Abstract
The swift progress in smart sensor technologies, Internet of Things, Industrial Internet of
Things, and Cyber-Physical Systems has led to evolving the sensor standards to enable
Industry 4.0, the industrial domain where adaptability, efficiency, and reliability are es-
sential. The sensor applications of the new industry era necessitate increasingly adaptable
sensor electronics and signal processing capabilities based on machine learning (ML) and
artificial intelligence (AI) with other cutting-edge technologies.
This thesis presents a literature review of the design and applications of evolvable
hardware and reconfigurable/programmable electronics that can be tailored for smart
sensory electronics (SSEs) in Industry 4.0. Through an interdisciplinary approach that
weaves together elements from bio-inspired systems, evolvable hardware, and advanced
signal processing techniques, this work introduces a suite of design methodologies and
implementations for analog front-end (AFEX) systems endowed with self-X capabilities,
namely self-optimization, self-configuration, and self-calibration.