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自己收藏的关于分组交换的simulink的仿真程序(their collections on the packet switching of the Simulink simulation program)

In this paper, we consider a coded cooperation diversity scheme that is suitable for L-relay channels that operate in the decode–forward mode. The proposed scheme is based on convolutional coding, where each codeword of the source node is partitioned into two frames that are transmitted in two phases.

多径瑞林信道仿真，可以应用于通信领域以及诸多信号处理方面(Multipath Ruilin channel simulation, can be applied to the communication field and many signal processing.)

采样定理的验证，即采样信号的频率大于等于原信号频率的两倍时才能准确的恢复原信号(Verification sampling theorem, the sampling frequency of the signal frequency is greater than or equal to twice the original signal in order to accurately restore the original signal)

实现扩频里随机信号和伪码序列的产生，以及扩频信号的产生，用本地载波实现bpskt调制，在接收端用差分相干积分完成伪码捕获的相应MATLAB程序(Spread spectrum signal generated in the realization and pseudo random code sequence, and generate a spread spectrum signal modulation to achieve bpskt local carrier, at the receiving end with the completion of the pseudo-differential coherent integration of the corresponding MATLAB code acquisition program)

数字信号处理第九章 IIR滤波器的设计 代码实现(Digital Signal Processing Chapter IX IIR filter design code)

DTMF decode for Telephony

庞中华《系统辨识与自适应控制MATLAB仿真》电子版，供大家学习。(Pang Zhonghua 《system identification and adaptive control MATLAB simulation》)

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.

This is a code for a Chebyschef filter