ORACLE DBA 简历如何写

我们心电采集检测电路的设计，以AD620 和OP07 为核心元件，针对心电信号的组成和干扰频率范围，进行了分析，对由电极采集到的心电信号，通过前置放大电路将微弱的心电心电信号放大，并通过低通滤波器、高通滤波器、及50Hz 陷波电路滤除干扰最后通过后置放大电路进一步放大得到清晰的心电波形。然后电平抬升，使用MPS430单片机进行采集，分析和显示，系统具有较好的效果。

stm32-sx1278-lora驱动程序，Mdk5直接编译，工程文件，亲测可用。2个模块，2个单片机，烧录同样程序即可互相收发（会通过串口1打印接收到的数据）

Dijkstra算法的Matlab程序,用于求各点之间的最短路距离。该程序解决了一个有九个点的无向图中求任意两点之间最短路距离的例子。程序中的每一步都有详细说明。

C++ 三维球体堆积程序 可指定容器体积 球半径分布

竞赛程序 IEEE杯电脑鼠程序 2010获奖程序 ，希望指教

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】自第一座带有智能化概念的建筑物于1984年在美国诞生以来，楼宇智能化已经成为当今建筑发展的主流技术。目前，随着信息产业的高速发展，智能系统这个名词已逐渐被人们所认识，在北京、上海、广州、深圳等地也不断地涌现出了一大批智能楼宇大厦。所谓的智能楼宇，就是一种基于计算机网络控制平台，对照明、变配电、电梯、安防、通讯、广播、空调、消防、门禁、会议等各项子系统的监测与建筑物有机的结合，最大限度的满足使用者舒适性、方便性的要求，以达到节能、高效管理、快速的信息化服务等目的。照明控制系统作为建筑物的一个必不可少的电气系统，其发展随着信息技术的不断发展也在发生日新月异的变化，传统的照明控制逐渐被智能照明控制所替代。

此文档对AXI总线协议进行了系统的详解，有急需的同学可以下载看看！

用于电力系统潮流计算的IEEE数据和相关图片，包括14/30/57/118/300节点等

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