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

基本信息

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

项目摘要

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，可满足社会的交通、健康、安全和通信需求。此外，传统的传感方法以固定的方式获取数据，而与数据的用途无关。数据处理管道中的重建和推理块彼此独立并分别进行优化，这种方法导致数据生成速度呈指数级增长，这对当今的传感系统目标的功率、存储、处理和通信要求产生了难以承受的需求。这个的该项目旨在通过开发自适应、面向任务和物理感知的数据到决策管道来推进基于学习的传感和处理技术的科学，该管道联合优化数据驱动学习中的数据采集、重建和推理阶段拟议的研究将为未来智能、自适应和资源节约型传感系统的各种应用奠定基础，包括生物医学成像、遥感、雷达和无线通信。该项目具有三个相互关联的目标（i）发展学习-基于物理感知通过与正则化反问题的基本关系和受现有信号处理模型启发的可解释架构来实现多维信号重建技术，(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，并联合优化传感、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，该框架直接从少量学习测量中推断出可操作的信息（分类、参数估计）计划协同教育的中心主题。外展活动旨在提高 K-12 学生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，因为传感技术处于信息感知和提取的前沿，并且至关重要。该项目将在广泛的应用中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，或者从智能家居技术到自动驾驶汽车。该奖项反映了 NSF 的法定使命，并通过评估被认为值得支持基金会的智力价值和更广泛的影响审查标准。

项目成果

期刊论文数量（20）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
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
影响因子：
0
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
通讯作者：
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