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
9.4k累计阅读
9.4k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
AI摘要
本文介绍了WCDMA中比特、符号、码片的概念，经过信源编码的数据为比特，信道编码和交织后为符号，最终扩频得到码片。还阐述了符号速率、码片速率的计算公式，以及不同调制方式下符号速率与比特速率的关系，如BPSK调制时bit rate=symbol rate等。
比特（Bit），符号（Symbol） ，码片（Chip）
经过信源编码的含有信息的数据称为“比特”
经过信道编码和交织后的数据称为“符号”
经过最终扩频得到的数据称为“码片”
符号速率＝（业务速率＋校验码）×信道编码×重复或打孔率
码片速率＝符号速率×扩频因子. 码片速率＝symbol rate×SF是正确的。symbol rate和bit rate的对应关系要看调制方式了.如果是BPSK调制，那么1bit可以代表0，1两种信息，此时bit rate=symbol rate。如果是QPSK调制，星座图中的4个信息就需要2bit来表示，此时bit rate=2 symbol rate.同理HSDP
LoRa 速率.pdf
LORA/LORAWAN 官方培训手册，说明了数据速率、码片速率、符号速率，以及码片持续时间和符号持续时间；
西安电子科技大学841学长经验分享（总成绩第三名）
西安电子科技大学841学长经验分享（总成绩第三名）
数字电路基础（CMOS电路、低功耗方法）
文章目录一、CMOS器件1.1.CMOS反相器1.2. CMOS与非门1.3.CMOS或非门1.4 CMOS与门和CMOS或门二、CMOS电路参数——功耗 一、CMOS器件 1.1.CMOS反相器 CMOS反相器实现非门功能，由一个PMOS管和一个NMOS管组成,其逻辑电路图如下： 其真值表如下： In PMOS NMOS Out 0 √ 导通 ×关闭 1 1 ×关闭 √...
单片机多级菜单的简单实现
单片机多级菜单的简单实现 多级菜单的思路 最近想把stm32上的oled显示屏做的好看一些，于是动手写了写一个多级菜单，思路很简单，每个页面用单独的函数封装好，每个功能也是用单独的函数进行封装，用了3个按键，两个切换键，一个确定键。 首先来看看用啥判断页面的（这偷个懒，希望没人打我） //页面标志位 uint8_t s1 = 0; //主页面功能号 uint8_t s2 = 0; //主次页面判断标志位 uint16_t s3 = 1; //次页面功能号 主页面的显示函数，这是个很枯燥的一
基于Qt与PaddleOCR的跨平台OCR工具开发实战
本文详细介绍了基于Qt与PaddleOCR开发跨平台OCR工具的实战经验。通过Qt的跨平台特性和PaddleOCR的高效识别能力，开发者可以快速构建支持Windows、Linux和macOS的OCR工具。文章涵盖了环境配置、核心功能实现、性能优化及打包发布等关键环节，特别针对中文识别、内存管理和多线程处理等常见问题提供了解决方案。
Xilinx VU+系列多DIE FPGA时序约束与跨DIE信号优化策略
本文深入探讨了Xilinx VU+系列多DIE FPGA的时序约束与跨DIE信号优化策略。针对2.5D/3D封装技术带来的时序挑战，详细解析了SLR区域约束、Laguna寄存器配置及跨时钟域约束等关键技术，提供了实用的TCL脚本和RTL优化示例，帮助开发者有效提升设计性能和时序收敛效率。
FPGA实战：给你的UART串口通信加上Verilog奇偶校验位（Modelsim仿真验证）
本文详细介绍了如何在FPGA中实现UART串口通信的Verilog奇偶校验功能，包括发送端校验位生成和接收端校验检测的设计与实现。通过Modelsim仿真验证，确保数据传输的可靠性，适用于嵌入式系统和数字通信领域。
openclaw通俗入门系列4——智能体工作区
工作区 = 智能体的家，存放它的性格、记忆、技能。默认不是牢笼（可以用沙箱隔离）。建议放到私有 Git 备份，但绝对不要提交密钥！
第14.1章 高翔博士2025年11月最新开源框架Lightning-LM源码学习解读（一）：激光-IMU定位+建图算法各模块简介
Lightning-LM：高效激光SLAM定位系统 该框架基于模块化设计，通过激光-IMU融合实现快速建图与定位，核心特点包括： 极速性能：纯CPU运行（建图1.2核/定位0.8核） 模块化架构：包含激光里程计（LIO）、地图管理、回环检测等独立模块 动态场景支持：地图分块加载、动静态图层分离 多模式适配：支持ROS2环境下的在线/离线运行 关键模块包括前端里程计（core/lio）、2D/3D地图转换（core/g2p5）、轻量优化库（core/miao）等，通过系统控制器（core/system）协调流
锂电池充电器电路设计 里面有电路
锂电池充电器电路设计 里面有电路 3.7V升压到5V电路设计。
pscad IEEE标准系统搭建说明
pscad IEEE标准系统搭建说明，包含典型的IEEE 9节点、14节点、30节点、39节点和118节点系统。内含标准测试系统的原始数据和潮流结果。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
1083
扫一扫
