The-detection-of-high-speed-targets

认知无线电 2比特量化 EGC 硬判决仿真(Cognitive radio, two-bit quantization the EGC hard judgment simulation)

Implementation of Digital predistortion in simulink using Saleh Memoryless Non-linearity and Least Squares method. I modified inbuilt OFDM block with Raised cosine block

说明：  svm预测教程，适合新手学习，2020版本(The SVM prediction tutorial, suitable for beginners learning, 2020 edition)

ant clustering and rgb

基于Matlab的msk调制(Simulink文件）(Matlab based on the msk modulation (Simulink documentation))

基于matlab GUI的贪吃蛇游戏程序。(Matlab GUI based on the Snake games.)

matlab对机械四杆结构的仿真动画，很难找的，很有价值。(matlab mechanical simulation of four-bar structure animations, it is difficult to find, great value.)

说明：  发动机喷管设计以及性能优化计算，由isight实现，bat批处理(the calculation for out)

les contours actif parametrique (snakes)

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.)