Contribution to S-EMG Signal Compression in 1D by the Combination of the Modified Discrete Wavelet Packet Transform (MDWPT) and the Discrete Cosine Transform (DCT) — Oak Academic Publishing
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Contribution to S-EMG Signal Compression in 1D by the Combination of the Modified Discrete Wavelet Packet Transform (MDWPT) and the Discrete Cosine Transform (DCT)
Department of Fundamental Science, Faculty of Mines and Petroleum Industries, University of Maroua, Maroua, Cameroon
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School of Engineering of Masuku, Franceville, Gabon
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Physics Department, Faculty of Sciences, University of Ngaoundere, Ngaoundere, Cameroon
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Electrical Engineering and Telecommunications Department, National Advanced School of Engineering,University of Yaounde 1, Ya-oundé, Cameroon
1 Department of Fundamental Science, Faculty of Mines and Petroleum Industries, University of Maroua, Maroua, Cameroon
2 School of Engineering of Masuku, Franceville, Gabon
3 Physics Department, Faculty of Sciences, University of Ngaoundere, Ngaoundere, Cameroon
4 Electrical Engineering and Telecommunications Department, National Advanced School of Engineering,University of Yaounde 1, Ya-oundé, Cameroon
A new Modified Discrete Wavelets Packets Transform (MDWPT) based method for the compression of Surface EMG signal (s-EMG) data is presented. A Modified Discrete Wavelets Packets Transform (MDWPT) is applied to the digitized s-EMG signal. A Discrete Cosine Transforms (DCT) is applied to the MDWPT coefficients (only on detail coefficients). The MDWPT+ DCT coeffici ents are quantized with a Uniform Scalar Dead-Zone Quantizer (USD ZQ) . An arithmetic coder is employed for the entropy coding of symbol streams. The proposed approach was tested on more than 35 act uals S-EMG signals divided into three categories. The proposed approach was evaluated by the foll owing parameters: Compression Factor (CF), Signal to Noise Ratio (SN R), Percent Root mean square Difference (PRD), Mean Frequency Distortion (MFD) and the Mean Square Error (MSE). Simulation results show that the proposed coding algorithm outperforms some recently developed s-EMG compression algorithms.
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