dsp5

带通滤波函数，故障诊断时采集振动信号后对信号进行滤波后处理。(The signal is filtered after the signal is collected in the fault diagnosis)

hi,this book very excelennt realy o.k

matlab file for script logging. can plot file

- K means algorithm is performed with different initial centroids in order to get the best clustering. - The total cost is calculated by summing the distance of each point to its cluster centre and then summing over all the clusters.Based on the minimum overall cost achieved during each iteration of iterKMeans the pixel assignment to their respective clusters are made and final compressed image is obtained. This algorithm will run slower as the number of clusters , size of the image and number of iterations increase.

Digital modulation over fading channel

Using Advanced Signal Processing Techniques on Ambient Vibration Records of a Structure

分析相位耦合的两个算例，分别利用了双谱和双相干系数法(Analysis of phase coupling of two examples, namely the use of a dual spectrum and bicoherence France)

文件为一些实用的函数实现,其中包括小波,复制,剪切,粘贴,一年中的第几天,一个过程所需的总天数等等.(Documents for some useful function.Including wavelet, copy, cut, paste, a year in the first few days, a process required for the total number of days and so on.)

matlab code for pitch of speech signal

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)