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In the paper, we present a new approach to multi-way Blind Source Separation (BSS) and corresponding 3D tensor factorization that has many potential applications in neuroscience and multi-sensory or multidimensional data analysis, and neural sparse coding. We propose to use a set of local cost functions with flexible penalty and regularization terms whose simultaneous or sequential (one by one) minimization...
In this paper, we introduce a simple but novel model to detect abnormal event in surveillance video using sparse autoencoder and recurrent neuron network. In this model, we first train a sparse autoencoder to extract features and use a sequence of temporal continuous features to train a recurrent neuron network to predict the subsequent features. We classify the frame as normal and abnormal based...
Cardiovascular diseases is one of the main courses of death around the world. Electrocardiogram (ECG) supervising is the most important and efficient way of preventing heart attacks. Machine monitoring and analysis of ECG is becoming a major topic of the modern medical research. In this paper, we propose a system to detect cardiac arrhythmia using the ECG data form MIT-BIH database as a reference...
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