Compressive-Sensing

贝叶斯网络matlab程序，最新的版本的程序(bayesian network matlab)

matlab，karman谱，利用白噪声特性和ARMR模型，通过求解模型参数生成随机风速(Matlab, Karman spectrum, the characteristics of white noise and ARMR model, the generation of random wind velocity by solving the model parameters)

数字信号处理中功率谱的重要算法。代码中对比了经典谱估计直接法和间接法之间的关系(digital signal processing power spectrum in the algorithm. Code Comparison of classical spectrum estimated direct and indirect relations between the)

Simulation of continuous-time system and digital controller

MSK解调的新方法，除了差分解调，复数滤波器解调，提出了一种基于MSK信号特殊点FFT的实部或虚部比较判决的方法来实现MSK信号的数字解调。(MSK new method of demodulation, in addition to the differential demodulation, complex filter demodulation, this paper proposes a special point FFT based on MSK signal of real or imaginary part of comparative judgment method to realize the MSK signal digital demodulation.)

该程序实现了高斯白噪声环境下直流电平的检测.画出了似然比门限,虚警,漏警随先验概率,代价因自等变化的情况.(The program to achieve the detection of DC level Gauss white noise environment. Draw the likelihood ratio threshold, the false alarm, alarm with the prior probability, cost because of the changes.)

说明：  this module gives a basic image compression example

matlab NewtonRaphson

说明：  肌电信号多维特征提取 +均方根值+中值频率+平均功率频率(Multidimensional Feature Extraction of EMG Signal)

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)