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.

该 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 12 名美国原住民学生将参加为期三年的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，然后到中国北京的主办大学进行为期五周的访问和学习。所有三个学生群体都将专注于一个统一的主题 - 构建和测试支持物联网 (IoT) 的环境监测系统。在研究培训过程中以及项目完成后，将对项目的三个关键方面进行深入、全面的评估：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。该计划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少。该计划将为参与的学生提供成为全球科学家并建立国际科学网络的独特机会。北达科他州有五所代表历史部落国家的部落学院。从这些部落学院招募该项目将确保区域多元化的学生群体能够接受国际指导和基于研究的教育。美国很多大城市都存在类似北京的环境问题，因此参赛学生所学到的监测技术将提供有价值的参考和必要的技术储备。参与的学生将培养同伴指导技能，并从中国的文化中心北京了解中国独特的文化和传统。学生参与者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，可以跟踪所有情况环境污染物分为金属、放射性同位素、挥发性有机污染物和生物污染物四类。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、物联网（IoT）系统的无线传感器网络设计，以实现环境监测、在实验室和现场测试所提出的监测系统，并提高可靠性。基于寿命分析。该系统是环境决策急需的技术，将有助于大幅减少环境问题对生态和人类健康的威胁，所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。将使用定性和定量数据测量的组合按顺序收集评估数据，以评估计划的有效性。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，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。

项目成果

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

用于物联网边缘计算的基于忆阻器的、支持变异的差分隐私学习系统

• DOI: 10.1109/jiot.2020.3023623
• 发表时间: 2020-09-11
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Jingyan Fu;Zhiheng Liao;Jianqing Liu;S. Smith;Jinhui Wang
• 通讯作者: Jinhui Wang

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: 2021-08
• 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

Mapping Transformation Enabled High-Performance and Low-Energy Memristor-Based DNNs

映射转型支持基于忆阻器的高性能和低能耗 DNN

• DOI: 10.3390/jlpea12010010
• 发表时间: 2022-02-10
• 期刊: Journal of Low Power Electronics and Applications
• 影响因子: 2.1
• 作者: Md. Oli;Saleh Ahmad Khan;Geng Yuan;Zhiheng Liao;Jingyan Fu;Caiwen Ding;Yanzhi Wang;Jinhui Wang
• 通讯作者: Jinhui Wang

Linear Optimization for Memristive Device in Neuromorphic Hardware

神经形态硬件中忆阻器件的线性优化

• DOI: 10.1109/isvlsi.2019.00088
• 发表时间: 2019-07-15
• 期刊: 2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)
• 作者: Jingyan Fu;Zhiheng Liao;Na Gong;Jinhui Wang
• 通讯作者: Jinhui Wang

Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AI

使用权重修剪和差分交叉映射来提高基于 ReRAM 的边缘 AI 的 DNN 容错能力

• DOI: 10.1109/isqed51717.2021.9424332
• 发表时间: 2021-04-07
• 期刊: 2021 22nd International Symposium on Quality Electronic Design (ISQED)
• 作者: Geng Yuan;Zhiheng Liao;Xiaolong Ma;Yuxuan Cai;Zhenglun Kong;Xuan Shen;Jingyan Fu;Zhengang Li;Chengming Zhang;Hongwu Peng;Ning Liu;Ao Ren;Jinhui Wang;Yanzhi Wang
• 通讯作者: Yanzhi Wang