Feature selection algorithms for flexible analog-to-feature converter

Back, Antoine; Chollet, Paul; Fercoq, Olivier; Desgreys, Patricia

doi:10.1109/newcas49341.2020.9159817

Cited by 1 publication

(2 citation statements)

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“…Inspired by an acquisition method thought for IoT devices, a promising way to circumvent the above limitation is an alternative sampling technique called Analog-to-Feature (A2F) conversion that directly acquires features in the analog domain instead of traditional sensor digitization strategies 11 . Due to the unique way of directly extracting useful information only in the analog domain, it has been proven to harbor enormous potential to signi cantly improve the transmission bandwidth and energy e ciency of signal processing in various applications [12][13][14] . But most A2F methods reported in the literature are designed for speci c application such as signal recovery, rather than classi cation tasks, which suffers from noise folding and poor feature extraction accuracy 12,13,15 .…”

Section: Introductionmentioning

confidence: 99%

“…Due to the unique way of directly extracting useful information only in the analog domain, it has been proven to harbor enormous potential to signi cantly improve the transmission bandwidth and energy e ciency of signal processing in various applications [12][13][14] . But most A2F methods reported in the literature are designed for speci c application such as signal recovery, rather than classi cation tasks, which suffers from noise folding and poor feature extraction accuracy 12,13,15 .…”

Section: Introductionmentioning

confidence: 99%

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A sensing paradigm of analog-to-feature (A2F) based on the MEMS accelerometer-integrated reservoir computing

Zou,

Yang,

Sun

et al. 2024

Preprint

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The current boom in Micro-electromechanical Systems combined with the advent of the physical reservoir computing make it possible to break through the bottleneck of traditional sensing paradigms based on physically separated sensors and processors. Here, we report a pioneering work on a novel sensing paradigm of analog-to-feature using a stiffness modulated micromechanical resonant accelerometer. Specifically, the amplitude-acceleration nonlinearity of the accelerometer and its transient nonlinearity serve as the nonlinear dynamics of our physical reservoir computing to categorize the accelerometer input. Furthermore, we perform ten times tenfold cross-validation tests and use the confusion matrix to test its classification performance on a data set of the different acceleration corresponding to the different motion postures generated by a six-axis IMU. The results show a classification accuracy of 97.33%, which proves that our MEMS accelerometer-integrated reservoir computing enables data processing ability locally at the sensing side for efficient distributed processing.

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