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Robust Low-Power Algorithm for Random Sensing Matrix for Wireless ECG Systems Based on Low Sampling-Rate Approach

Journal of Signal and Information Processing 2013年第3期4卷 125-131页

作者： Mohammadreza Balouchestani kaamran raahemifar Sridhar krishnan Electrical and Computer Engineering Department Ryerson University Toronto Canada Electrical and Computer Engineering Department Ryerson University Toronto Canada.

The main drawback of current ECG systems is the location-specific nature of the systems due to the use of fixed/wired applications. That is why there is a critical need to improve the current ECG systems to achieve extended patient’s mobility and to cover security handling. With this in mind, Compressed Sensing (CS) procedure and the collaboration of Sensing Matrix Selection (SMS) approach are used to provide a robust ultra-low-power approach for normal and abnormal ECG signals. Our simulation results based on two proposed algorithms illustrate 25% decrease in sampling-rate and a good level of quality for the degree of incoherence between the random measurement and sparsity matrices. The simulation results also confirm that the Binary Toeplitz Matrix (BTM) provides the best compression performance with the highest energy efficiency for random sensing matrix.

关键词： Sensing Matrix Power Consumption Normal and Abnormal ECG Signal Compressed Sensing Block Sparse Bayesian learning

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Combined Dictionary Learning in Facial Expression Recognition

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Journal of Signal and Information Processing 2013年第3期4卷 86-90页

作者： Ziyang Zhang kaamran raahemifar Department of Electrical and Computer Engineering Ryerson University Toronto Canada.

Dictionary learning has been applied to face recognition and gets good results. However few works applied dictionary learning in facial expression recognition. This paper investigates the application of K-SVD in facial expression recognition. Since K-SVD focuses on reconstruction and lacks discriminant capability. It has similar classification performance with image pixel values. To address this problem, this paper proposes a Combined Dictionary Scheme, which uses combination of separate dictionaries. This yields better performance than the original single dictionary scheme in terms of both recognition rate and computation complexity.

关键词： Facial expression Recognition Dictionary Learning K-SVD

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