Neuromorphic Architectures for Space Internet of Things (IoT)

This research opens a new possibility for Space Internet of Things (IoT) using neuromorphic detectors. Space IoT network architectures aiming at an increased performance in terms of fast data transmission with reduced latency, data storage, reduced operating power, efficient computational are imperative. The performance of a new class of neuromorphic detectors, the so called “Polarimetric Dynamic Vision Sensors” (pDVS)s were studied, under different operating scenarios. The acquired imaging signals were processed via Stokes parameter analysis and then analyzed using polarimetric Figure-of-Merits (FOM)s and established metrics. The outcome of this study will allow one to design and optimize robust and efficient neuromorphic sensing and computing architectures for Space IoT with emphasis on Space Situational Awareness (SSA) and Space Surveillance applications.