Complex Signal

Complex signal processing focuses on analyzing and manipulating signals containing multiple interwoven components or frequencies, a challenge addressed through advanced machine learning and signal processing techniques. Current research emphasizes developing novel neural network architectures, such as implicit neural representations (INRs) with variable-periodic activation functions and residual neural fields (ResFields), alongside algorithms like bagged deep image priors and complex-valued transformers, to improve signal representation, separation, and feature extraction from noisy or undersampled data. These advancements have significant implications for diverse fields, including medical imaging (MRI), radar-based place recognition, and the analysis of complex biological signals like EEG, enabling improved data analysis and more accurate information extraction.