Signal and Data Processing Based on Statistical and Graphical Models

基于统计和图形模型的信号和数据处理

• 批准号: RGPIN-2015-04483
• 负责人: Zhang, XiaoPing
• 金额: $ 2.19万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=683625
• 关键词: Signal; Data; Processing; Based; Statistical

The objective of this proposed research program is to systematically develop new signal and data processing theories based on the state-of-the-art mathematical theory in statistical modeling, graph and network. New graph/network signal models will also be developed under various application constraints coming from multimedia, economic big data, and sensor/social networks.******With growing digital numerical data (big or small) in all areas including multimedia, e.g., video/image/audio, business and biomedical applications, and from all type of sources, such as sensor networks and online social media networks, there are tremendous demands and technical challenges in analyzing, processing, organizing and retrieving digital information in these emerging applications. Exploring statistical structure and relationships hidden in data can provide us powerful tools to process and understand a large amount of data in an efficient way by discovering and incorporating domain knowledge and intrinsic signal characteristics of the data. Therefore it is a particularly fertile and timely area of research in both theory and applications. ******The new technology and algorithms based on statistical signal analysis on graphs and networks, and probabilistic graphical modeling are promising to be effective in elaborating the sophisticated structure of large amount digital data acquired from various modalities, multiple sensors, and various networks, and therefore contribute to the emerging new generation data processing, information mining, retrieval and content analysis applications.******My previous research based on multiscale analysis and graphical statistical modeling has established preliminary theoretical framework in signal and data applications in multimedia, sensor networks, telecommunications and economics. My research also shows that graphical models, such as hidden Markov models (HMM) and conditional random field (CRF) models, combined with multiscale analysis and traditional statistical methods, are promising to capture the complex structure of data such as video event structure and image object structure, among others.******In this research, I propose to (i) develop new general network/graph signal processing theory and algorithms based on the existing state-of-the-art graph signal processing framework; (ii) to construct optimal graph signal models and graph basis and develop related learning methods by consolidating graphic probabilistic models and deterministic graph signal processing; and (iii) to develop algorithms and solutions based on graph signal processing and statistical models for emerging data application problems in multimedia and economic data. **

该研究项目的目标是基于统计建模、图形和网络领域最先进的数学理论，系统地开发新的信号和数据处理理论。新的图/网络信号模型也将在来自多媒体、经济大数据和传感器/社交网络的各种应用限制下开发。******随着包括多媒体在内的所有领域的数字数值数据（大或小）的不断增长，例如，视频/图像/音频、商业和生物医学应用，以及传感器网络和在线社交媒体网络等所有类型的来源，在这些新兴领域中分析、处理、组织和检索数字信息存在巨大的需求和技术挑战。应用程序。探索数据中隐藏的统计结构和关系可以为我们提供强大的工具，通过发现和整合领域知识和数据的内在信号特征，以有效的方式处理和理解大量数据。因此，它在理论和应用方面都是一个特别丰富和及时的研究领域。 ******基于图形和网络统计信号分析以及概率图形建模的新技术和算法有望有效地阐述从各种模态、多个传感器和各种设备获取的大量数字数据的复杂结构。网络，因此有助于新兴的新一代数据处理、信息挖掘、检索和内容分析应用。******我之前基于多尺度分析和图形统计建模的研究已经在信号和数据应用中建立了初步的理论框架多媒体、传感器网络、电信和经济学。我的研究还表明，图形模型，例如隐马尔可夫模型（HMM）和条件随机场（CRF）模型，与多尺度分析和传统统计方法相结合，有望捕获视频事件结构和图像等数据的复杂结构*****在这项研究中，我建议（i）基于现有最先进的图信号处理框架开发新的通用网络/图信号处理理论和算法； (ii) 通过巩固图概率模型和确定性图信号处理，构建最佳图信号模型和图基础并开发相关学习方法； (iii) 针对多媒体和经济数据中新出现的数据应用问题，开发基于图信号处理和统计模型的算法和解决方案。 **