Redefining Electronic Information Engineering: The Cross-Disciplinary Fusion and Its Impact on Signal Processing Paradigms

Abstract

As the fundamental enabler of modern electronic information systems, signal processing technology plays a pivotal role in driving continuous advancements in full data-lifecycle management through paradigm-shifting innovations. Recent breakthroughs in the artificial intelligence (AI) technology cluster, particularly of deep-learning algorithms with heterogeneous computing architectures, have fundamentally reshaped the theoretical boundaries and practical dimensions of classical signal processing methodologies. This transformative convergence has enabled unprecedented capabilities in real-time data analysis, adaptive filtering, and intelligent decision-making, thereby redefining the efficiency and scalability of electronic information systems. Against this backdrop, this paper systematically examines the evolution and innovation trajectory of information processing technology within electronic information engineering. By integrating theoretical analysis with empirical case studies, we elucidate the synergistic effects of AI-driven signal processing techniques on system performance optimization, energy efficiency, and fault tolerance. Furthermore, we explore emerging challenges such as computational complexity, data privacy concerns, and hardware constraints, while proposing potential solutions based on hybrid computing frameworks and edge intelligence paradigms. The insights derived from this study not only contribute to the academic discourse on advanced signal processing but also provide actionable guidance for researchers and practitioners seeking to leverage cutting-edge technologies in electronic information engineering. By fostering interdisciplinary collaboration between AI, signal processing, and hardware design, this work aims to catalyze further innovation in the field.