate template matching circuits for the detection and classification of neural action potentials; * A two transistor synapse that exhibits spike timing dependent plasticity and can implement adaptive pattern classification and silicon learning

Advancements in the Industrial Internet of Things for Energy Efficiency

The Internet of Things is an emerging field that leverages the connections of everyday objects for the betterment of society. A subfield of this trend, the Industrial Internet of Things (IIoT), has been referred to as an industrial revolution that enhances both productivity and safety in the industrial environment. While still in its early stages, identified improvements have the potential to markedly improve manufacturing productivity. Energy efficiency within manufacturing plants has traditionally received little focus. The Industrial Assessment Center Program demonstrates the potential energy improvements that can be realized in manufacturing plants, but these assessments also highlight some of the traditional barriers to energy efficiency. Some of these barriers include the lack of data to justify actionable improvements, unclear correlations between improvement costs and potential cost savings, and lack of knowledge on how energy improvements provide ancillary benefits to the plant. The IIoT has the potential to increase energy efficiency implementation in manufacturing plants by addressing these challenges. This dissertation discusses the framework in which energy efficiency enhancements within the IIoT environment can be realized. The dissertation initially details the potential benefits of IIoT for energy efficiency and presents a general framework for these improvements. While proposed IIoT frameworks vary, they all include the core elements of improved sensing capabilities, enhanced data analysis, and intelligent actuation. In addition to presenting the framework generally, the dissertation provides detailed case studies on how each of these framework elements lead to improved energy efficiency in manufacturing. The first case study demonstrates improved sensing capabilities in the IIoT framework. A non-intrusive flow meter for use in compressed air and other fluid systems is presented. The second case study discusses Autonomous Robotic Assessments of Energy, which use advanced data analysis to autonomously perform a lighting energy assessment in facilities. The third case study is then presented on intelligent actuation, which uses a novel k-means algorithm to autonomously determine appropriate times to actuate compressors for air systems in manufacturing plants. Each of the presented case studies includes experimental tests demonstrating their capabilities

Interfaces neuronales CMOS haute résolution pour l'électrophysiologie et l'optogénétique en boucle fermée

