A Practical Architecture for SAR-based ADCs with Embedded Compressed Sensing Capabilities

Paolino, Carmine; Pareschi, Fabio; Mangia, Mauro; Rovatti, Riccardo; Setti, Gianluca

doi:10.1109/prime.2019.8787816

Cited by 2 publications

(1 citation statement)

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“…This implies an analog storage to hold the intermediate sum value. Yet, independently of the actual implementation and technology, the approach is doomed to suffer from leakage and disturbance [9], [38]. These phenomena degrade the stored value along time and their effect increases with the hold time and the number of sums.…”

Section: Windowsmentioning

confidence: 99%

Deep Neural Oracles for Short-window Optimized Compressed Sensing of Biosignals

Mangia

Prono

Marchioni

et al. 2020

IEEE Trans. Biomed. Circuits Syst.

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The recovery of sparse signals given their linear mapping on lower-dimensional spaces can be partitioned into a support estimation phase and a coefficient estimation phase. We propose to estimate the support with an oracle based on a deep neural network trained jointly with the linear mapping at the encoder. The divination of the oracle is then used to estimate the coefficients by pseudo-inversion. This architecture allows the definition of an encoding-decoding scheme with state-of-the-art recovery capabilities when applied to biological signals such as ECG and EEG, thus allowing extremely low-complex encoders. As an additional feature, oracle-based recovery is able to selfassess, by indicating with remarkable accuracy chunks of signals that may have been reconstructed with a non-satisfactory quality. This self-assessment capability is unique in the CS literature and paves the way for further improvements depending on the requirements of the specific application. As an example, our scheme is able to satisfyingly compress by a factor of 2.67 an ECG or EEG signal with a complexity equivalent to only 24 signed sums per processed sample.

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Section: Windowsmentioning

confidence: 99%