matlabdrawing

说明：  终于调好最小二乘法拟合程序，很适合刚开始学习matlab者使用。(Finally adjust the least square fitting program)

kmeans clustering function in matlab

his is a direct and complete translation of the Gauss codes of Bai, J. and P. Perron (1998) Estimating and Testing Linear Models with Multiple Structural Changes Econometrica, vol 66, 47-78 and Bai, J. and P. Perron (2003) Computation and Analysis of Multiple Structural Change Models Journal of Applied Econometrics, 18, 1-22, provided by Pierre Perron (version 3, October 11, 2004). All the notice in the original Gaiss codes remain valid. For users, run brcode.m . The procedure is explained in the same m file and in each function file. June 20, 2012 Yohei Yamamoto Email: yohei.yamamoto@econ.hit-u.ac.jp

说明：  针对低信噪比环境下基于能零比的端点检测方法准确率低的问题,提出了一种结合谱减法和短时零熵法的端点检测方法。首先通过功率谱减法对带噪语音进行降噪处理,然后结合短时平均过零率特征与谱熵特征,构造出一种新的语音参数,即短时零熵,最后运用双门限算法进行端点检测。实验结果表明该方法在复杂噪声环境下具有良好的端点检测性能,降低了检测的误判率。(To solve the problem of low accuracy of endpoint detection method based on energy zero ratio in low SNR environment, an endpoint detection method combining spectral subtraction and Short-term zero entropy method is proposed. Firstly, the noise is reduced by power spectrum subtraction, then a new speech parameter, short-time zero entropy, is constructed by combining the short-time average zero crossing rate and spectral entropy. Finally, the double threshold algorithm is used for endpoint detection. The experimental results show that this method has good performance of endpoint detection in complex noise environment, and reduces the rate of detection error.)

Matlab script that solves the multiple Travelling Salesman Problem with fixed start points where the salesman do not return to their starting location

PID控制的仿真,<<PID控制与应用>>课程的作业.SIMULINK格式(PID control of the simulation,)

2014全国研究生数模竞赛B题Matlab源代码(2014CUMCM B CreativeFoldingTable)

Cooperative Relaying Matlab Prototype Report

Localization problem(A matlab code for sensor localization)

Simple function for mixed (between- and within-subjects, aka repeated-measures) ANOVA