改进离散小波包变换(MDWPT)与离散余弦变换(DCT)结合对一维s -肌电信号压缩的贡献

Q3 Computer Science
Colince Welba, A. Okassa, Pascal Ntsama Eloundou, P. Ele
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引用次数: 4

摘要

提出了一种新的基于改进离散小波包变换(MDWPT)的表面肌电信号数据压缩方法。将一种改进的离散小波包变换(MDWPT)应用于数字化的s-肌电信号。离散余弦变换(DCT)应用于MDWPT系数(仅对细节系数)。采用均匀标量死区量化器(USDZQ)对MDWPT+ DCT系数进行量化。采用算术编码器对符号流进行熵编码。所提出的方法在超过35个实际的S-EMG信号上进行了测试,这些信号被分为三类。该方法通过以下参数进行评估:压缩系数(CF)、信噪比(SNR)、均方根差(PRD)百分比、平均频率失真(MFD)和均方误差(MSE)。仿真结果表明,所提出的编码算法优于最近开发的s-EMG压缩算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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)
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 coefficients are quantized with a Uniform Scalar Dead-Zone Quantizer (USDZQ). An arithmetic coder is employed for the entropy coding of symbol streams. The proposed approach was tested on more than 35 actuals S-EMG signals divided into three categories. The proposed approach was evaluated by the following parameters: Compression Factor (CF), Signal to Noise Ratio (SNR), 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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CiteScore
3.20
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