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  2026 (14)
Demystifying semi-supervised graph convolutional networks: GCNs without graphs or convolution. Stankovic, N.; Brajovic, M.; Richard, C.; Gu, Y.; and Mandic, D. . 2026. Submitted
link   bibtex  
Wasserstein online change point detection with neural networks. Wang, X.; Borsoi, R.; and Richard, C. In 2026. Submitted
link   bibtex  
Online change-point detection in RKHS. A non-asymptotic performance analysis. Richard, C.; Wang, X.; and Borsoi, R. In 2026. Submitted
link   bibtex  
Kernel change-point detection with recurrent regime recall in open-world streams. Amorim de Araujo, B.; El-Bouch, S.; and Richard, C. In 2026. Submitted
link   bibtex  
Feature-based adjacency matrix refinement for citation network classification using GCNs. Stankovic, N.; Brajovic, M.; Richard, C.; Gu, Y.; and Mandic, D. In 2026. Submitted
link   bibtex  
Contrastive learning for open-set anomaly detection of radio transients. Amblard, B.; El-Bouch, S.; Dalla Mura, M.; Ferrari, A.; and Richard, C. In Signal Processing (EUSIPCO), 34nd European Conference on, 2026.
Contrastive learning for open-set anomaly detection of radio transients [pdf] paper   link   bibtex   1 download  
The open-set push-pull loss: A revisit of contrastive learning for open-set supervised anomaly detection. Amblard, B.; El-Bouch, S.; Ferrari, A.; Dalla Mura, M.; and Richard, C. . 2026. Submitted
link   bibtex  
Continual learning: From online adaptation to foundation models. Chen, J.; Richard, C.; Liu, S.; and Sayed, A. H. . 2026. Submitted
link   bibtex  
A hypergraph-based nuclear norm regularization for decentralized multitask learning. Faia, G.; Richard, C.; and Nassif, R. In Signals, Systems and Computers (ASILOMAR), 2026 Asilomar Conference on, 2026. Accepted
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FASS: Discrete Fourier transform adapters for speech separation with incremental learning. Yang, Z.; Song, X.; Zhao, M.; Chen, J.; Richard, C.; and Cohen, I. Audio, Speech and Language Processing, IEEE Transactions on, 34: 3116-3131. 2026.
FASS: Discrete Fourier transform adapters for speech separation with incremental learning [pdf] paper   link   bibtex  
Dynamic spatio-spectral-temporal modeling with sparse and robust abundance estimation for multitemporal hyperspectral unmixing. Zhang, Y.; Li, L.; Gao, W.; Chen, J.; and Richard, C. . 2026. Submitted
link   bibtex  
Bidirectional diffeomorphic temporal alignment network. pplication to distributed acoustic sensing. Vassalos, K.; and Richard, C. In Signals, Systems and Computers (ASILOMAR), 2026 Asilomar Conference on, 2026. Accepted
Bidirectional diffeomorphic temporal alignment network. pplication to distributed acoustic sensing [link] paper   link   bibtex   3 downloads  
Sample-diffusion kernel LMS over networks with agent-specific adaptive dictionaries. Richard, C.; and Chen, J. In 2026. Submitted
link   bibtex  
DTU-Net: a multi-scale dilated transformer network for nonlinear hyperspectral unmixing. Wang, C.; Gao, J.; Zhu, F.; Halimi, A.; and Richard, C. Geoscience and Remote Sensing, IEEE Transactions on, 64: 1-17. 2026.
DTU-Net: a multi-scale dilated transformer network for nonlinear hyperspectral unmixing [link] code   DTU-Net: a multi-scale dilated transformer network for nonlinear hyperspectral unmixing [pdf] paper   link   bibtex   12 downloads  
  2025 (11)
Riemannian change point detection on manifolds with robust centroid estimation. Wang, X.; Borsoi, R.; Breloy, A.; and Richard, C. In Signal Processing (EUSIPCO), 33nd European Conference on, 2025.
