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REU Site: Artificial Intelligence Powered Robotics in 5G Network

REU 网站：5G 网络中人工智能驱动的机器人

基本信息

批准号：
2051113
负责人：
Yufang Jin
金额：
$ 40.34万
依托单位：
University of Texas at San Antonio
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-01 至 2024-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2051113&HistoricalAwards=false
关键词：
REU Site Artificial Intelligence Powered

项目摘要

The REU site at the University of Texas at San Antonio (UTSA) will host ten undergraduate students from colleges nationwide to conduct research on applications of artificial intelligence (AI) into autonomous vehicles based on 5G communications. Rapidly deploying 5G core networks have enhanced mobility and service provisioning while increasing the amount and complexity of the data. Integrating AI into 5G networks could effectively orchestrate applications such as autonomous driving of unmanned vehicles and optimal charging management of electric vehicles. Autonomous vehicles, 5G communication network, and AI have been identified as national priorities to enhance the leadership of the U.S. in future technologies. However, there is a lack of students entering the workforce or graduate schools in this area, in particular female and underrepresented students. The REU site at the University of Texas at San Antonio is designed to train undergraduate students with hands-on research activities on AI, autonomous vehicles, and 5G networks with direct mentorship by faculty mentors. The REU site also seeks to encourage the REU participants to pursue their studies for graduate degrees in STEM, which is imperative to enhance the competitiveness of the future workforce.The goal of this project is to equip undergraduate students with knowledge and skills of the latest engineering research on AI, robotics, autonomous driving, and optimal charging of electric vehicles in 5G mobile networks. The designed REU projects address the frontier research problems through hands-on applications, such as practical assembly of real robotics, data collection with 5G mobile devices, data processing with AI algorithms, building charging converters for electrical vehicles, and establishing a framework for control and communication between multiple unmanned vehicles. These are valuable activities for novice undergraduate researchers to gain direct research experiences. The REU site will also integrate the learn-practice-service cycle into research activities, encourage team spirit, and build students’ self-esteem by recognition and rewards for achievements. The REU program integrates research and education activities to enable REU students to develop essential skills through various training sessions, hands-on lab activities, and seminars on research and career development. The research activities are also integrated with outreach activities to local communities including field trips to the NIWEEK event and local industries.This site is supported by the Department of Defense in partnership with the NSF REU program.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.

位于德克萨斯大学圣安东尼奥分校（UTSA）的REU网站将接待来自全国各大学的10名本科生，开展人工智能（AI）应用于基于5G通信的自动驾驶汽车的研究。快速部署5G核心网络增强了移动性和服务提供，同时增加了数据的数量和复杂性。将人工智能集成到5G网络中可以有效地协调无人驾驶汽车的自动驾驶和电动汽车的最佳充电管理等应用。自动驾驶汽车、5G通信网络和人工智能已被确定为国家优先事项，以增强美国在未来技术中的领导地位。然而，在这一领域，进入劳动力市场或进入研究生院的学生人数很少，特别是女生和代表性不足的学生。位于德克萨斯大学圣安东尼奥的REU网站旨在通过人工智能，自动驾驶汽车和5G网络的实践研究活动培训本科生，并由教师导师直接指导。REU网站还旨在鼓励REU参与者继续攻读STEM研究生学位，这对提高未来劳动力的竞争力至关重要。该项目的目标是让本科生掌握人工智能，机器人，自动驾驶和5G移动的网络中电动汽车最佳充电的最新工程研究的知识和技能。所设计的REU项目通过动手应用解决前沿研究问题，如真实的机器人的实际组装、用5G移动的设备进行数据收集、用AI算法进行数据处理、为电动汽车构建充电转换器、建立多辆无人车之间的控制和通信框架等。这些都是有价值的活动，新手本科研究人员获得直接的研究经验。REU网站还将把学习-实践-服务循环融入研究活动，鼓励团队精神，并通过对成就的认可和奖励来建立学生的自尊。REU计划整合了研究和教育活动，使REU学生能够通过各种培训课程，动手实验室活动以及研究和职业发展研讨会来培养基本技能。该研究活动还与当地社区的外展活动相结合，包括实地考察NIWEEK活动和当地工业。该网站由国防部与NSF REU计划合作支持。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Characterizing Macrophages Diversity in COVID-19 Patients Using Deep Learning.

DOI：
10.3390/genes13122264
发表时间：
2022-12-01
期刊：
GENES
影响因子：
3.5
作者：
Flores, Mario A.;Paniagua, Karla;Huang, Wenjian;Ramirez, Ricardo;Falcon, Leonardo;Liu, Andy;Chen, Yidong;Huang, Yufei;Jin, Yufang
通讯作者：
Jin, Yufang

A New Neural ODE Structure for Learning High-Order Dynamical Systems