MatlabGUI

C code version of Matlab function fir1.m (lowpass only)

实现时分秒的计数和校正实现时分秒的计数和校正(Realized and correction of minutes and seconds count)

学习并找到降噪方法，对语音信号背景噪声做处理，使得声音更清晰。(The background noise is the most common factor degrading the quality and intelligibility of speech in recordings. Spectral subtraction can lower the noise level without affecting the speech signal quality. In this paper, we have studied and implemented to reduce the background noise of the voice signal, and to make improvements to reduce the background noise of the experiment. Not only with the spectral subtraction of the noisy signal of the transformation, we improve the traditional spectral subtraction based on the introduction of the parameters, by adjusting the parameters(α、β、γ) to achieve better enhancement.)

各种算法的集合，自学matlab很不错的资料，在这里和大家共享啦(suanfa matlab)

针对六自由度机器人关节运动强耦合难分析的问题,开发了可视化机器人仿真平台。该平台以KLD-600型六自由度教学机器人为研究对象,利用MATLAB集成仿真环境,分别实现了机器人正逆运动学计算、机器人点动和联动操作的动画演示等功能,且所有功能以图形用户界面的形式进行调用。该仿真平台可作为机器人教学和科研的仿真工具。(Six degrees of freedom robot joint motion for strong coupling analysis of the problem difficult to develop a visual simulation platform robot. The platform KLD-600-type robot teaching six degrees of freedom for the study, an integrated simulation environment using MATLAB, respectively, to achieve the inverse kinematics of robot is calculated, and the linkage to move the robot animation operations and other functions, and all functions to the form of a graphical user interface calls. The robotic simulation platform can be used as teaching and research simulation tool.)

差集天线阵列分析\方向图、二维图、三维图(difference sets)

主要用来计算给定观测序列的概率（求解HMM中的第一个问题）(Mainly used to calculate the probability of a given observation sequence (HMM solving the first problem))

说明：  用fft进行特征提取，并画出相应的图。再进行神经网络分类(FFT is used to extract features and draw corresponding graphs.)

matlab实现线形调频z变化的程序(matlab linear frequency modulation to achieve changes in the procedures for z)

粒子滤波跟踪算法学习的好文档。为个人读书心得，主要从三个角度去理解粒子滤波，由浅入深，有学习价值！(Particle filter tracking algorithm to learn a good document. For personal reading experience, mainly from the three perspectives to understand the particle filter, Deep learning value!)