Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data

Similarity-based approaches represent a promising direction for time series analysis. However, many such methods rely on parameter tuning, and some have shortcomings if the time series are multivariate (MTS), due to dependencies between attributes, or the time series contain missing data. In this paper, we address these challenges within the powerful context of kernel methods by proposing the robust \emph{time series cluster kernel} (TCK). The approach taken leverages the missing data handling properties of Gaussian mixture models (GMM) augmented with informative prior distributions. An ensemble learning approach is exploited to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel. We evaluate the TCK on synthetic and real data and compare to other state-of-the-art techniques. The experimental results demonstrate that the TCK is robust to parameter choices, provides competitive results for MTS without missing data and outstanding results for missing data.Comment: 23 pages, 6 figure

A Computational Framework to Support the Automated Analysis of Routine Electroencephalographic Data

Epilepsy is a condition in which a patient has multiple unprovoked seizures which are not precipitated by another medical condition. It is a common neurological disorder that afflicts 1% of the population of the US, and is sometimes hard to diagnose if seizures are infrequent. Routine Electroencephalography (rEEG), where the electrical potentials of the brain are recorded on the scalp of a patient, is one of the main tools for diagnosing because rEEG can reveal indicators of epilepsy when patients are in a non-seizure state. Interpretation of rEEG is difficult and studies have shown that 20-30% of patients at specialized epilepsy centers are misdiagnosed. An improved ability to interpret rEEG could decrease the misdiagnosis rate of epilepsy. The difficulty in diagnosing epilepsy from rEEG stems from the large quantity, low signal to noise ratio (SNR), and variability of the data. A usual point of error for a clinician interpreting rEEG data is the misinterpretation of PEEs (paroxysmal EEG events) ( short bursts of electrical activity of high amplitude relative to the surrounding signals that have a duration of approximately .1 to 2 seconds). Clinical interpretation of PEEs could be improved with the development of an automated system to detect and classify PEE activity in an rEEG dataset. Systems that have attempted to automatically classify PEEs in the past have had varying degrees of success. These efforts have been hampered to a large extent by the absence of a \gold standard\u27 data set that EEG researchers could use. In this work we present a distributed, web-based collaborative system for collecting and creating a gold standard dataset for the purpose of evaluating spike detection software. We hope to advance spike detection research by creating a performance standard that facilitates comparisons between approaches of disparate research groups. Further, this work endeavors to create a new, high performance parallel implementation of ICA (independent component analysis), a potential preprocessing step for PEE classification. We also demonstrate tools for visualization and analysis to support the initial phases of spike detection research. These tools will first help to develop a standardized rEEG dataset of expert EEG interpreter opinion with which automated analysis can be trained and tested. Secondly, it will attempt to create a new framework for interdisciplinary research that will help improve our understanding of PEEs in rEEG. These improvements could ultimately advance the nuanced art of rEEG interpretation and decrease the misdiagnosis rate that leads to patients suering inappropriate treatment

Concepção e realização de um framework para sistemas embarcados baseados em FPGA aplicado a um classificador Floresta de Caminhos Ótimos

Orientadores: Eurípedes Guilherme de Oliveira Nóbrega, Isabelle Fantoni-Coichot, Vincent FrémontTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica, Université de Technologie de CompiègneResumo: Muitas aplicações modernas dependem de métodos de Inteligência Artificial, tais como classificação automática. Entretanto, o alto custo computacional associado a essas técnicas limita seu uso em plataformas embarcadas com recursos restritos. Grandes quantidades de dados podem superar o poder computacional disponível em tais ambientes, o que torna o processo de projetá-los uma tarefa desafiadora. As condutas de processamento mais comuns usam muitas funções de custo computacional elevadas, o que traz a necessidade de combinar alta capacidade computacional com eficiência energética. Uma possível estratégia para superar essas limitações e prover poder computacional suficiente aliado ao baixo consumo de energia é o uso de hardware especializado como, por exemplo, FPGA. Esta classe de dispositivos é amplamente conhecida por sua boa relação desempenho/consumo, sendo uma alternativa interessante para a construção de sistemas embarcados eficazes e eficientes. Esta tese propõe um framework baseado em FPGA para a aceleração de desempenho de um algoritmo de classificação a ser implementado em um sistema embarcado. A aceleração do desempenho foi atingida usando o esquema de paralelização SIMD, aproveitando as características de paralelismo de grão fino dos FPGA. O sistema proposto foi implementado e testado em hardware FPGA real. Para a validação da arquitetura, um classificador baseado em Teoria dos Grafos, o OPF, foi avaliado em uma proposta de aplicação e posteriormente implementado na arquitetura proposta. O estudo do OPF levou à proposição de um novo algoritmo de aprendizagem para o mesmo, usando conceitos de Computação Evolutiva, visando a redução do tempo de processamento de classificação, que, combinada à implementação em hardware, oferece uma aceleração de desempenho suficiente para ser aplicada em uma variedade de sistemas embarcadosAbstract: Many modern applications rely on Artificial Intelligence methods such as automatic classification. However, the computational cost associated with these techniques limit their use in resource constrained embedded platforms. A high amount of data may overcome the computational power available in such embedded environments while turning the process of designing them a challenging task. Common processing pipelines use many high computational cost functions, which brings the necessity of combining high computational capacity with energy efficiency. One of the strategies to overcome this limitation and provide sufficient computational power allied with low energy consumption is the use of specialized hardware such as FPGA. This class of devices is widely known for their performance to consumption ratio, being an interesting alternative to building capable embedded systems. This thesis proposes an FPGA-based framework for performance acceleration of a classification algorithm to be implemented in an embedded system. Acceleration is achieved using SIMD-based parallelization scheme, taking advantage of FPGA characteristics of fine-grain parallelism. The proposed system is implemented and tested in actual FPGA hardware. For the architecture validation, a graph-based classifier, the OPF, is evaluated in an application proposition and afterward applied to the proposed architecture. The OPF study led to a proposition of a new learning algorithm using evolutionary computation concepts, aiming at classification processing time reduction, which combined to the hardware implementation offers sufficient performance acceleration to be applied in a variety of embedded systemsDoutoradoMecanica dos Sólidos e Projeto MecanicoDoutor em Engenharia Mecânica3077/2013-09CAPE

