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说明：  VBLAST 2发2收情况下的链路,可以实现误码率的计算 (cgcfgx)

对解列的一个网络，判断出其子系统的个数及每个子系统的节点及其支路(For splitting a network, determine the number of its subsystems and each subsystem node and branch)

Berger Model for EEG

In this work, the Mel frequency Cepstrum Coefficient (MFCC) feature has been used for designing a text dependent speaker identification system. The extracted speech features (MFCC’s) of a speaker are quantized to a number of centroids using vector quantization algorithm. These centroids constitute the codebook of that speaker. MFCC’s are calculated in training phase and again in testing phase. Speakers uttered same words once in a training session and once in a testing session later. The Euclidean distance between the MFCC’s of each speaker in training phase to the centroids of individual speaker in testing phase is measured and the speaker is identified according to the minimum Euclidean distance. The code is developed in the MATLAB environment and performs the identification satisfactorily.

说明：  canny算法的一个简易实现,关于两个阈值的连接问题采用了膨胀的方法,较为使用,经比较发现与matlab自带函数相差不大(canny algorithm is a simple implementation, two thresholds on the issue by the expansion of the connection method, the more use, by the comparison function that comes with little difference matlab)

说明：  同样是用matlab编写的实现支持向量机的源代码(using Matlab is also prepared to achieve SVM source code)

程序实现了生成决策树并使用决策树对离散特征向量进行分类。在判断是否要继续生成叶节点时采用entropy来作为impurity的measurement.(Program and use the decision tree decision tree to generate discrete feature vectors for classification. In determining whether to continue generating leaf nodes using entropy as the impurity of the measurement.)

三元均匀线阵列的方位估计，用于水声信号的方位估计(three arrays ULA DOA)

也是一个MATLAB编程的程序，关于如何图像压缩的(MATLAB is a programming process, on how to image compression)

输入信号为方波信号+高斯白噪声，方波信号基 频为1kHz，幅值为1，高斯白噪声方差为0.2， 均值为0，采样频率为20kHz。试用最大熵估计 法估计此信号的AR模型及其功率谱，并分析结 果(The input signal is a square wave signal+ Gaussian white noise, square wave signal fundamental frequency 1kHz, amplitude is a Gaussian white noise variance is 0.2, the mean is 0, the sampling frequency is 20kHz. Try maximum entropy estimation method to estimate the AR model of this signal and its power spectrum, and analyze the results)