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CIF:Small:Submodular Optimization Techniques for Sensing, Signal Processing, and Inference.

CIF:Small:Submodular Optimization Techniques for Sensing, Signal Processing, and Inference.
CIF:Small:用于传感、信号处理和推理的子模块优化技术。
批准号:
1717610
负责人:
Gregory Wornell
金额:
$50.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-07-01 至 2022-06-30

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中文摘要
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英文摘要
A rapidly growing collection of modern systems rely on sensors to collect measurements from which inference is performed via suitably designed signal processing. These include autonomous vehicles, environmental monitoring systems, smart buildings, security and surveillance systems, air traffic control, and many others. Moreover,in many of these existing and emerging applications, there are often stringent energy, cost, bandwidth, or other constraints that must be taken into account in the system design. Problems of placement,scheduling, and functionality of sensors have traditionally been difficult optimization problems. This research develops the important role that submodular optimization techniques have to play in addressing such problems. Indeed, while there has been significant development in submodular optimization tools and applications in recent years, a wealth of applications involving sensing, signal processing and inference have yet to be explored.The research investigates several instances and aspects of these problems, including: optimization of spatial arrays in passive and active sensing systems subject to diverse practical constraints; joint optimization of sensor placement and scheduling for efficient learning of environmental dynamics; optimization of spatial and temporal sensing subject to computational constraints on the associated signal processing for the inference task of interest; development of families of submodular information measures for target applications involvingboth high- and low-level inference; development of robust, adaptive, and universal designs for sensing that mitigate the effects of incomplete knowledge of aspects of the system and environment; and optimization of sensing system parameters beyond space and time via weighted selection formulations exploiting submodularity in the absence of convexity. Each of these investigations involves the development of the required analysis, algorithms, and architectures, guided by contemporary motivating applications.
期刊论文(14)
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科研奖励(0)
会议论文
DOI: --
发表时间: 2021-11
期刊: ArXiv
影响因子: --
作者: [Yuheng Bu;Gholamali Aminian;L. Toni;Miguel L. Rodrigues;G. Wornell]
通讯作者: Yuheng Bu;Gholamali Aminian;L. Toni;Miguel L. Rodrigues;G. Wornell
DOI: --
发表时间: 2019
期刊: Proceedings of the International Workshop on Artificial Intelligence and Statistics
影响因子: --
作者: [Staib, M., Wilder, B., Jegelka, S.]
通讯作者: Jegelka, S.
DOI: 10.48550/arxiv.2208.04055
发表时间: 2022-08
期刊: ArXiv
影响因子: --
作者: [Nikolaos Karalias;Joshua Robinson;Andreas Loukas;S. Jegelka]
通讯作者: Nikolaos Karalias;Joshua Robinson;Andreas Loukas;S. Jegelka
Tighter Expected Generalization Error Bounds via Convexity of Information Measures
通过信息度量的凸性来更严格的预期泛化误差界限
DOI: 10.1109/isit50566.2022.9834474
发表时间: 2022
期刊: IEEE International Symposium on Information Theory
影响因子: --
作者: [Aminian, Gholamali, Bu, Yuheng, Wornell, Gregory W., Rodrigues, Miguel R.]
通讯作者: Rodrigues, Miguel R.
13
    CIF: Small: Occlusion-Based Computational Imaging and Scene Analysis: Theory, Methods and Applications
    CIF: Small: Theory, Algorithms, and Applications of Super-Nyquist Coding
    CIF: Small: Theory and Codes for Intermittent and Sparse Communication
    2008 Institute of Electrical and Electronics Engineers International Symposium on Information Theory
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