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

摘要：嵌入式系统技术已经达到了部署大量低功耗无线传感设备的实际水平，这些设备具有执行复杂算法的能力，并有选择地将资源分配到最关键的情况。该计划是为分布式、普及、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，该系统能够理解其周围环境，并通过不断评估和调整其行为来响应环境变化。该方法的新颖之处在于：(1)基于动态贝叶斯网络（DBN）的多传感器融合，提供了一个强大、智能和认知的学习框架；(2)协作和分布式蒙特卡罗（CDMC）跟踪方法，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模型的概率推理提供，这提供了跟踪器的自评估。科学目标将是开发一个框架和算法，用于受带宽、能量、可靠性和与低成本分布式传感器相关的位置不确定性约束的传感器信息的多视角、多模式融合。建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，再到本地和系统级别的资源/性能优化等主题。虽然许多单独的问题已经被单独研究，但挑战是在严格限制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情况下，将它们整合到一个新的环境中。分布式、协同传感将在安全和监视、环境监测、工业自动化和远程勘探等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，例如人体跟踪和行为分析，用于医疗应用，如老年人的远程监控。拟议活动的更广泛影响还包括西北大学信号处理和计算机视觉课程的改进和整合，以及本科生进行相关主题研究的机会。

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.