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International Journal of Engineering & Technology Sciences
IJETS 2025, 49 pages - Article ID: IJETS-2508132112960
A Comprehensive Review of Analog and Digital Filter Design: FPGA-Based Implementations, Real-Time Challenges, and Emerging Applications
Department of Electrical Engineering-Electronics, Mazandaran University of Science and Technology, Babol, Iran
ABSTRACT
Digital filter design remains a foundational pillar of modern signal processing, facilitating the extraction, enhancement, and suppression of signal components across a broad spectrum of applications, including wireless communication, biomedical imaging, the Internet of Things (IoT), industrial automation, and edge computing. This review comprehensively examines both classical approaches, such as analog filter designs (e.g., Butterworth, Chebyshev, Elliptic) and digital implementations (FIR and IIR), and advanced, optimization-driven techniques that incorporate machine learning, neural networks, reinforcement learning, and quantum-inspired algorithms. Particular emphasis is placed on practical FPGA-based realizations, highlighting their reconfigurable, low-latency architectures tailored for real-time systems. A comparative analysis of FIR and IIR filters is presented in terms of latency, computational complexity, and hardware–software resource trade-offs across CPU, DSP, and FPGA platforms. Furthermore, the study explores adaptive filtering in dynamic, resource-constrained environments, hybrid classical–deep learning filter structures, and secure designs integrating cryptographic methods and machine learning–based Trojan detection. Finally, emerging trends and research challenges are discussed, including reconfigurable and neuromorphic architectures, holistic hardware–algorithm co-design, and seamless integration into heterogeneous high-performance computing platforms, laying the groundwork for the next generation of intelligent, adaptive, and secure signal processing systems.
Digital Signal Processing (DSP); Filter Design; Analog Filters (Butterworth; Chebyshev; Elliptic); Digital Filters (FIR; IIR); Machine Learning; Neural Networks; Reinforcement Learning; Quantum-Inspired Algorithms; Field-Programmable Gate Arrays (FPGA); Real-Time Systems; Adaptive Filtering; Edge Computing; Secure Signal Processing; Reconfigurable Architectures; Neuromorphic Architectures; Internet of Things (IoT).