CAREER: Learning to Sense: Joint Learning of Task Oriented Cognitive Sensing with Data Driven Reconstruction and Inference

职业：学习感知：面向任务的认知感知与数据驱动的重建和推理的联合学习

• 批准号: 2047771
• 负责人: Ali Gurbuz
• 金额: $ 50万
• 依托单位: Mississippi State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-15 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047771&HistoricalAwards=false
• 关键词: CAREER; Learning; Sense; Joint; Task

Sensors are an indispensable part of our lives, assisting society’s transportation, health, safety, and communication needs. Conventional sensing approaches acquire data in a fixed fashion, independent of the task for which the data is being utilized. In addition, each of data acquisition, reconstruction and inference blocks in the data processing pipeline is independent of one another and optimized separately. This approach has led to exponential rates of data generation that creates an unbearable demand for power, storage, processing, and communication requirements in today’s sensing systems. The goal of this project is to advance the science of learning-based sensing and processing technologies by developing an adaptive, task-oriented and physics-aware data-to-decision pipeline, which jointly optimizes data acquisition, reconstruction, and inference stages in a data-driven learning framework. The proposed research will establish the foundations of future smart, adaptive, and resource-efficient sensing systems for a variety of applications, including biomedical imaging, remote sensing, radar, and wireless communications.This project has three interconnected objectives (i) Developing learning-based physics-aware multi-dimensional signal reconstruction techniques through foundational relations to regularized inverse problems and explainable architectures inspired from existing signal processing models, (ii) Developing mathematical and learning-based adaptive and task-oriented measurement design approaches with jointly optimized sensing, reconstruction and processing blocks, and demonstrate its impacts on real-world problems, (iii) Developing a learning-based data-to-decision framework, which infers actionable information (classification, parameter estimation) directly from low number of learned measurements. The central theme of planned synergistic educational and outreach activities is to increase the scientific literacy of both the K-12 and university students and the public regarding sensing systems, signal processing, and machine learning. Because sensing technologies are on the frontier of how information is perceived and extracted, and are essential to a wide range of applications, this project will have a high impact on sensing technologies being developed to improve the quality of our daily lives, ranging from applications of cameras to biomedical imaging, or from smart home technologies to autonomous vehicles.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.

传感器是我们生活中不可或缺的一部分，有助于满足社会的交通、健康、安全和通信需求。传统的感测方法以固定的方式获取数据，与数据被利用的任务无关。此外，数据处理流水线中的数据采集、重构和推理块中的每一个都是彼此独立的并且单独优化。这种方法导致了数据生成的指数速率，这在当今的传感系统中对功率、存储、处理和通信要求产生了难以承受的需求。该项目的目标是通过开发自适应，面向任务和物理感知的数据决策管道来推进基于学习的传感和处理技术的科学，该管道在数据驱动的学习框架中共同优化数据采集，重建和推理阶段。拟议的研究将为未来智能、自适应和资源高效的传感系统奠定基础，用于各种应用，包括生物医学成像、遥感、雷达，该项目有三个相互关联的目标：（i）开发基于学习的物理感知的多通过正则化逆问题的基本关系和可解释的架构，现有的信号处理模型，（ii）开发数学和基于学习的自适应和面向任务的测量设计方法，联合优化传感，重建和处理模块，并展示其对现实世界问题的影响，（iii）开发基于学习的数据决策框架，直接从少量的学习测量中推断可操作的信息（分类，参数估计）。计划的协同教育和推广活动的中心主题是提高K-12和大学生以及公众在传感系统，信号处理和机器学习方面的科学素养。由于传感技术处于如何感知和提取信息的前沿，并且对于广泛的应用至关重要，因此该项目将对正在开发的传感技术产生重大影响，以提高我们的日常生活质量，从相机应用到生物医学成像，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影响审查标准。

项目成果

Data Driven Learning of Constrained Measurement Matrices for Signal Reconstruction

用于信号重建的约束测量矩阵的数据驱动学习

• DOI: 10.1109/ieeeconf53345.2021.9723098
• 发表时间: 2021
• 期刊: and Computers
• 作者: Mdrafi, Robiulhossain;Gurbuz, Ali Cafer
• 通讯作者: Gurbuz, Ali Cafer

Radar-Lidar Fusion for Classification of Traffic Signaling Motion in Automotive Applications

雷达-激光雷达融合用于汽车应用中交通信号运动分类

• DOI: 10.1109/radar54928.2023.10371020
• 发表时间: 2023
• 期刊: 2023 IEEE International Radar Conference (RADAR
• 作者: Biswas, Sabyasachi;Ball, John E.;Gurbuz, Ali C.
• 通讯作者: Gurbuz, Ali C.

A Deep Learning-Based Soil Moisture Estimation in Conus Region Using Cygnss Delay Doppler Maps

使用 Cygnss 延迟多普勒图进行基于深度学习的圆锥区域土壤湿度估计

• DOI: 10.1109/igarss46834.2022.9883916
• 发表时间: 2022
• 期刊: 2022 IEEE International Geoscience and Remote Sensing Symposium
• 作者: Nabi, M M;Senyurek, Volkan;Gurbuz, Ali Cafer;Kurum, Mehmet
• 通讯作者: Kurum, Mehmet

Quasi-Global Assessment of Deep Learning-Based CYGNSS Soil Moisture Retrieval

• DOI: 10.1109/jstars.2023.3287591
• 发表时间: 2023
• 期刊: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• 影响因子: 5.5
• 作者: Moin Nabi;V. Senyurek;Fangni Lei;M. Kurum;A. Gurbuz

SMAP Radiometer RFI Prediction with Deep Learning using Antenna Counts

SMAP 辐射计 RFI 使用天线计数进行深度学习预测

• 发表时间: 2022
• 期刊: 2022 IEEE International Geoscience and Remote Sensing Symposium
• 作者: Alam, A. M.;Gurbuz, A. C.;Kurum, M.
• 通讯作者: Kurum, M.