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Browsing by Author "Davaria, Sheyda"

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    Developing Active Artificial Hair Cell Sensors Inspired by the Cochlear Amplifier
    Davaria, Sheyda (Virginia Tech, 2021-01-26)
    The mammalian cochlea has been the inspiration to develope contemporary cochlear implants and active dynamic sensors that operate in the sensor's resonance region and possess favorable nonlinear characteristics. In the present work, multi-channel and self-sensing active artificial hair cells (AHCs) made of piezoelectric cantilevers and controlled by a cubic damping feedback controller are developed numerically and experimentally. These novel AHCs function near a Hopf bifurcation and amplify or compress the output by a one-third power-law relationship with the input, analogous to the mammalian cochlear amplifier. The multi-channel AHCs have extended frequency bandwidth to sense over multiple resonant frequencies, unlike conventional single-channel AHCs. Therefore, the adoption of these AHCs reduces the number of required sensors to cover the desired bandwidth of interest in an array format. Furthermore, a novel self-sensing active AHC is created in this study using quadmorph beams for future cochlear implants or sensor design applications. The self-sensing scheme allows miniaturization of the system, embedding AHCs in a limited space, and fabrication of AHC arrays by omitting external sensors from the system for practical implementation. Preliminary research on the extension of this research to MEMS AHCs and arrays of AHCs is also presented. The active AHCs can lead to transformative improvements in the dynamic range, sharpness of the response, and threshold of sound detection in cochlear implants to aid individuals with sensorineural hearing loss. Additionally, they can enhance the dynamic properties of sensors such as fluid flow sensors, microphones, and vibration sensors for various applications.