IRES Track I: USA-China: International Research Experience for Native American Students in IoT Enabled Environmental Monitoring Technologies

IRES 轨道 I：美国-中国：美国原住民学生在物联网环境监测技术方面的国际研究经验

• 批准号: 1855646
• 负责人: Jinhui Wang
• 金额: $ 29.99万
• 依托单位: University of South Alabama
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1855646&HistoricalAwards=false
• 关键词: IRES; Track; USA; China; International

This IRES project is co-funded by OISE and EPSCoR.Twelve Native American students from five Tribal Colleges in the state of North Dakota will participate in a six-week summer program over the course of three years. Each year, four students will form a cohort and participate in pre-training in the U.S. and then visit and study at the host University in Beijing, China for five weeks. All three student cohorts will focus on one unifying topic - building and testing an Internet of Things (IoT)-enabled environmental monitoring system. During the course of the research training and after the project is finished, a deep and comprehensive evaluation will be conducted to assess three key aspects of the program: 1) instructional training and research experience, 2) global professional and cultural experience; and 3) student mentoring. This program will greatly benefit participating students. This is an international learning experience that may be the key highlight of student participants' academic careers, which may be especially true for Native American students, as they have less chances for performing research abroad. The program will provide participating students with unique opportunities to become globally-engaged scientists and to build their international scientific networks. There are five Tribal Colleges in the state of North Dakota that represent historic tribal nations. The recruitment of this program from these Tribal Colleges will ensure a regionally diverse student group will receive international mentoring and research-based education. In many large cities of U.S., there are environmental problems similar to Beijing, so the monitoring technology learned by participating students will provide valuable reference and an essential technology reserve. The participating students will build peer-mentoring skills and learn about the unique culture and traditions of China from Beijing, the culture center of China.Student participants will learn to design advanced environmental monitoring systems that integrate energy harvest and remote control, which can track all four categories of environmental contaminants including metals, radioisotopes, volatile organic contaminants, and biological contaminants. In this project, tasks for student participants include sensor developments, interface circuits designs, wireless sensor network designs for Internet of Things (IoT)-enabled systems to realize environmental monitoring, testing the proposed monitoring system on laboratory and field sites, and making reliability-based lifetime analyses. The proposed system is a much-needed technology for environmental decision-making that will help substantially reduce ecological and human-health threats from environmental problems, and the implemented prototype and test data will be a significant reference for the other academic researchers and industrial developers. Evaluation data collection will occur sequentially using a combination of qualitative and quantitative data measurements to assess the effectiveness of the program. The primary goals of this program are to: 1) provide students with training and mentoring in international-based engineering applications that will create a positive effect in their professional and personal lives, 2) create Native American experts in environmental monitoring and protection, and 3) improve specialized environmental management in the remote areas.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.

这个IRES项目由OISE和EPSCoR共同资助。来自北达科他州五所部落学院的十二名美国原住民学生将参加为期三年、为期六周的暑期项目。每年，四名学生将组成一个队列，在美国参加预培训，然后在中国北京的主办大学访问和学习五周。这三个学生小组将专注于一个统一的主题——构建和测试一个支持物联网（IoT）的环境监测系统。在研究培训过程中及项目结束后，将对项目进行深入全面的评估，评估三个关键方面：1)教学培训和研究经验；2)全球专业和文化经验；3)学生辅导。这个项目将使参与的学生受益匪浅。这是一种国际学习经历，可能是学生参与者学术生涯的关键亮点，对于美洲原住民学生来说尤其如此，因为他们很少有机会在国外进行研究。该项目将为参与的学生提供独特的机会，使他们成为参与全球事务的科学家，并建立自己的国际科学网络。北达科他州有五所部落学院，代表着历史上的部落国家。从这些部落学院招收这个项目将确保一个地区多样化的学生群体能够接受国际指导和以研究为基础的教育。在美国的许多大城市，都存在着与北京类似的环境问题，因此参与学生学习的监测技术将提供有价值的参考和必要的技术储备。参加活动的学生将在北京这个中国的文化中心学习中国独特的文化和传统。学生将学习设计先进的环境监测系统，该系统集成了能量收集和远程控制，可以跟踪所有四类环境污染物，包括金属、放射性同位素、挥发性有机污染物和生物污染物。在这个项目中，学生参与者的任务包括传感器开发、接口电路设计、为支持物联网（IoT）的系统设计无线传感器网络，以实现环境监测，在实验室和现场测试拟议的监测系统，并进行基于可靠性的寿命分析。所提出的系统是环境决策所急需的技术，将有助于大大减少环境问题对生态和人类健康的威胁，实现的原型和测试数据将为其他学术研究人员和工业开发人员提供重要参考。评估数据收集将使用定性和定量数据测量相结合的顺序进行，以评估项目的有效性。该计划的主要目标是：1)为学生提供国际工程应用方面的培训和指导，这将对他们的职业和个人生活产生积极影响；2)培养环境监测和保护方面的美洲原住民专家；3)改善偏远地区的专业环境管理。该奖项反映了美国国家科学基金会的法定使命，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

PAWN: Programmed Analog Weights for Non-Linearity Optimization in Memristor-Based Neuromorphic Computing System

• DOI: 10.1109/jetcas.2023.3235658
• 发表时间: 2023-03
• 期刊: IEEE Journal on Emerging and Selected Topics in Circuits and Systems
• 影响因子: 4.6
• 作者: Saleh Ahmad Khan;Md. Oli-Uz-Zaman;Jinhui Wang

Reconfigurable Mapping Algorithm based Stuck-At-Fault Mitigation in Neuromorphic Computing Systems

神经形态计算系统中基于可重构映射算法的卡故障缓解

• DOI: 10.1145/3583781.3590208
• 发表时间: 2023
• 期刊: GLSVLSI '23: Proceedings of the Great Lakes Symposium on VLSI 2023
• 作者: Oli-Uz-Zaman, Md.;Khan, Saleh Ahmad;Oswald, William;Liao, Zhiheng;Wang, Jinhui
• 通讯作者: Wang, Jinhui

Reliability Improvement in RRAM-based DNN for Edge Computing

• DOI: 10.1109/iscas48785.2022.9937260
• 发表时间: 2022-05
• 期刊: 2022 IEEE International Symposium on Circuits and Systems (ISCAS)
• 作者: Md. Oli-Uz-Zaman;Saleh Ahmad Khan;Geng Yuan;Yanzhi Wang;Zhiheng Liao;Jingyan Fu;Caiwen Ding;Jinhui Wang

Memristor-Based Neuromorphic Hardware Improvement for Privacy-Preserving ANN

• DOI: 10.1109/tvlsi.2019.2923722
• 发表时间: 2019-12-01
• 期刊: IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS
• 影响因子: 2.8
• 作者: Fu, Jingyan;Liao, Zhiheng;Wang, Jinhui
• 通讯作者: Wang, Jinhui

Ameliorate Performance of Memristor-Based ANNs in Edge Computing

改善基于忆阻器的 ANN 在边缘计算中的性能

• DOI: 10.1109/tc.2021.3081985
• 发表时间: 2021
• 期刊: IEEE Transactions on Computers
• 影响因子: 3.7
• 作者: Liao, Zhiheng;Fu, Jingyan;Wang, Jinhui
• 通讯作者: Wang, Jinhui