L’avenir de la recherche sur les maladies du cerveau repose sur le développement de nouvelles technologies qui permettront de comprendre comment cet organe si complexe traite, intègre et transfère l’information. Parmi celles-ci, l’optogénétique est une technologie révolutionnaire qui permet d’utiliser de la lumière afin d’activer sélectivement les neurones du cortex d’animaux transgéniques pour observer leur effet dans un vaste réseau biologique. Ce cadre expérimental repose typiquement sur l’observation de l’activité neuronale de souris transgéniques, car elles peuvent exprimer une grande variété de gènes et de maladies et qu’elles sont peu couteuses. Toutefois, la plupart des appareils de mesure ou de stimulation optogénétique disponible ne sont pas appropriés, car ils sont câblés, trop lourds et/ou trop simplistes. Malheureusement, peu de systèmes sans fil existent, et ces derniers sont grandement limités par la bande passante requise pour transmettre les données neuronales, et ils ne fournissent pas de stimulation optogénétique multicanal afin de stimuler et observer plusieurs régions du cerveau. Dans les dispositifs actuels, l’interprétation des données neuronales est effectuée ex situ, alors que la recherche bénéficierait grandement de systèmes sans fil assez intelligents pour interpréter et stimuler les neurones en boucle fermée, in situ. Le but de ce projet de recherche est de concevoir des circuits analogiques-numériques d’acquisition et de traitement des signaux neuronaux, des algorithmes d’analyse et de traitement de ces signaux et des systèmes electro-optiques miniatures et sans fil pour : i) Mener des expériences combinant l’enregistrement neuronal et l’optogénétique multicanal haute résolution avec des animaux libres de leurs mouvements. ii) Mener des expériences optogénétiques synchronisées avec l’observation, c.-à-d. en boucle fermée, chez des animaux libres de leurs mouvements. iii) Réduire la taille, le poids et la consommation énergétique des systèmes optogénétiques sans fil afin de minimiser l’impact de la recherche chez de petits animaux. Ce projet est en 3 phases, et ses principales contributions ont été rapportées dans dix conférences internationales (ISSCC, ISCAS, EMBC, etc.) et quatre articles de journaux publiés ou soumis, ainsi que dans un brevet et deux divulgations. La conception d’un système optogénétique haute résolution pose plusieurs défis importants. Notamment, puisque les signaux neuronaux ont un contenu fréquentiel élevé (_10 kHz), le nombre de canaux sous observation est limité par la bande passante des transmetteurs sans fil (2-4 canaux en général). Ainsi, la première phase du projet a visé le développement d’algorithmes de compression des signaux neuronaux et leur intégration dans un système optogénétique sans fil miniature et léger (2.8 g) haute résolution possédant 32 canaux d’acquisition et 32 canaux de stimulation optique. Le système détecte, compresse et transmet les formes d’onde des potentiels d’action (PA) produits par les neurones avec un field programmable gate array (FPGA) embarqué à faible consommation énergétique. Ce processeur implémente un algorithme de détection des PAs basé sur un seuillage adaptatif, ce qui permet de compresser les signaux en transmettant seulement les formes détectées. Chaque PA est davantage compressé par une transformée en ondelette discrète (DWT) de type Symmlet-2 suivie d’une technique de discrimination et de requantification dynamique des coefficients. Les résultats obtenus démontrent que cet algorithme est plus robuste que les méthodes existantes tout en permettant de reconstruire les signaux compressés avec une meilleure qualité (SNDR moyen de 25 dB _ 5% pour un taux de compression (CR) de 4.2). Avec la détection, des CR supérieurs à 500 sont rapportés lors de la validation in vivo. L’utilisation de composantes commerciales dans des systèmes optogénétiques sans fil augmentela taille et la consommation énergétique, en plus de ne pas être optimisée pour cette application. La seconde phase du projet a permis de concevoir un système sur puce (SoC) complementary metal oxide semiconductor (CMOS) pour faire de l’enregistrement neuronal et de optogénétique multicanal, permettant de réduire significativement la taille et la consommation énergétique comparativement aux alternatives commerciales. Ceci est une contribution importante, car c’est la première puce à être doté de ces deux fonctionnalités. Le SoC possède 10 canaux d’enregistrement et 4 canaux de stimulation optogénétique. La conception du bioamplificateur inclut une bande passante programmable (0.5 Hz - 7 kHz) et un faible bruit referré à l’entré (IRN de 3.2 μVrms), ce qui permet de cibler différents types de signaux biologiques (PA, LFP, etc.). Le convertisseur analogique numérique (ADC) de type Delta- Sigma (DS) MASH 1-1-1 est conçu pour fonctionner de faibles taux de sur-échantillonnage (OSR _50) pour réduire sa consommation et possède une résolution programmable (ENOB de 9.75 Bits avec un OSR de 25). Cet ADC exploite une nouvelle technique réduisant la taille du circuit en soustrayant la sortie de chaque branche du DS dans le domaine numérique, comparativement à la méthode analogique classique. La consommation totale d’un canal d’enregistrement est de 11.2 μW. Le SoC implémente un nouveau circuit de stimulation optique basé sur une source de courant de type cascode avec rétroaction, ce qui permet d’accommoder une large gamme de LED et de tensions de batterie comparativement aux circuits existants. Le SoC est intégré dans un système optogénétique sans fil et validé in vivo. À ce jour et en excluant ce projet, aucun système sans-fil ne fait de l’optogénétique en boucle fermée simultanément au suivi temps réel de l’activité neuronale. Une contribution importante de ce travail est d’avoir développé le premier système optogénétique multicanal qui est capable de fonctionner en boucle fermée et le premier à être validé lors d’expériences in vivo impliquant des animaux libres de leurs mouvements. Pour ce faire, la troisième phase du projet a visé la conception d’un SoC CMOS numérique, appelé neural decoder integrated circuit (ND-IC). Le ND-IC et le SoC développé lors de la phase 2 ont été intégrés dans un système optogénétique sans fil. Le ND-IC possède 3 modules : 1) le détecteur de PA adaptatif, 2) le module de compression possédant un nouvel arbre de tri pour discriminer les coefficients, et 3) le module de classement automatique des PA qui réutilise les données générées par le module de détection et de compression pour réduire sa complexité. Un lien entre un canal d’enregistrement et un canal de stimulation est établi selon