Desktop

simulation modulate & demod AM

Simple algorithm of wsn to localize mobile targets

One of the most popular adaptive algorithms available in the literature is the stochastic gradient algorithm also called least-mean-square (LMS) [1], [2]. Its popularity comes from the fact that it is very simple to be implemented. As a consequence, the LMS algorithm is widely used in many applications

依托行业,突出专业特色,共谋计算机教育发展之路——高职计算机专业发展战略研究 (Relying on the industry, highlighting the major characteristics, and seek common development path computer education- Computer Professional Development Strategy)

一个带用户界面的matlab语音合成和识别的程序，MainGuide01.fig通过提取MFCC，用dtw来计算实时录音和模板之间的最小距离，给出识别结果，并且给出波形图，频谱图，语谱图，基频和共振峰。MainGuide02.fig是语音合成程序，通过load一个wav文件用LPC，残差和pitch两种方式进行合成，并且能实时听合成语音的效果。注意用的是matlab2012b以上才能打开fig(This is an simple user interface program including speech recoginition and speech synthesis.MainGuide01.fig extracting MFCC and using DTW to calculate the minimum distance between the real-time recording and templates , the recognition results are achieved, and the waveform diagram, FFT result , spectrum diagram are shown. including Pitch and Formant Curve . MainGuide02.fig is a speech synthesis program, it load a wav file using LPC, synthesis by the residuals or by pitch two ways, you can listen to synthesized speech effect in the real time . You must ushe matlab version latest more than 2012b that could be OK )

遗传算法，二进制编码，单点交叉，随即变异(Genetic Algorithm. The single-point crossover and the random variation were used)

求解路径问题,目标函数时间最短,主要适用管科物流等专业学习使用,对时间,空间复杂性没有优化.(Solving the path problem, the objective function time is the shortest, which is mainly used for professional learning and learning, such as tube logistics, and it is not optimized for time and space complexity.)

article of pattern recognizer

heuristic optimization

In statistics, an expectation-maximization (EM) algorithm is a method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. EM is an iterative method which alternates between performing an expectation (E) step, which computes the expectation of the log-likelihood evaluated using the current estimate for the latent variables, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.