CST微波工作室学习资料--很全很全

基于cordic算法的NCO，包含matlab代码以及verilog代码与相关原理，亲测可用基于cordic算法的NCO，包含matlab代码以及verilog代码与相关原理，亲测可用基于cordic算法的NCO，包含matlab代码以及verilog代码与相关原理，亲测可用

包括特征提取的介绍以及实现代码，实现OpenCV特征提取，包括边缘、颜色直方；

多重分形谱计算程序matlab，可用于股市分析等。有问题希望大家及时反馈给我。

利用蒙特卡洛模拟法计算随机潮流，计算电力系统安全性

硬件接线：A+ GPIOB_Pin_6B+:GPIOB_Pin_7A-:GPIOC_Pin_6B-:GPIOC_Pin_7串口波特率:9600设置好这些转动电机就可以在串口上看到当前速度和位置，希望对初学者有一定帮助

通过SDK连接 java编写查看海康威视摄像头移动侦测和遮挡告警

本资源是作者研究生期间的永磁同步电机入门级仿真，采用旋转高频电压注入，通过高频电流响应来实现转子位置的观测，并采用该方法在DSP实验电机平台上实现。因此，仿真绝对可靠，并且仿真中采用FDAtool实现滤波器功能，可以自由的选择滤波器的截止频率。（因为里面技术含量还有点，上传者也希望通过该资源挣点积分，因此可能比较贵哦）

【实例简介】最大类间方差（OSTU 自适应阈值提取 图像边缘增强算法 MATLAB代码

稀疏自编码深度学习的Matlab实现，sparse Auto coding，Matlab codetrain, m/7% CS294A/CS294W Programming Assignment Starter CodeInstructions%%%This file contains code that helps you get started ontheprogramming assignment. You will need to complete thecode in sampleIMAgEsml sparseAutoencoder Cost m and computeNumericalGradientml For the purpose of completing the assignment, you domot need tochange the code in this filecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencodtrain.m∥%%========%6% STEP 0: Here we provide the relevant parameters valuesthat willl allow your sparse autoencoder to get good filters; youdo not need to9 change the parameters belowvisibleSize =8*8; number of input unitshiddensize 25number of hidden unitssparsity Param =0.01; desired average activation ofthe hidden units7 (This was denoted by the greek alpharho, which looks like a lower-case pcurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod4/57train.,m∥in the lecture notes)1 ambda=0.0001%o weight decay parameterbeta 3%o weight of sparsity penalty term%%==:79 STEP 1: Implement sampleIMAGESAfter implementing sampleIMAGES, the display_networkcommand shouldfo display a random sample of 200 patches from the datasetpatches sampleIMAgES;display_network(patches(:, randi(size(patches, 2), 204, 1)), 8)%为产生一个204维的列向量,每一维的值为0~10000curer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod5/57train.m/v%中的随机数,说明是随机取204个 patch来显示%o Obtain random parameters thetatheta= initializeParameters ( hiddenSize, visibleSize)%%=============三三三三====================================97 STEP 2: Implement sparseAutoencoder CostYou can implement all of the components (squared errorcost, weight decay termsparsity penalty) in the cost function at once, butit may be easier to do%o it step-by-step and run gradient checking (see STEP3 after each stepWecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod6/57train. m vb suggest implementing the sparseAutoencoder Cost functionusing the following steps(a) Implement forward propagation in your neural networland implement the%squared error term of the cost function. Implementbackpropagation tocompute the derivatives. Then (using lambda=beta=(run gradient Checking%to verify that the calculations corresponding tothe squared error costterm are correctcurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod7/57train. m vl(b) Add in the weight decay term (in both the cost funcand the derivativecalculations), then re-run Gradient Checking toverify correctnessl (c) Add in the sparsity penalty term, then re-run gradiChecking toverify correctnessFeel free to change the training settings when debuggingyour%o code. (For example, reducing the training set sizecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod8/57train m vl/number of hidden units may make your code run fasterand setting betaand/or lambda to zero may be helpful for debuggingHowever, in yourfinal submission of the visualized weights, please useparameters web gave in Step 0 abovecoS七grad]sparseAutoencoderCost(theta, visibleSize,hiddensize, lambda,sparsityParam, beta,patches)二〓二二二二二二二〓二〓二〓二〓=二====〓=curer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod9/57train.m vlll96% STeP 3: Gradient CheckingHint: If you are debugging your code, performing gradienchecking on smaller modelsand smaller training sets (e. g, using only 10 trainingexamples and 1-2 hiddenunits) may speed things upl First, lets make sure your numerical gradient computationis correct for a%o simple function. After you have implemented computeNumerun the followingcheckNumericalGradientocurer:YiBinYUyuyibintony@163.com,WuYiUniversityDeep Learning, MATLAB Code for Sparse Autoencode10/57

利用理论推导的方法，该matlab程序得出涡旋光束经相位全息光栅接收后一阶衍射光束的解析表达式，并且仿真分析分别得出在发生正常对准、横向偏移、角向倾斜及横向偏移和角向倾斜两者同时出现时衍射光束的质心偏移特性和中心强度变化特性。