Riemannian change point detection on manifolds with robust centroid estimation [pdf] paper   link   bibtex   17 downloads  
Distributed Riemannian optimization in geodesically non-convex environments. Wang, X.; Borsoi, R.; Richard, C.; and Sayed, A. H. . 2025. Submitted
link   bibtex  
Multi-head architecture for open-set anomaly detection of fast radio bursts. Amblard, B.; El-Bouch, S.; Dalla Mura, M.; Ferrari, A.; and Richard, C. In Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2025 IEEE International Workshop on, 2025.
Multi-head architecture for open-set anomaly detection of fast radio bursts [pdf] paper   link   bibtex   9 downloads  
Détection des sursauts radio rapides (FRB) basée sur une architecture neuronale hybride. Amblard, B.; El-Bouch, S.; Dalla Mura, M.; Ferrari, A.; and Richard, C. In Actes du 30e Colloque GRETSI sur le Traitement du Signal et des Images, 2025.
Détection des sursauts radio rapides (FRB) basée sur une architecture neuronale hybride [pdf] paper   link   bibtex   1 download  
Riemannian diffusion adaptation for distributed optimization on manifolds. Wang, X.; Borsoi, R.; Richard, C.; and Sayed, A. H. In Machine Learning (ICML), 2025 IEEE International Conference on, 2025.
Riemannian diffusion adaptation for distributed optimization on manifolds [pdf] paper   link   bibtex   13 downloads  
Noise-assisted graph multivariate empirical mode decomposition with non-uniform projections. Sun, X.; Nassif, R.; Richard, C.; and Wang, H. Circuits and Systems I, IEEE Transactions on, 72(4): 1707-1717. 2025.
Noise-assisted graph multivariate empirical mode decomposition with non-uniform projections [pdf] paper   link   bibtex   3 downloads  
Anomaly detection in graph signals with complex wavelet packet correlation mining. Sun, X.; Nassif, R.; Richard, C.; Yang, Z.; Chen, J.; and Wang, H. Circuits and Systems I, IEEE Transactions on, 72(7): 3450-3463. 2025.
Anomaly detection in graph signals with complex wavelet packet correlation mining [pdf] paper   link   bibtex   5 downloads  
Conjugate gradient and variance reduction based online ADMM for low-rank distributed networks. Chen, Y.; Jin, D.; Chen, J.; Richard, C.; Zhang, W.; Huang, G.; and Chen, J. Signal Processing Letters, IEEE, 32: 706-710. 2025.
Conjugate gradient and variance reduction based online ADMM for low-rank distributed networks [pdf] paper   link   bibtex   1 download  
Distributed online learning over multitask networks with rank-one model. Chen, Y.; Jin, D.; Chen, J.; Richard, C.; Zhang, W.; Huang, G.; and Chen, J. Signal and Information Processing over Networks, IEEE Transactions on, 11: 314-328. 2025.
Distributed online learning over multitask networks with rank-one model [pdf] paper   link   bibtex   2 downloads  
Precision traffic monitoring: Leveraging distributed acoustic sensing and deep neural networks. Khacef, Y.; Van den Ende, M.; Richard, C.; Ferrari, A.; and Sladen, A. Intelligent Transportation Systems, IEEE Transactions on, 26(6): 7678 - 7689. 2025.
Precision traffic monitoring: Leveraging distributed acoustic sensing and deep neural networks [pdf] paper   link   bibtex   21 downloads  
A hypergraph-based Lasso regularization for distributed learning. Faia, G.; Richard, C.; and Nassif, R. In Signals, Systems and Computers (ASILOMAR), 2025 Asilomar Conference on, 2025.
A hypergraph-based Lasso regularization for distributed learning [pdf] paper   link   bibtex   1 download  
  2024 (7)
Non-parametric online change point detection on Riemannian manifolds. Wang, X.; Borsoi, R.; Richard, C.; and Chen, J. In Machine Learning (ICML), 2024 IEEE International Conference on, 2024.
