Towards Cognizant Sensors: Making sense of data through physics

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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.