<p dir="ltr">In the ever-advancing landscape of technology, emerging technologies have emerged as<br>powerful catalysts for innovation across various domains. These technologies, situated at<br>the nexus of the physical and digital realms, hold tremendous potential for revolutionizing<br>industries, improving our quality of life, and addressing global challenges. Central to har-<br>nessing this potential is the efficient exchange of data and the processing of information, a<br>pivotal linchpin that underpins the success of emerging technologies.</p><p dir="ltr"><br>The thesis titled ”Empowering Emerging Technologies through Energy-Efficient Commu-<br>nication and In-Sensor Computing” delves into a critical facet of this technological revolution.<br>It explores the central role of energy-efficient communication and in-sensor computing in un-<br>locking the full potential of emerging technologies. This comprehensive exploration unfolds<br>across three distinct chapters, each addressing an essential aspect of the research undertaken.<br>The first two chapters are dedicated to the realm of wearable technology, where we delve<br>into the intricacies of Human Body Communication (HBC). Chapter 2 meticulously models<br>the human body, focusing on Galvanic excitation and termination, which are fundamental<br>to understanding communication within this unique domain. In Chapter 3, we introduce<br>a novel method employing resonance through the human body to enhance wearable device<br>functionality and efficiency, shedding light on its innovative potential.</p><p dir="ltr"><br>The fourth chapter takes us into the world of machine vision and computer vision, where<br>we unveil an ingenious solution—an ADC-less in-sensor image edge detection scheme. This<br>pioneering approach not only advances the field but also enables enhanced image processing<br>and analysis within sensors, thereby fostering the growth of machine vision applications.<br>This thesis represents a substantial contribution to the fields of HBC and in-sensor com-<br>puting. It models the Galvanic body channel, explores resonance-based power delivery and<br>communication, and demonstrates the importance of in-sensor image edge detection. Fur-<br>thermore, it presents a hardware-based CMOS image sensor capable of real-time edge image<br>extraction, enhancing computational efficiency while reducing latency.<br>As we embark on this intellectual journey, we invite the reader to delve deeper into the<br>realms of emerging technologies, energy-efficient communication, and in-sensor computing.</p><p dir="ltr">By the culmination of this thesis, it is our hope that the insights garnered from this re-<br>search will empower emerging technologies, inspire further innovation, and usher in a more<br>sustainable and technologically empowered future.</p>