Non-parametric online change point detection on Riemannian manifolds [pdf] paper   link   bibtex   32 downloads  
Multi-lane vehicular traffic analysis facilitated by distributed acoustic sensing and deep learning. Van den Ende, M.; Sladen, A.; Ferrari, A.; and Richard, C. . 2024. Submitted
link   bibtex  
Learning noise adapters for incremental speech enhancement. Yang, Z.; Song, X.; Chen, J.; Richard, C.; and Cohen, I. Signal Processing Letters, IEEE, 31: 2915-2919. 2024.
Learning noise adapters for incremental speech enhancement [pdf] paper   link   bibtex   20 downloads  
Online parameter estimation over distributed multitask networks with a rank-one model. Chen, Y.; Jin, D.; Chen, J.; Richard, C.; Zhang, W.; Huang, G.; and Chen, J. In Signal Processing (EUSIPCO), 32nd European Conference on, 2024.
Online parameter estimation over distributed multitask networks with a rank-one model [pdf] paper   link   bibtex   6 downloads  
Riemannian diffusion adaptation over graphs with application to online distributed PCA. Wang, X.; Borsoi, R.; and Richard, C. In Acoustics Speech and Signal Processing (ICASSP), 2024 IEEE International Conference on, 2024.
Riemannian diffusion adaptation over graphs with application to online distributed PCA [pdf] paper   link   bibtex   17 downloads  
Diffusion-based distributed wave-domain active noise control with convex sum of non-convex quadratic costs. Zhang, M.; Dong, Y.; Chen, J.; and Richard, C. Circuits and Systems II, IEEE Transactions on, 71(3): 1531-1535. 2024.
Diffusion-based distributed wave-domain active noise control with convex sum of non-convex quadratic costs [pdf] paper   link   bibtex   2 downloads  
Leveraging distributed acoustic sensing for vehicle counting in high-traffic-density environments. Khacef, Y.; Richard, C.; Cavalcante, C. C.; and Ferrari, A. In Signals, Systems and Computers (ASILOMAR), 2024 Asilomar Conference on, 2024.
Leveraging distributed acoustic sensing for vehicle counting in high-traffic-density environments [pdf] paper   link   bibtex   6 downloads  
  2023 (16)
Distributed pressure matching for personal sound zone control using diffusion adaptation. Zhang, M.; Junqing, Z.; Chen, J.; and Richard, C. In Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2023 IEEE International Workshop on, 2023.
Distributed pressure matching for personal sound zone control using diffusion adaptation [pdf] paper   link   bibtex   2 downloads  
Distributed change point detection in streaming manifold-valued signals over graphs. Wang, X.; Borsoi, R.; Richard, C.; and Ferrari, A. In Signals, Systems and Computers (ASILOMAR), 2023 Asilomar Conference on, 2023.
Distributed change point detection in streaming manifold-valued signals over graphs [pdf] paper   link   bibtex  
Distributed learning with convex sum-of-non-convex objective. Zhang, M.; Chen, J.; and Richard, C. In Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2023 IEEE International Workshop on, 2023.
Distributed learning with convex sum-of-non-convex objective [pdf] paper   link   bibtex   2 downloads  
Détection de changements dans des signaux sur graphe à valeur dans des variétés riemanniennes. Wang, X.; Borsoi, R.; Richard, C.; and Ferrari, A. In Actes du 29e Colloque GRETSI sur le Traitement du Signal et des Images, 2023.
Détection de changements dans des signaux sur graphe à valeur dans des variétés riemanniennes [pdf] paper   link   bibtex   4 downloads  
Deep hyperspectral and multispectral image fusion with inter-image variability. Wang, X.; Borsoi, R.; Richard, C.; and Chen, J. Geoscience and Remote Sensing, IEEE Transactions on, 61. 2023. Available online
Deep hyperspectral and multispectral image fusion with inter-image variability [link] code   Deep hyperspectral and multispectral image fusion with inter-image variability [pdf] paper   link   bibtex   33 downloads  
Online change point detection on Riemannian manifolds with Karcher mean estimates. Wang, X.; Borsoi, R.; and Richard, C. In Signal Processing (EUSIPCO), 31st European Conference on, 2023.
