Architectures for block Toeplitz systems

In this paper efficient VLSI architectures of highly concurrent algorithms for the solution of block linear systems with Toeplitz or near-to-Toeplitz entries are presented. The main features of the proposed scheme are the use of scalar only operations, multiplications/divisions and additions, and the local communication which enables the development of wavefront array architecture. Both the mean squared error and the total squared error formulations are described and a variety of implementations are given

Data-driven multivariate and multiscale methods for brain computer interface

This thesis focuses on the development of data-driven multivariate and multiscale methods for brain computer interface (BCI) systems. The electroencephalogram (EEG), the most convenient means to measure neurophysiological activity due to its noninvasive nature, is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its multichannel recording nature require a new set of data-driven multivariate techniques to estimate more accurately features for enhanced BCI operation. Also, a long term goal is to enable an alternative EEG recording strategy for achieving long-term and portable monitoring. Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary EEG signal into a set of components which are highly localised in time and frequency. It is shown that the complex and multivariate extensions of EMD, which can exploit common oscillatory modes within multivariate (multichannel) data, can be used to accurately estimate and compare the amplitude and phase information among multiple sources, a key for the feature extraction of BCI system. A complex extension of local mean decomposition is also introduced and its operation is illustrated on two channel neuronal spike streams. Common spatial pattern (CSP), a standard feature extraction technique for BCI application, is also extended to complex domain using the augmented complex statistics. Depending on the circularity/noncircularity of a complex signal, one of the complex CSP algorithms can be chosen to produce the best classification performance between two different EEG classes. Using these complex and multivariate algorithms, two cognitive brain studies are investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user attention to a sound source among a mixture of sound stimuli, which is aimed at improving the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments elicited by taste and taste recall are examined to determine the pleasure and displeasure of a food for the implementation of affective computing. The separation between two emotional responses is examined using real and complex-valued common spatial pattern methods. Finally, we introduce a novel approach to brain monitoring based on EEG recordings from within the ear canal, embedded on a custom made hearing aid earplug. The new platform promises the possibility of both short- and long-term continuous use for standard brain monitoring and interfacing applications

Parallel computing and the generation of basic plasma data

Comprehensive simulations of the processing plasmas used in semiconductor fabrication will depend on the availability of basic data for many microscopic processes that occur in the plasma and at the surface. Cross sections for electron collisions, a principal mechanism for producing reactive species in these plasmas, are among the most important such data; however, electron-collision cross sections are difficult to measure, and the available data are, at best, sketchy for the polyatomic feed gases of interest. While computational approaches to obtaining such data are thus potentially of significant value, studies of electron collisions with polyatomic gases at relevant energies are numerically intensive. In this article, we report on the progress we have made in exploiting large-scale distributed-memory parallel computers, consisting of hundreds of interconnected microprocessors, to generate electron-collision cross sections for gases of interest in plasma simulations

Perceptual-based textures for scene labeling: a bottom-up and a top-down approach

Due to the semantic gap, the automatic interpretation of digital images is a very challenging task. Both the segmentation and classification are intricate because of the high variation of the data. Therefore, the application of appropriate features is of utter importance. This paper presents biologically inspired texture features for material classification and interpreting outdoor scenery images. Experiments show that the presented texture features obtain the best classification results for material recognition compared to other well-known texture features, with an average classification rate of 93.0%. For scene analysis, both a bottom-up and top-down strategy are employed to bridge the semantic gap. At first, images are segmented into regions based on the perceptual texture and next, a semantic label is calculated for these regions. Since this emerging interpretation is still error prone, domain knowledge is ingested to achieve a more accurate description of the depicted scene. By applying both strategies, 91.9% of the pixels from outdoor scenery images obtained a correct label

