25 research outputs found

    Neuromorphic Engineering Editors' Pick 2021

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    This collection showcases well-received spontaneous articles from the past couple of years, which have been specially handpicked by our Chief Editors, Profs. André van Schaik and Bernabé Linares-Barranco. The work presented here highlights the broad diversity of research performed across the section and aims to put a spotlight on the main areas of interest. All research presented here displays strong advances in theory, experiment, and methodology with applications to compelling problems. This collection aims to further support Frontiers’ strong community by recognizing highly deserving authors

    Convolutional Neural Network in Pattern Recognition

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    Since convolutional neural network (CNN) was first implemented by Yann LeCun et al. in 1989, CNN and its variants have been widely implemented to numerous topics of pattern recognition, and have been considered as the most crucial techniques in the field of artificial intelligence and computer vision. This dissertation not only demonstrates the implementation aspect of CNN, but also lays emphasis on the methodology of neural network (NN) based classifier. As known to many, one general pipeline of NN-based classifier can be recognized as three stages: pre-processing, inference by models, and post-processing. To demonstrate the importance of pre-processing techniques, this dissertation presents how to model actual problems in medical pattern recognition and image processing by introducing conceptual abstraction and fuzzification. In particular, a transformer on the basis of self-attention mechanism, namely beat-rhythm transformer, greatly benefits from correct R-peak detection results and conceptual fuzzification. Recently proposed self-attention mechanism has been proven to be the top performer in the fields of computer vision and natural language processing. In spite of the pleasant accuracy and precision it has gained, it usually consumes huge computational resources to perform self-attention. Therefore, realtime global attention network is proposed to make a better trade-off between efficiency and performance for the task of image segmentation. To illustrate more on the stage of inference, we also propose models to detect polyps via Faster R-CNN - one of the most popular CNN-based 2D detectors, as well as a 3D object detection pipeline for regressing 3D bounding boxes from LiDAR points and stereo image pairs powered by CNN. The goal for post-processing stage is to refine artifacts inferred by models. For the semantic segmentation task, the dilated continuous random field is proposed to be better fitted to CNN-based models than the widely implemented fully-connected continuous random field. Proposed approaches can be further integrated into a reinforcement learning architecture for robotics

    Guidage non-intrusif d'un bras robotique à l'aide d'un bracelet myoélectrique à électrode sèche

