Sensors and Sensor Networks: Development and Testing of Practical Algorithms for Online Interpretation of Sensor Data through Wavelet Decomposition

传感器和传感器网络：通过小波分解在线解释传感器数据的实用算法的开发和测试

• 批准号: 0330145
• 负责人: Tapas Das
• 金额: $ 20万
• 依托单位: University of South Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0330145&HistoricalAwards=false
• 关键词: Sensors; Sensor; Networks; Development; Testing

Project Summary: Development and Testing of Practical Algorithms for Online Interpretation of Sensor Data through Wavelet Decomposition Proposal # 0330145 Recent growth in sensor technology and its applications have made it easy to gather abundance of data. Unfortunately, the data collection rate via sensors usually far exceeds our current ability to process them. A remedy to this bottleneck situation is the development of effective and online implementable statistical algorithms for data interpretation. Furthermore, continued miniaturization of the sensors have significantly increased the sophistication of the sensor fabrication technology which itself is heavily dependent on sensors. Thus, both ends of the sensor technology (fabrication and data interpretation) stand to gain from this research. The funding granted would allow us to 1) develop theoretical analysis needed to assess the effectiveness of online multiresolution algorithms in interpreting sensor data, and construct efficient univariate and multivariate online algorithms, 2) test online algorithms on sensor data collected from a nanoscale fabrication process, and 3) develop a new Wavelet-EWMA feedback control strategy using online sensor data analysis algorithms. Our strategy for objective #1 is to first build a mathematical model for the multiresolution data analysis scheme in order to assess performance measures such as false alarm rate, and average run length. Our testing strategy is an elaborate one. We will collect a variety of sensor data from the test bed that relate to a number of outstanding operational issues. With each of these data sets, we will not only test the performances of the algorithms, but also use the test results to provide answers to the above issues. If successful, the mathematical analysis developed by this research would provide a strong foundation for performance assessment of wavelet based online multiscale algorithms. This science base would lead to the development of online and practically viable algorithms for sensor data analysis. Transfer of knowledge discovered through the proposed research into technological innovations will be enhanced through our collaborations with the semiconductor industry.

项目摘要：开发和测试通过小波分解在线解释传感器数据的实用算法建议#0330145传感器技术及其应用的最新发展使收集大量数据变得容易。不幸的是，通过传感器收集数据的速度通常远远超过我们目前处理它们的能力。解决这一瓶颈问题的一个办法是开发有效的、可在线实施的数据解释统计算法。此外，传感器的不断小型化大大提高了传感器制造技术的复杂性，而传感器制造技术本身严重依赖于传感器。因此，传感器技术的两端(制造和数据解释)都将从这项研究中受益。获得的资金将使我们能够1)开发所需的理论分析，以评估在线多分辨率算法在解释传感器数据方面的有效性，并构建高效的单变量和多变量在线算法，2)测试从纳米级制造过程中收集的传感器数据的在线算法，以及3)使用在线传感器数据分析算法开发新的小波-EWMA反馈控制策略。对于目标1，我们的策略是首先为多分辨率数据分析方案建立一个数学模型，以便评估诸如虚警率和平均运行长度等性能指标。我们的测试策略是精心设计的。我们将从试验台收集各种传感器数据，这些数据与一些悬而未决的操作问题有关。对于每个数据集，我们不仅将测试算法的性能，还将使用测试结果为上述问题提供答案。如果成功，本研究开发的数学分析将为基于小波的在线多尺度算法的性能评估提供坚实的基础。这一科学基础将导致开发用于传感器数据分析的在线和实际可行的算法。我们将通过与半导体业的合作，加强将通过拟议研究发现的知识转化为技术创新。