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美国原住民学生将在三年的时间内参加一个为期六周的夏季计划。每年，有四个学生将组成一个队列，并在美国参加预培训，然后在中国北京的托管大学访问和学习五个星期。这三个学生队列将重点关注一个统一的主题 - 建立和测试物联网（IOT）支持的环境监测系统。在研究培训的过程中和项目完成后，将进行深入而全面的评估，以评估该计划的三个关键方面：1）教学培训和研究经验，2）全球专业和文化经验； 3）学生指导。该计划将极大地使参与的学生受益。这是一种国际学习经验，可能是学生参与者学术职业的关键亮点，这对于美国原住民学生来说尤其如此，因为他们在国外进行研究的机会较小。该计划将为参与的学生提供独特的机会，成为全球参与的科学家并建立其国际科学网络。北达科他州有五所部落学院代表历史悠久的部落国家。这些部落学院招募该计划将确保一个区域多样化的学生团体将接受国际指导和基于研究的教育。在美国的许多大城市中，存在类似于北京的环境问题，因此通过参与学生学习的监测技术将提供宝贵的参考和基本的技术储备。参与的学生将建立同行吸引技巧，并从中国文化中心从北京学习中国的独特文化和传统。研究员将学会设计高级环境监测系统，以整合能量收获和遥控器，可以跟踪所有四种环境污染物，包括金属，可旋转性，挥发性有机观点，挥发性观念和生物学抗生素和生物学。在此项目中，学生参与者的任务包括传感器开发，接口电路设计，无线传感器网络设计（IOT）支持的系统，以实现环境监控，测试实验室和现场站点上建议的监视系统，以及进行基于可靠性的终身分析。拟议的系统是一项急需的环境决策技术，它将有助于大大减少环境问题的生态和人类健康威胁，而实施的原型和测试数据将成为其他学术研究人员和工业开发人员的重要参考。评估数据收集将使用定性和定量数据测量的组合依次进行，以评估程序的有效性。该计划的主要目标是：1）为学生提供基于国际工程应用的培训和指导，这些培训将对他们的专业和个人生活产生积极影响，2）创建美国原住民在环境监控和保护方面的专家，以及3）改善偏远地区的专业环境管理。该奖项反映了NSF的法定任务，并通过评估了基金会的范围，并通过评估了基金会的范围。

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

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

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 Variation-Enabled Differentially Private Learning Systems for Edge Computing in IoT

• DOI: 10.1109/jiot.2020.3023623
• 发表时间: 2021-06-15
• 期刊: IEEE INTERNET OF THINGS JOURNAL
• 影响因子: 10.6
• 作者: Fu, Jingyan;Liao, Zhiheng;Wang, Jinhui
• 通讯作者: Wang, Jinhui