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翻译文章，翻译的有点渣，随便看看。
www.ieee802.org/linksec/meetings/Sep02/Gaglianello_sec_1_0902.pdf
MPCP指定连接到一个点对多点（P2MP）PON段的OLT与ONU之间的机制，以允许上行方向数据的有效传输。
执行的功能有：
5个新MAC控制消息：
EPON与GPON选型实战指南：光纤接入网技术匹配逻辑
无源光网络（PON）是千兆宽带落地的核心承载技术，其本质是通过分光器实现点到多点的光信号分配，依赖时间或波长复用机制完成上下行隔离。EPON与GPON作为当前主流标准，分别基于以太网帧和GEM封装，差异不仅体现在标称速率（1.25G vs 2.5G/1.25G），更深层在于MAC控制协议（MPCP）与T-CONT+DBA调度机制带来的QoS保障能力、业务隔离性及终端适配灵活性。在智慧家庭、政企专线、智慧城市等场景中，技术选型成败取决于对硬实时性、终端异构性、现网兼容性与演进路径的综合判断，而非单纯追求参数先
MPCP协议详解--带注释(EPON关键协议).doc
MPCP定义了5种消息，称之为MPCP DATA UNITs， GATE、REPORT、REGISTER_RQ、REGISTER、REGISTER_ACK，他们用于OLT和ONU之间的信息交换。所有的MPCPDU为64字节的MAC控制帧，包括以下域
EPON基础培训 原理 MPCP 协议 应用
EPON基础培训 EPON ONU OLT EPON的由来 EPON的标准化历程 EPON的核心技术 EPON系统构成 EPON基本原理 EPON帧结构 MPCP协议 EPON技术实现与应用
mpcp协议详解
不错的mpcp协议文档哦，全中文，深入理解mpcp管理过程
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中国电信EPON开发必备的总结查询文档
MPCP协议介绍
MPCP定义了5种消息，称之为MPCP DATA UNITs， GATE、REPORT、REGISTER_RQ、REGISTER、REGISTER_ACK，他们用于OLT和ONU之间的信息交换。所有的MPCPDU为64字节的MAC控制帧，包括以下域： DA（目的地址），6个字节域，所有的MPCPDU都使用一个共同的多播MAC目的地址，其值为：01-80-C2-00-00-01，但是REGISTER报文除外，其使用ONU的实际MAC地址。
思必驰离线语音芯片：智能家居控制的核心引擎
思必驰离线语音芯片TH1520通过本地化语音识别技术，为智能家居带来革命性体验。该芯片具备200-600毫秒的快速响应、75分贝抗噪能力和双核异构设计，显著提升设备交互效率。支持自定义指令和10种音色选择，开发周期缩短至3天，BOM成本降低30%，是智能家居控制的核心引擎。
全网最详细 解决戴维南定理的详细推导
替代定理，戴维南定理详细证明
OpenPnP/Arduino - 二手西门子电动飞达的通讯协议解析与实战
本文详细解析了二手西门子电动飞达的通讯协议，并通过Arduino控制板实现与开源贴片平台OpenPnP的集成。文章涵盖了硬件接口定义、协议帧结构分析、Arduino控制实战、OpenPnP集成技巧以及二手飞达翻新指南，为开发者提供了全面的技术指导和实战经验。
【stata基础代码大全 】 可独立完成一篇实证
用stata打开，直接替换自己需要的变量就行，小白友好❗️❗️❗️。八、八.中介效应检验：路径分析（三种方法）第二种方法即Sobel-Goodman检验。*（7）Winsor处理，剔除异常值。六、内生性问题解决方法（附具体模型）其三，工具变量：两阶段最小二乘。第三种方法即Bootstrap检验。二、Stata中数据的基本处理。*(5)生成行业、年度虚拟变量。PSM1————近邻匹配。七、调节效应检验：机制分析。*(2)剔除数据中的样本。*(4)给变量添加标签。*(3)生成新的变量。*(6)基本数据匹配。
2025高教社数学建模国赛A题 - 烟幕干扰弹的投放策略（完整参考论文）
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华为OD新系统机试真题-勇攀数字高峰(Java/Py/C/C++/Js/Go)
新系统 华为OD机试新系统【勇登数字高峰】，提供C语言、Java、C++、Python、Go、JavaScript源码实现，并提供完整思路讲解，以及在线OJ模拟做题。华为OD新系统机试真题【勇登数字高峰】
2023推免（夏令营+套实验室）面试总结（博文附问题汇总和参考答案）
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Power domain 划分该如何下手
假如PD1比PD3更always on的话，我们只需要插入普通buffer即可，因为PD3 on，PD1也肯定是ON，PD1 off，PD3肯定时off，PD1能cover PD3的电源情况，因此使用普通buffer也可，但是处于省电角度，我们还是使用AO buffer；在我们做多power domain时，在切分power domain时，需要谨慎，稍有不慎，就会出现congestion以及timing问题，那问题来了，我们在切分power domain时有什么考究么，今天小编就和大家一起讨论讨论。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
Statistics and Machine Learning Toolbox
Statistics and Machine Learning Toolbox（带书签），MATLAB统计学习工具
SIMATIC S7-1200, S7-1500 PID 控制[手册]
SIMATIC S7-1200, S7-1500 PID 控制[手册]
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。
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