Online change point detection on Riemannian manifolds with Karcher mean estimates [pdf] arxiv   link   bibtex   3 downloads  
Theoretical analysis of the performance of the data-reuse RLS algorithm. Gao, W.; Chen, J.; and Richard, C. Circuits and Systems II, IEEE Transactions on, 71(1): 490-494. 2023.
Theoretical analysis of the performance of the data-reuse RLS algorithm [pdf] paper   link   bibtex   4 downloads  
Online dominant generalized eigenvectors extraction via a randomized algorithm. Cai, H.; Kaloorazi, M. F.; Chen, J.; Chen, W.; and Richard, C. Vehicular Technology, IEEE Transactions on, 72(6): 7597-7612. 2023.
Online dominant generalized eigenvectors extraction via a randomized algorithm [pdf] paper   link   bibtex   16 downloads  
Change point detection with neural online density-ratio estimator. Wang, X.; Borsoi, R.; Richard, C.; and Chen, J. In Acoustics Speech and Signal Processing (ICASSP), 2023 IEEE International Conference on, 2023.
Change point detection with neural online density-ratio estimator [pdf] paper   link   bibtex   20 downloads  
Anomaly detection in graph signals with canonical correlation analysis. Sun, X.; Nassif, R.; and Richard, C. In Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2023 IEEE International Workshop on, 2023. Accepted
link   bibtex  
Spatial deep deconvolution U-Net for traffic analyses with distributed acoustic sensing. Yuan, S.; Van den Ende, M.; Liu, J.; Noy, H. Y.; Clapp, R.; Richard, C.; and Biondi, B. Intelligent Transportation Systems, IEEE Transactions on, 25(2): 1913-1924. 2023.
Spatial deep deconvolution U-Net for traffic analyses with distributed acoustic sensing [link] arxiv   Spatial deep deconvolution U-Net for traffic analyses with distributed acoustic sensing [pdf] paper   link   bibtex   19 downloads  
Tuning-free plug-and-play hyperspectral image deconvolution with deep priors. Wang, X.; Chen, J.; Wei, Q.; and Richard, C. Geoscience and Remote Sensing, IEEE Transactions on, 61: 1-13. 2023.
Tuning-free plug-and-play hyperspectral image deconvolution with deep priors [pdf] paper   link   bibtex   10 downloads  
Integration of physics-based and data-driven models for hyperspectral image unmixing: A summary of current methods. Chen, J.; Zhao, M.; Wang, X.; Richard, C.; and Rahardja, S. Signal Processing Magazine, IEEE, 40(2): 61-74. 2023.
Integration of physics-based and data-driven models for hyperspectral image unmixing: A summary of current methods [pdf] paper   link   bibtex   9 downloads  
Deep deconvolution for traffic analysis with distributed acoustic sensing data. Van den Ende, M.; Ferrari, A.; Sladen, A.; and Richard, C. Vehicular Technology, IEEE Transactions on, 24(3): 2947-2962. 2023.
Deep deconvolution for traffic analysis with distributed acoustic sensing data [pdf] paper   link   bibtex   26 downloads  
A self-supervised deep learning approach for blind denoising and waveform coherence enhancement in distributed acoustic sensing. Van den Ende, M.; Lior, I.; Ampuero, J.; Sladen, A.; Ferrari, A.; and Richard, C. Neural Networks and Learning Systems, IEEE Transactions on, 34(7). 2023.
A self-supervised deep learning approach for blind denoising and waveform coherence enhancement in distributed acoustic sensing [link] arxiv   link   bibtex   5 downloads  
Online change point detection with kernels. Ferrari, A.; Richard, C.; Bourrier, A.; and Bouchikhi, I. Pattern Recognition, 133: 109022. 2023.
