Energy-Efficient Compressed Sensing: A joint Algorithmic/Implementation Approach Using Deterministic Sensing

节能压缩传感：使用确定性传感的联合算法/实现方法

• 批准号: 1363447
• 负责人: Vladimir Stojanovic
• 金额: $ 11.85万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2015-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1363447&HistoricalAwards=false
• 关键词: Energy; Efficient; Compressed; Sensing; joint

The objective of this research is to develop low-power, robust, and energy-efficient sensors to satisfy a wide range of current applications including large-scale networks and implantable biomedical sensors. The aim is to develop the fundamental framework for sensor systems, connecting theory and algorithms with efficient hardware implementations and circuit metrics, such as power, footprint, quantization effects, and other circuit and channel non-idealities. The approach is to develop compressed sensing techniques that result in universal and efficient sensor designs. Intellectual Merit: The transformative aspect of this research is a joint investigation into both theoretical and hardware development of compressed sensing techniques for sensor systems. The plan is to optimize energy allocation in the information chain, by shifting from (the original) randomization techniques to more energy-efficient deterministic sensing techniques. The focus is on regimes relevant to practice, and the results of this research are foreseen to greatly impact both systems and hardware communities.Broader Impacts: Energy-efficient sensor nodes are in great demand today. For example in large-scale networks and implantable sensors, low power nodes are required to increase the average node life-time until maintenance, as well as improve the patient's quality of life. The goal is to convince practitioners, that compressed sensing can be mapped to hardware-efficient implementations. By creating an online portal, the project will also demonstrate the benefits of the developed sensing techniques to both experts and general population. To disseminate the fundamental systems/hardware framework, a new multi-disciplinary course will be created at MIT and offered to a wide-range of students.

这项研究的目标是开发低功耗、坚固且节能的传感器，以满足当前的广泛应用，包括大规模网络和植入式生物医学传感器。目的是开发传感器系统的基本框架，将理论和算法与高效的硬件实现和电路指标（例如功率、占用空间、量化效应以及其他电路和通道非理想性）联系起来。该方法旨在开发压缩传感技术，从而实现通用且高效的传感器设计。智力优点：这项研究的变革性方面是对传感器系统压缩传感技术的理论和硬件开发进行联合研究。该计划是通过从（原始）随机化技术转向更节能的确定性传感技术来优化信息链中的能量分配。重点是与实践相关的制度，预计这项研究的结果将对系统和硬件社区产生重大影响。更广泛的影响：当今对节能传感器节点的需求很大。例如，在大规模网络和植入式传感器中，需要低功耗节点来延长维护前的平均节点寿命，并提高患者的生活质量。目标是让从业者相信压缩感知可以映射到硬件高效的实现。通过创建一个在线门户，该项目还将向专家和普通大众展示所开发的传感技术的好处。为了传播基本系统/硬件框架，麻省理工学院将创建一门新的多学科课程并向广大学生提供。