l’association de chaque PA à un neurone, grâce à la classification, et selon l’activité de ce neurone dans le temps. Le ND-IC consomme 56.9 μW et occupe 0.08 mm2 par canal. Le système pèse 1.05 g, occupe un volume de 1.12 cm3, possède une autonomie de 3h, et est validé in vivo.The future of brain research lies in the development of new technologies that will help understand how this complex organ processes, integrates and transfers information. Among these, optogenetics is a recent technology that allows the use of light to selectively activate neurons in the cortex of transgenic animals to observe their effect in a large biological network. This experimental setting is typically based on observing the neuronal activity of transgenic mice, as they express a wide variety of genes and diseases, while being inexpensive. However, most available neural recording or optogenetic devices are not suitable, because they are hard-wired, too heavy and/or too simplistic. Unfortunately, few wireless systems exist, and they are greatly limited by the required bandwidth to transmit neural data, while not providing simultaneous multi-channel neural recording and optogenetic, a must for stimulating and observing several areas of the brain. In current devices, the analysis of the neuronal data is performed ex situ, while the research would greatly benefit from wireless systems that are smart enough to interpret and stimulate the neurons in closed-loop, in situ. The goal of this project is to design analog-digital circuits for acquisition and processing of neural signals, algorithms for analysis and processing of these signals and miniature electrooptical wireless systems for: i) Conducting experiments combining high-resolution multi-channel neuronal recording and high-resolution multi-channel optogenetics with freely-moving animals. ii) Conduct optogenetic experiments synchronized with the neural recording, i.e. in closed loop, with freely-moving animals. iii) Increase the resolution while reducing the size, weight and energy consumption of the wireless optogenetic systems to minimize the impact of research with small animals. This project is in 3 phases, and its main contributions have been reported in ten conferences (ISSCC, ISCAS, EMBC, etc.) and four published journal papers, or submitted, as well as in a patent and two disclosures. The design of a high resolution optogenetic system poses several challenges. In particular, since the neuronal signals have a high frequency content (10 kHz), the number of chanv nels under observation is limited by the bandwidth of the wireless transmitters (2-4 channels in general). Thus, the first phase of the project focused on the development of neural signal compression algorithms and their integration into a high-resolution miniature and lightweight wireless optogenetics system (2.8g), having 32 recording channels and 32 optical stimulation channels. This system detects, compresses and transmits the waveforms of the signals produced by the neurons, i.e. action potentials (AP), in real time, via an embedded low-power field programmable gate array (FPGA). This processor implements an AP detector algorithm based on adaptive thresholding, which allows to compress the signals by transmitting only the detected waveforms. Each AP is further compressed by a Symmlet-2 discrete wavelet transform (DWT) followed dynamic discrimination and requantification of the DWT coefficients, making it possible to achieve high compression ratios with a good reconstruction quality. Results demonstrate that this algorithm is more robust than existing approach, while allowing to reconstruct the compressed signals with better quality (average SNDR of 25 dB 5% for a compression ratio (CR) of 4.2). With detection, CRs greater than 500 are reported during the in vivo validation. The use of commercial components in wireless optogenetic systems increases the size and power consumption, while not being optimized for this application. The second phase of the project consisted in designing a complementary metal oxide semiconductor (CMOS) system-on-chip (SoC) for neural recording and multi-channel optogenetics, which significantly reduces the size and energy consumption compared to commercial alternatives. This is important contribution, since it’s the first chip to integrate both features. This SoC has 10 recording channels and 4 optogenetic stimulation channels. The bioamplifier design includes a programmable bandwidth (0.5 Hz -7 kHz) and a low input-referred noise (IRN of 3.2 μVrms), which allows targeting different biological signals (AP, LFP, etc.). The Delta-Sigma (DS) MASH 1-1-1 low-power analog-to-digital converter (ADC) is designed to work with low OSR (50), as to reduce its power consumption, and has a programmable resolution (ENOB of 9.75 bits with an OSR of 25). This ADC uses a new technique to reduce its circuit size by subtracting the output of each DS branch in the digital domain, rather than in the analog domain, as done conventionally. A recording channel, including the bioamplifier, the DS and the decimation filter, consumes 11.2 μW. Optical stimulation is performed with an on-chip LED driver using a regulated cascode current source with feedback, which accommodates a wide range of LED parameters and battery voltages. The SoC is integrated into a wireless optogenetic platform and validated in vivo.To date and excluding this project, no wireless system is making closed-loop optogenetics simultaneously to real-time monitoring of neuronal activity. An important contribution of this work is to have developed the first multi-channel optogenetic system that is able to work in closed-loop, and the first to be validated during in vivo experiments involving freely-moving animals. To do so, the third phase of the project aimed to design a digital CMOS chip, called neural decoder integrated circuit (ND-IC). The ND-IC and the SoC developed in Phase 2 are integrated within a wireless optogenetic system. The ND-IC has 3 main cores: 1) the adaptive AP detector core, 2) the compression core with a new sorting tree for discriminating the DWT coefficients, and 3 ) the AP automatic classification core that reuses the data generated by the detection and compression cores to reduce its complexity. A link between a recording channel and a stimulation channel is established according to the association of each AP with a neuron, thanks to the classification, and according to the bursting activity of this neuron. The ND-IC consumes 56.9 μW and occupies 0.08 mm2 per channel. The system weighs 1.05 g, occupies a volume of 1.12 cm3, has an autonomy of 3h, and is validated in vivo

