Towards Cognizant Sensors: Making sense of data through physics

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，这些数据在本地或远程收集和处理，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级别集成传感和认知能力，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。该研究计划建立在使用非线性材料和设备响应作为非常规学习平台的最新进展之上，以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，感知传感器。我们的方法基于一种称为水库计算的技术，其中输入被应用于互连非线性神经元的固定网络（即，储存器）以衰减的方式保留关于过去事件的信息。库的功能是将输入数据非线性地映射到高维空间上，使得目标事件是可分离的。然后，神经元的输出由一个简单的接口读取，并使用可训练算法自适应地组合。由于水库在整个过程中保持不变，水库计算是物理机器学习的合适平台。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证明。 我们将依靠我们在微纳米系统，统计信号处理和各种非线性现象研究领域的良好记录，进行开发物理储层所需的基础研究，并将其应用于解决该领域的三个选定挑战。具体而言，我们将开发：（i）用于机器状态监测的认知加速度计;（ii）在试图测量气体混合物中的特定气体浓度时解决交叉敏感性挑战的认知气体传感器;以及（iii）一旦作为服装的一部分穿着，就可以检测人类活动的认知纺织品。 通过该计划开发的核心知识和技术适用于微观或宏观尺度的其他领域。参与该计划的学员将相互密切合作，并将获得超越其个人项目框架的技能。该计划预计将通过后续项目为Cognizant Sensors提供新的解决方案和设计指南，并将促进未来与学术和工业合作伙伴的合作。这种伙伴关系为受训者提供实习和实践培训机会，拓宽他们的技术、专业和创业视野。

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.