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本文提供Source Insight 4.0安装包和破解文件下载地址，详细介绍安装步骤，包括下载解压、完成安装、替换程序、添加破解文件等。还说明了若之前安装过该软件，需清除注册表信息和删除全局配置信息，以确保顺利安装。
SourceInSight 4 完整安装及破解文件
第一步：安装 安装sourceinsight4.0 （可从下文地址下载） 第二步：替换 用下文地址中的 sourceinsight4.0.exe 替换安装后路径下的 sourceinsight4.0.exe 第三步：破解 运行sourceinsight4.0，选择破解文件破解 下载解压后先找到安装文件进行安装，然后用解压后的“source insight 4.0.exe”替换安装路径下的source insight 4.0.exe，然后运行SI4，在弹出的对话框中选择第三项并将下载的文件 si4.pediy.lic选中并“Next”即可破解！
SI4安装及破解文件.zip
source insight4.0版本，安装完了，拿里面的exe程序替换一下
SI4安装文件及操作流程
SI4安装文件及操作流程
STM32F407 CubeMX UART DMA高效解析BNO085九轴数据
本文详细介绍了如何在STM32F407平台上使用CubeMX配置UART和DMA高效解析BNO085九轴传感器数据。通过DMA循环接收机制实现低CPU占用的实时数据采集，并分享数据解析技巧、错误处理及实战调试经验，为嵌入式开发者提供完整解决方案。
学习心得——COMSOL Multiphysics with MATLAB联合仿真及寻优计算
学习心得——COMSOL Multiphysics with MATLAB联合仿真及寻优计算
西门子WinCC VBS脚本实战：5分钟搞定变量记录导出到Excel（附完整代码）
本文提供了一套高效的西门子WinCC VBS脚本方案，帮助工程师在5分钟内将变量记录数据导出至Excel。方案详细解析了时区转换、数据库连接、SQL查询优化等核心难点，并附带了完整、可直接使用的VBS代码，解决了工业自动化项目中数据导出的实际痛点。
VLM,VLA,VLN的区别与联系
VLM 提供了理解世界的“眼睛和大脑”，VLN 赋予了这个智能体在空间中“移动的双腿”，而 VLA 则进一步赋予了它“灵巧的双手”来改变世界。三者共同构成了一个完整具身智能体的核心能力模块。
5分钟搞定：用Ollama在MacBook上本地运行Qwen3大模型（附避坑指南）
本文详细介绍了如何在MacBook上使用Ollama快速部署和运行Qwen3大模型，包括安装步骤、性能优化技巧及常见问题解决方案。Ollama作为一款轻量级工具，特别适合M系列芯片的Mac用户，支持多种量化版本，显著降低资源消耗，提升推理速度。
首个FDA认证开源医学影像AI平台Comp2Comp：从CT自动分析到临床部署全解析
深度学习模型在医学影像分析领域正成为变革性力量，其核心原理是通过卷积神经网络自动学习图像特征，实现精准分割与检测。这一技术价值在于将高度依赖医生经验的定性、主观判断，转化为高效、客观、可重复的定量分析，极大提升了诊断的一致性和工作效率。在应用场景上，医学影像AI已广泛渗透于疾病筛查、辅助诊断、预后评估及治疗方案规划等多个环节。本文聚焦于获得FDA 510(k)认证的开源平台Comp2Comp，它集成了主动脉钙化评分和椎体骨密度分析双任务，通过串联式的U-Net分割网络与目标检测模型，构建了从DICOM预处理
室内无人机自主定位：融合Mid360激光雷达与光流传感器的无GPS导航实践
本文详细介绍了室内无人机自主定位技术，通过融合Mid360激光雷达与光流传感器实现无GPS导航。文章分析了技术挑战、硬件配置、算法优化及实战调试，展示了在复杂室内环境中如何实现厘米级定位精度，为仓库巡检、隧道勘测等场景提供可靠解决方案。
【SpringBoot 3.x 第56节】再见 Feign？原生 HTTP 接口强势上位，这才是微服务调用的正确姿势！
🏆本文收录于《滚雪球学SpringBoot 3》，专门攻坚指数提升，本年度国内最系统+最专业+最详细（永久更新）。 本专栏致力打造最硬核 SpringBoot3 从零基础到进阶系列学习内容，🚀均为全网独家首发，打造精品专栏，专栏持续更新中…欢迎大家订阅持续学习。 如果想快速定位学习，可以看这篇【SpringBoot3教程导航帖】，你想学习的都被收集在内，快速投入学习！！两不误。 若还想学习更多，可直接前往《滚雪球学SpringBoot(全版本合集)》，涵盖SpringBoot所有版本教学文章
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
怀旧版电脑GBC模拟器
再现原版GBC掌机的界面，电脑运行连游戏界面都一样大小，很怀旧，很真实，（英文版）强烈推荐，很小的文件，很难才找到的。
jre9下载 jdk9
(Java SE Runtime Environment 9.0) 9.0u175 官方最新版 【x86|x64】
Outlook Express 6 (OE6) for Win7/Win8/Win10
英文版 Outlook Express6.0， 兼容Win7/Win10/WinXP，解压就可用。
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