相关推荐
SEMI E40标准的加工任务状态模型(Process Job State Model)解析
半导体制造设备的自动化控制离不开对加工任务(Process Job)的精细管理。SEMI E40标准定义了加工任务的全生命周期状态模型。分层建模,支持嵌套与并发子状态,确保复杂流程的灵活控制。SEMI E40通过。
Linux 内核文件系统与设备操作流程分析
Linux 内核文件系统与设备操作流程分析 来源: ChinaUnix博客 日期: 2007.09.07 20:51 (共有条评论) 我要评论 Author: sinister Email: sinister@whitecell.org Homepage: http:
从量子兔AlphaPi硬件拆解到ESP32-C3库开发实战
本文详细拆解了量子兔AlphaPi开发板的硬件构成,重点分析了其核心ESP32-C3芯片及外围传感器。通过逆向工程揭示了GPIO引脚定义与通信协议,并实战演示了如何从零开发MicroPython库,驱动按键、加速度计及LED点阵,最终整合成一个互动平衡球游戏项目,展现了开源硬件在教育与二次开发中的潜力。
注册表隐藏
1. 使用系统工具:在Windows操作系统中,可以使用组策略编辑器(gpedit.msc)来隐藏注册表。打开组策略编辑器后,导航到“用户配置”>“管理模板”>“系统”,然后找到“阻止访问注册表编辑器”选项,将其启用。2. 修改注册表权限:通过修改注册表的权限设置,可以限制用户对注册表的访问权限。- 单击“编辑”按钮,然后在“基本权限”中撤消“查看”权限。- 在“高级安全设置”窗口中,选择当前用户的权限项。- 在“安全”选项卡中,选择“高级”。- 右键单击该项,选择“权限”。- 导航到需要隐藏的注册表项。
别再羡慕专业版!用这个批处理脚本,一键给Win11家庭版装上官方沙盒
本文详细介绍了如何通过批处理脚本在Windows 11家庭版上安装官方Windows沙盒(Windows Sandbox),无需复杂操作即可享受专业版功能。脚本自动激活系统内置组件,提供安全隔离的虚拟化环境,适用于软件测试、安全浏览等场景。
二进制、八进制、十六进制、十进制转换详解
二进制是计算机的语言,正如我们使用汉语表达与交流信息一样,任何信息在计算机中都以二进制的形式存储和计算。了解二进制与十进制等进制之间的转换方法,有助于我们了解计算机内部的数字计算过程,可以帮助我们在一定程度上理解信息在计算机中的编码和解码过程。本文先简单介绍位置化数字系统的概念,然后详细讲解二进制、八进制、十六进制、十进制之间的转换方法与原理。
隐藏任意进程,目录/文件,注册表,端口
Author: sinisterEmail: sinister@whitecell.orgHomepage:http://www.whitecell.org Date: 2002-05-08查找进程,目录/文件,注册表等操作系统将最终调用 ZwQueryDirectoryFile,ZwQuerySystemInformation,ZwXXXValueKey 等函数。要想拦截这些函数达到隐藏目的,需
43、FMCW雷达高度计仿真
本文介绍了FMCW雷达高度计的波形级建模与仿真过程,重点模拟了着陆和倾斜两种飞行场景以评估其性能。通过结合高程数据和飞行轨迹构建雷达场景,使用自定义信号处理器分析IQ样本并估计高度。结果表明,该高度计在多数情况下满足性能要求,但也存在误差较大的点,提出了地面检测算法优化的潜在方向。
C++隐藏任意进程,目录/文件,注册表,端口
查找进程,目录/文件,注册表等操作系统将最终调用 ZwQueryDirectoryFile,ZwQuerySystemInformation,ZwXXXValueKey 等函数。要想拦截这些函数达到隐藏目的,需先自己实现以上函数,并修改系统维护的一个SYSCALL 表使之指向自己预先定义的函数。因 SYSCALL 表在用户层不可见,所以要写 DRIVE 在 RING 0 下才可修改。关于如何修改已
游戏大厂技术护城河——为什么你抄不了原神?
