This paper reports a highly sensitive piezoelectric MEMS resonant microphone array for detection and classification of wheezing in lung sounds. The resonant microphone array (RMA) is composed of 8 width-stepped cantilever resonant microphones with Mel-distributed resonance frequencies from 230 to 630 Hz, the main frequency range of wheezing. At the resonance frequencies, the unamplified sensitivities of the microphones in the RMA are between 86 and 265 mV/Pa, while the signal-to-noise ratios (SNRs) for 1 Pa sound pressure are between 86.6 and 98.0 dBA. Over 200 – 650 Hz, the unamplified sensitivities are between 35 and 265 mV/Pa, while the SNRs are between 79 and 98 dBA. Wheezing feature in lung sounds recorded by the RMA is more distinguishable than that recorded by a reference microphone with traditional flat sensitivity, and thus, the automatic classification accuracy of wheezing is higher with the lung sounds recorded by the RMA than with those by the reference microphone, when tested with deep learning algorithms on computer or with simple machine learning algorithms on low-power wireless chip set for wearable applications.

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A complete system solution extracting signals from the patient chest with three leads including motion artifact removal in both analog and digital implementations are described. The resulting ECG signal is transferred via Bluetooth low energy to a mobile phone. Using deep sleep modes, the overall power consumption is less than 300µA and the device can operate for more than 20 days using a 150mAh battery. The screening software looks for suspicious traces such as those with missing pulses, tachycardia, bradycardia, etc. The mobile phone software also eliminates any remaining motion artifact. The traces are subsequently processed in detail in a cloud server and to a physicians dashboard for long-term monitoring.

This paper describes an integrated circuit (IC) authentication and tamper detection system, based on a Film Bulk Acoustic Resonator (FBAR) and passive Radio-Frequency Identification (RFID), which allows for wireless detection of tampering or counterfeiting in packaged ICs. We demonstrate the concept through the use of a 2.6 GHz FBAR based on a Zinc Oxide (ZnO) thin film. The FBAR is series connected to a piezoelectric energy harvester, which can generate voltage pulses with a peak amplitude of 56 V when tampering activity is detected. Our measurements validate this concept and demonstrate that we can permanently alter the high frequency resonance characteristics of the FBAR through dielectric breakdown caused by tampering.