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Android多媒体框架简介
一 主要内容
1 Android Media
2 mediaCodec
3 mediaCodec 流程
4 NuPlayer
//5 MediaRecorder
//6 MediaRecorder 流程
二 Android Media
2.1 android多媒体框架历史回顾
Android 1.0 ->Packet video - Open core
Android 1.6 ->Open core 2.0
Android 2.0 ->StageFright
Android 2.1 ->StageFright
Android 3.0 ->增强版S
Android13多媒体框架概览
Android 多媒体框架旨在为 Java 服务提供可靠的接口。它是一个系统，包括多媒体应用程序、框架、OpenCore 引擎、音频/视频/输入的硬件设备，输出设备以及一些核心动态库，比如 libmedia、libmediaplayservices 等。媒体服务器创建相应的媒体服务以供多媒体应用程序使用。媒体服务器与 Libmedia 之间的通信构成了客户端-服务器（CS）模型。PV 播放器通过解复用媒体数据流、解码视频/音频数据、同步视频和音频时间，并发送解码后的数据来处理媒体数据流。
Android多媒体架构
Android多媒体框架
Android多媒体——基础框架介绍（一）
Android 多媒体框架为开发者提供了一系列强大的工具和 API，用于处理音频、视频以及图像等多种媒体类型。这个框架允许应用程序轻松地播放、录制和操作媒体内容，并支持多种格式和编码解码器(codec)。
Android平台RMTP直播实现指南
实时消息传输协议（Real-Time Messaging Protocol，RTMP）是一种设计用于在高带宽下实时传输音频、视频和数据的应用层协议。它由Adobe公司开发，广泛应用于流媒体传输，特别是在直播领域。RTMP是基于TCP的协议，支持Flash Player和Adobe AIR平台上的实时通信，支持点播、直播、录制等多种媒体处理功能。
OpenEuler内核编译及替换
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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
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Word多级标题出现黑块的解决思路（word 数字必须介于1-1638之间）
遇到多级目录出现黑块问题，看到一篇博客完美解决，在此记录。
把收款差异从扯皮变成闭环：SAP S/4HANA Dispute Resolution 模块到底解决什么问题？
本文摘要： SAP S/4HANA的争议解决模块（Dispute Resolution）通过标准化流程管理企业应收争议，解决发票与收款差异问题。核心是创建可追踪的Dispute Case工单，明确处理角色（Processor和Manager），并与财务系统联动实现自动清账。系统支持手工/自动创建争议工单，通过动态默认值减少错误，并最终实现争议金额归零的闭环管理。文中给出制造业客户扣款的完整处理案例，并强调组织落地需统一权限与目录。最后提供ABAP代码示例展示如何通过BAPI创建争议工单。（150字）
杰理JL701N V142的SDK实现双备份自定义OTA教程和代码分享
摘要 本文档详细介绍了基于JL701N SDK的UART OTA升级功能开发方案。该方案采用双备份(Dual Bank)机制，通过串口实现安全可靠的固件远程更新。核心特性包括：1) 双备份升级机制确保升级失败可回滚；2) CRC-16/XMODEM校验保障数据完整性；3) 灵活的传输控制，支持自定义波特率和数据包大小。文档提供了完整的系统架构、API接口说明、通信协议规范以及移植指南，适用于生产烧录、现场升级等多种场景。配套Python升级工具简化了升级流程，使开发人员能够快速实现固件更新功能。
手把手教你学Simulink实例--使用 Simulink 进行电力电子系统的小信号模型仿真
电力电子系统（Power Electronics System）包括各种功率转换器（如 DC-DC 转换器、逆变器等）。这些设备中的关键组件（如 MOSFET、IGBT、电容器、电感器等）不仅影响系统的性能，还直接关系到系统的稳定性、效率和可靠性。因此，在设计阶段进行详细的建模和仿真至关重要。小信号模型仿真的目的是通过线性化非线性系统来评估其动态特性，包括增益、相位裕度、带宽等指标。通过本教程，我们介绍了如何使用 Simulink 进行电力电子系统的小信号模型仿真。背景介绍。
7、树莓派故障排查全攻略
本文详细介绍了树莓派在使用过程中常见的各类故障及其排查方法。内容涵盖键盘鼠标诊断、电源诊断、显示诊断、启动诊断、网络诊断以及紧急内核的使用，提供了具体的解决方法和实用建议。同时，文章总结了故障排查流程、常见故障及解决方法的汇总，并给出了注意事项和操作建议，帮助用户高效解决树莓派使用过程中的问题，确保其稳定运行。
霍尼韦尔SoftMaster使用说明
霍尼韦尔SoftMaster使用说明
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
TMS_WebGMaps_1.9.8.0_Full_Source
TMS_WebGMaps_1.9.8.0_Full_Source 全源码 delphi cbuilder
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
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