Collaborative Research: IIBR Multidisciplinary: mSAIL (Michigan Small Animal Integrated Logger): a milligram-scale, multi-modal sensor and analytics monitoring platform

合作研究：IIBR 多学科：mSAIL（密歇根小动物综合记录仪）：毫克级、多模式传感器和分析监测平台

• 批准号: 2045017
• 负责人: Delbert Green
• 金额: $ 61.36万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 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: 2021-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2045017&HistoricalAwards=false
• 关键词: Collaborative; Research; IIBR; Multidisciplinary; mSAIL

An award is made to the University of Michigan and the University of Pittsburgh to develop and demonstrate a new logger and analytics platform for small terrestrial animal monitoring called mSAIL (Michigan Small Animal Integrated Logger). mSAIL is a millimeter-scale miniaturized tag attached to animals to record, store, and relay environmental, physiological, and behavioral data. This information is critical not only for a richer mechanistic understanding of animal and ecosystem function, but also in order to predict how these systems will change as Earth’s climate changes. This research and resulting technology will have a significant broad impact such as engaging the public and raising scientific literacy because the machine learning algorithms for mSAIL will be trained using real-world data collected by community volunteers. As well, this work will provide substantial mentoring and training opportunities for diverse participants in a cross-disciplinary environment.This research program tackles technical challenges for developing and applying millimeter-scale data loggers with improved information processing and visualization. The program aims to build a 50 mg millimeter-scale mSAIL device that 1) simultaneously measures light intensity, temperature, air pressure, and acceleration, 2) stores the recorded data in non-volatile memory, and 3) wirelessly communicates collected data. An integrated, chip-size battery will support device operation for one month and an energy-harvesting photovoltaic cell will provide essentially perpetual operation with as little as 500 lux light level. Using mSAIL, increasingly complex datasets that simultaneously document animal behavior and environment will be acquired from an expanded repertoire of species, down to those of sub-gram weight. mSAIL will spur cross-disciplinary collaborations to address a range of problems including the impact of anthropogenic environmental change (e.g. light pollution) on wildlife and the long-range migration of small animals such as monarch butterflies. This award is co-funded with support from the Animal Behavior Program in the Division of Integrative Organismal Systems.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.

密歇根大学和匹兹堡大学获得了一项奖项，用于开发和演示一种用于小型陆生动物监测的新型记录仪和分析平台，称为 mSAIL（密歇根小动物综合记录仪），是一种附着在动物身上的毫米级小型标签。记录、存储和转发环境、生理和行为数据，这些信息不仅对于更丰富地了解动物和生态系统功能至关重要，而且对于预测这些系统将如何随着地球气候的变化至关重要。这项研究和由此产生的技术将产生重大的广泛影响，例如吸引公众和提高科学素养，因为 mSAIL 的机器学习算法将使用社区志愿者收集的真实世界数据进行训练。在跨学科环境中为不同的参与者提供指导和培训机会。该研究项目旨在解决开发和应用毫米级数据记录仪的技术挑战，并改进信息处理和可视化。该项目旨在构建一个 50 毫克毫米级 mSAIL 设备。 1）同时测量光强度、温度、气压和加速度，2) 将记录的数据存储在非易失性存储器中，3) 无线传输收集到的数据，集成的芯片大小电池将支持设备运行一个月和能量收集。光伏电池将在低至 500 勒克斯的光照水平下提供基本上永久的运行，使用 mSAIL，可以从扩展的物种库（低至亚克级）中获取同时记录动物行为和环境的日益复杂的数据集。 mSAIL 将促进跨学科合作，解决一系列问题，包括人为环境变化（例如光污染）对野生动物的影响以及帝王蝶等小动物的远距离迁徙。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。

项目成果

Migrating Monarch Butterfly Localization Using Multi-Modal Sensor Fusion Neural Networks

使用多模态传感器融合神经网络迁移帝王蝶定位

• DOI: 10.23919/eusipco47968.2020.9287842
• 发表时间: 2021-01
• 期刊: 2020 28th European Signal Processing Conference (EUSIPCO
• 作者: Yang, Mingyu;Hsiao, Roger;Carichner, Gordy;Ernst, Katherine;Lim, Jaechan;Green, Delbert A.;Lee, Inhee;Blaauw, David;Kim, Hun
• 通讯作者: Kim, Hun

Siamese Learning-Based Monarch Butterfly Localization

基于暹罗学习的帝王蝶定位

• DOI: 10.1109/dslw53931.2022.9820497
• 发表时间: 2022-05
• 期刊: 2022 IEEE Data Science and Learning Workshop (DSLW
• 作者: Shoouri, Sara;Yang, Mingyu;Carichner, Gordy;Li, Yuyang;Hamed, Ehab A.;Deng, Angela;Green, Delbert A.;Lee, Inhee;Blaaw, David;Kim, Hun
• 通讯作者: Kim, Hun