墨衍会员
·
AI 创作全网分发
墨衍智能分发
本文由作者通过墨衍一键同步至各平台
1分发平台
200+累计阅读
244平均单平台
已同步平台
优快云
微信公众号
微博
知乎
掘金
百家号
博客园
抖音
小红书
你的文章也可以这样分发
墨衍会员 ¥399起/年，写一次发全网
我也要
引言：
在当今数字化时代，音视频传输技术的发展对于实现高质量、实时的多媒体内容传输至关重要。选择适当的音视频传输技术方案是确保有效传输和优化用户体验的关键。本文将探讨几种常见的音视频传输技术，并提供相应的源代码示例，帮助您在实际应用中做出明智的选择。
一、WebRTC技术
WebRTC（Web Real-Time Communication）是一种支持实时音视频通信的开源项目。它利用Web浏览器提供的API，实现了点对点的音视频传输。WebRTC具有低延迟、高质量的特点，适用于实时通信应用，如视频会议、实时游戏等。
以下是使用WebRTC进行音视频传输的简单示例代码：
// 创建本地媒体流
navigator.mediaDevices.getUserMedia({
video: true一篇NS SAR ADC的学习记录
一篇NS SAR ADC的学习记录 内容包含： OverSampling Noise Shaping Dynamic Amplifier DAC with Redundancy 最近看了些有意思的SAR ADC结构，但是因为确实对模拟这个领域不太熟，导致了一系列的级联查找，最终达到了一个大概能懂的状态，为了理清思路，顺便别让看了这么些天的东西浪费了，还是计划写篇文章。 ps：由于不是一篇特别正紧的报告，参考文献就写的比较随便，且我还是个做数字的 A 13.5-ENOB, 107-μW Noise-Sha
WSL2网络配置全攻略：如何让Windows和Linux子系统互相访问端口（附防火墙设置）
本文深入解析WSL2的网络架构，并提供详细的端口互通配置指南。针对WSL2与Windows宿主机之间常见的网络访问问题，文章从原理出发，逐步讲解如何识别网络环境、配置Windows防火墙规则、调整服务监听地址，并介绍镜像网络模式等进阶优化方案，最终实现稳定可靠的双向端口访问。
云计算系统等保测评对象和指标选取
参考GBT22239-2019《基本要求》附录D 云计算应用场景说明。简要理解下图，主要是云计算系统安全保护责任分担原则和云服务模式适用性原则，指导后续的测评对象和指标选取。
SY6982C 7.4V锂电池1A充电模块原理图设计，已量产
在手持设备、便携工具与小型无人机等场景中，7.4V（2S）锂聚合物电池的应用愈发广泛。这类电池对充电系统的安全性、效率与兼容性提出了明确要求：需支持 USB 5V 输入、1A 安全充电，同时具备多重保护机制。本文将结合一款基于 SY6982C 的充电原理图，深入解析其电路设计与技术亮点。
3、提升管壳式换热器能效：双螺旋盘管的应用研究
本研究探讨了在管壳式热交换器中应用双螺旋盘管以提升能效的可行性。通过实验对比双螺旋与单螺旋盘管的热传递效率，结果显示双螺旋盘管达到54.33%的效率，显著优于单螺旋的48.88%。双螺旋结构不仅增大了换热面积，还因产生剪切应力而减少结垢，延长运行周期。采用NTU方法评估热传递性能，验证了该设计在工业应用中的高效性与实用性，建议在制冷、空调、石油精炼等领域推广使用。
Claude Code 完整详解（Anthropic 官方 AI 编程 Agent）
它是运行在终端（Terminal）的 AI 编程代理，不是普通 IDE 插件；你用自然语言发指令，它自主规划步骤、读写项目文件、执行系统命令、排查 Bug、处理 Git 工作流，是能真正动手干活的 AI 程序员。真正的AI 开发代理人，不止写代码，还能执行开发全流程超长全局上下文，大型项目理解碾压传统补全工具终端原生 + IDE 双接入，开发流程无割裂代码质量、可读性、工程规范性普遍偏高权限安全机制完善，修改前预览、可随时中断回滚。
使用Transformer实现自动调制识别（RML2018）
image_size = 1024 # resize重置输入大小patch_size = 16 # 从输入图片中将被提取的块大小# 分成 num_patches 个块num_patches = (image_size // patch_size) #(72/6)**2 横着裁剪),],return x})
33、嵌入式系统中的内存操作与实时操作系统
本博客详细探讨了嵌入式系统中的内存操作和实时操作系统（RTOS）的基本原理及应用。内容涵盖C、C++和MicroPython三种语言下的SDRAM读写示例，并结合机器人真空吸尘器的实际应用说明内存的使用场景。此外，还介绍了RTOS的核心概念、组件及其工作机制，包括任务、事件、互斥锁、信号量、队列等，并以FreeRTOS和Mbed OS为例展示了RTOS的配置与使用方法。最后，通过常见问题和实践项目加深对嵌入式系统开发的理解。
数学建模模型总结
这是本人在两年内准备参加数学建模竞赛搜集到的模型资料，已经经过了本人的筛选和整理，希望能够对准备参加数学建模竞赛的学弟学妹们提供一些帮助！
STM32F407中文手册
STM32f407中文手册 本参考手册面向应用开发人员，提供有关使用 STM32F405xx/07xx、STM32F415xx/17xx、 STM32F42xxx 和 STM32F43xxx 微控制器存储器与外设的完整信息。STM32F405xx/07xx、STM32F415xx/17xx、STM32F42xxx 和 STM32F43xxx 构成一个微 控制器系列，各产品具有不同的存储器大小、封装和外设。有关订购信息以及器件的机械与电气特性，请参见数据手册。有关 ARM Cortex™-M4F 内核的信息，请参见《Cortex™-M4F 技术参考手册》。
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")
visio安装包
Microsoft Office Visio 是一款专业的制图软件。利用经过更新的形状、协作工具和数据链接图表，创建专业级图表以简化复杂的信息。
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
订阅专栏 解锁全文
8712
扫一扫
