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

快速增长的现代系统依赖于传感器来收集测量结果，通过适当设计的信号处理来执行推断。 其中包括自动驾驶汽车、环境监测系统、智能建筑、安全和监控系统、空中交通管制等。 此外，在这些现有和新兴应用中的许多应用中，通常存在在系统设计中必须考虑的严格的能量、成本、带宽或其他约束。传统上，传感器的放置、调度和功能问题是困难的优化问题。 这项研究开发的重要作用，子模块优化技术必须发挥在解决这些问题。 事实上，虽然近年来在子模块优化工具和应用方面有了很大的发展，但涉及传感、信号处理和推理的大量应用还有待于探索，本研究探讨了这些问题的几个实例和方面，包括：受各种实际约束的无源和有源传感系统中的空间阵列的优化;- 传感器布置和调度的联合优化，用于环境动态的有效学习;-受感兴趣的推理任务的相关信号处理的计算约束的空间和时间感测的优化;为涉及高、低层次推理的目标应用程序开发子模块信息测度族;发展稳健的、适应性的和通用的传感设计，以减轻对系统和环境方面的不完整知识的影响;以及通过在没有凸性的情况下利用子模块性的加权选择公式来优化空间和时间之外的感测系统参数。 每一项研究都涉及到所需分析、算法和架构的开发，并以当代激励应用程序为指导。

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.