Signal and Information Processing in Analysis and Modeling of Complex Structures

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Computational technology advances in storing and processing large amounts of complex-structured data have inevitably created a demand for fast and reliable transformation of data to accurate information about its underlying structure. The structures of interest range from man-made ones such as modern energy infrastructures or hyperspectral images, to living cells, organs of the human body or speech. These structures are modeled mainly for the purpose of analysis, prediction, and/or control of their behavior. All of these purposes heavily rely on the accuracy of the modeling process, which itself depends on our prior knowledge of the system and the data available from it. Proposing practical methods for data-driven modeling as well as efficient schemes for evaluating the models are critical challenges. These issues become vital especially in modeling health-related systems and structures where use of an unreliable model that can not be validated may cause irreversible damages.