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    Depuis plusieurs années la robotique est vue comme une solution clef pour améliorer la qualité de vie des personnes ayant subi une amputation. Pour créer de nouvelles prothèses intelligentes qui peuvent être facilement intégrées à la vie quotidienne et acceptée par ces personnes, celles-ci doivent être non-intrusives, fiables et peu coûteuses. L’électromyographie de surface fournit une interface intuitive et non intrusive basée sur l’activité musculaire de l’utilisateur permettant d’interagir avec des robots. Cependant, malgré des recherches approfondies dans le domaine de la classification des signaux sEMG, les classificateurs actuels manquent toujours de fiabilité, car ils ne sont pas robustes face au bruit à court terme (par exemple, petit déplacement des électrodes, fatigue musculaire) ou à long terme (par exemple, changement de la masse musculaire et des tissus adipeux) et requiert donc de recalibrer le classifieur de façon périodique. L’objectif de mon projet de recherche est de proposer une interface myoélectrique humain-robot basé sur des algorithmes d’apprentissage par transfert et d’adaptation de domaine afin d’augmenter la fiabilité du système à long-terme, tout en minimisant l’intrusivité (au niveau du temps de préparation) de ce genre de système. L’aspect non intrusif est obtenu en utilisant un bracelet à électrode sèche possédant dix canaux. Ce bracelet (3DC Armband) est de notre (Docteur Gabriel Gagnon-Turcotte, mes co-directeurs et moi-même) conception et a été réalisé durant mon doctorat. À l’heure d’écrire ces lignes, le 3DC Armband est le bracelet sans fil pour l’enregistrement de signaux sEMG le plus performant disponible. Contrairement aux dispositifs utilisant des électrodes à base de gel qui nécessitent un rasage de l’avant-bras, un nettoyage de la zone de placement et l’application d’un gel conducteur avant l’utilisation, le brassard du 3DC peut simplement être placé sur l’avant-bras sans aucune préparation. Cependant, cette facilité d’utilisation entraîne une diminution de la qualité de l’information du signal. Cette diminution provient du fait que les électrodes sèches obtiennent un signal plus bruité que celle à base de gel. En outre, des méthodes invasives peuvent réduire les déplacements d’électrodes lors de l’utilisation, contrairement au brassard. Pour remédier à cette dégradation de l’information, le projet de recherche s’appuiera sur l’apprentissage profond, et plus précisément sur les réseaux convolutionels. Le projet de recherche a été divisé en trois phases. La première porte sur la conception d’un classifieur permettant la reconnaissance de gestes de la main en temps réel. La deuxième porte sur l’implémentation d’un algorithme d’apprentissage par transfert afin de pouvoir profiter des données provenant d’autres personnes, permettant ainsi d’améliorer la classification des mouvements de la main pour un nouvel individu tout en diminuant le temps de préparation nécessaire pour utiliser le système. La troisième phase consiste en l’élaboration et l’implémentation des algorithmes d’adaptation de domaine et d’apprentissage faiblement supervisé afin de créer un classifieur qui soit robuste au changement à long terme.For several years, robotics has been seen as a key solution to improve the quality of life of people living with upper-limb disabilities. To create new, smart prostheses that can easily be integrated into everyday life, they must be non-intrusive, reliable and inexpensive. Surface electromyography provides an intuitive interface based on a user’s muscle activity to interact with robots. However, despite extensive research in the field of sEMG signal classification, current classifiers still lack reliability due to their lack of robustness to short-term (e.g. small electrode displacement, muscle fatigue) or long-term (e.g. change in muscle mass and adipose tissue) noise. In practice, this mean that to be useful, classifier needs to be periodically re-calibrated, a time consuming process. The goal of my research project is to proposes a human-robot myoelectric interface based on transfer learning and domain adaptation algorithms to increase the reliability of the system in the long term, while at the same time reducing the intrusiveness (in terms of hardware and preparation time) of this kind of systems. The non-intrusive aspect is achieved from a dry-electrode armband featuring ten channels. This armband, named the 3DC Armband is from our (Dr. Gabriel Gagnon-Turcotte, my co-directors and myself) conception and was realized during my doctorate. At the time of writing, the 3DC Armband offers the best performance for currently available dry-electrodes, surface electromyographic armbands. Unlike gel-based electrodes which require intrusive skin preparation (i.e. shaving, cleaning the skin and applying conductive gel), the 3DC Armband can simply be placed on the forearm without any preparation. However, this ease of use results in a decrease in the quality of information. This decrease is due to the fact that the signal recorded by dry electrodes is inherently noisier than gel-based ones. In addition, other systems use invasive methods (intramuscular electromyography) to capture a cleaner signal and reduce the source of noises (e.g. electrode shift). To remedy this degradation of information resulting from the non-intrusiveness of the armband, this research project will rely on deep learning, and more specifically on convolutional networks. The research project was divided into three phases. The first is the design of a classifier allowing the recognition of hand gestures in real-time. The second is the implementation of a transfer learning algorithm to take advantage of the data recorded across multiple users, thereby improving the system’s accuracy, while decreasing the time required to use the system. The third phase is the development and implementation of a domain adaptation and self-supervised learning to enhance the classifier’s robustness to long-term changes

