CAREER: InteractiveRF: Fully-Adaptive, Physics-Aware RF-Enabled Cyber-Physical Human Systems

职业：InteractiveRF：完全自适应、物理感知、支持 RF 的网络物理人体系统

• 批准号: 2238653
• 负责人: Sevgi Gurbuz
• 金额: $ 54.78万
• 依托单位: University of Alabama Tuscaloosa
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238653&HistoricalAwards=false
• 关键词: CAREER; InteractiveRF; Fully; Adaptive; Physics

As technology advances and an increasing number of devices enter our homes and workplace, humans have become an integral component of cyber-physical systems (CPS). One of the grand challenges of cyber-physical human systems (CPHS) is how to design autonomous systems where human-system collaboration is optimized through improved understanding of human behavior. A new frontier within this landscape is afforded by the advent of low-cost, low-power millimeter-wave radio frequency (RF) transceivers, which can be exploited almost anywhere as part of the Internet-of-Things, smart environments, and personal devices. RF sensors provide a unique, information rich dataset of high-resolution measurements of distance, direction-of-arrival, and micro-Doppler signature in a non-contact, non-intrusive fashion in most weather conditions and in the dark. This CAREER project aims to pave the way for new and innovative RF-enabled CPHS applications in service of society and a better quality-of-life by transforming current fixed-transmission RF sensors into intelligent devices that can autonomously respond to human and environmental dynamics to optimize CPHS performance. Due to the burgeoning commercial sector utilizing radar across a variety of fields, such as transportation, health and human-computer interaction, this project features integrated academic preparation for multi-disciplinary, convergence research at both undergraduate and graduate levels to educate a new generation of engineers with experience in RF sensing, machine learning, signal processing and CPHS applications. Through K-12 outreach activities and recruiting at local historically black colleges and universities (HBCUs), this project will enrich and motivate students to study STEM fields, laying the foundations for a diverse and globally competitive STEM workforce for the future.This CAREER project simultaneously addresses critical challenges currently limiting effective exploitation of RF sensors in CPHS, such as the problem of RF data scarcity for training deep models, the wide range and continuity of possible human movements, the presence of other people and obstacles, and the dynamic nature of real-world scenes. Specific contributions include the development of 1) physics-aware ML techniques that leverage the domain knowledge embodied in models with data-driven deep learning; 2) spatio-temporal parsing techniques to extract and recognize human signal components from RF data streams to improve robustness of RF-CPHS under real-world conditions; and 3) a new task-cognizant, fully-adaptive RF sensing framework to improve performance and robustness of RF-CPHS for varying tasks in dynamic real-world environments. The proposed fully-adaptive RF framework also paves the way for collaborative, multi-modal RF-CPHS by exploiting information learned from RF and other sensor modalities in its decision process.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着技术的进步和越来越多的设备进入我们的家庭和工作场所，人类已经成为网络物理系统（CPS）的组成部分。网络物理人类系统（CPHS）面临的重大挑战之一是如何设计自主系统，通过提高对人类行为的理解来优化人与系统的协作。 低成本、低功耗毫米波射频（RF）收发器的出现为这一领域提供了一个新的前沿，它几乎可以在任何地方作为物联网、智能环境和个人设备的一部分加以利用。 RF传感器在大多数天气条件下和黑暗中以非接触、非侵入的方式提供独特的、信息丰富的距离、到达方向和微多普勒特征的高分辨率测量数据集。 该CAREER项目旨在为新的和创新的RF使能的CPHS应用铺平道路，通过将当前的固定传输RF传感器转换为智能设备，可以自主响应人类和环境动态，以优化CPHS性能，从而为社会服务和更好的生活质量。 由于新兴的商业部门利用雷达在各种领域，如交通，健康和人机交互，该项目的特点是综合学术准备多学科，融合研究在本科和研究生水平，教育新一代的工程师在射频传感，机器学习，信号处理和CPHS应用的经验。 通过K-12外展活动和在当地历史悠久的黑人学院和大学（HBCU）招聘，该项目将丰富和激励学生学习STEM领域，为未来多元化和具有全球竞争力的STEM劳动力奠定基础。该CAREER项目同时解决目前限制CPHS中有效利用RF传感器的关键挑战，例如，用于训练深度模型的RF数据稀缺问题，可能的人类运动的广泛性和连续性，其他人和障碍物的存在，以及真实世界场景的动态性质。 具体贡献包括：1）物理感知ML技术的开发，该技术利用数据驱动的深度学习模型中包含的领域知识; 2）时空解析技术，从RF数据流中提取和识别人体信号分量，以提高RF-CPHS在真实世界条件下的鲁棒性;以及3）一种新的任务认知的、完全自适应的RF感测框架，以针对动态真实世界环境中的不同任务来提高RF-CPHS的性能和鲁棒性。 建议的完全自适应RF框架也铺平了道路，通过利用信息，从RF和其他传感器模式在其决策过程中了解到的协作，多模式RF-CPHS。这个奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的知识价值和更广泛的影响审查标准。