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作者：EasyWave 时间：2012.12.26
类别：Linux内核- 内核空间映射 声明：转载，请保留链接
注意：如有错误，欢迎指正。这些是我学习的日志文章......
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一：简介
共享内存可以说是最有用的进程间通信方式，也是最快的IPC形式。两个不同进程A、B共享内存的意思是，同一块物理内存被映射到进程A、B各自的进程地址空间。进程A可以即时看到进程B对共享
XTDrone平台FAST-LIO2与ego-planner结合路径规划部署
之前做了XTDrone平台FAST-LIO2的部署现在将其结合ego-planner进行导航规划，之前查看教程是使用视觉进行定位导航的，现在重新学习。修改advanced_param_xtdrone.xml，这里有视觉信息，不修改会报错显示为输入视觉话题信息。怎么描述 ，感觉当时觉得很难的问题突然就给解决了，一顿复杂，最近勤更新吧，继续开始学习。还有一个想法就是两个rviz结合在一起启动，写了一个demo，融合的不太好感觉额。改为只使用雷达点云，这里仿真的话题是/scan。
Mac用户必看：Typora最新版永久激活+弹窗屏蔽保姆级教程（附崩溃解决方案）
本文为Mac用户提供了一份详尽的Typora最新版永久激活与弹窗屏蔽教程。通过修改关键JS文件，实现软件激活状态并彻底关闭成功提示弹窗。文章深入剖析了操作原理，并重点提供了解决由此引发的多文档崩溃问题的实用临时方案，同时客观讨论了该方法的局限性与潜在风险。
从零到一：ArduPilot无人船PID调参实战指南
本文详细介绍了ArduPilot无人船PID调参的实战指南，从基础参数设定到高级调参技术，帮助用户掌握水面环境下的PID控制逻辑。通过案例分析和参数示例，解决无人船在空载、负载变化及不同水域环境中的航行问题，提升无人船的稳定性和精确度。
hpm6750evk2开发板（一）菜鸟入门
hpm6750与我们平时常用的stm单片机在开发环境和开发方法上有很大的区别，所以上手还是需要花一点时间的，拿到板子先不要急着上电调试，我们先找资料学习一下了，免得多走冤枉路，以下是我整理的一些学习资源，包括开发板使用手册、芯片SDK、开发工具、视频资料等，建议先把使用手册和学习视频先看了，再取搭建环境测试。最近刚入手一块hpm6750evk2开发板，花了几天时间学习，算是菜鸟入门了，今天分享一下学习过程。看完资料后，把开发工具安装上，下一篇文章我们分享一下调试工程的过程和遇到的问题。
ESP8266连接阿里云（STM32）
一、概述 之前通过串口调试助手+ESP8266已经连接上阿里云了 ESP8266连接阿里云（串口调试助手） 这次我们使用STM32+ESP8266来接入阿里云，其实本质上就是STM32和ESP8266进行串口通信，相当于使用STM32代替之前使用的串口调试助手来发送AT指令。只是发送的字符串数据可能有些细节需要注意一下。 二、保存连接的AT指令 定义几个字符串数据保存需要发送的AT指令 //注意： //末尾记得加\r\n //字符...
[Dify] 法律文档解析助手怎么做才敢用？先把条款提取、依据核对和人工复核分开
这篇文章围绕用 Dify 搭建法律文档解析助手时最容易被忽略的可靠性问题展开：文档能上传、模型能总结，并不等于结果就能直接用于法务判断。文章会从文档抽取、条款定位、召回片段核对、结构化输出和人工复核几个角度梳理设计思路，适合正在用 Dify 处理合同、制度、招投标文件或企业知识库问答的读者阅读。
am62x A53与R5F通信
AM62x是Ti的一款异构多核芯片。remoteproc驱动，硬编码（写死）了R5F/M4F固件的名字，这样驱动启动时，就会找硬件编码的固件，下面是其对应关系。值得注意的是，linux内核中的M4F remoteproc驱动启动M4F，而linux内核中的R5F remoreteproc驱动不启动R5F,相反，它attach到已经在boot阶段启动的R5F固件上。在/lib/firmware的真实固件名：，而我们生成的固件名如下所示，没有直接对应的，经确认,.out文件就是我们需要的，重命名后即可。
【SCADA】KingSCADA将数据插入SQLServer的方法
1、创建数据库及数据表格；2、配置ODBC数据源。3、通过KingSCADA与数据库建立连接，将数据写入数据库对应表格。
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。
IEM模型，地表粗糙度模拟
利用微波反演土壤水分时候，利用IEM模型可以实现对随机地表的模拟
VC中sqlite的使用
在VC中对sqlite的使用，实现数据库大批数据的快速插入，读取数据库中的数据在ListCtrl中显示
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
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