CCSS: Hyper-Graph Signal Processing for Multimedia Data Analysis in Cyber System Applications

Waves of research and development activities on Data Science (DS) and Artificial Intelligence (AI) that are fueling some of the most exciting and game-changing technological advances. The widespread applications of AI and IoT generate huge volume of complex data incessantly. These new datasets are much bigger and more complex than in traditional applications.

To explore the underlying structure and interaction of various datasets, important geometric signal processing tools such as graph and hypergraph models can reveal new insights and have found broad successes in sensor networks, IoT, cyber-physical systems, and multimedia data analysis. This research project centers on a comprehensive investigation of hypergraph signal processing (HSP).

Capable of capturing multilateral relationships and features among signals or data, HSP provides inherit analytic strength in a wider range of practical applications.

This project represents a systematic and comprehensive effort to broaden the theoretical foundation of geometric signal processing and to develop innovative solutions in response to the huge influx of real-life datasets. One goal is to establish a well-defined and comprehensive HSP framework for data analysis, compatible with traditional graph signal processing.

The team rigorously pursues analytical frameworks for HSP and demonstrate its superior performance in specific applications. The PI and the team further present an integrative framework to systematically exploit geometric data features in key practical scenarios. The research team plans to develop innovative solutions for analyzing and modeling large and complex datasets from a variety of new applications.

Addressing key data analytic topics, innovations arising from this work can directly impact data compression and analysis in cyber-systems. HSP inspired modeling of large data collections can transform learning outcomes in IoT and sensor networks. This research outcomes shall contribute substantially to the theoretical foundation of signal processing and modeling.

The ability to explore and discover new data structures and features will enable better understanding and optimized decision making in various data science and artificial intelligence application.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.