ABSTRACT
This paper reports a highly sensitive piezoelectric microelectromechanical systems (MEMS) resonant microphone array (RMA) for detection and classification of wheezing in lung sounds. The RMA is composed of eight 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-1, 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-1, 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.
ABSTRACT
This paper presents active noise cancelation (ANC) based on MEMS resonant microphone array (RMA) which offers very high sensitivities (and thus very low noise floors) near resonance frequencies and also provides filtering in acoustic domain. The ANC is targeted to actively cancel out any sound between 5 - 9 kHz (above the speech range of 300 - 3,400 Hz). The ANC works best around the resonance frequencies of the resonant microphones where the sensitivities are high. The ANC has been implemented with analog inverter, digital phase compensator, digital adaptive filter, and deep learning, and shown to perform better with a digital adaptive filter for both RMA-based and flat-band-microphone-based ANC. At the same time, when the sound intensity over 5 - 9 kHz is low, RMA-based ANC with adaptive filter works the best among different approaches tested. Automatic speech recognition under different noises (of different intensity levels) has been tested with ANC. In all the tested cases, word error rate improves with ANC.
ABSTRACT
Current methods of prostate cancer diagnosis and therapy rely on accurate imaging of the prostate using real-time ultrasound. Transurethral ultrasound (TUUS) may improve upon the current gold standard through improved 3D visualization and co-registration (fusion) with CT and MRI. A prototype transurethral ultrasound (TUUS) catheter-based transducer array and system was developed, featuring 32 elements with a diameter of 18F (6mm). A robust, multi-channel ultrasound transceiver was also developed to enable TUUS imaging using pulse-echo and frequency-based signal processing methods. The feasibility of a TUUS imaging system suitable for multi-modal image fusion and novel ultrasound signaling techniques was demonstrated.
