Radio Frequency Identi cation (RFID) technology is o en deployed for inventory management scenarios. In inventory applications, a known or unknown number of RFID tags are queried in a discrete manner and for a single, short period of time, until each tag is recognized by the interrogator device. Passive RFID provides several bene ts conducive to ubiquitous deployment, including RFID tags that are energized from the wireless RF interrogation signal itself that obviates the need for a ba ery or wired power, and antenna assemblies that can be integrated with the chip with only a small footprint. We have utilized these bene ts to enable continuous biomedical sensing devices with minimal footprint and ba eryless deployment. ese devices are fabric-based smart garments with an embedded RFID tag and antenna assembly. However, traditional inventory-based RFID interrogation presents several challenges due to the RFID protocols and regulations that govern their use. In this paper, we discuss the considerations necessary to utilize RFID interrogation to enabling passive, continuous sensor monitoring, and the techniques we employed in developing so ware to do so.•Computer systems organization →Sensor networks; •Hardware →Digital signal processing; •So ware and its engineering →So ware design engineering;
KEYWORDSIoT sensor processing framework, Signal processing, So ware architecture ACM Reference format:
Many speech processing systems struggle in conditions with low signal-to-noise ratios and in changing acoustic environments. Adaptation at the transduction level with integrated signal processing could help to address this; in human hearing, transduction and signal processing are integrated and can be adaptively tuned for noisy conditions. Here we report a microelectromechanical cochlea as a bio-inspired acoustic sensor with integrated signal processing functionality. Real-time feedback is used to tune the sensing and processing properties, and dynamic switching between linear and nonlinear characteristics improves the detection of signals in noisy conditions, increases the sensor dynamic range and enables adaptation to changing acoustic environments. The transition to nonlinear behaviour is attributed to a Hopf bifurcation and we experimentally validate its dependence on sensor and feedback parameters. We also show that output-signal coupling between two coupled sensors can increase the frequency coverage.
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