Data acquisition optimization for smart monitoring networks

智能监控网络的数据采集优化

• 批准号: RGPIN-2014-05827
• 负责人: Groza, Voicu
• 金额: $ 2.26万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=588642
• 关键词: Data; acquisition; optimization; smart; monitoring

Data Acquisition Systems (DAS) digitize analog signals in order to be digitally processed by embedded systems in monitoring or control applications. DAS are at the front-end of the chain of data processing and, as such, any quantization and sampling error that is input to the computing system may cause catastrophic failures in the correct interpretation of the acquired signals. Current DAS have no provisions for Measurement Quality Assurance (MQA) to certify in real-time the correctness and trustworthiness of measurements by providing metadata on the precision, accuracy, conditions and utility of measurements, and which are essential requirements for operation in remote unsupervised environments. The lack of this information made impossible the development of viable monitoring technologies for personal healthcare applications or constrained their proliferation in other domains. Losing data because of unreliable operation, due to scarce resources caused by unexpected events, fuelled a lack of confidence in these technologies, as well. In order to address the resources efficiency, reliability and versatility, while increasing accuracy, trustworthiness and adequacy of the collected information, we propose to conceiving, developing and optimizing architectures, algorithms and models at different levels of data acquisition. The quantization process will be marked by MQA qualifiers which will objectively and globally characterize the measurands (acquired signals), measurement uncertainty, acquisition conditions (compliance with the requirements of standards), and measuring devices (Hardware and Software), including the accuracy of the indirect measurement algorithms. This research will employ compressed sensing techniques in order to reduce digitization relative errors and optimize the density of transmitted data. Floating-point differential quantization methods will be used to insure a broad dynamic range of the acquired signals and eliminate redundant information. Adaptive sampling rate algorithms will be studied in the context of the acquisition of sparse signals with strong deterministic components, but which may occasionally deviate from stable operation. Adaptive compressive sampling and quantization will mitigate the power consumption if resources get scarcely, while maximizing the data acquisition capacity and minimizing the measurement uncertainty. One of the most challenging applications of DAS is the non-invasive personal health monitoring. Assessment of quotidian physiological parameters relies on measurement and processing of bio-signals that are strongly correlated with these parameters and which can be acquired non-invasively. Optimization of methods in estimation of physiological parameters based on multi-sensors and algorithms fusion will be a central objective of this research plan. The amount of collected data will be optimally adapted to the resources constraints. Current technologies require more power for data transmission rather than processing. Hence, the local DAS management will decide to calculate locally the parameters whenever is possible, to minimize communication, in order to save energy. This research project will be validated by implementing its findings on a multi-sensor DAS platform that will be capable of fusing data, adapting its operation and structure to the dynamics of the monitored signals, selftesting, self-calibrating and estimating the MQA parameters. This project aims at improving the quality of health services in Canada by enabling people to monitor and report their health conditions. Not only will medical practitioners have access to trustworthy data, but governmental agencies will also be able to concurrently monitor the efficiency of their policies and investments.

数据采集​​系统 (DAS) 将模拟信号数字化，以便由监控或控制应用中的嵌入式系统进行数字处理。 DAS 位于数据处理链的前端，因此，输入到计算系统的任何量化和采样误差都可能导致无法正确解释所获取的信号。目前的 DAS 没有提供测量质量保证 (MQA) 的规定，可以通过提供有关测量精度、准确度、条件和实用性的元数据来实时证明测量的正确性和可信度，而这些元数据是在远程无监督环境中操作的基本要求。缺乏这些信息使得不可能为个人医疗保健应用开发可行的监控技术，或者限制了它们在其他领域的扩散。由于运行不可靠、意外事件导致资源稀缺而导致数据丢失，也加剧了人们对这些技术缺乏信心。 为了解决资源效率、可靠性和多功能性，同时提高所收集信息的准确性、可信度和充分性，我们建议在不同数据采集级别上构思、开发和优化架构、算法和模型。量化过程将通过MQA限定符来标记，这些限定符将客观地、全局地表征被测量（采集的信号）、测量不确定度、采集条件（符合标准要求）和测量设备（硬件和软件），包括间接测量算法的准确性。 这项研究将采用压缩传感技术来减少数字化相对误差并优化传输数据的密度。浮点差分量化方法将用于确保所采集信号的宽动态范围并消除冗余信息。自适应采样率算法将在采集具有强确定性成分的稀疏信号的背景下进行研究，但有时可能会偏离稳定运行。如果资源稀缺，自适应压缩采样和量化将减少功耗，同时最大化数据采集能力并最小化测量不确定性。 DAS 最具挑战性的应用之一是非侵入式个人健康监测。日常生理参数的评估依赖于与这些参数密切相关且可以非侵入性获取的生物信号的测量和处理。基于多传感器和算法融合的生理参数估计方法的优化将是该研究计划的中心目标。收集的数据量将根据资源限制进行最佳调整。当前的技术需要更多的电力来传输数据而不是处理。因此，本地 DAS 管理层将决定尽可能在本地计算参数，以尽量减少通信，从而节省能源。 该研究项目将通过在多传感器 DAS 平台上实施其研究结果进行验证，该平台能够融合数据、根据监测信号的动态调整其操作和结构、自测试、自校准和估计 MQA 参数。 该项目旨在通过使人们能够监测和报告自己的健康状况来提高加拿大的医疗服务质量。医生不仅可以获得可靠的数据，而且政府机构也能够同时监控其政策和投资的效率。