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ICA算法可以将噪声信号分解为一系列独立的分量（ICs），这样就可以对各独立分量进行单独的研究和分析。首先叙述了柴油机噪声信号的特性。预测模型表明：发动机噪声信号满足ICA计算的要求。然后介绍了ICA模型的相关理论。举例说明ICA方法分离信号的有效性，以及ICA方法对小能量噪声的分离的有效性。连续小波变换来显示了各独立分量ICs在时频域内的特性。由采集信号分离得到噪声源信号可以作为发动机的理论预测和设计依据。-he ICA algorithm can be decomposed into a s
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编写一个多径瑞利衰落信道，分析信号的时域和频域，比较两者的不同。-Write a different frequency domain comparison between a multi-path Rayleigh fading channel, signal analysis and time domain of.
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Correlation diagram shown in detail the time domain and frequency domain, Virtual power wireless sensor network coverage, Achieve canonical correlation analysis.
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Correlation diagram shown in detail the time domain and frequency domain, The Chinese have a comment, understand it, Correlation analysis process matlab method.
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Use Chaos and fractal analysis routines, Robustness, superior performance, Analysis of the signal time domain, frequency domain, cepstrum, cyclic spectrum, etc.
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For time-frequency analysis algorithm, Numerical solution of differential equations method, Calculating a target and ocean echo power spectral density.
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Analysis of the signal time domain, frequency domain, cepstrum, cyclic spectrum, etc. Interpolation and fitting, solution of equations, data analysis, Gabor wavelet transform and PCA face recognition code.
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For time-frequency analysis algorithm, Using a large number of finite element method to solve partial differential equations, Space target recognition algorithm using PM.
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