Online change point detection with kernels [link] arxiv   Online change point detection with kernels [link] code   link   bibtex   14 downloads  
  2022 (10)
Self-supervised velocity field learning for high-resolution traffic monitoring with distributed acoustic sensing. Khacef, Y.; Van den Ende, M.; Ferrari, A.; Richard, C.; and Sladen, A. In Signals, Systems and Computers (ASILOMAR), 2022 Asilomar Conference on, 2022.
Self-supervised velocity field learning for high-resolution traffic monitoring with distributed acoustic sensing [pdf] paper   Self-supervised velocity field learning for high-resolution traffic monitoring with distributed acoustic sensing [link] youtube   link   bibtex   49 downloads  
Transient performance analysis of the L1-RLS algorithm. Gao, W.; Chen, J.; Richard, C.; Shi, W.; and Zhang, Q. Signal Processing Letters, IEEE, 29: 90-94. 2022.
Transient performance analysis of the L1-RLS algorithm [link] arxiv   link   bibtex   7 downloads  
Factorisation tensorielle couplée en termes de rangs (L, L, 1) : application conjointe à la super-résolution hyperspectrale et au démélange en présence de variabilité spectrale. Prévost, C.; Borsoi, R.; Usevich, K.; Brie, D.; Bermudez, J. M.; and Richard, C. In Actes du 28e Colloque GRETSI sur le Traitement du Signal et des Images, 2022.
Factorisation tensorielle couplée en termes de rangs (L, L, 1) : application conjointe à la super-résolution hyperspectrale et au démélange en présence de variabilité spectrale [pdf] paper   link   bibtex   6 downloads  
Tensor-based image fusion accounting for inter-image variability: Recoverability and algorithms. Borsoi, R.; Prévost, C.; Usevich, K.; Brie, D.; Bermudez, J. M.; and Richard, C. In Actes du 28e Colloque GRETSI sur le Traitement du Signal et des Images, 2022.
Tensor-based image fusion accounting for inter-image variability: Recoverability and algorithms [pdf] paper   link   bibtex   10 downloads  
Transient analysis of diffusion recursive least squares via signed error algorithm for cyclostationary colored inputs. Gao, W.; Xu, Y.; Chen, J.; and Richard, C. In Machine Learning for Signal Processing (MLSP), 2022 IEEE International Workshop on, pages 1-6, 2022. IEEE
Transient analysis of diffusion recursive least squares via signed error algorithm for cyclostationary colored inputs [pdf] paper   link   bibtex   6 downloads  
Hyperspectral image super-resolution with deep priors and degradation model inversion. Wang, X.; Chen, J.; and Richard, C. In Acoustics Speech and Signal Processing (ICASSP), 2022 IEEE International Conference on, 2022.
Hyperspectral image super-resolution with deep priors and degradation model inversion [pdf] paper   link   bibtex   9 downloads  
Transient analysis of clustered multitask diffusion RLS algorithm. Gao, W.; Chen, J.; Richard, C.; Shi, W.; and Zhang, Q. In Acoustics Speech and Signal Processing (ICASSP), 2022 IEEE International Conference on, 2022.
Transient analysis of clustered multitask diffusion RLS algorithm [pdf] paper   link   bibtex   7 downloads  
Hyperspectral super-resolution accounting for spectral variability: Coupled tensor LL1-based recovery and blind unmixing of the unknown super-resolution image. Prévost, C.; Borsoi, R.; Usevich, K.; Brie, D.; Bermudez, J. M.; and Richard, C. SIAM Journal on Imaging Sciences, 15(1). 2022.
Hyperspectral super-resolution accounting for spectral variability: Coupled tensor LL1-based recovery and blind unmixing of the unknown super-resolution image [link] paper   link   bibtex   2 downloads  
Graph topology inference with derivative-reproducing property in RKHS: algorithm and convergence analysis. Moscu, M.; Borsoi, R.; Richard, C.; and Bermudez, J. M. Signal and Information Processing over Networks, IEEE Transactions on, 8: 78-91. 2022.
