Neuromorphic RF sensing and processing

神经形态射频传感和处理

• 批准号: 2888964
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2888964
• 关键词: Neuromorphic; RF; sensing; processing

The aim of this project will be to investigate neuromorphic solutions in RF sensing at both hardware and software level. The main objectives of the project will be:- To become familiar with fundamental concepts of radar principles and RF sensing systems.- To become familiar with fundamental concepts in the field of AI and ML, with a particular interest in sensing systems.- To identify constraints and training requirements for SNNs when dealing with RF data;- To identify and design brain inspired passive RF sensing modes to generate spiking outputs;- To design and develop novel NM/SNN frameworks, for RF systems, based on simulated and real data.- To validate the developed framework on real data provided by Leonardo and acquired at the SSP&S labs.- To devise a testing strategy to evaluate and demonstrate the performance of the novel NM/SNN models and techniques developed, and compare performance with traditional processing methods.- To investigate the experimentation with Neuromorphic hardware of the developed solutions;- To disseminate outcomes from this research, in world leading journals and conferences.

该项目的目的是在硬件和软件层面研究射频传感中的神经形态解决方案。该项目的主要目标是：- 熟悉雷达原理和射频传感系统的基本概念。- 熟悉人工智能和机器学习领域的基本概念，特别是对传感系统感兴趣。- 识别处理 RF 数据时 SNN 的约束和培训要求； - 识别和设计受大脑启发的无源 RF 传感模式以生成尖峰输出； - 基于模拟和真实数据，为 RF 系统设计和开发新颖的 NM/SNN 框架。 - 真实验证开发的框架Leonardo 提供并在 SSP&S 实验室获取的数据。- 制定测试策略来评估和演示所开发的新型 NM/SNN 模型和技术的性能，并将性能与传统处理方法进行比较。- 研究神经形态硬件的实验已开发的解决方案；- 在世界领先的期刊和会议上传播这项研究的成果。