tq

Emergency System in Power Substation

Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of speech production, for recovering compressive sensed speech. Since the linear transform is signal dependent and unknown, unlike the standard CS formulation, a codebook of transfer functions is proposed in a matching pursuit (MP) framework for CS recovery. It is found that MP is efficient and effective to recover CS encoded speech as well as jointly estimate the linear model. Moderate number of CS measurements and low order sparsity estimate will result in MP converge to the same linear transform as direct VQ of the LP vector derived the original signal. There is also high positive correlation between signal domain approximation and CS measurement domain approximation for a large variety of speech spectra.

叶图像提取研究及虚拟植物可视化实现(Extraction of leaf images and virtual plant visualization realize)

Hidden Markov Model (HMM) Toolbox written by Kevin Murphy A problem of fundamental interest is characterizing such real-world signals in terms of signal models. HMM is referred to as Markov sources or probabilistic functions of Markov chains in the communications literature.

角度和延时估计，里面一共5个小程序，已经写成函数形式，亲测好用(to estimate angle and time delay in array signal processing)

matlab_fuzzymeasure fuzzymeasure

matlab source code very useful to science study

finite element method code

各种颜色空间介绍(Introduced a variety of color space)

MIMO多径瑞利信道下的迫零+串行干扰抵消+自适应功率分配预编码算法仿真链路（simulink+m files）(Rayleigh MIMO multi-path channel under the zero forcing serial interference cancellation adaptive power distribution pre-coding algorithm simulation Chains (Simulink m files))