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必不可少的一部分，可以协助社会的运输，健康，安全和沟通需求。传统的灵敏度方法以固定方式获取数据，而与使用数据的任务无关。此外，数据处理管道中的每个数据采集，重建和推理块彼此独立并分别进行了优化。这种方法导致了数据生成的指数率，从而在当今的灵敏度系统中对电力，存储，处理和通信需求产生了难以忍受的需求。该项目的目的是通过开发一种自适应，面向任务和物理意识的数据对登录管道来推动基于学习的灵敏度和处理技术的科学，该管道在数据驱动的学习框架中共同优化了数据采集，重建和推理阶段。拟议的研究将建立针对各种应用的未来智能，适应性和资源效率的灵敏度系统的基础，包括生物医学成像，遥感，远程灵敏度，雷达和无线通信。此项目具有三个相互联系的目标（i）通过基于学习的物理学来开发基于学习的物理学的启发，并从创建的架构构造技术启发了构造界面，并启发了基于学习的物理学，并将其定期化。 （ii）通过共同优化的感应，重建和处理块开发基于数学和学习的自适应和以任务为导向的测量设计方法，并证明了其对现实世界问题的影响，（iii）开发基于学习的数据对决定性框架，从而直接介绍了可行的信息（分类，参数估计，参数估计），直接从低知识的测量值中引入。计划的协同教育和外展活动的中心主题是提高K-12和大学生以及公众在敏感性系统，信号处理和机器学习方面的科学素养。因为传感技术是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，因此该项目将对开发的感应技术产生很大的影响，以改善我们的日常生活质量，从摄像机的应用到生物医学的应用到生物医学成像，或者通过智能家居技术进行自动奖励，并在自动级别上进行了自动化奖，并且已经反映了NESF的代表。基金会的智力优点和更广泛的影响评论标准。

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.

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

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

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.