REU Site: Research Experiences for Undergraduates in Smart & Connected Communities

本科生研究体验(REU)网站吸引学生参与智能互联社区(S和CC)这一新兴领域的研究，这是智能城市的基本组成部分。通过全国范围的招生程序，10名本科生将参加一个为期10周的暑期研究项目，为期三个暑期，由德克萨斯州立大学主办。特别努力招收以教学为主的大学和学院的学生，同时确保妇女、退伍军人和少数民族的参与。该项目包括由经验丰富的计算机科学教员提供指导、技术研讨会和工作坊、学生演示和职业发展机会。这些活动是在智力环境中进行的，通过提高学生的技术、沟通和社交技能来发展他们的职业生涯。这个REU网站的研究内容涉及三个主要方面：(1)智能人与环境交互的模型、机制和工具，以监控和保护智能基础设施免受意外故障和恶意攻击；(2)收集和分析从人体生物信号到活动和众包数据的各种类型的数据，以促进城市环境中公民的健康和福祉；以及(3)智能交通系统的新调度和监测方法，将减少城市环境中的拥堵，促进安全和减少不利的环境影响。对提出的方法的评估是通过理论和数值分析、模拟实验和使用各种设备、传感器和机器人的实际实施来完成的。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This Research Experience for Undergraduates (REU) Site engages students in research in the emerging area of Smart & Connected Communities (S&CC), the essential building blocks of smart cities. Through a nationwide recruitment process, cohorts of 10 undergraduate students will participate in a 10-week summer research program, for three summers, hosted at Texas State University. Special efforts are made to recruit students from primarily teaching universities and colleges while ensuring the participation of women, veterans, and minorities. The project includes mentoring by experienced computer science faculty members, technical seminars and workshops, student presentations, and professional development opportunities. These activities are carried out in an intellectual environment that develops the students' professional careers through enhancing their technical, communication, and social skills.The research component in this REU Site falls along three main thrusts in building the next generation of information and communication technologies supporting the development of S&CC: (1) models, mechanisms and tools for smart human-environment interactions to monitor and protect smart infrastructure against accidental failures and malicious attacks; (2) new methods for collecting and analyzing various types of data ranging from human biosignals to activity and crowd-sourcing data, to promote health and well-being of citizens in urban settings; and (3) novel scheduling and monitoring methods for intelligent transportation systems that would decrease congestion, promote safety and reduce adverse environmental impacts in urban settings. Evaluation of the proposed methods is done through theoretical and numerical analysis, simulation experiments, and real implementations with various devices, sensors, and robots.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.