Accelerating SNN Training with Stochastic Parallelizable Spiking Neurons

Spiking neural networks (SNN) are able to learn spatiotemporal features while using less energy, especially on neuromorphic hardware. The most widely used spiking neuron in deep learning is the Leaky Integrate and Fire (LIF) neuron. LIF neurons operate sequentially, however, since the computation of state at time t relies on the state at time t-1 being computed. This limitation is shared with Recurrent Neural Networks (RNN) and results in slow training on Graphics Processing Units (GPU). In this paper, we propose the Stochastic Parallelizable Spiking Neuron (SPSN) to overcome the sequential training limitation of LIF neurons. By separating the linear integration component from the non-linear spiking function, SPSN can be run in parallel over time. The proposed approach results in performance comparable with the state-of-the-art for feedforward neural networks on the Spiking Heidelberg Digits (SHD) dataset, outperforming LIF networks while training 10 times faster and outperforming non-spiking networks with the same network architecture. For longer input sequences of 10000 time-steps, we show that the proposed approach results in 4000 times faster training, thus demonstrating the potential of the proposed approach to accelerate SNN training for very large datasets

Sensing and Signal Processing in Smart Healthcare

In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included

Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

With the advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors, new opportunities are emerging for applying deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge. This can facilitate the advancement of the medical Internet of Things (IoT) systems and Point of Care (PoC) devices. In this paper, we provide a tutorial describing how various technologies ranging from emerging memristive devices, to established Field Programmable Gate Arrays (FPGAs), and mature Complementary Metal Oxide Semiconductor (CMOS) technology can be used to develop efficient DL accelerators to solve a wide variety of diagnostic, pattern recognition, and signal processing problems in healthcare. Furthermore, we explore how spiking neuromorphic processors can complement their DL counterparts for processing biomedical signals. After providing the required background, we unify the sparsely distributed research on neural network and neuromorphic hardware implementations as applied to the healthcare domain. In addition, we benchmark various hardware platforms by performing a biomedical electromyography (EMG) signal processing task and drawing comparisons among them in terms of inference delay and energy. Finally, we provide our analysis of the field and share a perspective on the advantages, disadvantages, challenges, and opportunities that different accelerators and neuromorphic processors introduce to healthcare and biomedical domains. This paper can serve a large audience, ranging from nanoelectronics researchers, to biomedical and healthcare practitioners in grasping the fundamental interplay between hardware, algorithms, and clinical adoption of these tools, as we shed light on the future of deep networks and spiking neuromorphic processing systems as proponents for driving biomedical circuits and systems forward.Comment: Submitted to IEEE Transactions on Biomedical Circuits and Systems (21 pages, 10 figures, 5 tables

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Advanced photonic and electronic systems WILGA 2016

Young Researchers Symposium WILGA on Photonics Applications and Web Engineering has been organized since 1998, two times a year. Subject area of the Wilga Symposium are advanced photonic and electronic systems in all aspects: theoretical, design and application, hardware and software, academic, scientific, research, development, commissioning and industrial, but also educational and development of research and technical staff. Each year, during the international Spring edition, the Wilga Symposium is attended by a few hundred young researchers, graduated M.Sc. students, Ph.D. students, young doctors, young research workers from the R&D institutions, universities, innovative firms, etc. Wilga, gathering through years the organization experience, has turned out to be a perfect relevant information exchange platform between young researchers from Poland with participation  of international guests, all active in the research areas of electron and photon technologies, electronics, photonics, telecommunications, automation, robotics and information technology, but also technical physics. The paper summarizes the achievements of the 38th Spring Edition of 2016 WILGA Symposium, organized in Wilga Village Resort owned by Warsaw University of technology