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
基本信息
- 批准号:RGPIN-2015-04483
- 负责人:
- 金额:$ 2.19万
- 依托单位:
- 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:
- 关键词:
项目摘要
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. **
该研究计划的目标是基于统计建模,图形和网络中最先进的数学理论,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约束下开发。*随着包括多媒体在内的所有领域的数字数据(或大或小)的增长,视频/图像/音频、商业和生物医学应用,以及来自所有类型的源,例如传感器网络和在线社交媒体网络,在这些新兴应用中,在分析、处理、组织和检索数字信息方面存在巨大的需求和技术挑战。探索隐藏在数据中的统计结构和关系可以为我们提供强大的工具,通过发现和整合数据的领域知识和内在信号特征,以有效的方式处理和理解大量数据。因此,它是一个特别肥沃和及时的研究领域,在理论和应用。** 基于图和网络上的统计信号分析以及概率图建模的新技术和算法有望有效地阐述从各种模态、多个传感器和各种网络获取的大量数字数据的复杂结构,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程序。**我以前的研究基于多尺度分析和图形统计建模,建立了初步的理论框架,在信号和数据的多媒体应用,传感器网络,电信和经济。我的研究还表明,图形模型,如隐马尔可夫模型(HMM)和条件随机场(CRF)模型,结合多尺度分析和传统的统计方法,有希望捕捉复杂的数据结构,如视频事件结构和图像对象结构等。在本研究中,我提出(i)在现有的最先进的图信号处理框架的基础上,发展新的通用网络/图信号处理理论和算法;(ii)通过整合图概率模型和确定性图信号处理,构建最优的图信号模型和图基,并发展相关的学习方法;以及(iii)基于图形信号处理和统计模型开发算法和解决方案,以解决多媒体和经济数据中出现的数据应用问题。 **
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Zhang, XiaoPing其他文献
MicroRNA 107 Partly Inhibits Endothelial Progenitor Cells Differentiation via HIF-1 beta
MicroRNA 107 通过 HIF-1 beta 部分抑制内皮祖细胞分化
- DOI:
- 发表时间:
- 期刊:
- 影响因子:3.7
- 作者:
Meng, Shu;Cao, JiaTian;Wang, LianSheng;Zhou, Qing;Li, YiGang;Shen, ChengXing;Zhang, XiaoPing;Wang, ChangQian - 通讯作者:
Wang, ChangQian
Enhanced expression of caveolin-1 possesses diagnostic and prognostic value and promotes cell migration, invasion and sunitinib resistance in the clear cell renal cell carcinoma
Caveolin-1 表达增强具有诊断和预后价值,可促进透明细胞肾细胞癌中的细胞迁移、侵袭和舒尼替尼耐药
- DOI:
10.1016/j.yexcr.2017.07.004 - 发表时间:
2017-09-15 - 期刊:
- 影响因子:3.7
- 作者:
Ruan, HaiLong;Li, Xiang;Zhang, XiaoPing - 通讯作者:
Zhang, XiaoPing
PLIN3 is up-regulated and correlates with poor prognosis in clear cell renal cell carcinoma
PLIN3 上调并与透明细胞肾细胞癌的不良预后相关
- DOI:
10.1016/j.urolonc.2018.04.006 - 发表时间:
2018-07-01 - 期刊:
- 影响因子:2.7
- 作者:
Wang, Keshan;Ruan, HaiLong;Zhang, XiaoPing - 通讯作者:
Zhang, XiaoPing
Overexpression of PPT2 Represses the Clear Cell Renal Cell Carcinoma Progression by Reducing Epithelial-to-mesenchymal Transition
- DOI:
10.7150/jca.36477 - 发表时间:
2020-01-01 - 期刊:
- 影响因子:3.9
- 作者:
Yuan, ChangFei;Xiong, ZhiYong;Zhang, XiaoPing - 通讯作者:
Zhang, XiaoPing
Expression of AMPA receptor subunits in hippocampus after status convulsion
- DOI:
10.1007/s00381-012-1747-3 - 发表时间:
2012-06-01 - 期刊:
- 影响因子:1.4
- 作者:
Hu, Yue;Jiang, Li;Zhang, XiaoPing - 通讯作者:
Zhang, XiaoPing
Zhang, XiaoPing的其他文献
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{{ truncateString('Zhang, XiaoPing', 18)}}的其他基金
Statistical Signal Processing and Learning on Networks and Graphs
网络和图的统计信号处理和学习
- 批准号:
RGPIN-2020-04661 - 财政年份:2022
- 资助金额:
$ 2.19万 - 项目类别:
Discovery Grants Program - Individual
Statistical Signal Processing and Learning on Networks and Graphs
网络和图的统计信号处理和学习
- 批准号:
RGPAS-2020-00106 - 财政年份:2022
- 资助金额:
$ 2.19万 - 项目类别:
Discovery Grants Program - Accelerator Supplements
Statistical Signal Processing and Learning on Networks and Graphs
网络和图的统计信号处理和学习
- 批准号:
RGPAS-2020-00106 - 财政年份:2021
- 资助金额:
$ 2.19万 - 项目类别:
Discovery Grants Program - Accelerator Supplements
Advanced Localization Technology and RF Propagation Models for Emergency Radio Communication Enhancement System (ERCES)
用于紧急无线电通信增强系统(ERCES)的先进定位技术和射频传播模型
- 批准号:
558257-2020 - 财政年份:2021
- 资助金额:
$ 2.19万 - 项目类别:
Alliance Grants
Statistical Signal Processing and Learning on Networks and Graphs
网络和图的统计信号处理和学习
- 批准号:
RGPIN-2020-04661 - 财政年份:2021
- 资助金额:
$ 2.19万 - 项目类别:
Discovery Grants Program - Individual
COVID-19 - AI Based Screening and Monitoring of COVID-19 Respiration Patterns using Acoustic Sensors
COVID-19 - 使用声学传感器进行基于 AI 的 COVID-19 呼吸模式筛查和监测
- 批准号:
550079-2020 - 财政年份:2020
- 资助金额:
$ 2.19万 - 项目类别:
Alliance Grants
Statistical Signal Processing and Learning on Networks and Graphs
网络和图的统计信号处理和学习
- 批准号:
RGPIN-2020-04661 - 财政年份:2020
- 资助金额:
$ 2.19万 - 项目类别:
Discovery Grants Program - Individual
Statistical Signal Processing and Learning on Networks and Graphs
网络和图的统计信号处理和学习
- 批准号:
RGPAS-2020-00106 - 财政年份:2020
- 资助金额:
$ 2.19万 - 项目类别:
Discovery Grants Program - Accelerator Supplements
Advanced Localization Technology and RF Propagation Models for Emergency Radio Communication Enhancement System (ERCES)
用于紧急无线电通信增强系统(ERCES)的先进定位技术和射频传播模型
- 批准号:
558257-2020 - 财政年份:2020
- 资助金额:
$ 2.19万 - 项目类别:
Alliance Grants
Predicting voting behaviour based on machine learning and signal processing algorithms
基于机器学习和信号处理算法预测投票行为
- 批准号:
536609-2018 - 财政年份:2018
- 资助金额:
$ 2.19万 - 项目类别:
Engage Grants Program
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