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本节重点：
让学生了安装上Python，配置好环境变量
Python目前已支持所有主流操作系统，在Linux,Unix,Mac系统上自带Python环境，在Windows系统上需要安装一下！超级简单~~~
打开官网 https://www.python.org/downloads/windows/——下载中心
测试安装是否成功！
windows --> 运行 --> 输入cmd ，然后回车，弹出cmd程序！输入python,如果能进入交互环境 ！代表安装成功！
提示：以下是本篇文章正文内容，下面安装过程仅供参考！！
Python官网下载地址：🔗
进入界面后点击Download Python
根据系统选择下载的版本，此处演示Windows的版本！！
下载的文件！
1）安装
1.双击python-3.9.2-amd6
python-3.9.2-amd64.exe
python-3.9.2-amd64官方安装包
局放起始电压（PDIV）与击穿电压（BDV）：绝缘系统设计的核心参数 [特殊字符]
在电力设备绝缘系统设计中，**局放起始电压（Partial Discharge Inception Voltage, PDIV）和击穿电压（Breakdown Voltage, BDV）**是两个至关重要的参数。它们直接反映了绝缘材料的性能和绝缘系统的可靠性，对变压器的设计、制造和运行具有重要指导意义。本文将深入探讨这两项参数的定义、测试方法及其与绝缘设计制造的关系，帮助读者全面理解其在电力设备中的核心作用。
如何理解反步法设计控制器？
假设以二阶系统为例，被控对象的系统状态方程为： 基于反步控制的具体方法如下： 1.1步骤一 定义期望状态为xd，状态误差e1，为： e1=x1-xd; 则e1的导数为： 1.2步骤二 定义虚拟控制量为： e2定义为： 1.3步骤三 定义Lyapunov能量函数为： 1.4步骤四 定义总Lyapunov能量函数为： 因此设计的控制律为： 设计c>0，则： 满足Lyapunov的稳定性判定条件...
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各种加法器的比对分析与Verilog实现（3）
了Kogge-Stone加法器和brent-kung加法器，本文将用Verilog代码进行实现。
从零构建：openEuler WSL下的ARM交叉编译工具链自动化部署方案
本文详细介绍了在openEuler WSL环境下使用Ansible自动化部署ARM交叉编译工具链的方案。通过编写Ansible角色和Playbook，实现从基础环境配置到工具链安装、环境变量设置的一键部署，大幅提升嵌入式开发效率。方案支持多种ARM架构，并扩展了QEMU仿真、VS Code远程开发等实用功能，是嵌入式开发者的高效解决方案。
RC滤波电路工作原理：【带图+公式计算】
容抗Xc和电容C成反比，和频率f也成反比，C越大，交流电容易通过电容，说明电容量大，电容的阻碍作用小；交流电的频率高，交流电也容易通过电容，说明频率高，电容的阻碍作用也小。我们可以看到，一个2V的纹波，经常RC电路后，幅值下降到mv级别，基本可以忽略不计，这就是RC滤波电路的工作原理。一个频率为1Khz的信号，通过一个容值为0.1uf的电容，计算电容对该信号产生的阻抗。将RC滤波器电路中的电阻，取值为1KΩ，电容取值为0.47uf，纹波幅值为2V。从公式1可以看到，Xc越大，Vout值就越接近Vin的值。
基于Spring Security 6的OAuth2 系列之二 - 客户端--非代码演示授权码模式
至此，我们就使用浏览器+postman工具演示了通过授权码模式去获取Gitee的用户信息。这过程中，我们只是遵循OAuth2的流程，就能够实现授权方法。那么接下来，我们采用代码方式来实现这个流程。
实战 | OpenCV实现扫描文本矫正应用与实现详解(附源码)
导读 本文主要介绍使用OpenCV对扫描文本矫正的应用实例及详细实现步骤。 背景介绍 在使用打印机或扫描仪扫描文档时，由于摆放位置差异难免造成扫描文档的倾斜。本文将使用OpenCV将倾斜的文档矫正水平并去除黑边。 实现步骤 本文只针对包含大部分文字的文档做倾斜矫正，思路来源是大部分文字倾斜后同一行的也在同一条直线，通过拟合直线计算文本倾斜角度，然后旋转来实现文本倾斜矫正。 详细步骤： ①加载倾斜图像 ②灰度转换+腐蚀+膨胀 ③Canny边缘检测 + 霍夫线变换检测直线 ...
NTscan扫描工具
ntscan是一个扫描器，可以扫描没有密码和弱密码的共享，先到配置文件中填写猜测的用户名和密码，扫描过程中右侧出现的是扫描日志。
大中型超市收银系统(可直接运行)
光步前台收银系统系统功能：商品销售、商品退货、挂单、收银员权限、会员销售、单品取消、整单取消、商品查询、脱机销售、脱机数据上传，基本资料下载，开钱箱、小票打印、收款结算，支持多币种付款方式（现金、储值卡、礼券、微信等）,支持多种促销。运行环境：.net 4.0登陆信息用户名:1001 密码:123456内置商品信息，商品编码可输入00001000 - 00010000,测试效果相关功能键F5 结算 F9挂单 F11商品查询 F8退货 Insert单品取消 Del整单取消 F12开钱箱F2 会员 Ctrl+Z退出系统 Home 锁屏 相关源码待公开
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
C# WinForm 基于DirectShow.dll编写的程序，可边预览边保存视频，音频和图片
基于微软的DirectShow.dll的二次开发1.自动识别连接的摄像头跟麦克风且可以对其进行选择（默认选中第一个）2.可录制wmv和avi两种格式的视频并且都带有音频，avi可以录制的分辨率和帧数进行选择，wmv默认进行了淸晰度优化如不喜欢可去除代码中有相关提示3.可在预览点开之后进行拍照，也可边录制视频边拍照4.添加了视频和音频的压缩功能（酌情使用）这次二次开发发现C#对DirectShow的相关信息太少，这次做完也不敢藏私共享出来，还望大家指正
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