matlab

多项式求值的四种算法的运行时间复杂度的比较。将四个算法在一个程序中实现，并在一个运行结果图中比较显示。(Value of the four algorithms polynomial time complexity of running the comparison. Four algorithms will be implemented in a program and run the results in a comparison chart display.)

Genetic Algorithm matlab code

interface camera with matlab

白噪声的正太分布检验法 详细理论参见何书元《应用时间序列分析》(White noise distribution of the test is too detailed theory see Dr Shu-yuan, " the application of time series analysis" )

在瑞丽衰落加高斯白噪声信道上进行的BPSK调制代码，代码解释详细，非常适合初学者(Decline in Ruili plus white Gaussian noise channel for the BPSK modulation code, the code to explain in detail, very suitable for beginners)

利用AR模型和Lomb算法的心率变异性分析。(Analyzed using AR model and Lomb algorithm HRV.)

在一个自己设置的已知障碍的环境下，用势场法做路径规划，起点，目标位置都是已知。我用matlab做的(themselves in a set of known obstacles to the environment, with potential field do path planning, the starting point, target locations are known. I used Matlab to do)

This book presents the MATLAB computer programming system as a problemsolving tool for scientists and engineers who have no prior knowledge of computer programming. It is based on a teach-yourself approach readers are frequently invited to experiment for themselves in order to discover how particular constructs work. The text was originally written as a companion to a hands-on course at the University of Cape Town. Most of the students taking the course had little or no computing experience and came from disadvantaged backgrounds. Consequently the book assumes that readers have no knowledge of computing, unlike most similar books on MATLAB.((2007 Elsevier)Essential Matlab for Engineers and Scientists(3rd Edition))

详尽介绍了matlab语言的函数应用，对于初学者很有帮助。是matlab学习的入门教程。(A detailed description of the matlab language function applications, very helpful for beginners. Is the matlab learning introductory tutorial.)

压缩感知方法演示，1-D信号压缩传感的实现(正交匹配追踪法Orthogonal Matching Pursuit) 测量数M>=K*log(N/K),K是稀疏度,N信号长度,可以近乎完全重构(1-D signal is compressed sensing to achieve (orthogonal matching pursuit method Orthogonal Matching Pursuit) number of measurements M> = K* log (N/K), K is the sparsity, N signal length, can be almost completely reconstructed)