VPNet: Variable Projection Networks

In this paper, we introduce VPNet, a novel model-driven neural network architecture based on variable projections (VP). The application of VP operators in neural networks implies learnable features, interpretable parameters, and compact network structures. This paper discusses the motivation and mathematical background of VPNet as well as experiments. The concept was evaluated in the context of signal processing. We performed classification tasks on a synthetic dataset, and real electrocardiogram (ECG) signals. Compared to fully-connected and 1D convolutional networks, VPNet features fast learning ability and good accuracy at a low computational cost in both of the training and inference. Based on the promising results and mentioned advantages, we expect broader impact in signal processing, including classification, regression, and even clustering problems

Sistema de medida de sinais eletrofisiológicos utilizando eléctrodos e-textiles embebidos numa cadeira de rodas com localização GPS e RFID

Dissertação de Mestrado em Integração de Sistemas IndustriaisO objetivo deste projeto é desenvolver uma cadeira de rodas inteligente que permita avaliar o estado de saúde do paciente com dificuldades motoras, doenças crónicas ou idade avançada. A partir de informações obtidas em tempo real, que permitam a monitorização dos sinais electrofisiológicos do electrocardiograma (ECG) e da condutividade da pele, de forma a analisar esses parâmetros e acessíveis remotamente a uma central de informação. Este projeto pretende dar apoio às pessoas com mobilidade reduzida de forma a serem monitorizadas nas suas próprias habitações. Os dados monitorizados são analisados em tempo real por um médico, que poderá estar no seu local de trabalho e deslocar-se ou pedir auxilio, para socorrer o mais rapidamente possível o paciente sempre que necessário. Assim, é possível uma mais rápida e atempada intervenção em caso de detecção de anomalias que coloquem em risco a vida do paciente. Alguns dos problemas de saúde em que este projecto se enquadra são doenças crónicas, como por exemplo os diabetes, hipertensão arterial, colesterol, obesidade, problemas cardíacos (enfarte e ataque cardíaco), acidentes vasculares cerebrais (AVC) ou até em casos de idade avançada ou mesmo devido a acidentes. Os sensores de aquisição de sinais são embutidos nos braços de apoio da cadeira de rodas, de modo a que o paciente esteja a ser monitorizado sem se aperceber

Improved parametrized multiple window spectrogram with application in ship navigation systems

In analyzing non-stationary noisy signals with time-varying frequency content, it's convenient to use distribution methods in joint, time and frequency, domains. Besides different adaptive data-driven time-frequency (TF) representations, the approach with multiple orthogonal and optimally concentrated Hermite window functions is an effective solution to achieve a good trade-off between low variance and minimized stable bias estimates. In this paper, we propose a novel spectrogram method with multiple optimally parameterized Hermite window functions, with parameterization which includes a pair of free parameters to regulate the shape of the window functions. The computation is performed in the optimization process to minimize the variable projection (VP) functional problem. The proposed parametrized distribution method improves TF concentration and instantaneous frequency (IF) estimation accuracy, as shown in experimental results for synthetic signals and real-life ship motion response signals. With the optimization of nonlinear least-squares approximation of the ship response signals, the Hermite spectra are centralized, and only up to 15 basis functions are sufficient for concentration improvement in the TF domain

ON SOME COMMON COMPRESSIVE SENSING RECOVERY ALGORITHMS AND APPLICATIONS

Compressive Sensing, as an emerging technique in signal processing is reviewed in this paper together with its’ common applications. As an alternative to the traditional signal sampling, Compressive Sensing allows a new acquisition strategy with significantly reduced number of samples needed for accurate signal reconstruction. The basic ideas and motivation behind this approach are provided in the theoretical part of the paper. The commonly used algorithms for missing data reconstruction are presented. The Compressive Sensing applications have gained significant attention leading to an intensive growth of signal processing possibilities. Hence, some of the existing practical applications assuming different types of signals in real-world scenarios are described and analyzed as well