Event-Based Sensing and Signal Processing in the Visual, Auditory, and Olfactory Domain: A Review

Abstract

The nervous systems converts the physical quantities sensed by its primary receptors into trains of events that are then processed in the brain. The unmatched efficiency in information processing has long inspired engineers to seek brain-like approaches to sensing and signal processing. The key principle pursued in neuromorphic sensing is to shed the traditional approach of periodic sampling in favor of an event-driven scheme that mimicks sampling as it occurs in the nervous system, where events are preferably emitted upon the change of the sensed stimulus. In this paper we highlight the advantages and challenges of event-based sensing and signal processing in the visual, auditory and olfactory domains. We also provide a survey of the literature covering neuromorphic sensing and signal processing in all three modalities. Our aim is to facilitate research in event-based sensing and signal processing by providing a comprehensive overview of the research performed previously as well as highlighting conceptual advantages, current progress and future challenges in the field.

Event-based imaging velocimetry: an assessment of event-based cameras for the measurement of fluid flows

“…This has broadened the range of applications as testified in a steadily increasing number of publications (see, e.g., Robotics and Perception Group 2022) and also has made the present feasibility study possible. For recent reviews of event-based sensing technology and underlying concepts, the reader is referred to Gallego et al (2022) and Tayarani-Najaran and Schmuker (2021).…”

Challenges in Materials and Devices of Electronic Skin

“…Each taxel can be individually controlled via row/column addressing, and requires 2.5 s to latch to a different state . Great usage of time and energy consumption will be costly and information loss still exists during periodic sampling . Inspired by a biological neural network, event-based model provides a time and energy savings method to get certain necessary information .…”

“…Inspired by a biological neural network, event-based model provides a time and energy savings method to get certain necessary information . A threshold is set to decide whether the signal is transmitted, so energy consumption and response time are greatly saved . This also means that there are higher requirements for the delay of a single sensor.…”

“…227 Great usage of time and energy consumption will be costly and information loss still exists during periodic sampling. 228 Inspired by a biological neural network, event-based model provides a time and energy savings method to get certain necessary information. 229 A threshold is set to decide whether the signal is transmitted, so energy consumption and response time are greatly saved.…”

“…229 A threshold is set to decide whether the signal is transmitted, so energy consumption and response time are greatly saved. 228 This also means that there are higher requirements for the delay of a single sensor. The event-based model, when combined with deep learning, neural network, and algorithms, has great application potential in vision, auditory, and olfactory domains.…”