AI and Adaptive-Based Digital Signal Processing and Optimal Implementation of DSP Algorithms

Dear Colleagues,

Artificial intelligence (AI) is increasingly transforming the field of many electronics areas. Digital Signal Processing  (DSP)  systems  are becoming more complex, requiring advanced approaches that go beyond traditional techniques. In this context, AI offers powerful tools to address nonlinearities, parameter variations, and uncertainties, enabling intelligent, adaptive, and data-driven solutions.

Recent developments in AI applications for DSP have led to significant improvements in optimal performances and efficiency, but also in reliability and fault tolerance. Key advances include the use of machine learning and deep learning for system modeling and optimization, fault diagnosis, reinforcement learning for adaptive real control, and for system optimization. In addition, the integration of edge AI,  embedded intelligence, and adaptive techniques is allowing real-time decision-making within DSP system functionality.

Also, Generative AI  has significantly transformed the area of image and signal processing, enabling unprecedented improvements for DSP techniques, such as  synthesis, reconstruction, and enhancement capabilities. For example, denoising and super-resolution Generative AI, as well as other AI and adaptive techniques, have redefined how signal is represented and processed.

Optimizing the implementation of DSP algorithms and architectures is an essential part of research and design for many modern applications, e.g., multimedia, big data, Internet of Things (IoT), etc. Nevertheless, the optimization of such computationally intensive applications is a challenging issue that requires a clever design and/or restructuring the algorithm or architecture.

These advancements are essential for the future of DSP as they contribute to higher performance, reduced design costs, and improved system resilience. In this context, the convergence of Adaptive/Machine Learning  and Optimal Implementation is expected to play a pivotal role in the future of DSP. Also, obtaining efficient implementation of DSP algorithms (both in software and VLSI) is essential for a real-time utilization of such solutions.

This Special Issue aims to bring together cutting-edge research at the intersection of signal processing and AI. We invite contributions exploring theoretical advances, computational models, and real-world applications, which exploit the synergy between traditional signal processing principles and modern ones.

The scope of this Special Issue, titled “AI and Adaptive-Based Digital Signal Processing and Optimal Implementation of DSP Algorithms”, encompasses a broad range of research areas, including, but not limited to, the following:

• AI-assisted modeling, design, and optimization of digital signal processing;
• Machine learning and deep learning for the optimization of DSP systems and fault detection;
• Reinforcement learning and adaptive algorithms for real-time DSP system optimization and control;
• Edge AI and embedded intelligence for high-performance, real-time applications;
• Data-driven approaches for efficiency improvement, energy management, and system reliability;
• VLSI signal processing;
• Signal processing methods for an efficient implementation;
• Optimization of the VLSI implementation of multimedia blocks;
• Low-power circuits and systems for DSP applications;
• Efficient adaptive/learning algorithms (low complexity/fast versions, optimized parameters, etc.);
• Tensor-based signal processing (efficient decomposition methods, low-rank approximations, etc.);
• Sparsity-aware algorithms.

This Special Issue focuses on papers that demonstrate how these design challenges can be overcome using innovative solutions. Overall, this Special Issue will highlight the growing role of artificial intelligence and adaptive techniques and optimal implementations in advancing Digital Signal Processing and electronic systems, with the goal of creating more intelligent, efficient, and sustainable DSP  systems.

Prof. Dr. Doru Florin Chiper
Prof. Dr. Constantin Paleologu
Guest Editors

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• machine learning
• adaptive algorithms
• embedded intelligence
• learning algorithms
• edge AI for signal processing