RET Site: Sensor, Signal and Information Processing Algorithms and Software

RET 站点：传感器、信号和信息处理算法和软件

• 批准号: 1953745
• 负责人: Andreas Spanias
• 金额: $ 56万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953745&HistoricalAwards=false
• 关键词: RET; Site; Sensor; Signal; Information

The Internet of Things (IoT), the ecosystem of physical objects connected via the internet, has seen rapid growth over recent years and has been enhanced by mobile applications. Machine Learning (ML) algorithms and sensors are critical to this technology, leading to a demand in developing sensors that are more efficient and less expensive. This award creates a new Research Experiences for Teachers (RET) site focused on applications of ML methods for sensor and mobile IoT. Each summer, ten high school teachers and two community college instructors will participate in research activities at Arizona State University. High school teachers will be recruited from the Queen Creek Unified School District (QCUSD) and Salt River Schools (SRS), and community college instructors will be recruited from Cochise College (CC), all of whom serve a large population of underrepresented students. After a hands-on bootcamp centered on key concepts in ML, sensors, and IoT, teachers will be immersed in a 6-week research program and mentored by a team of ASU faculty, graduate student advisors, and industry leaders. ASU will continue their engagement with teachers throughout the school years and offer assistance and feedback on transferring their research experiences into the classroom. The goal of this RET Site is to give teachers a deeper understanding of ML and IoT such that they can develop engaging materials around these topics for their classrooms. Moreover, teachers’ experiences will motivate and energize their students to engage in STEM activities and career pathways.Industry labs strive to produce inexpensive sensors for mobile IoT whose performance hinges on signal conditioning and classification software. Training faculty and teachers in this area requires an integrative approach as software designers need to understand application/sensor limitations. RET Site participants will be immersed in application-driven algorithm and software development for sensor and IoT testbeds. In addition, the RET will require a short hands-on bootcamp in machine learning designed to build their knowledge in ML, sensors, IoT with the focus on developing materials for their classes. Industrial mentors and curriculum specialists will be engaged to provide reviews of research and instructional plans. The RET site objectives are to: a) introduce teachers and instructors to research practices by immersing them in government/industry research activities, b) engage them in the development of machine learning and signal processing methods for sensor and IoT health monitoring research, c) motivate and guide teachers to adapt RET experiences into compelling teaching materials. This RET features multidisciplinary synergies with access to unique technology, exceptional talents, and increased opportunities to broaden participation. RET Site partners, QCUSD, SRS and CC, serve a large number of minority students. In fact, the SRS serve 100% Native American students. The RET will disseminate publications and outcomes to conferences and teaching standard organizations. Teachers will create hand-outs and presentations to support their instructional plans. The RET site will include industry participation from the sensor signal and information processing (SenSIP) industry-university center which is also an I/UCRC site.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.

物联网 (IoT) 是通过互联网连接的物理对象的生态系统，近年来发展迅速，并通过移动应用程序得到增强，机器学习 (ML) 算法和传感器对此技术至关重要，从而催生了物联网 (IoT)。该奖项创建了一个新的教师研究体验 (RET) 网站，专注于传感器和移动物联网的机器学习方法的应用。每年夏天，十名高中高效教师和两名社区大学教师将参与其中。亚利桑那州立大学高中教师的研究活动。将从皇后溪联合学区 (QCUSD) 和盐河学校 (SRS) 招募，社区大学教师将从科奇斯学院 (CC) 招募，所有这些教师都经过精心培训，为大量代表性不足的学生提供服务。在以机器学习、传感器和物联网关键概念为中心的训练营中，教师将沉浸在为期 6 周的研究项目中，并由亚利桑那州立大学教师、研究生顾问和行业领导者组成的团队进行指导。在整个学年中与教师进行交流，并就将他们的研究经验转移到课堂上提供帮助和反馈。该 RET 网站的目标是让教师更深入地了解 ML 和 IoT，以便他们能够围绕这些主题为课堂开发引人入胜的材料。此外，教师的经验将激励和激励学生参与 STEM 活动和职业道路。行业实验室致力于为移动物联网生产廉价的传感器，其性能取决于信号调理和分类软件。作为软件的整体方法设计人员需要了解应用/传感器的局限性 RET 现场参与者将沉浸在传感器和物联网测试台的应用驱动算法和软件开发中。此外，RET 将需要一个简短的机器学习实践训练营，旨在积累他们的知识。 RET 网站的目标是：a) 向教师和讲师介绍研究实践。让他们沉浸在政府/行业研究活动中，b) 让他们参与传感器和物联网健康监测研究的机器学习和信号处理方法的开发，c) 激励和指导教师将 RET 经验改编成引人注目的教材。通过获得独特的技术、杰出的人才以及扩大参与范围的更多机会，QCUSD、SRS 和 CC 为大量少数族裔学生提供服务。事实上，SRS 为 100% 的美国原住民学生提供服务。 RET 将向会议和教学标准组织分发出版物和成果。教师将制作讲义和演示文稿以支持他们的教学计划。RET 网站将包括来自传感器信号和信息处理 (SenSIP) 工业大学中心的行业参与。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。