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安装opencv：
/Users/your_user_name/opencv下解压，则解压后opencv文件夹位置为/Users/your_user_name/opencv/opencv-3.4.3brew install cmake/Users/your_user_name/opencv/opencv-3.4.3新建文件夹buildcd build，利用cmake编译，cmake -G "Unix Makefiles" ..make -j8sudo make install配置Xcode
Command Line ToolmacOS版homebrew+VScode配置opencv（c/c++）亲测可用
之前在win10的vscode上搭建过opencv环境，这次试了下Mac下的，虽然踩坑不少，但最终还是成功了。下面是具体步骤： 一、Homebrew的安装 我试了官网下的安装代码，虽然能成功，但是速度很慢，而且有几个环境因为墙的原因，没有下载。最后我用的是清华大学的镜像。 教程可以参考：清华大学镜像安装homebrew 二、利用Homebrew安装opencv 有三个步骤： brew instal...
手把手教学：MacOS系统下Xcode配置使用OpenCV方法
从windows换成MacOS系统，需要重新配置openCV用一用，顿时想起被Visual Studio和各种环境Path支配的恐惧……所幸，OS系统配置opencv算是十分方便了，但首先，您要先有Xcode（快先到APP store搜索下载吧，5GB+，要下比较久的）。正好平时做iOS开发，Xcode已经装好，此时就很庆幸了，剩下的工作，就是在终端（terminal）中敲几行命令的事儿了！就是这...
mac os+python3+opencv3环境配置
环境：macOS python3.5.2 安装： opencv3.2 用终端可以直接安装opencv，但还不能用，需要将cv2链接到python3类库目录下之后才能用。具体代码如下： step1:用终端安装opencv $ brew install opencv3 --with-contrib --with-python3 --without-python $ brew link
学习OpenCV（蝴蝶书/C++）相关——2.MacOS下使用VSCode调试cpp程序
之前在用过简单的的配置。但是不足以支撑我这里调用OpenCV这些第三方库的调试，因此认真看了一下。
【OpenCV】在MacOS上源码编译OpenCV
在做视觉任务时，我们经常会用到开源视觉库OpenCV，OpenCV是一个基于Apache2.0许可（开源）发行的跨平台计算机视觉和机器学习软件库，它具有C++，Python，Java和MATLAB接口，并支持Windows，Linux，Android和Mac OS。 最近在项目中，我遇到了在MacOS上使用OpenCV需求，目前OpenCV官网上并没有提供OpenCV现成的安装包，因此在此处我们需要自己进行编译.
mac 安装opencv3.0 以及测试例子
简单易懂的描述教你如何在MAC上搭建opencv3.0开发环境，并附带实例程序，包含人脸标记。
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Multisim 14.0元件库下载完整指南：从安装到配置
详细介绍Multisim 14.0版本的元件库下载方法，涵盖安装步骤与后续配置流程，帮助用户快速搭建仿真环境。内容覆盖常见问题及解决方案，让multisim元件库下载更高效、稳定。
DC-DC电源PCB布局实战：从EMI优化到热管理
本文深入探讨了DC-DC电源PCB布局的核心实战技巧。文章从分析电流环路入手，强调通过优化“黄金三角”布局以最小化环路面积，从而有效抑制EMI并提升效率。详细阐述了功率器件摆放、地线分割、敏感信号布线、电感下方铺铜策略以及热管理设计等关键步骤，为工程师提供了一套从理论到实践的完整布局指南。
思科模拟器Cisco Packet Tracer交换机的管理配置
一、交换机的管理方式：带内管理和带外管理 通过交换机的 Console 端口管理交换机属于带外管理；这种管理方式不占用交换机的网络端口，第一次配置交换机必须利用 Console 端口进行配置。 通过 Telnet、拨号等方式属于带内管理。 二、控制台配置思科交换机 1.新建拓扑图 新建拓扑，选择console线连接交换机和PC，一端接交换机的console口，一端接PC的RS 232 口（...
Photoshop实战技巧（9）——高效去除复杂水印的五种进阶方法
本文详细介绍了Photoshop中高效去除复杂水印的五种进阶方法，包括色阶通道爆破法、频率分离高阶应用、智能填充+手动修复组合拳等专业技巧。针对半透明水印、密集文字水印等复杂场景，提供分步操作指南和实战案例，帮助用户精准去除水印同时保留原始图像细节，大幅提升工作效率。
别再死记硬背参数了！用ADS手把手教你仿真一个4GHz镜像抑制混频器（附完整工程文件）
本文详细解析了4GHz镜像抑制混频器的ADS仿真全流程，从理论到实践，涵盖混频器设计基础、ADS工程搭建、二极管选型、非线性仿真及性能优化。通过实战案例，帮助工程师掌握关键参数平衡与调试技巧，提升射频电路设计效率。
小梅哥Xilinx ZYNQ学习笔记2——38译码器
译码器（Decoder）是一种多输入多输出的组合逻辑电路，负责将二进制代码翻译为特定的对象（如逻辑电平等），功能与编码器相反。译码器一般分为通用译码器和数字显示译码器两大类。本设计的是通用译码器。三八译码器，即是 3 种输入状态翻译成 8 种输出状态。1.case开始开始，endcase结束；2.数字声明时，合法的基数格式有 4 中，包括：十进制('d 或 'D)，十六进制('h 或 'H)，二进制（'b 或 'B），八进制（'o 或 'O）。数值可指明位宽，也可不指明位宽。
RK3568平台（camera篇）点亮imx586摄像头
mipi摄像头硬件确认点：1.供电：5V，2.8V，1.2V，1.8V，reset脚（硬拉3.3，上电的时候从低到高），pwron脚外接3.3V。2,时钟：MCLKOUT是24兆时钟。3.4组差分mipi线。mipi的data线的波形为方波。
VB摄像头监控
该VB模块可对摄像头捕获的画面进行录像等，可在创建的窗口进行控制
visio安装包
Microsoft Office Visio 是一款专业的制图软件。利用经过更新的形状、协作工具和数据链接图表，创建专业级图表以简化复杂的信息。
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")
efm32 入门.pdf
本PDF共计22页，介绍的是操作驱动API，下载与否自己斟酌。
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