Signal Processing using Phase Diagram Analysis - New Perspectives in Understanding Transient Features in Data Science

In today’s digital era, advancements in communications, artificial intelligence and big data are present in both professional and personal spheres, with data science playing a pivotal role in processing and decision-making across various domains such as multimedia, engineering and finance. Among the diverse data analysis techniques, transient phenomena analysis is crucial for understanding process variations and informing decisions. Traditional approaches to transient analysis include energy-based and model-based techniques. However, phase diagram analysis, originally used in dynamical systems, has emerged as a potent tool for uncovering transient features in signal processing. This method graphically represents system state trajectories over time, making transient features like sudden changes and anomalies more discernible. Its versatility is evident across multiple fields: in wireless communications for signal characterization and modulation recognition; in the energy sector for predictive maintenance of power grids, detecting early degradation and assessing risks; and in high-frequency and THz domains for enhancing imaging and sensing technologies by improving resolution and sensitivity. Thus, phase diagram analysis significantly contributes to advancements in communication, energy management and high-frequency technology.