Management and Processing of Continuous Streams from Moving Sensors

管理和处理来自移动传感器的连续流

• 批准号: 0307908
• 负责人: Shahram Ghandeharizadeh
• 金额: $ 30.04万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-15 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0307908&HistoricalAwards=false
• 关键词: Management; Processing; Continuous; Streams; Moving

The goal of this project is to investigate a framework to capture both the semantics and essence of an activity pertaining to moving sensor data streams. Moving sensors refers to an emerging class of data intensive applications that impacts disciplines such as communication, health-care, scientific applications, etc. These applications consist of a fixed number of sensors that move and produce streams of data as a function of time. With communication, for example, a hearing impaired individual might utilize a cyber glove that translates hand signs into written (spoken) words. The glove consists of a sensor for each finger joint that reports its location as a function of time, producing streams of data.The framework consumes streams of data and constructs their multi level, spatio temporal representation at different levels of abstraction. This multi-layer design ensures extensibility, modularity, and physical data independence from the stream producing devices. While this framework is demonstrated in the context of several applications, its key demonstration is with an application to recognize hand signs (in particular, American Sign Language) using a virtual-reality glove. This shows the extensibility of the framework to recognize new signs and its modularity to handle new input devices. The framework improves the quality of detected patterns by (a) employing buffers for delayed decision making, (b) different layers maintain context to provide hints when input data is noisy, and (c) employs the concept of Envelope of Limits (EoL) to compensate for a user's slight variations when performing a spatio-temporal sign. More information can be obtained from the project web sites (http://dblab.usc.edu) and (http://infolab.usc.edu). The broader impact of this project is to further the field of multimedia by incorporating hand-gesture as a new mode for conveying information, with applications for the people with disabilities.

该项目的目标是研究一个框架，以捕获与移动传感器数据流有关的活动的语义和本质。移动传感器是指一类新兴的数据密集型应用，影响通信、医疗保健、科学应用等学科。这些应用由固定数量的传感器组成，这些传感器移动并产生作为时间函数的数据流。例如，在交流方面，听力受损的人可能会使用一种将手势翻译成书面（口头）文字的网络手套。该手套由每个手指关节上的传感器组成，该传感器可以根据时间报告其位置，从而产生数据流。该框架使用数据流，并在不同的抽象层次上构建它们的多层次、时空表示。这种多层设计确保了可扩展性、模块化和物理数据独立于流生成设备。虽然这个框架在几个应用程序的背景下进行了演示，但其关键演示是使用虚拟现实手套识别手势（特别是美国手语）的应用程序。这显示了框架识别新符号的可扩展性和处理新输入设备的模块化。该框架通过以下方式提高了检测模式的质量：(a)使用缓冲区来延迟决策，(b)不同层维护上下文以在输入数据有噪声时提供提示，以及(c)使用极限包络（EoL）的概念来补偿用户在执行时空符号时的微小变化。更多信息可从项目网站（http://dblab.usc.edu）和（http://infolab.usc.edu）获得。这项计划的更广泛影响是将手势作为一种传递信息的新模式，并应用于残疾人士，从而进一步推动多媒体领域的发展。