A Distributed Cognitive Information Processing System [NWU-FY05-067]

分布式认知信息处理系统 [NWU-FY05-067]

• 批准号: 0515929
• 负责人: Thrasyvoulos Pappas
• 金额: --
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-03-01 至 2008-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0515929&HistoricalAwards=false
• 关键词: Distributed; Cognitive; Information; Processing; System

ABSTRACTNSF-0515929Pappas, ThrasyvoulosEmbedded systems technology has reached the level at which it is practical to deploy numerous low-power wireless sensing devices with the capacity to execute sophisticated algorithms and to selectively allocate resources to the most critical situations. The plan is to develop a robust platform for distributed, pervasive, small sensing and computing devices to implement a cognitive information processing system that understands its surroundings and responds to changes in its environment by constantly evaluating and adapting its behavior. The novelty of the approach lies in the use of (1) multiple sensor fusion based on a dynamic Bayesian network (DBN) which provides a powerful, intelligent, and cognitive learning framework, and (2) collaborative and distributed Monte Carlo (CDMC) tracking methods for establishing the collaboration among multiple computational units. Once the DBN model is learned, solutions tothe problems under consideration are provided by the probabilistic inference of the model, which provides a self-evaluation of the tracker. The scientific objective will be to develop a framework and algorithms for multi-perspective, multi-modal fusion of sensor information subject to bandwidth, energy, reliability,and location-uncertainty constraints associated with low-cost distributed sensors. The proposed research covers topics from sensor calibration and feature extraction, to data fusion and collaborative processing, to resource/performance optimizations at the local and system level. While many of the individual problems have been studied on their own, the challenge is to bring them all together in a new context under tight constraints on power consumption for computing and sensor communication, as well as latency constraints and sensor failure considerations.Distributed, collaborative sensing will play an increasingly critical role ina variety of applications involving security and surveillance, environmental monitoring, industrialautomation, and remote exploration. The techniques can also be usedin a number of other applications, such as human tracking and behavior analysis for medicalapplications such as telemonitoring of elderly people. The broader impacts of the proposed activity also include improvements and integration of the signal processing and computer vision curricula at Northwestern and the opportunity for undergraduates to perform research on related topics.

嵌入式系统技术已经达到了部署大量低功率无线传感设备的实用水平，这些设备能够执行复杂的算法并有选择地将资源分配给最关键的情况。该计划是为分布式、无处不在的小型传感和计算设备开发一个强大的平台，以实现一个认知信息处理系统，该系统通过不断评估和调整自己的行为来了解周围环境并对环境的变化做出反应。该方法的创新之处在于使用了(1)基于动态贝叶斯网络(DBN)的多传感器融合，它提供了一个强大的、智能的和认知的学习框架；(2)协作和分布式蒙特卡罗(CDMC)跟踪方法用于建立多个计算单元之间的协作。一旦学习了DBN模型，所考虑的问题的解决方案就由该模型的概率推理来提供，该模型提供了对跟踪器的自我评估。科学目标是开发一个框架和算法，用于多角度、多模式融合传感器信息，受与低成本分布式传感器相关的带宽、能量、可靠性和位置不确定性约束。拟议的研究涵盖了从传感器校准和特征提取，到数据融合和协作处理，到本地和系统级的资源/性能优化的主题。虽然许多单独的问题已经被单独研究，但挑战是在计算和传感器通信的功耗以及延迟限制和传感器故障考虑的严格限制下，将这些问题结合在一起。分布式协作传感将在涉及安全和监视、环境监测、工业自动化和远程探测的各种应用中发挥越来越重要的作用。这些技术还可以用于许多其他应用，例如人体跟踪和医疗应用的行为分析，例如远程监护老年人。拟议活动的更广泛影响还包括改进和整合西北大学的信号处理和计算机视觉课程，以及为本科生提供进行相关专题研究的机会。