Imaging photoplethysmography: towards effective physiological measurements

Since its conception decades ago, Photoplethysmography (PPG) the non-invasive opto-electronic technique that measures arterial pulsations in-vivo has proven its worth by achieving and maintaining its rank as a compulsory standard of patient monitoring. However successful, conventional contact monitoring mode is not suitable in certain clinical and biomedical situations, e.g., in the case of skin damage, or when unconstrained movement is required. With the advance of computer and photonics technologies, there has been a resurgence of interest in PPG and one potential route to overcome the abovementioned issues has been increasingly explored, i.e., imaging photoplethysmography (iPPG). The emerging field of iPPG offers some nascent opportunities in effective and comprehensive interpretation of the physiological phenomena, indicating a promising alternative to conventional PPG. Heart and respiration rate, perfusion mapping, and pulse rate variability have been accessed using iPPG. To effectively and remotely access physiological information through this emerging technique, a number of key issues are still to be addressed. The engineering issues of iPPG, particularly the influence of motion artefacts on signal quality, are addressed in this thesis, where an engineering model based on the revised Beer-Lambert law was developed and used to describe opto-physiological phenomena relevant to iPPG. An iPPG setup consisting of both hardware and software elements was developed to investigate its reliability and reproducibility in the context of effective remote physiological assessment. Specifically, a first study was conducted for the acquisition of vital physiological signs under various exercise conditions, i.e. resting, light and heavy cardiovascular exercise, in ten healthy subjects. The physiological parameters derived from the images captured by the iPPG system exhibited functional characteristics comparable to conventional contact PPG, i.e., maximum heart rate difference was <3 bpm and a significant (p < 0.05) correlation between both measurements were also revealed. Using a method for attenuation of motion artefacts, the heart rate and respiration rate information was successfully assessed from different anatomical locations even in high-intensity physical exercise situations. This study thereby leads to a new avenue for noncontact sensing of vital signs and remote physiological assessment, showing clear and promising applications in clinical triage and sports training. A second study was conducted to remotely assess pulse rate variability (PRV), which has been considered a valuable indicator of autonomic nervous system (ANS) status. The PRV information was obtained using the iPPG setup to appraise the ANS in ten normal subjects. The performance of the iPPG system in accessing PRV was evaluated via comparison with the readings from a contact PPG sensor. Strong correlation and good agreement between these two techniques verify the effectiveness of iPPG in the remote monitoring of PRV, thereby promoting iPPG as a potential alternative to the interpretation of physiological dynamics related to the ANS. The outcomes revealed in the thesis could present the trend of a robust non-contact technique for cardiovascular monitoring and evaluation

Data reduction algorithms to enable long-term monitoring from low-power miniaturised wireless EEG systems

Objectives: The weight and volume of battery-powered wireless electroencephalography (EEG) systems are dominated by the batteries. Battery dimensions are in turn determined by the required energy capacity, which is derived from the system power consumption and required monitoring time. Data reduction may be carried out to reduce the amount of data transmitted and thus proportionally reduce the power consumption of the wireless transmitter, which dominates system power consumption. This thesis presents two new data selection algorithms that, in addition to achieving data reduction, also select EEG containing epileptic seizures and spikes that are important in diagnosis. Methods: The algorithms analyse short EEG sections, during monitoring, to determine the presence of candidate seizures or spikes. Phase information from different frequency components of the signal are used to detect spikes. For seizure detection, frequencies below 10 Hz are investigated for a relative increase in frequency and/or amplitude. Significant attention has also been given to metrics in order to accurately evaluate the performance of these algorithms for practical use in the proposed system. Additionally, signal processing techniques to emphasize seizures within the EEG and techniques to correct for broad-level amplitude variation in the EEG have been investigated. Results: The spike detection algorithm detected 80% of spikes whilst achieving 50% data reduction, when tested on 992 spikes from 105 hours of 10-channel scalp EEG data obtained from 25 adults. The seizure detection algorithm identified 94% of seizures selecting 80% of their duration for transmission and achieving 79% data reduction. It was tested on 34 seizures with a total duration of 4158 s in a database of over 168 hours of 16-channel scalp EEG obtained from 21 adults. These algorithms show great potential for longer monitoring times from miniaturised wireless EEG systems that would improve electroclinical diagnosis of patients

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies