MEMS Microphones as Ultrasonic Transducers: Nonlinear Electrostatic Actuation and a Parametric Array Prototype

This paper investigates commercial-style capacitive MEMS microphone dies as air-coupled ultrasonic transmitters under nonlinear pull-in and snap-back actuation and demonstrates a compact parametric-array prototype. A single die produces large diaphragm displacement and measurable ultrasonic pressure in air. A 28-die array driven at 83 and 93 kHz generates a directional component at the 10 kHz difference frequency. Measurements are compared with analytical radiation theory and finite-element modeling, and the effects of aperture, fill factor, device uniformity, and receiver nonlinearity are discussed.

An early prediction of covid-19 associated hospitalization surge using deep learning approach

The global pandemic caused by COVID-19 affects our lives in all aspects. As of September 11, more than 28 million people have tested positive for COVID-19 infection, and more than 911,000 people have lost their lives in this virus battle. Some patients can not receive appropriate medical treatment due the limits of hospitalization volume and shortage of ICU beds. An estimated future hospitalization is critical so that medical resources can be allocated as needed. In this study, we propose to use 4 recurrent neural networks to infer hospitalization change for the following week compared with the current week. Results show that sequence to sequence model with attention achieves a high accuracy of 0.938 and AUC of 0.850 in the hospitalization prediction. Our work has the potential to predict the hospitalization need and send a warning to medical providers and other stakeholders when a re-surge initializes.