Signal and Information Processing in Analysis and Modeling of Complex Structures

计算技术在存储和处理大量复杂结构数据方面的进步不可避免地产生了对快速可靠地将数据转换为关于其底层结构的准确信息的需求。感兴趣的结构范围从人造的，如现代能源基础设施或高光谱图像，到活细胞，人体器官或语音。对这些结构进行建模主要是为了分析、预测和/或控制它们的行为。所有这些目的在很大程度上依赖于建模过程的准确性，而建模过程本身又依赖于我们对系统的先验知识和从中获得的数据。提出数据驱动建模的实用方法以及评估模型的有效方案是关键挑战。这些问题变得至关重要，特别是在建模健康相关的系统和结构，使用一个不可靠的模型，无法验证可能会导致不可逆的损害。

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.