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

CCSS：用于网络系统应用中多媒体数据分析的超图信号处理

• 批准号: 2029848
• 负责人: Zhi Ding
• 金额: $ 30万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029848&HistoricalAwards=false
• 关键词: CCSS; Hyper; Graph; Signal; Processing

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.

数据科学（DS）和人工智能（AI）的研究和开发活动浪潮正在推动一些最令人兴奋和改变游戏规则的技术进步。人工智能和物联网的广泛应用不断产生大量复杂的数据。这些新的数据集比传统应用程序更大，更复杂。为了探索各种数据集的底层结构和交互，重要的几何信号处理工具（如图和超图模型）可以揭示新的见解，并在传感器网络，物联网，网络物理系统和多媒体数据分析中取得了广泛的成功。本研究计画以超图讯号处理为中心，进行全面的研究。HSP能够捕捉信号或数据之间的多边关系和特征，在更广泛的实际应用中提供了继承的分析优势。该项目代表了一个系统和全面的努力，以扩大几何信号处理的理论基础，并开发创新的解决方案，以应对大量涌入的现实生活中的数据集。一个目标是建立一个定义良好的和全面的HSP框架的数据分析，与传统的图形信号处理兼容。该团队严格追求HSP的分析框架，并在特定应用中展示其上级性能。PI和团队进一步提出了一个综合框架，以系统地利用关键实际场景中的几何数据特征。该研究团队计划开发创新的解决方案，用于分析和建模来自各种新应用的大型复杂数据集。针对关键的数据分析主题，这项工作产生的创新可以直接影响网络系统中的数据压缩和分析。HSP启发的大型数据集建模可以改变物联网和传感器网络的学习成果。这些研究成果将为信号处理和建模的理论基础做出重大贡献。探索和发现新的数据结构和功能的能力将有助于更好地理解各种数据科学和人工智能应用并优化决策。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估而被认为值得支持。

项目成果

Point Cloud Resampling via Hypergraph Signal Processing

• DOI: 10.1109/lsp.2021.3119257
• 发表时间: 2021-02
• 期刊: IEEE Signal Processing Letters
• 影响因子: 3.9
• 作者: Qinwen Deng;Songyang Zhang-;Zhi Ding

An Efficient Hypergraph Approach to Robust Point Cloud Resampling

• DOI: 10.1109/tip.2022.3149225
• 发表时间: 2021-03
• 期刊: IEEE Transactions on Image Processing
• 影响因子: 10.6
• 作者: Qinwen Deng;Songyang Zhang;Zhi Ding

Signal Processing Over Multilayer Graphs: Theoretical Foundations and Practical Applications

• DOI: 10.1109/jiot.2023.3294470
• 发表时间: 2021-08
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Songyang Zhang-;Qinwen Deng;Zhi Ding

RME-GAN: A Learning Framework for Radio Map Estimation Based on Conditional Generative Adversarial Network

• DOI: 10.1109/jiot.2023.3278235
• 发表时间: 2022-12
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Songyang Zhang-;Achintha Wijesinghe;Zhi Ding

Exemplar-Based Radio Map Reconstruction of Missing Areas Using Propagation Priority

• DOI: 10.1109/globecom48099.2022.10001269
• 发表时间: 2022-09
• 期刊: GLOBECOM 2022 - 2022 IEEE Global Communications Conference
• 作者: Songyang Zhang-;Tianhang Yu;Jonathan Tivald;Brian Choi;Feng Ouyang;Zhi Ding