EMPOWERING EMERGING TECHNOLOGIES THROUGH ENERGY-EFFICIENT COMMUNICATION AND IN-SENSOR COMPUTING

In the ever-advancing landscape of technology, emerging technologies have emerged as
powerful catalysts for innovation across various domains. These technologies, situated at
the nexus of the physical and digital realms, hold tremendous potential for revolutionizing
industries, improving our quality of life, and addressing global challenges. Central to har-
nessing this potential is the efficient exchange of data and the processing of information, a
pivotal linchpin that underpins the success of emerging technologies.

The thesis titled ”Empowering Emerging Technologies through Energy-Efficient Commu-
nication and In-Sensor Computing” delves into a critical facet of this technological revolution.
It explores the central role of energy-efficient communication and in-sensor computing in un-
locking the full potential of emerging technologies. This comprehensive exploration unfolds
across three distinct chapters, each addressing an essential aspect of the research undertaken.
The first two chapters are dedicated to the realm of wearable technology, where we delve
into the intricacies of Human Body Communication (HBC). Chapter 2 meticulously models
the human body, focusing on Galvanic excitation and termination, which are fundamental
to understanding communication within this unique domain. In Chapter 3, we introduce
a novel method employing resonance through the human body to enhance wearable device
functionality and efficiency, shedding light on its innovative potential.

The fourth chapter takes us into the world of machine vision and computer vision, where
we unveil an ingenious solution—an ADC-less in-sensor image edge detection scheme. This
pioneering approach not only advances the field but also enables enhanced image processing
and analysis within sensors, thereby fostering the growth of machine vision applications.
This thesis represents a substantial contribution to the fields of HBC and in-sensor com-
puting. It models the Galvanic body channel, explores resonance-based power delivery and
communication, and demonstrates the importance of in-sensor image edge detection. Fur-
thermore, it presents a hardware-based CMOS image sensor capable of real-time edge image
extraction, enhancing computational efficiency while reducing latency.
As we embark on this intellectual journey, we invite the reader to delve deeper into the
realms of emerging technologies, energy-efficient communication, and in-sensor computing.

By the culmination of this thesis, it is our hope that the insights garnered from this re-
search will empower emerging technologies, inspire further innovation, and usher in a more
sustainable and technologically empowered future.