Attentive Adversarial Learning for Domain-Invariant Training

Adversarial domain-invariant training (ADIT) proves to be effective in suppressing the effects of domain variability in acoustic modeling and has led to improved performance in automatic speech recognition (ASR). In ADIT, an auxiliary domain classifier takes in equally-weighted deep features from a deep neural network (DNN) acoustic model and is trained to improve their domain-invariance by optimizing an adversarial loss function. In this work, we propose an attentive ADIT (AADIT) in which we advance the domain classifier with an attention mechanism to automatically weight the input deep features according to their importance in domain classification. With this attentive re-weighting, AADIT can focus on the domain normalization of phonetic components that are more susceptible to domain variability and generates deep features with improved domain-invariance and senone-discriminativity over ADIT. Most importantly, the attention block serves only as an external component to the DNN acoustic model and is not involved in ASR, so AADIT can be used to improve the acoustic modeling with any DNN architectures. More generally, the same methodology can improve any adversarial learning system with an auxiliary discriminator. Evaluated on CHiME-3 dataset, the AADIT achieves 13.6% and 9.3% relative WER improvements, respectively, over a multi-conditional model and a strong ADIT baseline.Comment: 5 pages, 1 figure, ICASSP 201

Supported diagnosis of adhd from eeg signals based on hidden markov models and probability product kernels

Attention deficit hyperactivity disorder (ADHD), most often present in childhood, may persist in adult life, hampering personal development. However, ADHD diagnosis is a real challenge since it highly depends on the clinical observation of the patient, the parental and scholar information, and the specialist expertise. Despite demanded objective diagnosis aids from biosignals, the physiological biomarkers lack robustness and significance under the non-stationary and non-linear electroencephalographic dynamics. Therefore, this work presents a supported diagnosis methodology for ADHD from the dynamic characterization of EEG based on hidden Markov models (HMM) and probability product kernels (PPK). Based on the symptom of impulsivity, the proposed approach trains an HMM for each subject from EEG signals in failed inhibition tasks. In the first instance, PPK measures the similarity between subjects through the inner product between their trained HMMs. Then, given the computational costs, fast computation of PPK for HMM facilitates parameter tuning of kernel similarity. Finally, the Kernel Principal Component Analysis (KPCA) projects the PPK to a lower dimensional space, allowing the interpretability of the results. Thus, a support vector machine supports the diagnosis of ADHD as a classification task using PPK as the inner product operator. The methodology compared classification results on EEG signals with all channels, channels of interest (COI), and analysis in the Theta, Alpha, and Beta frequency bands. The results show an accuracy rate of 97.0% in the Beta band in COI, which supports the assumption that this frequency rhythm may be correlated to differences between ADHD and controls regarding attentional allocation during the execution of the cognitive task.El trastorno por déficit de atención e hiperactividad (TDAH), que suele presentarse en la infancia, puede persistir en la vida adulta, obstaculizando el desarrollo personal. Sin embargo, el diagnóstico del TDAH es un verdadero reto, ya que depende en gran medida de la observación clínica del paciente, de la información de los padres y de los estudiosos, y de la experiencia de los especialistas. A pesar de la demanda de ayudas para el diagnóstico objetivo a partir de bioseñales, los biomarcadores fisiológicos carecen de robustez y significación bajo la dinámica electroencefalográfica no estacionaria y no lineal. Por lo tanto, este trabajo presenta una metodología de diagnóstico apoyada para el TDAH a partir de la caracterización dinámica del EEG basada en modelos ocultos de Markov (HMM) y productos de kernel de probabilidad (PPK). Basándose en el síntoma de impulsividad, el enfoque propuesto entrena un HMM para cada sujeto a partir de las señales del EEG en tareas de inhibición fallidas. En primer lugar, el PPK mide la similitud entre los sujetos a través del producto interno entre sus HMMs entrenados. Luego, dados los costes computacionales, el cálculo rápido de PPK para los HMM facilita el ajuste de los parámetros de similitud del kernel. Por último, el Análisis de Componentes Principales del Kernel (KPCA) proyecta el PPK a un espacio de menor dimensión, lo que permite la interpretabilidad de los resultados. Así, una máquina de vectores de apoyo apoya el diagnóstico del TDAH como una tarea de clasificación utilizando el PPK como operador de producto interno. La metodología comparó los resultados de clasificación en señales de EEG con todos los canales, canales de interés (COI), y análisis en las bandas de frecuencia Theta, Alpha, y Beta. Los resultados muestran una tasa de precisión del 97,0% en la banda Beta en COI, lo que apoya la suposición de que este ritmo de frecuencia puede estar correlacionado con las diferencias entre el TDAH y los controles en cuanto a la asignación atencional durante la ejecución de la tarea cognitiva.MaestríaMagíster en Ingeniería EléctricaContents 1 List of Symbols and Abbreviations 5 1.1 Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2 Abbrevations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2 Introduction 7 2.1 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Justification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3 State of the art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.4 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4.1 General objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4.2 Specific objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3 Develop a multichannel time series classification methodology taking into account signal dynamics 13 3.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.1 Similarity between time series . . . . . . . . . . . . . . . . . . . . . . 13 3.2 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.2.1 EEG Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.2.2 HMM training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.2.3 Parameter tuning and Classification . . . . . . . . . . . . . . . . . . . 17 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4 Develop a time series classification methodology that takes into account spectral information and reduces the computational cost of training. 21 4.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.1.1 Fast computation of PPK for HMM . . . . . . . . . . . . . . . . . . . 22 4.2 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.2.1 Synthetic Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.2.2 Training and Parameter tuning and classification . . . . . . . . . . . 24 4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 1 CONTENTS 4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 5 Develop a methodology for visualizing stochastic representations to facilitate the interpretability of inference machines 32 5.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 5.1.1 Model interpretability . . . . . . . . . . . . . . . . . . . . . . . . . . 32 5.1.2 Low-dimensional HMM visualization . . . . . . . . . . . . . . . . . . 33 5.1.3 Low-dimensional state visualization . . . . . . . . . . . . . . . . . . 34 5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 5.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 6 Conclusions 4

