Information processing

This research area is devoted to the development of methodologies for processing information in different application scenarios. The current trend in the scientific research is more and more oriented towards methods and algorithms that can be used to extract knowledge from data. We are witnessing a data deluge, the so-called big data: the development of effective techniques to manage and exploit this huge amount of information is of paramount importance for maximizing the impact of information and communication technologies on our society.

The study of techniques to properly process, analyze, and exploit the available information requires an interdisciplinary approach, including expertise from different fields like signal processing, statistics, and control systems. In this area, the research is oriented both to classical signal processing and statistical tools, like estimation, detection, classification, stochastic modelling, and to innovative machine learning tools, like deep neural networks and adversarial generative networks.  Also, the interplay and cross-fertilization between those different approaches is a key aspect towards the development of novel and effective solutions.

Due to their universality, the studied methodologies can be applied in very diverse scenarios. The main application scenarios considered by DET researchers include biomedical data processing and classification, image and video processing, data transmission and coding for next generation communication systems, network analysis and monitoring, navigation systems.  In those scenarios, information processing methodologies are often applied to address important issues in modern societies, like energy efficiency, user data privacy, security of network infrastructures, environmental and atmospheric monitoring.