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一、GridView横向的实现：
1.DisplayMetrics类简单介绍：
DisplayMetrics主要是获得屏幕物理参数，比如size、density、font scaling.
而这个信息的获取只需要通过上图的两行代码。
这是我们可以获得的部分参数，这里我们主要是通过密度density来操作。
2.gridview的横向实现：
我们首先定义gridview的宽度
int gridviewWidth = (int) (size * (length + 4) * density); //这里加四是为了区别下面的itemwidth，要大于itemwidthint itemWidth = (int) (lengtAndroid自定义HorizontalGridView实现横向滑动与行数限制的完整解决方案
在Android UI开发中，GridView作为经典的二维列表控件，长期以来被广泛用于展示网格化数据。然而其默认行为是垂直滚动、逐列填充，无法满足现代应用中日益增长的横向滑动需求。为了突破这一限制，开发者必须深入理解Android视图系统的底层机制，并基于已有容器类进行定制扩展。本章聚焦于构建一个功能完整、性能可控的自定义HorizontalGridView，重点剖析从继承开始的技术选型依据、测量流程重构逻辑以及控件生命周期管理策略。通过继承。
Android 实现横向滑动的GridView
GridView的横向滑动，非常简单，一目了然，不用计算复杂的GridView长度，而是直接设置横向滑动即可，可供参考；
AndroidTV首页横向滑动源码，
AndroidTV首页横向滑动源码，
USB3.0一致性测试方案
为各测试项目具体测试结果包括脉冲串突发时间、脉冲串重复时间、时钟周期以及眼图等完整USB3.0测试项目，工程师可以看到各项测试的具体结果方便观察。RIGOL 提供了一套完善的工具，包括数字示波器、专业的测试夹具以及全自动一致性测试软件，使得USB 3.0设备设计人员能够高效精确地进行信号捕捉、测试与分析。报告第二部分测试摘要，在此可以看到USB3.0测试所有测试项目，并且可以观看到测试是否正常，可以帮助工程师更好的判断测试结果。，以简化下载、存储以及对于多媒体的大量内容的共享。
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本文详细介绍了如何利用HipSTR工具从二代测序(NGS)数据中精准解析短串联重复序列(STR)的遗传多态性。通过三步走策略，HipSTR能够高效识别STR区域、估算重复次数并判定基因型，适用于法医鉴定、疾病研究和细胞系鉴定等多个领域。文章还提供了实战操作指南和常见问题解决方案，帮助研究者充分利用NGS数据开展STR分析。
使用 Python 和可视化编程控制树莓派机械臂myCobot
这些模块包括逻辑、循环、数学、文本、列表、颜色、变量、函数以及控制机械臂的函数等等，所以用户可以轻松入门编程，只需单击右上角的运行就可以启动程序，做一些有趣的应用。可以搭配任意摄像头进行图像识别，具有4个 USB 端口，可以通过 Raspberry Pi 的微型 HDMI 端口连接到显示器. 最后，40 个GPIO 接头可以进行更多扩展应用，机械臂每个接口都有清晰的标记，面板上还有一个电源开关和一个直流电源插孔。您现在可以将机器人放在地板或桌子上，并用力拧紧吸盘，以确保机械臂的底座在操作过程中不会移动。
CATIA P3 V5-6R2023在Windows 11 23H2上的稳定安装与系统级配置指南
CATIA是高端制造业核心的三维参数化设计平台，其运行依赖严格的系统底层支撑。理解CATIA的BRep几何内核原理与Windows子系统（如WSL、Hyper-V）的协同机制，是保障建模稳定性与计算精度的技术前提。CATIA Run-Time Library和OpenGL硬件加速能力共同决定了曲面重建、螺纹建模等关键操作的可靠性；而.NET Framework 3.5、VC++ 2022运行库及显卡Studio驱动的精准匹配，则构成工业软件在Win11环境落地的工程基础。本文聚焦于航空、轨交、能源等领域高频
机器学习的学习准则（期望风险最小化、经验风险最小化、结构风险最小化）
要评价模型f(x, θ)的好坏，可通过期望风险R(θ)来衡量：求期望损失R(θ)不仅需要知道损失函数还需要知道真实分布,损失函数可以通过一定的准则计算得到，但是样本空间的真实分布是未知的。所以不能直接求R(θ)。期望风险R(θ)无法计算，给定一个训练集D =，我们可以计算经验风险，经验风险就是在训练集上的平均损失：为了解决过拟合问题，引入了参数正则化来限制模型的能力。使其不要过度的最小化经验风险。这种准则就是结构风险最小化准则
Scale-up，scale-out技术概述
本文系统分析了Scale-up与Scale-out两种扩展技术在现代计算中的应用与发展。Scale-up通过增强单节点性能，适用于高耦合任务，而Scale-out通过横向扩展节点，实现高可用性和无限扩展。在芯片层面，Chiplet技术推动Scale-up发展；在数据中心层面，二者协同工作构建强大集群。光模块在长距离高带宽传输中发挥关键作用。文章对比了NVIDIA、AMD和Broadcom三家公司的技术路线，指出NVIDIA采用封闭生态，AMD选择开放标准，Broadcom则主导底层连接技术。未来发展方向将聚
【ESP32使用MAX98357播放音频】
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具身智能中的（VLA）算法与实践——第一讲 基础与概览
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ssm框架搭建
这个是一个在idea上成功跑起来的ssm框架项目，保存在这里方便以后使用直接下载
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")
文档：亲测可用解决vnc灰屏，黑屏，鼠标大黑叉情况
主要解决vnc-viewer灰屏，黑屏，鼠标大黑叉等现象，详细见文档。
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