dynamic-simulation

Program to encode in JPEG algorithm. Note, this doesnot output JPEG file. but a matlab demonstration of the process how jpeg is created.

输出一个正整数的所有格雷码序列及一正整数的所有升序拆分序列(output a positive integer Gray all sequences and a positive integer of all ascending sequence Resolution)

L2TP netlink layer, for management.

Includes source codes to find DFT of a sequence, to find odd and even components of a signal, to perform PSK, FSK, QPSK.

BP神经网络代码，使用LM算法（高斯牛顿法的改进，具有很好的鲁棒性）(BP neural network code, use the LM algorithm (modified Gauss-Newton method has good robustness))

PCM编码实例。包括均匀量化编码，非均匀量化编码，及A率PCM编码。(PCM coding examples. Including uniform quantization coding, non-uniform quantization coding, and A PCM coding rate.)

随机子空间法用于模态参数识别 能够识别频率阻尼(Stochastic subspace method for modal parameter identification )

main executing reference usage: observeWaveletDecompositionBenchmark_N_levels.m The objective is to illustrate wavelet decomposition, and to describe how operations may be done in the wavelet domain before the reconstruction. The demo decouples the wavelet operations (transform, operation in wavelet domain, and reconstruction) from the plotting. A PSNR and MSE describes the possible distortion (however minimal) of the forward and inverse wavelet transform. * Caveat: The image display may be subjected to the selected target image. The objective is to provide a simple framework for the understanding of working with wavelets.

deconvolution deconvolve.m Given a system impulse response h[n] and test response y[n], this algorithm deconvolves the test response with the impulse response to determine the input x[n] as X(jw) = Y(jw)/H(jw). Eliminate some noise, but is still very rough NOTE: Assumes all signals are the same length. Use sigcorrect.m to equalize their lengths if they are different. use: deconvolve(impresp,test,testresp) variables: impresp: impulse response of system test: test input to system testresp: test response of system

基于SVR的线性系统逆模型辨识建模及校验的仿真程序，由于辨识信号采用的都是随机信号，不是数值固定的信号，所以程序每次运行的结果都可能有所不同。(Linear inverse model identification modeling and verification system simulation program based on SVR identification signal are random signal, not the value of the fixed signal, so the program each time you run the results are likely to differ.)