A model for peak matrix performance on FPGAs

Computations involving matrices form the kernel of a large spectrum of computationally demanding applications for which FPGAs have actively been utilized as accelerators. The performances of such matrix operations on FPGAs are related to underlying architectural parameters such as computational resources, memory and I/O bandwidth. A model that gives bounds on the peak performance of matrix-vector and matrix-matrix multiplication operations on FPGAs based on these parameters is presented. The architecture and efficiency of existing implementations are compared against the model. Future trends in matrix performance on FPGA devices are estimated based on the performance model and system parameters from the past decade. © 2011 IEEE.published_or_final_versio

Feature Analysis for Discrimination of Motor Unit Action Potentials

© 2018 IEEE. In electrophysiological signal processing for intramuscular electromyography data (nEMG), single motor unit activity is of great interest. The changes of action potential (MUAP) morphology, motor unit (MU) activation, and recruitment provide the most informative part to study the nature causality in neuromuscular disorders. In practice, for a single nEMG recording, more than one motor unit activities (in the surrounding area of a needle electrode) are usually collected. Such a fact makes the MUAP discrimination that separates single unit activities a crucial task. Most neurology laboratories worldwide still recruit specialists who spend hours to manually or semi-automatically sort MUAPs. From a machine learning perspective, this task is analogous to the clustering-based classification problem in which the number of classes and other class information are unfortunately missing. In this paper, we present a feature analysis strategy to help better utilize unsupervised (i.e., totally automated) methods for MUAP discrimination. To that end, we extract a large pool of features from each MUAP. Then we select the top ranked candidates using clusterability scores as selection criteria. We found spectrograms of wavelet decomposition as a top-ranking feature, highly correlated to the motor unit reference and was more separable than existing features. Using a correlation-based clustering technique, we demonstrate the sorting performance with this feature set. Compared with the reference produced by human experts, our method obtained a comparable result (e.g., equivalent number of classes was found, identical MUAP morphology in each pair of corresponding MU class, and similar histograms of MUs). Taking the manual labels as references, our method got a much higher sensitivity and accuracy than the compared unsupervised sorting method. We obtained a similar result in MUAP classification to the reference

Freezing of Gait Detection in Parkinson's Disease: A Subject-Independent Detector Using Anomaly Scores

© 2012 IEEE. Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this paper, we aim to develop an automated FoG detector for subject independent. After extracting highly relevant features, we apply anomaly detection techniques to detect FoG events. Specifically, feature selection is performed using correlation and clusterability metrics. From a list of 244 feature candidates, 36 candidates were selected using saliency and robustness criteria. We develop an anomaly score detector with adaptive thresholding to identify FoG events. Then, using accuracy metrics, we reduce the feature list to seven candidates. Our novel multichannel freezing index was the most selective across all window sizes, achieving sensitivity (specificity) of 96% (79%). On the other hand, freezing index from the vertical axis was the best choice for a single input, achieving sensitivity (specificity) of 94% (84%) for ankle and 89% (94%) for back sensors. Our subject-independent method is not only significantly more accurate than those previously reported, but also uses a much smaller window (e.g., 3 s versus 7.5 s) and/or lower tolerance (e.g., 0.4 s versus 2 s)

Significant papers from the First 25 Years of the FPL Conference

The list of significant papers from the first 25 years of the Field-Programmable Logic and Applications conference (FPL) is presented in this paper. These 27 papers represent those which have most strongly influenced theory and practice in the field.postprin

Abuso cibernético nas relações dos jovens adultos: relação entre o uso problemático de internet, recurso a estratégias cibernéticas abusivas e existência de traços de psicopatia

As Tecnologias de Informação e Comunicação vieram revolucionar a forma como comunicamos e marcam presença constante no quotidiano dos jovens. A experienciar as suas primeiras relações afetivas, os jovens adultos recorrem às tecnologias para comunicar com o parceiro íntimo. Se por um lado estes meios de comunicação podem aproximar os jovens, por outro servem de veículo para situações de abuso. Considerando estes elementos, esta investigação teve por objetivo estudar a relação entre o uso problemático da internet, traços de psicopatia e comportamentos de abuso cibernético. A amostra é constituída por 105 jovens, 85 raparigas e 20 rapazes, com uma idade média de 23.2 (DP = 3.65). Os resultados indicaram que há um forte uso das tecnologias para manter a comunicação com o parceiro. Já nos resultados do abuso cibernético foi percetível a elevada prevalência de comportamentos de vitimação e perpetração, com especial destaque para a dimensão do controlo. Não se encontraram diferenças no abuso cibernético entre rapazes e raparigas. Por fim, o estudo da relação entre os traços de psicopatia, uso problemático na internet e comportamentos de abuso cibernético não apresentou relevância estatística. Este estudo permitiu perceber que os comportamentos de abuso cibernético são transversais a ambos os sexos e apresentam uma tendência de crescimento da sua prevalência.Information and Communication Technologies have revolutionized the way we communicate and mark a constant presence in the daily lives of young people. Experiencing their first affective relationships, young adults’ resort to technologies to communicate with their intimate partner. In one hand, communication by technologies can promotes closer relationships, on the other hand they could be a vehicle for abuse situations. So, this research aimed to study the relationship between problematic use of internet, psychopathy traits and cyber dating abuse behavior. The sample is constituted by 105 emerging adults, 80 girls, and 20 boys, with an average age of 23.2 (SD = 3.65). The results suggest that there is a strong use of technologies to maintain communication with the partner. The results of cyber dating abuse were perceptible the high prevalence of victimization and perpetration behaviors, with special emphasis on the dimension of control. No differences were found between the averages of cyber dating abuse among boys and girls. Finally, the study of the relationship between traits of psychopathy, problematic use on the Internet, and cyber dating abuse behaviors was not statistically relevant. The study showed that cyber dating abuse behaviors are transversal to both sexes and show a tendency to increase their prevalence

