2017 IEEE Biomedical Circuits and Systems Conference (BioCAS) 2017
DOI: 10.1109/biocas.2017.8325130
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Bio-inspired active amplification in a MEMS microphone using feedback computation

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Cited by 6 publications
(4 citation statements)
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“…Miles et al [116] have demonstrated active Q control aimed to reduce damping, here using a proportional and differential gain and feedback scheme to an electrostatic mesh, successfully broadening the resonant response without noise gain. A similar effect can be achieved with pulse train stimulation, changing the phase timing of the pulse with respect to the diaphragm oscillations [117]. Active control over the damping in this manner relies on separate methods of measurement and feedback; for example, piezoelectric measurement of membrane motion and capacitive comb feedback [118], or laser diffraction-based measurement and actuation through a capacitive backplate [119,120].…”
Section: Bio-inspired Active Amplification Sensorsmentioning
confidence: 99%
“…Miles et al [116] have demonstrated active Q control aimed to reduce damping, here using a proportional and differential gain and feedback scheme to an electrostatic mesh, successfully broadening the resonant response without noise gain. A similar effect can be achieved with pulse train stimulation, changing the phase timing of the pulse with respect to the diaphragm oscillations [117]. Active control over the damping in this manner relies on separate methods of measurement and feedback; for example, piezoelectric measurement of membrane motion and capacitive comb feedback [118], or laser diffraction-based measurement and actuation through a capacitive backplate [119,120].…”
Section: Bio-inspired Active Amplification Sensorsmentioning
confidence: 99%
“…Motivated by the active mechanisms of signal-detection and processing within biological sensors and systems, past studies have presented novel sensor system architectures that exploit a bio-inspired neuronal model approach namely using the leaky-integrate-and-fire (LIF) neuron [14][15]. It led to the creation of a novel design approach for an acoustic signal processing methodology performed at the transducer level [16][17]. This can be modelled using a feedback control process approach using a mechanical detector (e.g., analogue function) equipped with some sort of electrical computation capabilities (e.g., digital functions) that together can perform peripheral sound processing, hence the "transducer can be part of the signal processing chain", as illustrated in Fig.…”
Section: Adaptive Sound Processingmentioning
confidence: 99%
“…Diagram overview of the feedback control system that is used to implement the concept of adaptive sound processing. Image adapted and redrawn from[16].…”
mentioning
confidence: 99%
“…Inspired by the cochlear characteristics, various vibration sensors, microphones, flow sensors, artificial hair cells (AHCs), and artificial cochleae have been designed (Guerreiro et al, 2017; Knisely, 2017; Shintaku et al, 2010). Among these, the development of artificial cochleae and AHCs with the purpose of creating novel cochlear implants and treating sensorineural hearing loss caused by damage to the hair cells (Robles and Ruggero, 2001) has gained traction in recent years (Davaria and Tarazaga, 2022; Davaria et al, 2020; Joyce and Tarazaga, 2015b; Zhao et al, 2017).…”
Section: Introductionmentioning
confidence: 99%