│ │ │ │ ↓ 通过 │ │ │ │。│ │ │ │ ↓ 成功 │ │ │ │。│ │ │ │ ↓ 完成 │ │ │ │。│ │ │ │ ↓ 达标 │ │ │ │。
Unity内存优化:从崩溃到流畅的终极指南
Emscripten 为 WebAssembly 设定的内存布局(Static、Stack 和 Heap)为开发者提供了一个清晰的内存管理框架。理解这些内存区域的特性和用途,有助于开发者在 WebGL 项目中更有效地管理内存,优化性能,减少内存使用峰值。在 Unity WebGL 中,Dynamic Memory 是 UnityHeap 的主要组成部分,负责存储游戏运行时所需的各种数据。Dynamic Memory 主要分为两类:Managed Memory 和 Native Memory。
利用驱动程序隐藏任意进程,目录/文件,注册表,端口
利用驱动程序隐藏任意进程,目录/文件,注册表,端口2006-07-26 12:27 作者: 来源: 发表时间:2006-05-19 -- 利用驱动程序隐藏任意进程,目录/文件,注册表,端口[转帖]查找进程,目录/文件,注册表等操作系统将最终调用 ZwQueryDirectoryFile,ZwQuerySystemInformation, ZwXXXValueKey 等函数
基于Simulink的电机轴电压与轴电流抑制仿真
本文带你完成了电机轴电压与轴电流抑制的Simulink仿真,实现了:✅ 揭示了共模电压 → 寄生电容 → 轴电压 → 轴承电蚀的完整链路✅ 搭建了含寄生参数的高频电机等效模型共模电压最小化PWM(源头抑制)dv/dt滤波器(路径阻断)轴接地(终端泄放) ✅ 通过仿真验证了轴电压从68 V降至<3 V的显著效果 ✅ 为高可靠性电机系统提供低成本、高效益的轴承保护方案。
[转载]隐藏任意进程,目录/文件,注册表,端口
Author : sinister Email : sinister@whitecell.org HomePage: http://www.whitecell.org 查找进程,目录/文件,注册表等操作系统将最终调用 ZwQueryDirectoryFile,ZwQuerySystemInformation,ZwXXXValueKey 等函数。要想拦截这些函数达到隐藏目的,需先自己实现以上...
Devexpress16.2.6破解工具
Devexpress16.2.6破解工具Devexpress16.2.6破解工具Devexpress16.2.6破解工具Devexpress16.2.6破解工具
NTscan扫描工具
ntscan是一个扫描器,可以扫描没有密码和弱密码的共享,先到配置文件中填写猜测的用户名和密码,扫描过程中右侧出现的是扫描日志。
隐藏任意进程,目录文件,注册表,端口
隐藏任意进程,目录文件,注册表,端口 隐藏任意进程,目录/文件,注册表,端口 查找进程,目录/文件,注册表等操作系统将最终调用 ZwQueryDirectoryFile,ZwQuerySystemInformation, ZwXXXvalueKey 等函数。要想拦截这些函数达到隐藏目的,需先自己实现以上函数,并修改系统维护的一个 SYSCALL 表使之指向自己预先
廖雪峰Python教程[完整版]
廖雪峰的python教程整理的,已经排好版了,而且还是完整的,适合小白
隐藏任意进程,目录/文档,注册表,端口
来源: 作者: 发布时间:2007-05-24
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")
进程的深度隐藏
/********************************************************* 查找进程操作系统最终调用native api ZwQuerySystemInformation, 因此拦截此函数可以达到隐藏进程的目的。 *********************************************************/ /...
如何在Windows NT中隐藏自己[转]
导读: =============================[ 在NT“盒子”里消失 ]============================= 如何在Windows NT中隐藏自己 -------------------------- 作者:Holy_Father 版本:1.2 英语 日期:05.08.2003 翻译:pker / CVC
文件隐藏
/********************************************************* 查找目录/文件操作系统最终调用native api ZwQueryDirectoryFile, 因此拦截此函数可以达到隐藏文件/目录的目的。 *********************************************************/ /*...
Windows下MySQL卸载不干净?5个隐藏文件夹和注册表项你删了吗?
本文提供了一份详尽的Windows系统下MySQL彻底卸载教程。针对常规卸载后残留导致重装失败的问题,指南深入揭示了必须手动清理的5个关键残留点,包括隐藏的数据文件夹和注册表项,并给出了每一步的安全操作步骤与验证方法,确保系统环境纯净,为成功重装扫清障碍。
1777



被折叠的 条评论
为什么被折叠?



