基于MATLAB的JPEG的编码和解码

股票系统模型 前端：页面显示模块化 k线显示 后端：django框架，调用第三方获取数据代码说明：views.py 逻辑代码urls.py　路由跳转models.py　用于数据库ORMdata.py 调用tushare获取股票实时数据forms.py　定义注册登录form表单templates　相关页面 主要页面： company.html 股票信息详情页 hot.html　热点资讯页面 index.html　首页 k.html　K线图页面 left.html　左侧默认显示页面 login.html　登录页 logi

用matlab写的dpso算法，适合教师做研究，学生写论文

无刷直流电机simulink仿真模型，可以直接运行

编写的MATLAB 多导体传输线模型下 的MTLAB程序 请大家多多知道

实现功能： 运用串口发送指令（MODBUS协议）控制芯片STM32F103C8T6产生PWM波，从而控制86步进电机的运动和停止，本程序添加不同频率PWM波，进而可以控制86步进电机的运动速度。

BLE开发板 主从一体串口透传.zip CC2540/CC2541主从一体化透传源码（支持串口和APP,带AT命令），可以直接下载使用

徕卡最新TS15，Geocom开发使用手册○○二

本方案主要参照了华为公布的HBase二级索引实现方案。为每个DataTable创建一个与之对应的IndexTable，通过各种途径，保证IndexTable Region与DataTable Region一一对应，并且存储在同一个RegionServer上。

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

【实例简介】基于MATLAB实现OFDM系统基带信号在频率选择性衰落信道条件下的发送与接收。 题目及要求： 仿真系统构成：信号输入（为随机比特流）、OFDM调制、仿真信道传输、OFDM解调、信号输出（可能存在误码的比特率）； 仿真分析内容：根据输入、输出比特流计算不同信噪比条件下的误码率，并绘制曲线。 对调制的要求：OFDM调制的子载波间隔为15KHz，循环前缀长度及子载波数目可调，各子载波使用QPSK调制。 对应博客：https://blog.csdn.net/weixin_43723423/article/details/106343471