java做的简单订餐系统

这是一篇关于基于PLC的自动化立体车库毕业设计（北航），该设计详细介绍了基于PLC的自动化立体车库设计理念和实际论证，希望帮到打家

求根MUSIC算法，不同于谱搜索MUSIC，可以给出角度

三维Otsu图像分割，灰度-平均灰度-梯度，利用粒子群算法加速。

主要研究人脸检测算法，分析了现有人脸检测算法的特点和不足之处。采用基于YCbCr 空间的高斯肤色模型，利用颜色信息把彩色图像分割成皮肤区、头发区和背景区。对皮肤区进行去噪处理，实现脸部区域的具体定位，然后对人脸上的眼睛、嘴巴和鼻子定位。给出了人脸检测的模块设计和算法流程。

1、 设计一个交通信号灯控制器，由一条主干道和一条支干道汇合成十字路口，在每个入口处设置红、绿、黄三色信号灯，红灯亮禁止通行，绿灯亮允许通行，黄灯亮则给行驶中的车辆有时间停在禁行线外。2、 红、绿、黄发光二极管作信号灯，用传感器或逻辑开关作检测车辆是否到来的信号。3、 主干道处于常允许通行的状态，支干道有车来时才允许通行。主干道亮绿灯时，支干道亮红灯；支干道亮绿灯时，主干道亮红灯。4、 主、支干道均有车时，两者交替允许通行，主干道每次放行45秒，支干道每次放行25秒，设立45秒、25秒计时、显示电路。5、 在每次由绿灯亮到红灯亮的转换过程中，要亮5秒黄灯作为过渡

一个基于STM32F103的蓝牙4.0控制程序，对于想要学习蓝牙的初学者有比较大的帮助

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

LD3320语音识别芯片的PCB封装图文件，已经芯片的说明文档，实测可用。

图像处理所用的标准测试图像，512*512的灰度图，包括：lena,aerial,airfield,baboon,barbara,blonde,boat,bridge,cameraman,couple,darkhair,dollar,fingerprint,goldhill,lake,peppers,priate,plane,yacht。共计19副图像，位深度为8.

MATLAB编程处理体表肌电信号，包括文件读取，滤波，画图显示时域、频域图。