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Git 是一种分布式版本控制系统，常用于协同开发和管理代码。在嵌入式系统开发中，使用 Git 可以方便地跟踪和管理项目代码，协同开发人员之间的代码修改和合并。本文将介绍一些常用的 Git 命令，并提供相应的示例代码。
要开始使用 Git，首先需要在项目目录中初始化一个 Git 仓库。可以使用以下命令进行初始化：
$ git init
如果有一个远程仓库已经存在，并且你想在本地进行开发和修改，可以使用以下命令来克隆远程仓库到本地：
$ git clone <远程仓库地址>
在进行代码修改后，需要将修改的代码添加到 Git 的暂存区，并提交到本地仓库。可以使用以下命令完成此操作：
$ git 最优化理论与自动驾驶（三）：LQR原理、公式及代码演示
LQR 是基于线性系统和二次型代价函数的最优控制方法，在实际工程应用中，我们一般采用离散时间系统的LQR设计，其目标是通过设计反馈控制律，使系统在离散时间步长内稳定运行，最小化某个二次型代价函数。给定一个离散时间系统，其状态和控制输入通过以下方程表示：是时刻k的系统状态，是时刻k的控制输入，是状态转移矩阵，是输入矩阵。系统描述的物理意义：系统状态通过矩阵A演变到下一时刻，同时控制输入通过矩阵B影响系统的状态。
【物理模拟】PBD算法详解
参考MatthiaMuller的十分钟物理（他就是PBD算法的发明者）
FDTD材料nk值拟合优化指南：利用材料资源管理器提升光学模型精度
本文详细介绍了如何利用FDTD仿真软件中的材料资源管理器，对材料nk值进行精确拟合与优化，从而提升光学模型的仿真精度。文章通过实战案例，解析了拟合参数调整、报告解读以及针对varFDTD求解器的特殊注意事项，帮助用户从根本上解决仿真结果不准的常见问题。
Creo报错代码-9？手把手教你重新生成许可证文件（基于Creo 10.0）
本文详细解析Creo报错代码-9的成因及解决方案，重点介绍如何重新生成许可证文件以应对网络环境变化导致的验证失败。通过分步指南和高级技巧，帮助用户有效管理多网络环境下的Creo许可证问题，提升工作效率。
域用户无法修改密码问题的解决方案
问题描述：加域的电脑无法更改密码，提示“无法更新密码,为新密码提供的值不符合字符域的长度、复杂度或历史要求”。 问题原因：域控制器中相关设置问题，主要如下图所示（如启用复杂性、最短期限和强制密码历史） 解决方案：在域控制器中更改对应的密码策略即可： ① 禁用复杂性要求 ② 最短使用期限改为0天 ③ 强制密码历史改为0 如果出现无法更改的情况，如下图箭头所指的图标所示 代表需要在域控制器的组策...
手把手教你用Multisim搭建FM直接调频电路（附完整波形分析）
本文通过Multisim仿真软件，手把手教你搭建FM直接调频电路。详细介绍了从核心概念理解、压控振荡器（VCO）参数设置、电路搭建与波形观测，到带通滤波器设计与信号优化的完整流程。通过对比滤波前后的时域波形与频谱分析，帮助读者直观理解频率调制原理，并提供了常见问题的排查思路与参数调试方法，将抽象的通信理论转化为可操作的实践技能。
基于YOLO的印章检测及文字识别
针对印章自动化检测与文字识别需求，提出了一种融合改进YOLO模型与OCR技术的端到端解决方案，旨在解决复杂背景干扰、印章残缺变形及环形文字排列等难题
使用abagen工具箱获取基于脑区的基因表达矩阵
脑区模板定义了您感兴趣的脑区的空间位置。abagen提供了一些预定义的脑区模板，您可以选择使用，也可以使用自定义的脑区模板。它提供了一种简便的方式来获取基于脑区的基因表达矩阵。abagen需要使用Allen脑图谱数据来获取基因表达矩阵。您可以使用abagen内置的下载功能来获取这些数据。这就是使用abagen工具箱获取基于脑区的基因表达矩阵的详细步骤。通过按照上述步骤操作，您可以轻松获取并使用Allen脑图谱数据中的基因表达信息。一旦您加载了脑区模板，您可以使用abagen来获取基于脑区的基因表达矩阵。
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
TMS_WebGMaps_1.9.8.0_Full_Source
TMS_WebGMaps_1.9.8.0_Full_Source 全源码 delphi cbuilder
OV9734 DATASHEET，完整规格书
OV9734完整规格书，包括电气参数，尺寸规格，寄存器配置表等
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
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