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用于小波滤波器的谱分析，mallat分解法。实现信号的重构-for wavelet spectral analysis, Mallat decomposition. Signal Reconstruction
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采用db1基本小波来分解信号,比较第三层近似信号与原始信号,重构最大误差,比较第三层近似信号与原始信号.-used db1 to the basic wavelet decomposition signal, the third layer approximate comparison with the original signal signal, the largest reconstruction error Comparing the third layer similar to the
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稀疏分解信号重建程序，利用匹配追踪原理不断对信号匹配，最后达到重建信号目的。-sparse decomposition signal reconstruction procedures, the use of matching principle constantly tracking the signal matching, Finally signal to the redevelopment purposes.
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压缩传感理论的一个简单例子，首先对信号进行稀疏采样，然后利用MP算法对信号进行重建。,Compressed sensing theory of a simple example, first of all, the signal sparse sampling, and then use MP algorithm of signal reconstruction.
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压缩感知中压缩采样匹配追踪算法，用于稀疏信号的重构-Compressed sensing algorithm in the compressed sample matching pursuit for sparse signal reconstruction
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采用BP算法来实现压缩感知的信号重构示例。BP算法由线性规划来实现，稀疏基为DCT基，信号为语音信号-an example of using BP algorithm for signal reconstruction in compressed sensing. BP algorithm is implemented by linear programming, sparse basis is the DCT basis, the signal used is speech
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基于DD算法的先验信噪比估计的维纳语音降噪完整程序，包括语音分帧，动态信噪比估计,噪声估计更新和帧的重构。很完整。-DD algorithm based on a priori signal to noise ratio is estimated that the integrity of the process noise reduction Wiener voice, including voice sub-frame, dynamic signal to noise ratio estim
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压缩感知信号重建
梯度投影法信号重建代码-Compressed sensing signal reconstruction signal reconstruction code gradient projection method
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基于线性预测系数(LPC)的语音信号重构。给出了完整的LPC matlab程序，包括语音信号采集，LP预测系数，阶数的选择以及预测结果误差曲线。（很好用）-Based on the linear prediction coefficient (LPC) voice signal reconstruction. Given a complete LPC matlab procedures, including voice signal acquisition, LP prediction coef
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这个程序是用来显示经过经验模态分解后所得的各个固有模态函数和残余信号，以及由它们重建的信号。-This procedure is used to show through after EMD from various intrinsic mode function and the residual signal, as well as the reconstruction of the signal from them.
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应用稀疏信号重组法来进行的传感器阵列声源定位。是MIT的Dmitry M. Malioutov的博士毕业论文。-A Sparse Signal Reconstruction Perspective
for Source Localization with Sensor Arrays_by
Dmitry M. Malioutov
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1）验证抽样定理。
2）掌握用数字方式分析模拟信号的频谱的基本原理。
3）掌握理想内插方式重构模拟信号。
4）掌握衡量模拟信号相似程度的参数。
-1) verify the sampling theorem. 2) have used digital spectrum analysis of analog signals of the basic principles. 3) interpolation ideal way to master analog signal r
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信号的采样与重建，对已知连续信号进行采样，然后通过离散信号恢复原始信号。-Signal sampling and reconstruction of continuous signals to known samples, and then restore the original signal through the discrete signal.
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基于小波变换的碰磨故障信号的特征提取，可以画出信号原图，轴心轨迹，频谱图以及多层小波变换的重构信号-Based on wavelet transform rubbing fault signal feature extraction, the signal can be drawn artwork, orbit, spectrum and signal reconstruction wavelet multi-
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基于TMS320F2808的信号重建方案设计，PWM da转换方法借鉴-TMS320F2808-based design of signal reconstruction
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用于压缩感知信号重建的算法研究，介绍的很详细-For compressed sensing signal reconstruction algorithm, described in detail
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重构是用分解得到的多分辨率下的小波系数，将多尺度小波合成原信号.不是分解完了马上重构的，中间可能有去噪的过程啊，压缩的过程.-Reconstruction is under multi-resolution decomposition of the wavelet coefficients, the wavelet multi-scale synthesis of the original signal reconstruction is not immediately broken down
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稀疏信号重构的远景分析与传感器信源定位综述分析 -A Sparse Signal Reconstruction Perspective for Source Localization With Sensor Arrays
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压缩感知可以用远低于奈奎斯特速率对信号进行采样，然后用重构算法对测量的信号进行重构，这程序就是CS的重构算法。-Compressed sensing can be used far less than the Nyquist sampling rate of the signal, and then use the measurement signal reconstruction algorithm to reconstruct this program is CS reconstructio
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The following Matlab project contains the source code and Matlab examples used for orthogonal least squares algorithms for sparse signal reconstruction. Added after previous version ols_gp: Sparse reconstruction by Orthogonal Least Squares followed b
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