DSP

dvb-t 内交织matlab程序，非常有用(dvb-t interleaving code)

用于视频监控的核心代码，基于matlab的，用的是usb摄像头就可以。(video surveillance 基本翻译 视频监控 视频监视 网络释义 Video Surveillance:视频监控 | 视讯监控 | 电视监视 )

考虑L的三个不同值：L＝256（3个数据段），L＝128(7个数据段)和L=64（15个数据段）。各自的谱估计图如上图所示。可以明显的看到，加窗明显的减小了频谱上的假谱峰，但也更加进一步平滑了谱峰。所以，对于L＝64的情况，在ω＝0.8π的谱线可以很确定的辨认，但是那两个靠近的谱峰不容易区分。对于L＝128的情况，这种情况提供了在分离和检测间最好的均衡。当然，对于在L＝256时的情况，效果是更好的，能够从谱估计图上明显的分辨出三条谱线的存在以及它们幅度关系的强弱。 除了Welch法外，还可以采用对多个周期图求平均的功率谱估计方法的其他方法如Bartlett法等等，在功率谱估计上也能取得较好的结果。(consider three different values : L = 256 (3 data), L = 128 (7 data) and L = 64 (15 data). Their spectral estimation map as shown in Fig. It is clear to see that increase significantly decreasing window of the spectrum of false peaks, but much more to further smooth the peaks. Therefore, L = 64, in the case = 0.8 spectrum can determine the identification, but that the two peaks around is not easy to distinguish. L = 128 for the situation, which provided the separation and detection among the best balanced. Of course, L = 256 in the case, the effect is better, from the spectral estimation map clearly distinguish between the three lines of the existence and their relationship to the rate of strength. In addition to Welch law, but also can be used to map multiple cycle for the average power)

matlab插值与拟合的程序及讲义。对于数值计算的学习有帮助。(matlab interpolation and fitting procedures and handouts. Numerical study for the help.)

各种灰色T型关联度代码，修正的T型关联度，改进的T型关联度,邓氏关联度，绝对关联度，相对关联度等。非常方便！(Various gray T-correlation code, modified T-correlation, improved T-correlation, Tangs correlation, absolute degree, associate degree relative. Very convenient!)

BP神经网络公路运量预测 实例讲述如何在matlab中使用BP神经网络(BP neural network prediction road traffic)

遗传算法matlab代码，很不错，值得看看(Genetic algorithm matlab code, very good, worth a look)

离散Hopfield神经网络的数字识别，可作为学习参考(Digital identification of discrete Hopfield neural networks can be used as a reference to learn)

改进的卡尔曼滤波程序，并比较了传统卡尔曼滤波的性能(UKF)

基于声卡和Matlab的数据采集系统在杨氏模量测量中的应用 (Based on the sound card and Matlab of data acquisition system in Yang s modulus to the measurement of the application )