A New Language for Phone Networks

Scientists love the cell phone. Researchers have used the ubiquitous device as a portable polling station, a tracking device, and a sensor. Now, computer scientists want to use mobile phones to exchange data without using the phone's network, instead of communicating directly with cellular towers, base stations, and the occasional wireless network.

Node clusters: The topology of pocket-switched networks changes quickly over time as humans move about, causing nodes to leave and join local networks. Researchers group devices by community, clusters that share the same information, and the average delay to receive information. Nodes with the same color receive information after a similar delay.
Credit: University of Cambridge

That's the vision of a group of computer scientists who believe that spreading data virally could open up a whole new manner of applications on peer-to-peer mobile device networks, known more formally as "pocket-switched networks." Such an ad hoc network--sort of a Sneakernet on steroids--could allow victims of a natural disaster to pass messages from one person to another even if the cell towers are destroyed. In another scenario, visitors to specific locations could have important information forwarded to them via the local folks' devices. And groups of friends could poll each other on where to eat dinner that night, without using the Internet.

"If this gets traction, we hope that people come up with a whole slew of applications," says Jon Crowcroft, professor of computer science at the University of Cambridge and the leader of a team of researchers working on the problem.

Technologies such as pocket-switched networks are a form of delay-tolerant networking, such as the Interplanetary Internet. Delay-torrent networks are part of a class of infrastructure that includes any collection of occasionally connected nodes that could be disconnected from the network for a long time and forward messages opportunistically.

Pocket-switched networks typically consist of a sparse collection of devices that are disconnected much of the time and are, of course, mobile. Communications are accomplished through Bluetooth or wireless connections between devices using a publish-and-subscribe technique dependent on the content preferences of the device's owner.

"It is an infrastructure-less approach," says Kevin Fall, a principal engineer at Intel Research Berkeley and an expert on delay-tolerant networking. "You don't need base stations, you don't need cell towers, you just have to carry around a device that can connect to other devices."

Yet, what the technology does not have is simplicity. Crowcroft and his team from the University of Cambridge hope to solve that problem. Last week, the research group unveiled a programming language designed to make developing complex programs far simpler. The language, known as the Data-Driven Declarative Networking (D3N) language, allows simple programs to take advantage of inherent characteristics of pocket-switched networks, including asynchronous communications and simple-to-express queries. The language is declarative, allowing the programmer to focus on the application logic instead of the algorithms specific to pocket-switched networks.

【电能质量扰动】基于ML和DWT的电能质量扰动分类方法研究(Matlab实现)内容概要:本文研究了一种基于机器学习(ML)和离散小波变换(DWT)的电能质量扰动分类方法,并提供了Matlab实现方案。首先利用DWT对电能质量信号进行多尺度分解,提取信号的时频域特征,有效捕捉电压暂降、暂升、中断、谐波、闪变等常见扰动的关键信息;随后结合机器学习分类器(如SVM、BP神经网络等)对提取的特征进行训练与分类,实现对不同类型扰动的自动识别与准确区分。该方法充分发挥DWT在信号去噪与特征提取方面的优势,结合ML强大的模式识别能力,提升了分类精度与鲁棒性,具有较强的实用价值。; 适合人群:电气工程、自动化、电力系统及其自动化等相关专业的研究生、科研人员及从事电能质量监测与分析的工程技术人员;具备一定的信号处理基础和Matlab编程能力者更佳。; 使用场景及目标:①应用于智能电网中的电能质量在线监测系统,实现扰动类型的自动识别;②作为高校或科研机构在信号处理、模式识别、电力系统分析等课程的教学案例或科研实验平台;③目标是提高电能质量扰动分类的准确性与效率,为后续的电能治理与设备保护提供决策依据。; 阅读建议:建议读者结合Matlab代码深入理解DWT的实现过程与特征提取步骤,重点关注小波基选择、分解层数设定及特征向量构造对分类性能的影响,并尝试对比不同机器学习模型的分类效果,以全面掌握该方法的核心技术要点。
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