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MRI: Development of an Instrument for Student and Faculty Research on Multimodal Environmental Observations

MRI：开发用于学生和教师多模态环境观测研究的仪器

基本信息

批准号：
2018611
负责人：
Naphtali Rishe
金额：
$ 261.03万
依托单位：
Florida International 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：
2020-10-01 至 2025-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2018611&HistoricalAwards=false
关键词：
MRI Development Instrument Student Faculty

项目摘要

Focusing on ecological research in the dynamic Everglades Ecosystem, this Instrument provides a platform to aggregate and compare many environmental data sets. It also evaluates the effect of algorithms on ecological model outputs. The instrument leverages experiences and regional access to pilot studies on the Florida Everglades. The expected outcomes are likely to constitute a significant advance in state-of-the-art, novel discoveries in the fields of computing and ecology. By developing a better understanding of spatial and temporal synergies in ecosystem dynamics, scientists will be able to predict complex ecosystem responses and guide risk assessment, planning, and solution development. The enabled research will produce evidence-based science, useful in land-management decisions. The Instrument is a pilot, expandable to other regions and to the national or global scale. All software and FIU-produced data will be open-sourced.This Instrument supports research that requires the integration of large multimodal heterogeneous datasets to address the challenges with current data-driven ecosystems research and instruments, including scaling of different data; generic global models and algorithms; calibration; and exponentially-growing acquisition of remotely sensed data. Specific enabled research activities involve multi-dimensional fuzzy logic on spatiotemporal data; multimodal image analysis; and machine learning. The system comprises a data repository, a Multimodal Ecosystem Data Aggregator, and analytical and visualization tools. Existing large repositories of global datasets do not readily allow the incorporation of vast amounts of multidimensional, historical locally-acquired datasets. The platform provides comprehensive local data integration and its correlation to global data. While the Instrument will house a well-curated collection of environmental data, its unique service will transcend mere curation, with built-in tools for data mining, fuzzy-logic reasoning, image analysis, and visualization -- developed and interfaced to empower both student and faculty researchers.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.

专注于动态沼泽地生态系统的生态研究，该仪器提供了一个平台，汇总和比较许多环境数据集。它还评估了算法对生态模型输出的影响。该工具利用经验和区域获得佛罗里达大沼泽地试点研究的机会。预期的结果可能构成计算和生态学领域最先进的新发现的重大进展。通过更好地了解生态系统动态中的空间和时间协同作用，科学家将能够预测复杂的生态系统响应，并指导风险评估，规划和解决方案的制定。这一研究将产生对土地管理决策有用的循证科学。该仪器是一个试点，可扩展到其他地区以及国家或全球规模。该仪器支持需要整合大型多模态异构数据集的研究，以应对当前数据驱动的生态系统研究和仪器的挑战，包括不同数据的缩放;通用全球模型和算法;校准;以及遥感数据的指数增长获取。具体的研究活动涉及时空数据的多维模糊逻辑;多模态图像分析;和机器学习。该系统包括一个数据储存库、一个多模式生态系统数据聚合器以及分析和可视化工具。现有的全球数据集的大型存储库不容易允许合并大量的多维的，历史的本地获取的数据集。该平台提供全面的本地数据集成及其与全球数据的相关性。虽然该仪器将容纳精心策划的环境数据集，但其独特的服务将超越单纯的策划，内置数据挖掘，模糊逻辑推理，图像分析，和可视化--该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的评估来支持。影响审查标准。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Geographic Boosting Tree: Modeling Non-Stationary Spatial Data

地理提升树：非平稳空间数据建模

DOI：
10.1109/icmla51294.2020.00190
发表时间：
2020
期刊：
2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA
影响因子：
0
作者：
Deng, Liangdong;Adjouadi, Malek;Rishe, Naphtali
通讯作者：
Rishe, Naphtali

Deep convolutional neural network for mixed random impulse and Gaussian noise reduction in digital images

DOI：
10.1049/iet-ipr.2019.0931
发表时间：
2020-12-15
期刊：
IET IMAGE PROCESSING
影响因子：
2.3
作者：
Mafi, Mehdi;Izquierdo, Walter;Adjouadi, Malek
通讯作者：
Adjouadi, Malek

Combining Perception Considerations with Artificial Intelligence in Maritime Threat Detection Systems

在海上威胁检测系统中将感知考虑因素与人工智能相结合

DOI：
10.1109/sose55472.2022.9812640
发表时间：
2022
期刊：
2022 17th Annual System of Systems Engineering Conference (SOSE
影响因子：
0
作者：
Chen, Ian;Huang, Lucy;Qiao, Jack;Tamir, Dan E.;Rishe, Naphtali
通讯作者：
Rishe, Naphtali

Inverse Distance Weighted Random Forests: Modeling Unevenly Distributed Non-Stationary Geographic Data

反距离加权随机森林：对不均匀分布的非平稳地理数据进行建模

DOI：
10.1109/icacsis51025.2020.9263208
发表时间：
2020
期刊：
2020 International Conference on Advanced Computer Science and Information Systems (ICACSIS
影响因子：
0
作者：
Deng, Liangdong;Adjouadi, Malek;Rishe, Naphtali
通讯作者：
Rishe, Naphtali

Addressing the missing data challenge in multi-modal datasets for the diagnosis of Alzheimer’s disease