jingdianchangbianzhi

这个是用MATLAB中的SIMULINK做成的PCM编码器。(This is the MATLAB SIMULINK caused by the PCM encoder.)

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.

MATLAB的梯度法源程序，按照提示输入，可直接运行(MATLAB source code of the gradient method, in accordance with the prompt input, can be directly run)

设模拟带通信号频率范围1KHz—1.1KHz，设计一个采样频率8KHz数字频谱分析系统，要求频率分辨率0.1Hz。若对信号直接用FFT进行频谱分析(simulation based communications, with the frequency range 1KHz- 1.1KHz. Design of an 8 KHz sampling frequency digital spectrum analysis system requirements frequency resolution 0.1 Hz. If the signal directly FFT spectrum analysis)

最近从书上看到的非线性解题法的一个程序，共享下(Recently from the book to see non-linear problem-solving method of a program to share the)

泊松过程检测和估计。MATLAB。有详细的注释。(poission process.by MATLAB.)

伪随机码调相来实现定距的作用，伪随机码的自相关性有很好的定距性能(the pseudo--random code)

编写针对一个搜索点宏块比较的下面两个匹配函数：均方误差(MSE)、绝对值误差(MAD)。函数名直接使用简写。要求当前帧和参考帧的数据均采用二维数组表示。(Writing for a search point macroblock compare the following two matching functions: the mean square error (MSE), absolute error (MAD). The function name directly using shorthand. Requirements of the current frame and reference frame data are used two-dimensional array.)

说明：  近年来，基于启发式的多目标优化技术得到了很大的发展，研究表明该技术比经典方法更实用和高效。有代表性的多目标优化算法主要有NSGA、NSGA-II、SPEA、SPEA2、PAES和PESA等。粒子群优化(PSO)算法是一种模拟社会行为的、基于群体智能的进化技术，以其独特的搜索机理、出色的收敛性能、方便的计算机实现，在工程优化领域得到了广泛的应用，多目标PSO(MOPSO)算法应用到了不同的优化领域[9~11]，但存在计算复杂度高、通用性低、收敛性不好等缺点。 多目标粒子群（MOPSO）算法是由CarlosA. Coello Coello等在2004年提出来的(In recent years, heuristic-based multi-objective optimization technology has been greatly developed, and research shows that this technology is more practical and efficient than classical methods. Representative multi-objective optimization algorithms mainly include NSGA, NSGA-II, SPEA, SPEA2, PAES and PESA. Particle Swarm Optimization (PSO) algorithm is an evolutionary technology based on swarm intelligence that simulates social behavior. With its unique search mechanism, excellent convergence performance, and convenient computer implementation, it has been widely used in the field of engineering optimization. The objective PSO (MOPSO) algorithm is applied to different optimization fields [9~11], but it has shortcomings such as high computational complexity, low versatility, and poor convergence. The multi-objective particle swarm optimization (MOPSO) algorithm was proposed by Carlos A. Coello Coello et al. in 2004)

A matrix of symbolic elements is reasonably useful for many analytic linear algebra applications, and for analytic vector, matrix and tensor differentiation. This function produces a matrix of arbitrary dimension of symbolic elements that are indexed by their position in the symbolic matrix for ease of reference. EXAMPLE >> M = symMat([2 2], m , real ) M = [ m11, m12] [ m21, m22] >> class(M) ans = sym >> det(M) ans = m11*m22 - m12*m21