Time Encoding via Unlimited Sampling: Theory, Algorithms and Hardware Validation

Florescu, Dorian; Bhandari, Ayush

doi:10.1109/tsp.2022.3210748

Cited by 8 publications

(1 citation statement)

References 45 publications

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“…Very recently, non-idealities in hardware prototyping were considered in [45,46]; a computational sampling strategy in the form of unlimited sampling with hysteresis [47,48] was found to be more flexible for circuit design specifications. Unlimited sampling has also been used with event-driven sampling (EDS) [49,50], wherein the modulo-hysteresis model addresses the DR limitations of an asynchronous sigma-delta modulator (ASDM) that transmits trigger times that capture changes in the input integral from the output of a time encoding machine. Event-driven sampling exclusively records transitions, sampling amplitudes, in contrast to one-bit sensing where both amplitudes and time bins are sampled.…”

Section: A Prior Artmentioning

confidence: 99%

UNO: Unlimited Sampling Meets One-Bit Quantization

Eamaz,

Mishra,

Yeganegi

et al. 2024

IEEE Trans. Signal Process.

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Recent results in one-bit sampling provide a framework for a relatively low-cost, low-power sampling, at a high rate by employing time-varying sampling threshold sequences. Another recent development in sampling theory is unlimited sampling, which is a high-resolution technique that relies on modulo ADCs to yield an unlimited dynamic range. In this paper, we leverage the appealing attributes of the two aforementioned techniques to propose a novel unlimited one-bit (UNO) sampling approach. In this framework, the information on the distance between the input signal value and the threshold is stored and utilized to accurately reconstruct the one-bit sampled signal. We then utilize this information to accurately reconstruct the signal from its one-bit samples via the randomized Kaczmarz algorithm (RKA). In the presence of noise, we employ the recent plug-and-play (PnP) priors technique with alternating direction method of multipliers (ADMM) to exploit integration of stateof-the-art regularizers in the reconstruction process. Numerical experiments with RKA and PnP-ADMM-based reconstruction illustrate the effectiveness of our proposed UNO, including its superior performance compared to the one-bit Σ∆ sampling.

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