Graph topology inference with derivative-reproducing property in RKHS: algorithm and convergence analysis [pdf] arxiv   link   bibtex   17 downloads  
Deep deconvolution applied to distributed acoustic sensing for traffic analysis. Van den Ende, M.; Ferrari, A.; Sladen, A.; and Richard, C. In Signal Processing (EUSIPCO), 30th European Conference on, 2022.
Deep deconvolution applied to distributed acoustic sensing for traffic analysis [pdf] paper   link   bibtex   14 downloads  
  2021 (12)
A self-supervised deep learning approach for improving signal coherence in distributed acoustic sensing. Van den Ende, M.; Lior, I.; Ampuero, J.; Sladen, A.; and Richard, C. In European Geosciences Union General Assembly, 2021.
A self-supervised deep learning approach for improving signal coherence in distributed acoustic sensing [link] youtube   link   bibtex   14 downloads  
In vitro and in vivo multispectral photoacoustic imaging for the evaluation of chromophore concentration. Dolet, A.; Ammanouil, R.; Petrilli, V.; Richard, C.; Tortoli, P.; and Varray, F. Sensors, 21(10). 2021.
In vitro and in vivo multispectral photoacoustic imaging for the evaluation of chromophore concentration [link] paper   link   bibtex   9 downloads  
Coupled tensor models accounting for inter-image variability. Prévost, C.; Borsoi, R.; Usevich, K.; Brie, D.; Bermudez, J. M.; and Richard, C. In Signals, Systems and Computers (ASILOMAR), 2021 Asilomar Conference on, 2021.
Coupled tensor models accounting for inter-image variability [link] youtube   link   bibtex   10 downloads  
From time-frequency to vertex-frequency and back. Stankovic, L.; Lerga, J.; Mandic, D.; Brajovic, M.; Richard, C.; and Dakovic, M. Mathematics, 9(12). 2021.
From time-frequency to vertex-frequency and back [pdf] paper   link   bibtex   5 downloads  
Fast unmixing and change detection in multitemporal hyperspectral data. Borsoi, R.; Imbiriba, T.; Bermudez, J. M.; and Richard, C. Computational Imaging, IEEE Transactions on, 7: 975-988. 2021.
Fast unmixing and change detection in multitemporal hyperspectral data [link] arxiv   link   bibtex   3 downloads  
Coupled tensor decomposition for hyperspectral and multispectral image fusion with inter-image variability. Borsoi, R.; Prévost, C.; Usevich, K.; Brie, D.; Bermudez, J. M.; and Richard, C. Signal Processing, IEEE Journal of Selected Topics in, 15(3): 702-717. 2021.
Coupled tensor decomposition for hyperspectral and multispectral image fusion with inter-image variability [pdf] arxiv   link   bibtex   25 downloads  
Convergence analysis of the graph-topology-inference kernel LMS algorithm. Moscu, M.; Nassif, R.; Hua, F.; and Richard, C. In Acoustics Speech and Signal Processing (ICASSP), 2021 IEEE International Conference on, 2021.
Convergence analysis of the graph-topology-inference kernel LMS algorithm [pdf] paper   link   bibtex   27 downloads  
Next-generation traffic monitoring with distributed acoustic sensing arrays and optimum array processing. Van den Ende, M.; Ferrari, A.; Sladen, A.; and Richard, C. In Signals, Systems and Computers (ASILOMAR), 2021 Asilomar Conference on, 2021.