Cycle-to-cycle control of swing phase of paraplegic gait induced by surface electrical stimulation

Parameterised swing phase of gait in paraplegics was obtained using surface electrical stimulation of the hip flexors, hamstrings and quadriceps; the hip flexors were stimulated to obtain a desired hip angle range, the hamstrings to provide foot clearance in the forward swing, and the quadriceps to acquire knee extension at the end of the swing phase. We report on two main aspects; optimisation of the initial stimulation parameters, and parameter adaption (control). The initial stimulation patterns were experimentally optimised in two paraplegic subjects using a controlled stand device, resulting in an initial satisfactory swinging motion in both subjects. Intersubject differences appeared in the mechanical output (torque joint) per muscle group. During a prolonged open-loop controlled trial with the optimised but unregulated stimulation onsets and burst duration for the three muscle groups, the hip angle range per cycle initially increased above the desired value and subsequently decreased below it. The mechanical performance of the hamstrings and quadriceps remained relatively unaffected. A cycle-to-cycle controller was then designed, operating on the basis of the hip angle ranges obtained in previous swings. This controller successfully adapted the burst duration of the hip flexors to maintain the desired hip angle range

Analytic structure of the S-matrix for singular quantum mechanics

The analytic structure of the S-matrix of singular quantum mechanics is examined within a multichannel framework, with primary focus on its dependence with respect to a parameter (Ω) that determines the boundary conditions. Specifically, a characterization is given in terms of salient mathematical and physical properties governing its behavior. These properties involve unitarity and associated current-conserving Wronskian relations, time-reversal invariance, and Blaschke factorization. The approach leads to an interpretation of effective nonunitary solutions in singular quantum mechanics and their determination from the unitary family.Instituto de Física La PlataConsejo Nacional de Investigaciones Científicas y Técnica