H Hasan Mohammad, Abbasalipour Amin, Nikfarjam Hamed, Pourkamali Siavash, Emad-Ud-Din Muhammad, Jafari Roozbeh, Alsaleem Fadi
Department of Earth and Space Sciences, Columbus State University, Columbus, GA 31909, USA.
Department of Electrical and Computer Engineering, University of Texas at Dallas, Dallas, TX 75080, USA.
Micromachines (Basel). 2021 Mar 5;12(3):268. doi: 10.3390/mi12030268.
The goal of this paper is to provide a novel computing approach that can be used to reduce the power consumption, size, and cost of wearable electronics. To achieve this goal, the use of microelectromechanical systems (MEMS) sensors for simultaneous sensing and computing is introduced. Specifically, by enabling sensing and computing locally at the MEMS sensor node and utilizing the usually unwanted pull in/out hysteresis, we may eliminate the need for cloud computing and reduce the use of analog-to-digital converters, sampling circuits, and digital processors. As a proof of concept, we show that a simulation model of a network of three commercially available MEMS accelerometers can classify a train of square and triangular acceleration signals inherently using pull-in and release hysteresis. Furthermore, we develop and fabricate a network with finger arrays of parallel plate actuators to facilitate coupling between MEMS devices in the network using actuating assemblies and biasing assemblies, thus bypassing the previously reported coupling challenge in MEMS neural networks.
本文的目标是提供一种新颖的计算方法，可用于降低可穿戴电子产品的功耗、尺寸和成本。为实现这一目标，引入了使用微机电系统（MEMS）传感器进行同时传感和计算的方法。具体而言，通过在MEMS传感器节点本地实现传感和计算，并利用通常不需要的吸合/释放滞后现象，我们可以消除云计算的需求，并减少模数转换器、采样电路和数字处理器的使用。作为概念验证，我们展示了一个由三个商用MEMS加速度计组成的网络的仿真模型可以利用吸合和释放滞后现象对一系列方波和三角波加速度信号进行固有分类。此外，我们开发并制造了一个具有平行板致动器手指阵列的网络，以利用致动组件和偏置组件促进网络中MEMS设备之间的耦合，从而绕过先前报道的MEMS神经网络中的耦合挑战。
