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Full-Text Articles in Architecture
Modeling And Design Parameter Optimization To Improve The Sensitivity Of A Bimorph Polysilicon-Based Mems Sensor For Helium Detection, Sulaiman Mohaidat, F. M. Alsaleem
Modeling And Design Parameter Optimization To Improve The Sensitivity Of A Bimorph Polysilicon-Based Mems Sensor For Helium Detection, Sulaiman Mohaidat, F. M. Alsaleem
Durham School of Architectural Engineering and Construction: Faculty Publications
Helium is integral in several industries, including nuclear waste management and semiconductors. Thus, developing a sensing method for detecting helium is essential to ensure the proper operation of such facilities. Several approaches can be used for helium detection, including based on the high thermal conductivity of helium, which is several times higher than air. This work utilizes the high thermal conductivity of helium to design and analyze a bimorph MEMS sensor for helium sensing applications. COMSOL Multiphysics software (version 6.2) is used to carry out this investigation. The sensor is constructed from poly-silicon and SiO2 materials with a trenched …
Simulation For A Mems-Based Ctrnn Ultra-Low Power Implementation Of Human Activity Recognition, Muhammad Emad-Ud-Din, Mohammad H. Hasan, Roozbeh Jafari, Siavash Pourkamali, Fadi M. Alsaleem
Simulation For A Mems-Based Ctrnn Ultra-Low Power Implementation Of Human Activity Recognition, Muhammad Emad-Ud-Din, Mohammad H. Hasan, Roozbeh Jafari, Siavash Pourkamali, Fadi M. Alsaleem
Durham School of Architectural Engineering and Construction: Faculty Publications
This paper presents an energy-efficient classification framework that performs human activity recognition (HAR). Typically, HAR classification tasks require a computational platform that includes a processor and memory along with sensors and their interfaces, all of which consume significant power. The presented framework employs microelectromechanical systems (MEMS) based Continuous Time Recurrent Neural Network (CTRNN) to performHAR tasks very efficiently. In a real physical implementation, we show that the MEMS-CTRNN nodes can perform computing while consuming power on a nano-watts scale compared to the micro-watts state-of-the-art hardware. We also confirm that this huge power reduction doesn’t come at the expense of reduced …
Exploiting Pull-In/Pull-Out Hysteresis In Electrostatic Mems Sensor Networks To Realize A Novel Sensing Continuous-Time Recurrent Neural Network, Mohammad H. Hasan, Amin Abbasalipour, Hamed Nikfarjam, Siavash Pourkamali, Muhammad Emad-Un-Din, Roozbeh Jafari, Fadi Alsaleem
Exploiting Pull-In/Pull-Out Hysteresis In Electrostatic Mems Sensor Networks To Realize A Novel Sensing Continuous-Time Recurrent Neural Network, Mohammad H. Hasan, Amin Abbasalipour, Hamed Nikfarjam, Siavash Pourkamali, Muhammad Emad-Un-Din, Roozbeh Jafari, Fadi Alsaleem
Durham School of Architectural Engineering and Construction: Faculty Publications
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 …