Advanced Computational and Intelligent Methods in Signal Processing

Dear Colleagues,

It is often the case that the analysis and interpretation of real-world signals—from biomedical recordings and financial time series to multimedia and sensor data—require sophisticated computational and intelligent methods. The challenges posed by high dimensionality, non-stationarity, noise, and complex nonlinear patterns demand solutions that go beyond traditional techniques, drawing on advances in numerical computation, deep learning, machine learning, and optimization theory.

Applications span a wide range of domains, including speech and audio processing, computer vision, biomedical engineering, wireless communications, autonomous systems, financial engineering, and remote sensing.

The development and theoretical analysis of these advanced computational and intelligent methods are crucial for building efficient, robust, and interpretable systems. By exploiting the mathematical structures of both algorithms and signals, we can design scalable models, establish performance guarantees, and gain deeper insights into the data they process.

This Special Issue gathers contributions showcasing the development and application of advanced computational and intelligent methods for signal processing. Special emphasis is placed on the mathematical foundations of these methods—including optimization, linear algebra, statistical learning, and information theory—as well as on the design of efficient algorithms capable of solving large-scale and complex signal processing problems. We seek contributions that demonstrate state-of-the-art performance while also offering novel theoretical insights or innovative computational frameworks.

Dr. Ping Lang
Guest Editor

Manuscript Submission Information

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