Frame-Based SEMG-to-Speech Conversion

This paper presents a methodology that uses surface electromyogram (SEMG) signals recorded from the cheek and chin to synthesize speech. A neural network is trained to map the SEMG features (short-time Fourier transform coefficients) into vector-quantized codebook indices of speech features (linear prediction coefficients, pitch, and energy). To synthesize a word, SEMG signals recorded during pronouncing a word are blocked into frames; SEMG features are then extracted from each SEMG frame and presented to the neural network to obtain a sequence of speech feature indices. The waveform of the word is then constructed by concatenating the pre-recorded speech segments corresponding to the feature indices. Experimental evaluations based on the synthesis of eight words show that on average over 70% of the words can be synthesized correctly and the neural network can classify SEMG frames into seven phonemes and silence at a rate of 77.8%. The rate can be further improved to 88.3% by assuming medium-time stationarity of the speech signals. The experimental results demonstrate the feasibility of synthesizing words based on SEMG signals only.Department of Electronic and Information EngineeringRefereed conference pape

Unsupervised discrimination of motor unit action potentials using spectrograms

© 2014 IEEE. Single motor unit activity study is a major research interest because changes of MUAP morphology, MU activation, and MU recruitment provide the most informative part in diagnosis and treatment of neuromuscular disorders. Intramuscular recordings often provide a more than one motor unit activities, thus MUAP discrimination is a crucial task to study single unit activities. Most neurology laboratories worldwide still need specialists who spend hours to classify MUAPs. In this study, we present a new real-time unsupervised method for MUAP discrimination. After automatically detect MUAPs, we extract features of spectrogram images from the wavelet coefficients of MUAPs. Unlike benchmark methods, we do not calculate Euclidean distances which assumes a spherical distribution of data. Instead, we measure correlation between spectrogram images. Then MUAPs are automatically discriminated without any prior knowledge of the number of clusters as in previous works. MUAP were detected on a real data set with a precision PPV of 94% (tolerance of 2 ms). We obtained a similar result in MUAP classification to the reference. The difference in percentages of MU proportions between our method and the reference were 3% for MU1, 0.4% for MU2, and 12% for MU3. In contrast, F1-score for MU3 reached the highest level at 91% (PPV at the highest of 96.64% as well)

An anomaly detection technique in wearable wireless monitoring systems for studies of gait freezing in Parkinson's disease

© 2017 IEEE. Wearable monitoring systems have been in need for studies of gaits especially freezing of gait detection in patients with Parkinson's disease. The causality of gait freezing is still not fully understood. The histogram of gait freezing is the key assessment of the disease, thus monitoring them in patients' daily life is much appreciated. A real-Time signal processing platform for wearable sensors can help record freezing time instances. However, current monitor systems are calibrated with offline training (patient-dependent) that is cumbersome and time-consuming. In this work, by using acceleration data and spectral analysis, we propose an online/real-Time detection technique. Periods of low acceleration and low spectral coherence are identified and patient-independent parameters are then extracted. Using this set of new features, we validated our method by comparing it with clinicians' labels. The proposed approach achieved an overall mean (±SD) sensitivity (specificity) of 87 ± 0.3% (94±0.3%). To our best knowledge, this is the best performance for automated subject-independent approaches

Wearable healthcare systems: A single channel accelerometer based anomaly detector for studies of gait freezing in Parkinson's disease

© 2017 IEEE. The causality of gait freezing in patients with advanced Parkinson's disease is still not fully understood. Clinicians are interested in investigating the freezing of gait (FoG) histogram of patients in their daily life. To that end, one needs a real-time signal processing platform that can help record freezing information (e.g., timing and the duration of every gait freezing occurrences). Wearable wireless sensors have been proposed to monitor FoG epochs. Existing automated methods using accelerometers have been introduced with high accuracy performance only for subject-dependent settings (e.g., an individual offline training process). This is a troublesome for large scale out-of-lab deployment and time-consuming. In this work, we used spectral coherence analysis for accelerometer data to apply an anomaly detection approach. Conventional features such as energy and freezing index are introduced to help refine normal epochs while the anomaly scores from spectral coherence measures define FoG epochs. Using this new set of features, our new FoG detector for subject-independent settings achieves the mean ±SD sensitivity (specificity) of 89.2±0.3% (95.6 ± 0.3%). To our best knowledge, this is the best performance for automated subject-independent approaches in literature of freezing of gait detection