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我也要
在开始写聊天软件，类似于微信这样的软件之前，一直在写单机游戏。之所以要写聊天软件，只是因为要会联网的一些操作，完善自己的编程能力。
首先，我了解了一个名词IM，即时通讯的意思，在发送资源，不管是文本还是视频这些流文件，都能在短间内到达对方，这就是实时通信的用途。在我开始准备看服务器端的代码时，我突然发现了更好的资源leancloud，可以自行百度，他的平台提供了很好的接口，可以免费使用leancloud管理服务器上的资源，自己只需要使用他的sdk，就可以在短时间内完成一个项目。
首先，得到创建app应用，得到appId，和appKey
得到appId和appKey后，就可以在android studio上开发了。
在开发前，必须要做的事情就是看api文档，官网上有中文文档，不会的可以翻阅，如何有些小代码，小demo，一点点跟着做就可以。
最近在看界面的部分，最主要的就几个特别重要的类：
安卓高仿QQ聊天软件项目源码实战
在现代Android应用开发中，用户界面（UI）不仅是功能的载体，更是用户体验的核心体现。尤其在社交类应用如QQ、微信等产品中，高度还原的设计稿与流畅自然的交互体验已成为衡量一个App品质的重要标准。本章将深入探讨如何从零开始高精度复刻QQ登录页及主界面布局，并通过自定义View技术打造可复用、高性能的UI组件。我们将聚焦于ConstraintLayout的响应式能力、自定义绘制机制、事件分发处理等多个关键技术点，构建一套既美观又具备工程化价值的UI体系。
Android仿微信聊天软件开发与Socket网络通信实现
Android操作系统架构基于Linux内核，它由四个主要组成部分构成：应用层、应用框架层、系统运行库和Linux内核。在这一章节中，我们将重点介绍Android的层次结构和各层次间的关联性，以便读者能够建立一个清晰的系统概念模型。
Android聊天软件开发（基于网易云IM即时通讯）——第三部分
Android聊天软件开发（基于网易云IM即时通讯）——添加好友（三）
安卓开发的仿qq的社交聊天软件项目源码，功能已经非常完善，可以作为安卓学习的不二首选
安卓开发的仿qq的社交聊天软件项目源码，功能已经非常完善，可以作为安卓学习的不二首选
Android 开发聊天工具 模仿QQ，实现网络即时聊天功能。
Android 开发聊天工具 模仿QQ，实现网络即时聊天功能。
磁编码器选型避坑指南：为什么MT6816比霍尔传感器更适合你的伺服系统？
本文深入对比了MT6816磁编码器与传统霍尔传感器在伺服系统中的应用。MT6816基于AMR技术，对磁场方向敏感而非强度，具备高精度、强抗干扰、高安装容差和极低延时等优势，尤其适合高转速、恶劣环境下的工业场景，能有效提升系统可靠性与长期稳定性。
LaTeX新手入门以及TeXlive和TeXstudio的安装使用
作为一个新手，LaTeX的了解和使用很是费了一番功夫，因此，在这里给找不到路的同志们稍微介绍一下。如有错误，敬请指正。本篇内容基于Win10系统，因此安装在Mac或者Linux上的同学可以再找找其他的介绍文章。 目录 LaTeX介绍 LaTeX环境配置 一、TeXlive下载 二、TeXlive安装 1. 安装main installer 2. 正式安装 三、TeXstudio安装...
H3C VRF实验
POP-vpn-instance-RT1]route-distinguisher 11:11 # VPN实例-RT1的路由区分器值11:11。配置路由泄露，其实就是在一个VPN示例中添加另一个VPN实例的下一跳，比如在VPN实例RT1中添加去往SRV的默认路由。但是也有了一个新的问题，那就是RT1、RT2和RT3也能互访了，这让VPN实例变得没有意义了。[POP]ip vpn-instance RT1 #配置vpn实例-RT1。绑定了VPN实例，所以要把路由添加到VPN实例POP中。
ADC INL&DNL测试方法和实例
本文介绍了一种可操作的ADC INL和DNL的测试方法--直方图法（也称码密度法）。并实际使用该方法测试了两款芯片的ADC INL和DNL
【08】C#实战篇——C#调用C++ 动态库dll,报错 “无法加载DLL“xxx.dll”: 找不到指定的模块。 (异常来自 HRESULT:0x8007007E)。”
摘要： 本文详细分析了C#调用C++封装的DLL时出现“无法加载DLL”错误的排查与解决方法。通过案例演示，列举了常见原因（如平台不匹配、路径错误、依赖缺失等），并重点介绍了使用VS的dumpbin工具检测DLL依赖项的方法。最终定位问题为缺失依赖的LineApi.dll和opencv_world455d.dll，将其放入C#的exe目录后解决。文章还对比了C++与C#的报错差异，提供了通用解决方案，包括检查运行时库、环境变量等，帮助开发者高效解决类似问题。 关键词： C#调用C++ DLL、dumpbin
零基础做完YOLOv8船只检测系统：HRSC2016数据集 + 完整UI，稳过答辩
亲爱的同学，通过这篇教程，你已经掌握了“基于YOLOv8的HRSC2016微小船只检测系统”从数据集处理、模型训练到Tkinter界面开发的全流程。这个课题既贴合“海事智能化”的产业战略，又涵盖了“目标检测、深度学习、界面开发”等多个毕设评分点，非常适合作为计算机或海事工程专业的毕业设计。在实践中，你可能会遇到“海面背景复杂导致船只漏检”“微小船只识别精度不足”“UI与模型推理同步卡顿”等挑战——别担心，这些都是技术成长的必经之路。
OCS2 入门教程（一）
OCS2 是专为切换系统优化控制（ptimalontrol forwitchedystems，OCS2）定制的 C++ 工具箱。该工具箱提供以下算法的高效实现：SLQ：连续时间域约束 DDPiLQR：离散时域约束 DDPSQP：基于 HPIPM 的多重打靶算法IPM： 基于非线性内点法的多重搜索算法SLP：基于 PIPG 的顺序线性规划算法OCS2 通过增强拉格朗日法（Augmented Lagrangian）或松弛障碍法（relaxed barrier methods）处理一般路径约束。
IEM模型，地表粗糙度模拟
利用微波反演土壤水分时候，利用IEM模型可以实现对随机地表的模拟
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")
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。
TMS_WebGMaps_1.9.8.0_Full_Source
TMS_WebGMaps_1.9.8.0_Full_Source 全源码 delphi cbuilder
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