Multivariate Signal

Multivariate signal processing focuses on analyzing and interpreting data with multiple interconnected variables, aiming to extract meaningful information and build predictive models. Current research emphasizes robust methods for denoising, domain adaptation (handling variations in data sources), and efficient representation learning using techniques like graph neural networks, transformers, and hypercomplex algebras. These advancements are crucial for applications ranging from industrial equipment maintenance (predicting remaining useful life) to biomedical signal analysis and image/video processing, improving accuracy and generalization capabilities in diverse fields.