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. **

该提出的研究计划的目的是系统地基于统计建模，图形和网络中最先进的数学理论开发新的信号和数据处理理论。新的图形/网络信号模型还将在来自多媒体，经济大数据以及传感器/社交网络的各种应用程序限制下开发。****** ******在所有领域的数字数值数据（大或小）中不断增长，包括多媒体，例如视频/图像/图像/图像/图像/音频，商业，商业和生物医学应用程序，以及来自所有类型的trormiss and Semboring Networks and Onlyise Services Networks and Sensering Sensering Media，以及在其中的Sensering Sensering Media，以及在其中进行了传感器媒体，以及在其中涉及传感器媒体，以及在其中的技术媒体，以及在其中的技术媒体，以及在其中挑战了Sensering Sersistions，以及在其中挑战的。在这些新兴应用程序中处理，组织和检索数字信息。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，以通过发现和合并数据的域知识和内在信号特征来以有效的方式处理和理解大量数据。因此，在理论和应用中，这是一个特别肥沃和及时的研究领域。 ******基于对图和网络的统计信号分析以及概率图形模型的新技术和算法，有望有效阐述从各种模式，多个传感器以及各种网络中获得的大量数字数据的复杂结构，并因此对新的生成数据处理，信息进行分析，以前的分析，以前的信息分析，我的记录图，以前的分析。统计模型已在多媒体，传感器网络，电信和经济学中建立了信号和数据应用中的初步理论框架。我的研究还表明，图形模型，例如隐藏的马尔可夫模型（HMM）和有条件的随机场（CRF）模型，结合了多尺度分析和传统的统计方法，有望捕获数据的复杂结构，例如视频事件结构和图像对象结构等。 （ii）通过巩固图形概率模型和确定性图形信号处理来构建最佳图形信号模型和图形基础并开发相关的学习方法； （iii）基于图形信号处理以及多媒体和经济数据中新兴数据应用问题的统计模型开发算法和解决方案。 **