Impementation of mathematical morphology algorithms on FPGA : Signal processing applications.

Abstract

This master’s thesis presents the implementation of mathematical morphology algorithms on Field-Programmable Gate Arrays (FPGAs) for signal processing applications. Mathematical morphology plays a crucial role in various image and signal processing tasks. By leveraging the parallelism, reconfigurability, and low power consumption capabilities of FPGAs, efficient hardware-based implementations of these algorithms are achieved, enabling real-time processing. The thesis explores different fields and applications that demonstrate the capabilities and flexibility of the implemented techniques and algorithms in handling both images and signals. It addresses challenges associated with adapting mathematical morphology algorithms to FPGA architectures, considering trade-offs between accuracy, performance, and resource utilization. The research findings contribute to the development of optimized and hardwareaccelerated solutions for signal processing applications that rely on mathematical morphology. This work aims to provide insights into the fundamental concepts of mathematical morphology, pushing the boundaries of its application in conjunction with AI models and FPGA implementations. It explores the intersection between mathematical morphology and other emerging technologies.