    Machine learning for quantum and complex systems

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    Machine learning now plays a pivotal role in our society, providing solutions to problems that were previously thought intractable. The meteoric rise of this technology can no doubt be attributed to the information age that we now live in. As data is continually amassed, more efficient and scalable methods are required to yield functional models and accurate inferences. Simultaneously we have also seen quantum technology come to the forefront of research and next generation systems. These technologies promise secure information transfer, efficient computation and high precision sensing, at levels unattainable by their classical counterparts. Although these technologies are powerful, they are necessarily more complicated and difficult to control. The combination of these two advances yields an opportunity for study, namely leveraging the power of machine learning to control and optimise quantum (and more generally complex) systems. The work presented in thesis explores these avenues of investigation and demonstrates the potential success of machine learning methods in the domain of quantum and complex systems. One of the most crucial potential quantum technologies is the quantum memory. If we are to one day harness the true power of quantum key distribution for secure transimission of information, and more general quantum computating tasks, it will almost certainly involve the use of quantum memorys. We start by presenting the operation of the cold atom workhorse: the magneto-optical trap (MOT). To use a cold atomic ensemble as a quantum memory we are required to prepare the atoms using a specialised cooling sequence. During this we observe a stable, coherent optical emission exiting each end of the elongated ensemble. We characterise this behaviour and compare it to similar observations in previous work. Following this, we use the ensemble to implement a backward Raman memory. Using this scheme we are able to demonstrate an increased efficiency over that of previous forward recall implementations. While we are limited by the optical depth of the system, we observe an efficiency more than double that of previous implementations. The MOT provides an easily accessible test bed for the optimisation via some machine learning technique. As we require an efficient search method, we implement a new type of algorithm based on deep learning. We design this technique such that the artificial neural networks are placed in control of the online optimisation, rather than simply being used as surrogate models. We experimentally optimise the optical depth of the MOT using this method, by parametrising the time varying compression sequence. We identify a new and unintuitive method for cooling the atomic ensemble which surpasses current methods. Following this initial implementation we make substantial improvements to the deep learning approach. This extends the approach to be applicable to a far wider range of complex problems, which may contain extensive local minima and structure. We benchmark this algorithm against many of the conventional optimisation techniques and demonstrate superior capability to optimise problems with high dimensionality. Finally we apply this technique to a series of preliminary problems, namely the tuning of a single electron transistor and second-order correlations from a quantum dot source

    MOCAST 2021

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    The 10th International Conference on Modern Circuit and System Technologies on Electronics and Communications (MOCAST 2021) will take place in Thessaloniki, Greece, from July 5th to July 7th, 2021. The MOCAST technical program includes all aspects of circuit and system technologies, from modeling to design, verification, implementation, and application. This Special Issue presents extended versions of top-ranking papers in the conference. The topics of MOCAST include:Analog/RF and mixed signal circuits;Digital circuits and systems design;Nonlinear circuits and systems;Device and circuit modeling;High-performance embedded systems;Systems and applications;Sensors and systems;Machine learning and AI applications;Communication; Network systems;Power management;Imagers, MEMS, medical, and displays;Radiation front ends (nuclear and space application);Education in circuits, systems, and communications

    Machine Learning Methods with Noisy, Incomplete or Small Datasets

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    In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios

