PHYSICS-DRIVEN STRUCTURED COSPARSE MODELING FOR SOURCE LOCALIZATION

被引:0
|
作者
Nam, Sangnam [1 ]
Gribonval, Remi [1 ]
机构
[1] INRIA Rennes Bretagne Atlantique, Rennes, France
关键词
Sparsity; cosparsity; structured cosparsity; dictionary; analysis operator; pursuit algorithm; SIGNAL RECONSTRUCTION;
D O I
暂无
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Cosparse modeling is a recent alternative to sparse modeling, where the notion of dictionary is replaced by that of an analysis operator. When a known analysis operator is well adapted to describe the signals of interest, the model and associated algorithms can be used to solve inverse problems. Here we show how to derive an operator to model certain classes of signals that satisfy physical laws, such as the heat equation or the wave equation. We illustrate the approach on an acoustic inverse problem with a toy model of wave propagation and discuss its potential extensions and the challenges it raises.
引用
收藏
页码:5397 / 5400
页数:4
相关论文
共 50 条
  • [1] JOINT ESTIMATION OF SOUND SOURCE LOCATION AND BOUNDARY IMPEDANCE WITH PHYSICS-DRIVEN COSPARSE REGULARIZATION
    Bertin, N.
    Kitic, S.
    Gribonval, R.
    2016 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING PROCEEDINGS, 2016, : 6340 - 6344
  • [2] Physics-Driven Inverse Problems Made Tractable With Cosparse Regularization
    Kitic, Srdan
    Albera, Laurent
    Bertin, Nancy
    Gribonval, Remi
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2016, 64 (02) : 335 - 348
  • [3] Versatile and Scalable Cosparse Methods for Physics-Driven Inverse Problems
    Kitic, Srdan
    Bensaid, Siouar
    Albera, Laurent
    Bertin, Nancy
    Gribonval, Remi
    COMPRESSED SENSING AND ITS APPLICATIONS, 2017, : 291 - 332
  • [4] BRAIN SOURCE LOCALIZATION USING A PHYSICS-DRIVEN STRUCTURED COS PARSE REPRESENTATION OF EEG SIGNALS
    Albera, L.
    Kitic, S.
    Bertin, N.
    Puy, G.
    Gribonval, Remi
    2014 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP), 2014,
  • [5] Rank estimation and tensor decomposition using physics-driven constraints for brain source localization
    Taheri, Nasrin
    Kachenoura, Amar
    Karfoul, Ahmad
    Han, Xu
    Ansari-Asl, Karim
    Merlet, Isabelle
    Senhadji, Lotfi
    Albera, Laurent
    2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2019,
  • [6] Physics-Driven Interface Modeling for Drainage and Imbibition in Fractures
    Prodanovic, Masa
    Bryant, Steven L.
    SPE JOURNAL, 2009, 14 (03): : 532 - 542
  • [7] SOLVING PHYSICS-DRIVEN INVERSE PROBLEMS VIA STRUCTURED LEAST SQUARES
    Murray-Bruce, John
    Dragotti, Pier Luigi
    2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2016, : 331 - 335
  • [8] A Sampling Framework for Solving Physics-Driven Inverse Source Problems
    Murray-Bruce, John
    Dragotti, Pier Luigi
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2017, 65 (24) : 6365 - 6380
  • [9] Filled Elastomers: Mechanistic and Physics-Driven Modeling and Applications as Smart Materials
    Xian, Weikang
    Zhan, You-Shu
    Maiti, Amitesh
    Saab, Andrew P.
    Li, Ying
    POLYMERS, 2024, 16 (10)
  • [10] Demonstration of Decentralized Physics-Driven Learning
    Dillavou, Sam
    Stern, Menachem
    Liu, Andrea J.
    Durian, Douglas J.
    PHYSICAL REVIEW APPLIED, 2022, 18 (01)