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该原创文章首发于微信公众号：字节流动
OpenGL PBO（Pixel Buffer Object），被称为像素缓冲区对象，主要被用于异步像素传输操作。PBO 仅用于执行像素传输，不连接到纹理，且与 FBO （帧缓冲区对象）无关。
OpenGL PBO（像素缓冲区对象） 类似于 VBO（顶点缓冲区对象），PBO 开辟的也是 GPU 缓存，而存储的是图像数据。
与 PBO 绑定相关的 Target 标签有 2 个：GL_PIXEL_UNPACK_BUFFER 和 GL_PIXEL_PACK_BUFFER 。
其中将 PBO 绑定为 GL_PIXEL_UNPACK_BUFFER 时，glTexImage2D() 和 glTexSubImage2D() 表示从 PBO 中解包（unpack）像素数据并复制到帧缓冲区 。
将 PBO 绑定为 GL_PIXEL_PACK_BUFFER 时，glReadPixels() 表示从帧缓冲区中读取像素数据并打包进（pack） PBO 。
OpenGL ES 3.0 开发（二十二）：PBO
该原创文章首发于微信公众号：字节流动 PBO 是什么 OpenGL PBO（Pixel Buffer Object），被称为像素缓冲区对象，主要被用于异步像素传输操作。PBO 仅用于执行像素传输，不连接到纹理，且与 FBO （帧缓冲区对象）无关。 OpenGL PBO（像素缓冲区对象） 类似于 VBO（顶点缓冲区对象），PBO 开辟的也是 GPU 缓存，而存储的是图像数据。 与 PBO 绑定...
OpenGL ES之深入解析PBO、UBO与TBO的功能和使用
OpenGL PBO（Pixel Buffer Object），被称为像素缓冲区对象，主要被用于异步像素传输操作。PBO 仅用于执行像素传输，不连接到纹理，且与 FBO （帧缓冲区对象）无关。UBO，就是一个装载 uniform 变量数据的缓冲区对象，本质上跟 OpenGL ES 的其他缓冲区对象没有区别，创建方式也大致一致，都是显存上一块用于储存特定数据的区域。TBO 即“纹理缓冲区对象”，是 OpenGL ES 3.2 引入的概念，因此在使用时首先要检查 OpenGL ES 的版本。...
Android OpenGL 渲染图像读取哪家强
该原创文章首发于微信公众号：字节流动 glReadPixels glReadPixels 是 OpenGL ES 的 API ，OpenGL ES 2.0 和 3.0 均支持。 使用非常方便，下面一行代码即可搞定，但是效率也是最低的。 glReadPixels(0, 0, outImage.width, outImage.height, GL_RGBA, GL_UNSIGNED_BYTE, buffer); 当调用 glReadPixels 时，首先会影响 CPU 时钟周期，同时 GPU 会等待当前帧.
从解压到编译：手把手带你用TeXstudio搞定第一篇ACM LaTeX论文
本文详细指导如何使用TeXstudio从解压ACM LaTeX模板到成功编译第一篇学术论文的全过程。涵盖环境准备、模板配置、TeXstudio设置及常见问题排查，特别针对ACM会议论文格式要求，帮助学术新人快速掌握LaTeX写作技巧。
【Python深度学习】做深度学习训练时，如果数据集太大，无法一次性加载进GPU，应该如何做？
方法核心思想优点缺点适用场景实现复杂度小批量训练（Mini-Batch Training）与数据加载器（DataLoader）将数据集划分为小批量，每次只加载一个小批量到GPU中进行训练。- 易于实现- 是深度学习的标准做法- 支持多进程加载- 内存可能成为瓶颈- 需要调整批量大小避免内存不足适用于大部分场景低数据分片与分布式训练（Data Sharding & Distributed Training）将数据集分片，每个GPU或节点处理其中一部分数据，分布式训练。
Linux内核编程（十三）Input输入子系统
Input 子系统是 Linux 内核中专门为输入设备设计的一个子系统，它提供了一个通用的框架来管理各种输入设备，如键盘、鼠标、触摸屏、游戏手柄等。这个子系统的主要目的是简化和规范化输入设备驱动的开发，同时提高驱动的通用性和兼容性。（1）输入设备：如键盘、鼠标、触摸屏、游戏手柄等，它们是用户与计算机进行交互的主要方式。（2）输入设备驱动的复杂性：传统上，为每种输入设备编写驱动都需要创建文件、进行硬件初始化、实现文件操作函数集和处理中断等。每种设备的驱动可能都有许多重复性的工作。
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静电放电噪声耦合的方式主要是：传导耦合与辐射耦合，辐射耦合主要以磁场耦合、 电场耦合为主要形式。电场耦合发生的主要条件是：静电放电高频在参考地平面寄生电感中产生电平波动，平行于参考地平面上的较长信号布线就很容易通过分布电容发生电场耦合。根据高速信号回流路径的特性可知，其回流路径是贴近信号布线下方，或者信号两侧，若信号回流路径跨分割后，环路面积会随之增大；信号环路面积大从EMI角度看其空间辐射更强，从EMS角度看则更容易耦合外部噪声干扰（雷击浪涌、EFT、ESD、RS）。
华为OD技术面真题 - 数据库Mysql - 1
汇总华为OD技术面高频Mysql相同问题，提供详细解答。
YOLO26改进策略【模型轻量化】| 替换骨干网络 CVPR-2024 StarNet，超级精简高效的轻量化模块
本文记录的是。YOLO26主要用于轻量化应用部署，相关轻量化改进与其整体架构非常适配。StarNet设计简洁，没有复杂的设计和精细调整的超参数，仅是一个 4 阶段的分层架构。并且其中能够在低维空间计算的同时考虑极高维的特征这一特性，进而提高模型精度。本文在替换骨干网络中配置了原论文中的starnet_s1starnet_s2starnet_s3和starnet_s4七种模型，以满足不同的需求。
QNX侧触摸屏（TP）Bringup流程与源码深度解析
QNX侧TP Bringup的核心在于构建。
大中型超市收银系统(可直接运行)
光步前台收银系统系统功能：商品销售、商品退货、挂单、收银员权限、会员销售、单品取消、整单取消、商品查询、脱机销售、脱机数据上传，基本资料下载，开钱箱、小票打印、收款结算，支持多币种付款方式（现金、储值卡、礼券、微信等）,支持多种促销。运行环境：.net 4.0登陆信息用户名:1001 密码:123456内置商品信息，商品编码可输入00001000 - 00010000,测试效果相关功能键F5 结算 F9挂单 F11商品查询 F8退货 Insert单品取消 Del整单取消 F12开钱箱F2 会员 Ctrl+Z退出系统 Home 锁屏 相关源码待公开
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。
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