Towards Cognizant Sensors: Making sense of data through physics

迈向认知传感器：通过物理学理解数据

• 批准号: RGPIN-2020-06348
• 负责人: Bahreyni, Behraad
• 金额: $ 3.35万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717990
• 关键词: Towards; Cognizant; Sensors; Making; sense

Distributed sensor networks, such as wearables and Internet-of-Things, produce growing amounts of data that is collected and processed locally or remotely to generate context using sophisticated machine learning algorithms. The next step in developing sensing systems is to integrate the sensing and cognition abilities at the sensor level so that patterns in the data are detected as it arrives without the need for back and forth transmission of raw data. This research program builds upon the latest advances in using nonlinear material and device responses as unconventional learning platforms to develop sensing systems that not only measure the target parameters but simultaneously recognize patterns and provide cognitive information; i.e., Cognizant Sensors. Our approach is based on a technique known as Reservoir Computing, where the input is applied to a fixed network of interconnected nonlinear neurons (i.e., the reservoir) that retain, in a decaying manner, information about the past events. The function of the reservoir is to map the input data onto a high-dimensional space nonlinearly such that the target events are separable. The outputs of neurons are then read by a simple interface and adaptively combined using a trainable algorithm. As the reservoir remains unchanged throughout the process, reservoir computing is a suitable platform for physical machine learning. Examples of such systems based on nonlinear photonic, electronic, and fluidic neurons have been proposed and demonstrated. We will rely on our proven track record in the fields of micro- and nano-systems, statistical signal processing, and study of various nonlinear phenomena to conduct the fundamental research required to develop physical reservoirs and apply them to solve three selected challenges in the field. Specifically, we will develop: (i) A Cognizant Accelerometer for the condition monitoring of machinery; (ii) A Cognizant Gas Sensor to address the cross-sensitivity challenge when trying to measure a particular gas concentration in a gas mixture; and (iii) A Cognizant Textile that can detect human activity once worn as part of a garment. The core knowledge and techniques developed through this program apply to other areas at micro- or macro-scales. The trainees involved in this program will collaborate closely with each other and will attain skillsets that expand beyond the framework of their individual projects. The program is expected to result in new solutions and design guidelines for Cognizant Sensors through follow up projects and will instigate future collaborations with academic and industrial partners. Such partnerships provide internship and hands-on training opportunities to the trainees, broadening their technical, professional, and entrepreneurial horizons.

分布式传感器网络，如可穿戴设备和物联网，产生越来越多的数据，这些数据被收集并在本地或远程处理，以使用复杂的机器学习算法生成上下文。开发传感系统的下一步是在传感器层面整合传感和认知能力，以便在数据到达时检测到数据中的模式，而不需要来回传输原始数据。这项研究计划建立在使用非线性材料和设备响应作为非传统学习平台的最新进展的基础上，开发不仅测量目标参数而且同时识别模式并提供认知信息的传感系统，即认知传感器。我们的方法是基于一种称为水库计算的技术，其中输入被应用于由相互连接的非线性神经元(即水库)组成的固定网络，该网络以衰减的方式保留关于过去事件的信息。储存库的功能是将输入数据非线性地映射到高维空间，使得目标事件是可分离的。然后，神经元的输出被一个简单的界面读取，并使用可训练的算法进行自适应组合。由于水库在整个过程中保持不变，水库计算是适合物理机器学习的平台。基于非线性光子、电子和流体神经元的这种系统的例子已经被提出和演示。 我们将依靠我们在微纳系统、统计信号处理和各种非线性现象研究领域的成熟记录，开展开发物理油藏所需的基础研究，并将其应用于解决该领域选定的三个挑战。具体地说，我们将开发：(I)用于机械状况监测的Cognizant加速度计；(Ii)Cognizant气体传感器，用于解决试图测量气体混合物中特定气体浓度时的交叉敏感性挑战；以及(Iii)Cognizant纺织品，它可以检测穿在衣服上的人类活动。 通过该方案开发的核心知识和技术适用于微观或宏观范围的其他领域。参与该计划的学员将相互密切合作，并将获得超出各自项目框架的技能。该计划预计将通过后续项目为Cognizant传感器带来新的解决方案和设计指南，并将推动未来与学术和工业合作伙伴的合作。这种伙伴关系为学员提供实习和实践培训机会，拓宽了他们的技术、专业和创业视野。