《使用SpringBoot+jSerialComm实现Java串口通信详解》 在现代软件开发中，串口通信作为一种基础的硬件交互方式，依然广泛应用于各种设备的数据交换。本文将详细讲解如何使用SpringBoot框架配合jSerialComm库，实现Java串口通信功能，并在Windows和Linux操作系统上进行读写操作。 一、SpringBoot简介 SpringBoot是由Pivotal团队提供的全新框架，旨在简化Spring应用的初始搭建以及开发过程。它集成了大量常用的Java企业级应用功能，如数据源、JPA、WebSocket等，极大地提高了开发效率。 二、jSerialComm介绍 jSerialComm是一款轻量级的Java串口通信库，它提供了简单易用的API，支持在Java中轻松地进行串口读写操作。无需额外的驱动程序或库文件，jSerialComm在多种操作系统环境下都能运行，包括Windows和Linux。 三、配置SpringBoot项目 1. 创建SpringBoot项目：我们需要创建一个SpringBoot项目，可以使用Spring Initializr在线生成，或者通过IDEA等工具直接创建。 2. 引入jSerialComm依赖：在`pom.xml`文件中添加jSerialComm的Maven依赖： ```xml com.fazecast jserialcomm 2.0.5 ``` 确保版本号与标题匹配。 四、实现串口通信 1. 创建串口服务类：在SpringBoot项目中，我们可以创建一个名为`SerialPortService`的类，该类将负责处理串口的打开、关闭、读写等操作。需要注入`SerialPort`对象，以便调用其提供的方法。 2. 打开串口：使用`SerialPort.getCommPorts()`获取可用的串口列表，选择需要的串口，然后调用`openPort()`方法打开。 3. 配置串口参数：通过`setBaudRate()`, `setParity()`, `setDataBits()`, `setStopBits()`等方法设置串口参数，如波特率、校验位、数据位和停止位。 4. 读写操作：使用`writeByte()`, `writeBytes()`, `readByte()`, `readBytes()`等方法进行串口的读写操作。 5. 监听串口事件：可以注册事件监听器，通过`addSerialPortEventListener()`方法监听串口的打开、关闭、数据接收等事件。 6. 关闭串口：完成串口通信后，记得调用`closePort()`关闭串口，释放资源。 五、跨平台兼容性 由于jSerialComm库的跨平台特性，同样的代码在Windows和Linux系统下都能正常工作。只需要注意不同系统下的串口号可能会有所不同，Windows下通常为"COM1", "COM2"等，而Linux下可能是"/dev/ttyS0", "/dev/ttyUSB0"等。 六、实际应用示例 在实际应用中，例如工业自动化、物联网设备监控等场景，我们可以利用SpringBoot的定时任务功能，定期从串口读取数据并进行处理，或者根据接收到的命令控制硬件设备。通过编写控制器接口，还可以将串口通信集成到Web应用中，实现远程监控和控制。 总结，结合SpringBoot和jSerialComm，我们可以构建一个高效、稳定的Java串口通信应用，无论是在服务器后台还是Web前端，都能灵活地实现串口数据的读写和管理。同时，由于其跨平台特性，使得这种解决方案具有广泛的适用性。

React PDF表示例 经过测试： Firefox 86.0（Ubuntu）和Chrome版本89.0.4389.82（64位）（Ubuntu） 让我知道你们是否喜欢 :victory_hand: 为什么这个仓库甚至存在？ 我见过无处不在的人问你该如何在react-pdf中建立表格。 可悲的事实是，图书馆中仍然没有为您提供帮助的官方组件。 但是，这并不意味着这是不可能的，实际上，这甚至还不难，您只需要在一小部分CSS上变得肮脏即可。 在此存储库中，我构建了一个简单的示例，该示例说明如何使用，和一些忍者CSS轻松模拟表的外观。 怎么跑 克隆存储库 在终端中，转到存储库文件夹： cd react-pdf-table-example 使用yarn或npm安装依赖项，无论哪种方式： yarn install或npm install 使用以下命令启动项目： yarn start或npm

keithley2002 labview example,非常实用.

CityEngine-Philadelphia_example案例操作详解

FullCalendar示例项目 一组简单的示例项目，展示了如何在各种构建设置中使用FullCalendar。 请阅读每个项目子目录中的自述文件。

从swfupload官网下载 MIT协议 支持 flashplayer8 到 10

Python Machine Learning By Example by Yuxi (Hayden) Liu English | 31 May 2017 | ASIN: B01MT7ATL5 | 254 Pages | AZW3 | 3.86 MB Key Features Learn the fundamentals of machine learning and build your own intelligent applications Master the art of building your own machine learning systems with this example-based practical guide Work with important classification and regression algorithms and other machine learning techniques Book Description Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal. What you will learn Exploit the power of Python to handle data extraction, manipulation, and exploration techniques Use Python to visualize data spread across multiple dimensions and extract useful features Dive deep into the world of analytics to predict situations correctly Implement machine learning classification and regression algorithms from scratch in Python Be amazed to see the algorithms in action Evaluate the performance of a machine learning model and optimize it Solve interesting real-world problems using machine learning and Python as the journey unfolds About the Author Yuxi (Hayden) Liu is currently a data scientist working on messaging app optimization at a multinational online media corporation in Toronto, Canada. He is focusing on social graph mining, social personalization, user demographics and interests prediction, spam detection, and recommendation systems. He has worked for a few years as a data scientist at several programmatic advertising companies, where he applied his machine learning expertise in ad optimization, click-through rate and conversion rate prediction, and click fraud detection. Yuxi earned his degree from the University of Toronto, and published five IEEE transactions and conference papers during his master's research. He finds it enjoyable to crawl data from websites and derive valuable insights. He is also an investment enthusiast. Table of Contents Getting Started with Python and Machine Learning Exploring the 20 newsgroups data set Spam email detection with Naive Bayes News topic classification with Support Vector Machine Click-through prediction with tree-based algorithms Click-through rate prediction with logistic regression Stock prices prediction with regression algorithms Best practices

感觉比rust编程指南之类循规蹈矩的书籍更容易上手。 毕竟呆头呆脑地学几天语法的成本太高了。 建议直接读所需要的项目的代码，不懂的语法直接查这本书。

几个用java写的小程序，实现了bio和nio

The Python Standard Library by Example --Doug Hellmann 那本的源代码哟，欢迎下载！