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我们在 OpenHarmony实战STM32MP157开发板 “控制” Hi3861开发板 – 上篇 中介绍到了，App面板的开发，以及JS API接口的开发和调用。
那么本篇文章，会详解：BearPi-HM Nano开发板，如何实现数据上报和指令接收响应的。
看到这里，可能有同学可能已经知道思路了，因为我们前面有3篇文章介绍过：
1、OpenHarmony之cJSON库使用介绍
2、OpenHarmony使用Socket实现一个UDP客户端详解
3、OpenHarmony使用Socket实现一个TCP服务端详解
所以，动手能力强的
OpenHarmony（鸿蒙南向开发）——小型系统STM32MP1芯片移植案例
本文章基于意法半导体STM32MP157芯片的小熊派BearPi-HM Micro开发板，进行小型带屏开发板的移植，实现了ace_engine_lite、arkui_ui_lite、aafwk_lite、appexecfwk_lite、HDF等部件基于OpenHarmony LiteOS-A内核的适配。移植架构上采用Board与SoC分离的方案。
鸿蒙南向开发：Board和SoC解耦的设计思路
按照硬件进行层次划分为芯片架构层、片上系统层和单板层。从下向上依次进行包含关系
OpenHarmony5.0南向设备开发：轻量系统芯片移植（轻量系统STM32F407芯片移植案例）
介绍基于STM32F407IGT6芯片在拓维信息Niobe407开发板上移植OpenHarmony LiteOS-M轻量系统，提供交通、工业领域开发板解决方案。
OpenHarmony轻量系统在STM32上的初体验：除了点灯，还能玩出什么花样？
本文详细介绍了OpenHarmony轻量系统在STM32F407上的进阶实践，从基础点灯到构建物联网节点原型。通过硬件配置、外设驱动开发、轻量级组件集成及物联网节点设计，展示了如何在资源受限的MCU上实现高效能应用。适合开发者探索鸿蒙操作系统在嵌入式领域的潜力。
【OpenHarmony】STM32F407 实战：从零移植LiteOS-M 3.1内核
本文详细介绍了将OpenHarmony 3.1版本的LiteOS-M内核移植到STM32F407开发板的完整实战过程。内容涵盖环境搭建、源码获取、工程构建、编译脚本编写、内核配置、中断对接以及串口调试等关键步骤，旨在帮助开发者掌握在资源受限MCU上运行现代物联网操作系统的核心技术，为未来OpenHarmony生态应用打下基础。
实战指南：在STM32F4上构建OpenHarmony 3.0 LiteOS-M最小系统
本文详细介绍了在STM32F4上构建OpenHarmony 3.0 LiteOS-M最小系统的完整流程，包括环境准备、工程模板创建、内核移植、系统裁剪与优化等关键步骤。通过实战案例和调试技巧，帮助开发者快速掌握OpenHarmony在嵌入式设备上的移植方法，适用于物联网和智能硬件开发。
OpenHarmony实战STM32MP157开发板 “控制” Hi3861开发板 -- 上篇
我们会分3篇将实战分解完成，通过实战的方式带大家玩转这2款开发板。用户可感知的操控2款开发板。
OpenHarmony实战STM32MP157开发板 “控制” Hi3861开发板 -- 下篇
看完本篇文章，你会掌握STM32MP157里面的JS API接口的实现，以及如何联网，让安装的App通过JS调用接口的实现，和Hi3861开发板进行交互通信。
移植最新版OpenHarmony到STM32F407
OpenHarmony是华为推出的一款开源操作系统，旨在为各种设备提供统一的分布式应用框架和开发平台。本文将详细介绍如何将最新版本的OpenHarmony移植到STM32F407微控制器上。至此，你已经成功将最新版本的OpenHarmony移植到STM32F407微控制器上。你可以通过配置引脚和时钟，以及根据具体的硬件特性进行相应的调整，实现更多的功能和扩展。希望本文对你在将最新版本的OpenHarmony移植到STM32F407上有所帮助！步骤3：编译OpenHarmony源代码。
【VAE学习笔记】全面通透地理解VAE(Variational Auto Encoder)
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Electron - Electron failed to install correctly, please delete node_modules/electron and try install
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全15万字丨PyTorch 深度学习实践、基础知识体系全集；忘记时，请时常回顾。
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汽车电子测试过程中，除了实车测试，还有台架测试，在进行台架测试时，都需要通过电源来模拟汽车电瓶给ECU供电，如果要进一步进行比较高效的压力测试或自动化测试，就需要用到程控电源，所以本章主要介绍CAPL是如何来控制程控电源的。
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将图 1-2 中扫描单元按照扫描移位模式连接起来，就构成了扫描测试中的基本结构，这里介绍下全扫描。全扫描就是多个扫描单元相连接，上一单元的扫描输出，连接到目前单元的扫描输入，目前单元的扫描输出连接到下一单元的扫描输入，以此类推，构成一条移位寄存器链，如图1-3 所示：图 1-3 全扫描这样的扫描结构，可通过一个串行输入端，将期望的数据存入移位寄存器链并观察运行结果。
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win10 跳过U8环境检测的IE WebControls.rar
win10 安装u8的时候 提示IE WebControls 不通过，这个工具可以欺骗u8的检查。 跳过U8环境检测的IE WebControls。提示导入失败后，重新检测通过。
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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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