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优点：
异步处理、削峰填谷、服务解耦
缺点：
延迟问题、复杂度、数据不一致
rabbitmq（Exchange +队列）
同一份消息如果需要被多个消费者来消费，需要配置 Exchange 将消息发送到多个队列，
每个队列中都存放一份完整的消息数据，可以为一个消费者提供消费服务。
RocketMQ 为每个消费组在每个队列上维护一个消费位置
rocketmq（主题+队列）
kafka（主题+分区）
kafka的分区就是rocket的队列
Topic可以被分成若干分区分布于kafka集群中，方便扩容
5. Pulsar
在 Pulsar 的 Broker 中既不保存元数据，也不存储消息，存储计算分离
mq主要是io密集型的系统，io包含磁盘io和网络io，这里主要是网络io
- 异步
- 高性能异步网络传输（netty，nio）
传统的同步网络 IO，一般采用的都是一个线程对应一个 Channel 接收数据，很难支持高并发和高吞吐量- 如何通过网络传输结构化的数据（序列化与反序列化）
JSON、
北斗B1频点信号捕获实战：如何应对NH码跳变带来的挑战？
本文深入探讨了北斗B1频点信号捕获中NH码跳变带来的核心挑战。针对NH码以1kHz速率调制导致的相干积分中断和相关峰畸变问题，文章提出并详细解析了一种基于伪码与多普勒频率分离技术的优化捕获方案。该方案通过差分处理消除NH跳变影响，并利用匹配滤波器与FFT分别进行码相位和多普勒频率的快速估计，有效提升了弱信号环境下的捕获灵敏度和稳定性，为北斗接收机设计提供了实用的工程参考。
Claude Code 接入 DeepSeek-V4 教程
本文介绍Claude Code的安装配置流程：1) 安装Node.js 18+和Git（Windows）；2) 通过npm全局安装@anthropic-ai/claude-code；3) 配置DeepSeek API环境变量（需替换API Key）；4) 启动Claude Code或配置VSCode插件。重点包括Linux/macOS和Windows的环境变量设置方法，以及VSCode插件的settings.json配置项。完成配置后即可在终端或VSCode中使用Claude Code功能。
Java Web 图书管理系统的制作指南
实现对图书信息的增删改查（CRUD）操作。支持用户登录与权限管理。图书借阅与归还功能。系统管理员可以管理用户信息和图书库存。
计算机视觉-深度学习图像检测方法梳理
计算机视觉-深度学习图像检测方法梳理 由于之后要转方向啦，趁这段时间整理手中硕士研究方向的一些阅读笔记，这是一篇关于计算机视觉的基础知识梳理 先搞清一些小知识点 首先我们要弄清楚图像分类、目标定位、语义分割、实例分割的区别 a. 图像分类 ：给定一张输入图像，图像分类任务旨在判断该图像所属类别； b. 目标定位 :在图像分类的基础上，我们还想知道图像中的目标具体在图像的什么位置，通常是以包围盒的(bounding box)形式； c. 语义分割 ：语义分割是目标检测更进阶的任务，目标检测只需要框出每个目
Qt Android MySql 数据库驱动编译（详细图解步骤）
第一步.先介绍下环境搭建： Qt 版本 5.12.11 下载链接 https://download.qt.io/archive/qt/5.12/5.12.11/qt-opensource-linux-x64-5.12.11.run Ubuntu 18.04.6 LTS 下载链接 JDK版本 1.8.0_191 下载链接https://www.oracle.com/java/technologies/downloads/ Android Studio版本 ...
基于深度学习的水果新鲜程度检测系统（UI界面 + YOLOv8/v7/v6/v5代码 + 训练数据集）
本文详细介绍了如何使用YOLO系列模型（YOLOv5、YOLOv6、YOLOv7、YOLOv8）构建水果新鲜度检测系统。通过数据集准备、模型训练、推理过程以及UI界面的实现，我们能够构建一个实时检测水果新鲜度的系统。通过本博客，您可以全面了解如何应用深度学习技术解决实际问题，尤其是在智能监控和食品质量检测领域。
化工管道设计软件：CAESAR II二次开发_（6）.API接口与函数库
自定义函数库是开发者根据特定需求编写的函数集合。这些函数可以扩展CAESAR II的功能，实现更复杂的管道设计任务。自定义函数库通常需要开发者具备一定的编程基础，能够理解和编写与CAESAR II接口兼容的代码。定义函数：在Python或其他编程语言中定义所需的函数。注册函数：将自定义函数注册到CAESAR II的函数库中。调用函数：在CAESAR II中调用自定义函数，实现特定功能。假设我们需要创建一个自定义函数库，用于批量修改管道节点的坐标。定义函数：在Python中定义一个函数，用于批量修改节点坐标。
2023年下半年网络工程师案例分析真题答案解析
R1] ip route-static 0.0.0.0 0.0.0.0 GigabitEthemet2/0/1 (3) track (4) R1 to R2 //配置R1的缺省路由，联动BFD使得R1到2的流量优先走R1->S1->R2链路，当此链路发生故障时，流量切换到R1->R3->R2链路上。当同一VLAN内也需要网络隔离时，我们可采用交换机的(2)功能，若在配置了二层隔离后，部分主机有互通的需求，则可在需求主机的网关上配置(3)功能。不需要，MAC地址是数据链路层封装以太帧的地址标识。
Camera2Demo
在Camera2 的基础上进行封装，使得Camera2一堆初始化逻辑与Activity分离，降低耦合度，完成预览拍照并保存的操作
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")
老版QQ原生表情gif动态
这是老版的QQ表情，不过全是动态的的，Windows照片查看器是不会动的，可以右击用浏览器打开查看，再说一遍是老版的，只是分享给宁愿用老版也要动态的人。
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。
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