TY - GEN
T1 - Adapted Compressed Sensing with Incremental Encoder and Deep Performance Predictor for Low-Power Sensor Node Design
AU - Marchioni, A.
AU - Martinini, F.
AU - Manovi, L.
AU - Cortesi, S.
AU - Rovatti, R.
AU - Setti, Gianluca
AU - Mangia, M.
N1 - KAUST Repository Item: Exported on 2023-07-17
PY - 2023/7/13
Y1 - 2023/7/13
N2 - Wearable solutions, especially within the realm of health, are in high demand. Meeting this demand is possible thanks to energy-efficient sensors that acquire signals and transmit them as digital messages. Since transmission often heavily contributes to the overall energy consumption, compression can be beneficial. Inspired by Compressed Sensing, we propose an iterative compression scheme able to reduce the energy needed to transmit a signal. To meet a constraint in the quality of service, our novel scheme adapts the number of transmitted digital words for each signal instance. Adaptation is based on the output of a performance predictor embedded in the receiving decoder, that at every iteration estimates the quality of service. We test our novel compression paradigm over ECG signals and BLE communication protocol. Our method significantly reduces computation and transmission energy consumption compared to other already established techniques based on Compressed Sensing.
AB - Wearable solutions, especially within the realm of health, are in high demand. Meeting this demand is possible thanks to energy-efficient sensors that acquire signals and transmit them as digital messages. Since transmission often heavily contributes to the overall energy consumption, compression can be beneficial. Inspired by Compressed Sensing, we propose an iterative compression scheme able to reduce the energy needed to transmit a signal. To meet a constraint in the quality of service, our novel scheme adapts the number of transmitted digital words for each signal instance. Adaptation is based on the output of a performance predictor embedded in the receiving decoder, that at every iteration estimates the quality of service. We test our novel compression paradigm over ECG signals and BLE communication protocol. Our method significantly reduces computation and transmission energy consumption compared to other already established techniques based on Compressed Sensing.
UR - http://hdl.handle.net/10754/692962
UR - https://ieeexplore.ieee.org/document/10175954/
U2 - 10.1109/i2mtc53148.2023.10175954
DO - 10.1109/i2mtc53148.2023.10175954
M3 - Conference contribution
BT - 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
PB - IEEE
ER -
