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获取手机分辨率
//获取手机像素，用于处理图片内存溢出
private int metrisPixels = 0;
DisplayMetrics outMetrics = new DisplayMetrics();
this.getWindowManager().getDefaultDisplay().getMetrics(outMetrics);
metrisPixels = outMetrics.heightPixels*outMetrics.widthPixels;
【论文阅读】LIME：Low-light Image Enhancement via Illumination Map Estimation(笔记最全篇）
文章目录AbstractIntroductionContributionMethodSpeed-up Method(1)：ALM子问题 T子问题 G子问题 Z和μ\muμSpeed-up Method(2)：权重变量实验conclusion 今天要整理的一篇论文是使用传统的方法解决低照度的问题，Guo等人于2016年发表在IEEE上，论文《LIME：Low-light Image Enhance...
获取手机屏幕分辨率
<br /> //获取手机屏幕分辨率的类<br /> DisplayMetrics dm = new DisplayMetrics();<br /> getWindowManager().getDefaultDisplay().getMetrics(dm);<br /> //获得手机的宽带和高度像素单位为px<br /> String str = "手机屏幕分辨率为:" + dm.widthPixels<br />
HyperMesh二次开发实战：自动化Comp与材料属性批量绑定
本文详细介绍了HyperMesh二次开发中自动化Comp与材料属性批量绑定的实战技巧。通过TCL脚本实现批量创建属性卡、智能材料分配和异常处理，大幅提升前处理效率，适用于汽车、航空航天等领域的复杂模型处理。
获取分辨率
一、获取分辨率 使用getSize() 办法 [java] view plaincopyprint? Display display = getWindowManager().getDefaultDisplay(); Point size = new Point(); display.getSize(size);
实现高精度卷积混响--MATLAB仿真篇
一、前言 之前在STM32F407上现实了直播声卡，实现的经典Schroeder混响模型和Moorer混响模型，得到不错效果。后来得知卷积混响最为真实地表现空间的特性，所以也就尝试在MCU上实现一个高精度的卷积混响(卷积点数为32768)，目前MCU出来的混响效果算得上一流水准，本人也在计划在ADI的21489或者SC584上更高精度的卷积混响。 二、卷积方式 1、conv1是使用MATLAB自带卷积函数conv 2、conv2和conv3是改进后卷积过程，具有减少运算量特点，方便嵌入式移植 clear;
quick - lua 分辨率适配
Quick中CONFIG_SCREEN_AUTOSCALE的值有三种情况： FIXED_WIDTH：保持传入的设计分辨率宽度不变，根据屏幕分辨率修正设计分辨率的高度。 FIXED_HEIGHT：保持传入的设计分辨率高度不变，根据屏幕分辨率修正设计分辨率的宽度。 FILL_ALL：保证了设计区域总有一个方向铺满屏幕，另一个方向可能超出屏幕或留有黑边。 却没有全屏幕适配的情况，需要修改代码增加这个功能。
华为项目TR5评审后发现了重大缺陷的应急处理经验
华为TR5评审后发现重大缺陷时，启动分级应急机制：1）缺陷分级：按严重性（致命/严重/一般）评估影响；2）应急响应：跨部门会议制定方案，冻结生产并调配资源；3）根因修复：通过RCA定位问题，设计变更后回归测试；4）变更管理：评审修复影响，调整发布策略；5）持续改进：记录缺陷闭环，优化流程。案例显示，软件闪退问题通过热修复补丁和代码重构解决。关键工具包括Jira、FMEA等，确保缺陷在量产前拦截，降低风险。
ADB获取手机分辨率的多种方法，解决部分手机获取不到的问题
ADB获取手机分辨率的多种方法，解决部分手机获取不到的问题
Frida实战：高效dump Android内存中的so文件与修复技巧
本文详细介绍了使用Frida工具dump Android内存中已解密so文件的实战技巧。针对加固应用静态分析失效的痛点，文章提供了从环境搭建、使用frida_dump脚本自动化抓取内存数据，到利用SoFixer工具修复ELF文件结构的完整流程，帮助逆向分析人员高效获取可分析的so文件。
HRNet 【demo 复现过程】
Deep High-Resolution Representation Learning for Human Pose Estimation
获取Android手机的屏幕分辨率
获取手机屏幕分辨率分以下两种情况： 1、Activity中获取： DisplayMetrics metrics = new DisplayMetrics(); getWindowManager().getDefaultDisplay().getMetrics(metrics); int width = metrics.widthPixels; int height = metric
【CANoe】CAPL_ECU上电后首帧报文发出时间检测
【CANoe】CAPL_ECU上电后首帧报文发出时间检测,通过CANoe I/O口检测电压实现
【Android基础】获取手机分辨率
在开发手机应用时，除了对API的掌握外，更重要的还是画面。 对于画面，屏幕分辨率又是很重要的一个因素。 由于手机屏幕尺寸各不相同，在画面布局时就需要考虑这个问题。 Android可以设置为随着窗口大小调整缩放比例，但即便如此，手机程序设计人员还是必须知道手机屏幕边界，以免布局变形。 下面我们就讲一下，如何通过代码获取手机屏幕的分辨率，代码其实很简单，关键是android...
pscad IEEE标准系统搭建说明
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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")
ADB获取手机分辨率和dpi，以及指定手机分辨率和dpi的指令
将WIDTH和HEIGHT替换为您想要设置的分辨率的宽度和高度。将DPI替换为您想要设置的DPI值。1、在使用命令前，请确保手机已开启USB调试模式。
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。
Android 获取手机分辨率
获取手机屏幕的分辨率（尺寸）
Devexpress16.2.6破解工具
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android手机屏幕分辨率获取异常
TextView tv=(TextView)findViewById(R.id.tv); DisplayMetrics dm=new DisplayMetrics(); getWindowManager().getDefaultDisplay().getMetrics(dm); float width=dm.widthPixels*dm.density; float height=dm.he
获取手机实际分辨率
1.这个是获取手机分辨率 但是不包括状态栏和浸入式底部导航栏 DisplayMetrics dm = new DisplayMetrics(); context.getWindowManager().getDefaultDisplay().getMetrics(dm); return +dm.widthPixels + "*" + dm.heightPixels; 2.获取手机实际分...
android 获取手机的分辨率
为什么80%的码农都做不了架构师？>>> ...
html 自动获取屏幕分辨率,HTML5教程 如何获取手机屏幕分辨率和浏览器分辨率
本篇教程探讨了HTML5教程 如何获取手机屏幕分辨率和浏览器分辨率，希望阅读本篇文章以后大家有所收获，帮助大家HTML5+CSS3从入门到精通 。<在使用CSS3的中有如下类似代码Html代码@mediascreenand(max-width:480px)｛……｝意思是在最大宽度为480px的设备上应用{}里面的样式。这里的width，注意是手机浏览器的分辨率，而不是手机设备的屏幕分辨...
安卓编程获取手机分辨率
使用DisplayMetrics 创建的对象获取手机屏幕分辨率DisplayMetrics dm=new DisplayMetrics(); getWindowManager().getDefaultDisplay().getMetrics(dm); int height=dm.heightPixels; int width=dm.widthPixels;
Android 获取手机的分辨率两种方法
A，过时的API [2]获取手机的分辨率 WindowManager wm = (WindowManager) getSystemService(WINDOW_SERVICE); int width = wm.getDefaultDisplay().getWidth(); int height = wm.getDefaultDisplay...
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