RS232串口通信的传输格式和接收过程

串口通信是一种常见的数据传输方式,其中RS232是一种常用的串口通信标准。本文将介绍RS232串口通信的传输格式和接收过程,并提供相应的源代码示例。

  1. 传输格式:
    RS232串口通信使用一种异步传输方式,数据以字节为单位进行传输。每个字节由起始位、数据位、校验位和停止位组成。具体的传输格式如下:
  • 起始位(Start Bit):逻辑低电平(0),表示数据传输的开始。
  • 数据位(Data Bits):根据通信需求,可以选择使用5、6、7或8个数据位。
  • 校验位(Parity Bit):可选项,用于校验数据的正确性。常见的校验方式有奇校验、偶校验和无校验。
  • 停止位(Stop Bit):逻辑高电平(1),表示数据传输的结束。
  1. 接收过程:
    接收端需要按照相同的传输格式来接收数据。下面是一个简单的接收程序示例:
import serial

# 打开串口
ser = serial.Serial('COM1'
如何理解MDB-RS232适配器数据通信格式协议解析 如何理解MDB-RS232适配器数据通信格式解析是我们使用MDB-RS232时候经常遇到的困惑。MDB-RS232(MDB232)与MDB接口协议的纸币器,硬币器,刷卡器通信,因为一主多从MDB支付系统的设计结构,因此协议要进行一些优化和便于理解。 阅读详情

相关推荐

RS232通信简介及实例

RS232是一种串行通信协议,常用于连接计算机和外部设备。在这篇文章中,我们将详细介绍RS232通信的基本原理、步骤以及一个实例。

理论都是虚的,代码才是王道。 4717

RS232相关内容

什么是RS-232? ------------------------------------------     RS-232(ANSI/EIA-232标准)是IBM-PC及其兼容机上的串行连接标准。可用于许多用途,比如连接鼠标、打印机或者Modem,同时也可以接工业仪器仪表。用于驱动和连线的改进,实际应用中RS-232的传输长度或者速度常常超过标准的值。RS-232只限于PC串口和设备间点

lovelove5的专栏 836

10行代码搞定QMT实盘交易:手把手教你用Python自动化买卖股票(附完整源代码)

本文提供了一份详尽的QMT实盘交易教程,手把手教你使用Python构建自动化交易系统。内容涵盖环境配置、账户连接、查询与下单等核心代码,并附有完整的源代码示例,帮助开发者快速掌握从零到实盘的完整流程,实现安全可靠的股票自动化买卖。

weixin_29214335的博客 440

保姆级教程:2026版Windows电脑换新,微信QQ聊天记录无损搬家(附路径修改避坑指南)

本文提供2026版Windows电脑换新时微信QQ聊天记录无损迁移的保姆级教程,涵盖最新版微信/QQ的存储路径修改和避坑指南。通过详细步骤和实战技巧,确保数据迁移过程中不丢失任何珍贵对话记录和文件,特别适合需要保留完整聊天记录的用户。

weixin_42629815的博客 2484

从真题到策略:浙江工业大学计算机转专业机试全解析与备考指南

本文全面解析浙江工业大学计算机转专业机试的考试概况、典型真题和高频考点,提供详细的备考策略和面试准备建议。从机试题目类型到解题技巧,帮助考生高效备考,顺利通过这一竞争激烈的转专业关卡。

weixin_42524824的博客 422

第12章 STM32+BH1750光照传感器+OLED模块显示环境光照强度

相比光敏传感器,它可以直接输出环境光照强度的数值(范围是0~65535lx),非常适合懒人党,各位的传感器买到手后,赶紧跟着文章试试吧!

qq_44645742的博客 1万+

【即插即用模块】Transformer篇 | TCSVT 2025 | DAFM:双注意力聚合+反向跨模态筛选,特征精准捕捉并融合!

本文提出了一种双模态注意力融合模块(DAFM),用于增强多模态图像融合任务中的特征交互。该模块通过双注意力聚合机制,结合自注意力与跨模态注意力,有效提升特征融合效果。DAFM采用可学习平衡因子动态调整注意力权重,并运用深度卷积降低计算复杂度。实验结果表明,该模块能显著提升多模态图像融合质量,适用于RGB-红外等双模态视觉任务。模块代码实现简洁,可直接集成到现有网络中作为特征融合层使用。

主要更新底层视觉(去噪、超分等)相关的科研内容,形式为【论文精读】+【论文复现】 176

【医学图像处理】X-ray成像之X射线的产生

【医学图像处理】X-ray成像之X射线的产生 概述 电流流过钨丝,并将其加热。 随着加热,增加的能量使电子能够通过热电子发射从灯丝中释放出来。 电子被吸引到带正电的阳极,并以由管电位(电压)确定的最大能量撞击钨靶。 当电子轰击目标时,它们通过轫致辐射和特有的相互作用将能量转换为热量(99%)和X射线光子(1%)。 X射线光子以一束能量(X射线光谱)从光束中释放出来,构成X射线图像形成的基础。 设备 阴极 灯丝 用细的(0.2毫米)钨丝制成,因为钨: 原子序数高(A 184,Z 74) 是良好的热

ZhangPY的专栏 2358

华为OD机试双机位C卷-水库溃坝填补(Py/Java/C/C++/Js/Go)

华为OD机试(机考)双机位C卷 【水库溃坝填补】,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。水库溃坝填补。

qq_45776114的博客 2109

C#上位机实战:PLC与检测设备无缝对接,构建产品质量实时判定系统

本文分享了基于C#开发的汽车零部件质量检测系统实现经验。系统采用三层架构:设备层(PLC/视觉相机/三坐标)、通信层(OPC UA/SDK/串口)和应用层(数据采集/判定/存储)。重点解决了三大技术难点:1)通过OPC UA订阅模式将PLC通信延迟从500ms降至50ms;2)反编译厂商Demo破解视觉SDK对接难题;3)采用时间戳+产品ID实现多设备数据同步。系统上线后质检人员减少67%,误判率降至0.5%,数据追溯效率显著提升。文章强调工业场景下稳定性优先于技术复杂度,实用经验值得借鉴。

这里只有干货:从 Modbus 通信到 MES 对接,从 YOLO 训练到工控机部署,带你搞定工业软件开发全流程。 799

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');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

汉书HE8,好用的汇编代码编写工具

汉书HE8,好用的汇编代码编写工具早期编写汇编代码所用功能强大

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。

上一篇: 设备间子系统的知识学习:信息与通信
下一篇: Android/iOS手机通过音频接口与外设通信
普通网友
博客等级 码龄3年 53粉丝 0原创
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符  | 博主筛选后可见
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值