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第一章 计算机系统漫游
计算机系统：是由硬件和系统软件组成的，它们共同工作来运行应用程序。
源程序实际上就是一个有值0和1组成的位（bit）序列,8个位被组织成一组，成为字节。
进程是对处理器、主存和I/0设备的抽象。
文件是对I/O设备的抽象表示。
虚拟存储器是对主存和磁盘I/O设备的抽象表示。所以计算机重要的思想就是抽象。
进程是操作系统对一个正在运行的程序的一种抽象。并发运行是说一个进程的指令和另外一个进程的指令是交错运行的。
操作系统报错跟踪进程运行所需的所有状态信息，这种状态，就是上下文。
虚拟机是对整个计算机包括操作系统、操作系统和程序的抽象。
第二章 信息的表示和处理
C语言中，以0x或者0X开头的数字常量表示十六进制的值。
csapp-international.pdf
英文原版CSAPP 英文版 pdf A Tour of Computer Systems 1 1.1 Information Is Bits + Context 3 1.2 Programs Are Translated by Other Programs into Different Forms 4 1.3 It Pays to Understand How Compilation Systems Work 6 1.4 Processors Read and Interpret Instructions Stored in Memory 7 1.4.1 Hardware Organization of a System 7 1.4.2 Running the he llo Program 10 1.5 Caches Matter 12 1.6 Storage Devices Form a Hierarchy 13 1.7 The Operating System Manages the Hardware 14 1.7.1 Processes 16 1.7.2 Threads 17 1.7.3 Virtual Memory 17 1.7.4 Files 19 1.8 Systems Communicate with Other Systems Using Networks 20 1.9 Important Themes 21 1.9.1 Concurrency and Parallelism 21 1.9.2 The Importance of Abstractions in Computer Systems 24 1.10 Summary 25 Bibliographic Notes 26
DIY电磁炉核心驱动方案：IR2153PBF自振荡半桥芯片实战指南（附电路图）
本文详细介绍了IR2153PBF自振荡半桥驱动芯片在DIY电磁炉核心驱动方案中的实战应用。该芯片集成了振荡器与600V高压驱动能力，仅需少量外围元件即可生成互补带死区的PWM信号，极大简化了半桥逆变电路设计。文章深入解析了其工作原理、关键参数，并提供了完整的电路设计、PCB布局指南及调试步骤，帮助电子爱好者构建稳定高效的感应加热驱动核心。
【Docker】如何让docker容器正常使用nvidia显卡
描述了如何在docker容器中使用英伟达显卡的过程和问题解决方法。
国标Gb28181里Contact和Route的使用
国标Gb28181是基于Sip协议的，因此对Contact和Route的使用，也可以说是Sip里Contact和Route的使用。Contact和Route则是sip协议里的头字段。 现在，在使用国标Gb28181来做项目时，经常会遇到这种情况，国标的下级是在内网里，上级在公网上。如果不做处理，下级到上级会经过nat协议的转换从而导致上级向下级发送请求时(比如获取目录请求)不能到达
地震勘探原理---地震预处理
3.1 为什么说要对数据进行振幅补偿?具体来说，地震波在传播过程中会遇到各种各样的地质条件，例如不同的岩层、土壤、水层等，这些介质的密度、速度等特性不同，因而对地震波的能量消耗也有所不同。在传播距离较远的情况下，能量损失可能会导致接收到的地震信号振幅变小。相反，在传播距离较近的情况下，能量损失可能较小，导致接收到的地震信号振幅变大。如果不对数据进行振幅补偿，这种振幅不均衡会导致地震数据分析和解释的误差和不确定性增加，可能产生错误的结论。
【科研数据获取】根据Excel表个中的企业/公司信息批量获取经纬度坐标，并进行地图可视化
批量获取企业/公司的经纬度坐标，并进行地图可视化1 需求2 实现途径3 需求逐步实现 1 需求 在做项目的时候，老板或者项目答辩时候甲方最喜欢看到的就是可视化结果，尤其是涉及到地图可视化。很多时候会遇到企业/公司的数据，想要做地图可视化，必不可少的就是要获取它们具体地址：第一种就是像查户口一样精准一下具体的城镇街道，还有一种就是经纬度信息，前一种属于文字内容往往用于管理方面，第二种数字信息多用于数据处理与分析。 2 实现途径 获取地理信息数据，自然需要使用到地图的功能，这里直接采用百度地图的拾取坐标系统，
Python爬虫：如何抓取亚马逊商品评论和评分
通过本文的学习，您已经掌握了如何使用Selenium爬取亚马逊商品的评论数据，并处理反爬虫机制。特别是在亚马逊这样的大型电商平台，商品评论和评分不仅能够反映产品的质量和顾客的体验，还可以对商家进行竞争分析。通过爬取亚马逊的商品评论，您可以获得有价值的市场信息，如产品的优缺点、消费者的反馈等。通过查看源代码，您可以发现，亚马逊的商品评论和评分数据往往通过JavaScript动态加载，这也意味着在抓取时需要使用Selenium或Playwright等工具来模拟浏览器加载。亚马逊的评论页面支持分页加载。
Coze智能体开发：开始使用扣子
扣子全新升级！它不仅能帮你办公、做视频、写代码，更具备自主任务执行、探索进化等能力，真正从“陪你聊”走向“帮你干活”。
Unity零基础到入门 ☀️| 轻松学会 Unity界面布局和简单实例——入门级！(^_−)☆
入门级Unity知识，一起加油呀！
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。
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")
efm32 入门.pdf
本PDF共计22页，介绍的是操作驱动API，下载与否自己斟酌。
最新的sdio 4.0 spec 协议标准 带书签 高清完整PDF版
Part-E1-SDIO-Specification-Ver4.00-Final-120220.pdf：最新的sdio 4.0 spec 协议标准 带书签 高清完整PDF版，包括SDIO 接口的外设，SDIO 接口官方协议等。
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