    Learning Biosignals with Deep Learning

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    The healthcare system, which is ubiquitously recognized as one of the most influential system in society, is facing new challenges since the start of the decade.The myriad of physiological data generated by individuals, namely in the healthcare system, is generating a burden on physicians, losing effectiveness on the collection of patient data. Information systems and, in particular, novel deep learning (DL) algorithms have been prompting a way to take this problem. This thesis has the aim to have an impact in biosignal research and industry by presenting DL solutions that could empower this field. For this purpose an extensive study of how to incorporate and implement Convolutional Neural Networks (CNN), Recursive Neural Networks (RNN) and Fully Connected Networks in biosignal studies is discussed. Different architecture configurations were explored for signal processing and decision making and were implemented in three different scenarios: (1) Biosignal learning and synthesis; (2) Electrocardiogram (ECG) biometric systems, and; (3) Electrocardiogram (ECG) anomaly detection systems. In (1) a RNN-based architecture was able to replicate autonomously three types of biosignals with a high degree of confidence. As for (2) three CNN-based architectures, and a RNN-based architecture (same used in (1)) were used for both biometric identification, reaching values above 90% for electrode-base datasets (Fantasia, ECG-ID and MIT-BIH) and 75% for off-person dataset (CYBHi), and biometric authentication, achieving Equal Error Rates (EER) of near 0% for Fantasia and MIT-BIH and bellow 4% for CYBHi. As for (3) the abstraction of healthy clean the ECG signal and detection of its deviation was made and tested in two different scenarios: presence of noise using autoencoder and fully-connected network (reaching 99% accuracy for binary classification and 71% for multi-class), and; arrhythmia events by including a RNN to the previous architecture (57% accuracy and 61% sensitivity). In sum, these systems are shown to be capable of producing novel results. The incorporation of several AI systems into one could provide to be the next generation of preventive medicine, as the machines have access to different physiological and anatomical states, it could produce more informed solutions for the issues that one may face in the future increasing the performance of autonomous preventing systems that could be used in every-day life in remote places where the access to medicine is limited. These systems will also help the study of the signal behaviour and how they are made in real life context as explainable AI could trigger this perception and link the inner states of a network with the biological traits.O sistema de saúde, que é ubiquamente reconhecido como um dos sistemas mais influentes da sociedade, enfrenta novos desafios desde o ínicio da década. A miríade de dados fisiológicos gerados por indíviduos, nomeadamente no sistema de saúde, está a gerar um fardo para os médicos, perdendo a eficiência no conjunto dos dados do paciente. Os sistemas de informação e, mais espcificamente, da inovação de algoritmos de aprendizagem profunda (DL) têm sido usados na procura de uma solução para este problema. Esta tese tem o objetivo de ter um impacto na pesquisa e na indústria de biosinais, apresentando soluções de DL que poderiam melhorar esta área de investigação. Para esse fim, é discutido um extenso estudo de como incorporar e implementar redes neurais convolucionais (CNN), redes neurais recursivas (RNN) e redes totalmente conectadas para o estudo de biosinais. Diferentes arquiteturas foram exploradas para processamento e tomada de decisão de sinais e foram implementadas em três cenários diferentes: (1) Aprendizagem e síntese de biosinais; (2) sistemas biométricos com o uso de eletrocardiograma (ECG), e; (3) Sistema de detecção de anomalias no ECG. Em (1) uma arquitetura baseada na RNN foi capaz de replicar autonomamente três tipos de sinais biológicos com um alto grau de confiança. Quanto a (2) três arquiteturas baseadas em CNN e uma arquitetura baseada em RNN (a mesma usada em (1)) foram usadas para ambas as identificações, atingindo valores acima de 90 % para conjuntos de dados à base de eletrodos (Fantasia, ECG-ID e MIT -BIH) e 75 % para o conjunto de dados fora da pessoa (CYBHi) e autenticação, atingindo taxas de erro iguais (EER) de quase 0 % para Fantasia e MIT-BIH e abaixo de 4 % para CYBHi. Quanto a (3) a abstração de sinais limpos e assimptomáticos de ECG e a detecção do seu desvio foram feitas e testadas em dois cenários diferentes: na presença de ruído usando um autocodificador e uma rede totalmente conectada (atingindo 99 % de precisão na classificação binária e 71 % na multi-classe), e; eventos de arritmia incluindo um RNN na arquitetura anterior (57 % de precisão e 61 % de sensibilidade). Em suma, esses sistemas são mais uma vez demonstrados como capazes de produzir resultados inovadores. A incorporação de vários sistemas de inteligência artificial em um unico sistema pederá desencadear a próxima geração de medicina preventiva. Os algoritmos ao terem acesso a diferentes estados fisiológicos e anatómicos, podem produzir soluções mais informadas para os problemas que se possam enfrentar no futuro, aumentando o desempenho de sistemas autónomos de prevenção que poderiam ser usados na vida quotidiana, nomeadamente em locais remotos onde o acesso à medicinas é limitado. Estes sistemas também ajudarão o estudo do comportamento do sinal e como eles são feitos no contexto da vida real, pois a IA explicável pode desencadear essa percepção e vincular os estados internos de uma rede às características biológicas

    Safety and Reliability - Safe Societies in a Changing World

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    The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen
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