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Energy efficiency, long battery life, and low latency are key attributes of many emerging ultralow-power sensing and monitoring systems. Applications such as always-on reactive sensors for natural human-device interfaces, as well as multiple consumer and industrial applications for the Internet of Things (IoT), require ultralow-power designs beyond the promise of state-of-the art data converters. These devices demand a new approach to analog-digital system partitioning with the goal of significant overall reduction in energy consumption. Unlike most multimedia systems, many IoT applications require signal information extraction or signature extraction, rather than full reconstruction of the original sensed waveforms. Under these conditions, Nyquist rate sampling may no longer offer the optimal digitization scheme. Recent work on alternative sensor digitization strategies targets drastic sampling rate reductions in analog-to-digital conversion, while preserving the valuable relevant information (knowledge) present in the sensed signal. This article aims to give an overview of the emerging field of analog-to-information conversion in light of various sub-Nyquist sampling techniques recently appearing in literature,as well as to highlight some of the opportunities, challenges, and new applications such converters offer.
Verhelst et al. (Thu,) studied this question.