Next-generation traffic monitoring with distributed acoustic sensing arrays and optimum array processing [link] arxiv   Next-generation traffic monitoring with distributed acoustic sensing arrays and optimum array processing [pdf] paper   Next-generation traffic monitoring with distributed acoustic sensing arrays and optimum array processing [link] youtube   link   bibtex   27 downloads  
Transient theoretical analysis of diffusion RLS algorithm for cyclostationary colored inputs. Gao, W.; Chen, J.; and Richard, C. Signal Processing Letters, IEEE, 28: 1160-1164. 2021.
Transient theoretical analysis of diffusion RLS algorithm for cyclostationary colored inputs [pdf] arxiv   link   bibtex   9 downloads  
Hyperspectral image super-resolution via deep prior regularization with parameter estimation. Wang, X.; Chen, J.; Wei, Q.; and Richard, C. Circuits and Systems for Video Technology, IEEE Transactions on. 2021. Early Access
Hyperspectral image super-resolution via deep prior regularization with parameter estimation [link] arxiv   Hyperspectral image super-resolution via deep prior regularization with parameter estimation [link] code   link   bibtex   17 downloads  
Spectral variability in hyperspectral data unmixing: a comprehensive review. Borsoi, R.; Imbiriba, T.; Bermudez, J. M.; Richard, C.; Chanussot, J.; Drumetz, L.; Tourneret, J.; Zare, A.; and Jutten, C. Geoscience and Remote Sensing Magazine, IEEE, 9(4): 223-270. 2021.
Spectral variability in hyperspectral data unmixing: a comprehensive review [link] arxiv   Spectral variability in hyperspectral data unmixing: a comprehensive review [link] code   link   bibtex  
Deep generative models for library augmentation in multiple endmember spectral mixture analysis. Borsoi, R.; Imbiriba, T.; Bermudez, J. M.; and Richard, C. Geoscience and Remote Sensing, IEEE Letters on, 18(10): 1831-1835. 2021.
Deep generative models for library augmentation in multiple endmember spectral mixture analysis [pdf] arxiv   link   bibtex   2 downloads  
  2020 (18)
Online dominant generalized eigenvectors extraction via a randomized method. Cai, H.; Kaloorazi, M. F.; Chen, J.; Chen, W.; and Richard, C. In Signal Processing (EUSIPCO), 28th European Conference on, 2020.
Online dominant generalized eigenvectors extraction via a randomized method [pdf] paper   link   bibtex   6 downloads  
Convex combination of diffusion strategies over networks. Jin, D.; Chen, J.; Richard, C.; Chen, J.; and Sayed, A. H. Signal and Information Processing over Networks, IEEE Transactions on, 6: 714-731. 2020.
Convex combination of diffusion strategies over networks [pdf] paper   link   bibtex   8 downloads  
Online proximal learning over multitask networks over jointly sparse multitask networks with L(infty,1) regularization. Jin, D.; Chen, J.; Richard, C.; and Chen, J. Signal Processing, IEEE Transactions on, 68: 2087-2104. 2020.
Online proximal learning over multitask networks over jointly sparse multitask networks with L(infty,1) regularization [pdf] paper   link   bibtex   10 downloads  
Online kernel-based graph topology identification with partial-derivative-imposed sparsity. Moscu, M.; Borsoi, R.; and Richard, C. In Signal Processing (EUSIPCO), 28th European Conference on, 2020.
Online kernel-based graph topology identification with partial-derivative-imposed sparsity [pdf] paper   link   bibtex   11 downloads  
Online graph-based change point detection in multiband image sequences. Borsoi, R.; Richard, C.; Ferrari, A.; Chen, J.; and Bermudez, J. M. In Signal Processing (EUSIPCO), 28th European Conference on, 2020.
Online graph-based change point detection in multiband image sequences [pdf] paper   link   bibtex   10 downloads  
A regularization framework for learning over multitask graphs. Nassif, R.; Vlaski, S.; Richard, C.; and Sayed, A. H. In Acoustics Speech and Signal Processing (ICASSP), 2020 IEEE International Conference on, 2020.
A regularization framework for learning over multitask graphs [pdf] paper   link   bibtex   3 downloads  
Diffusion LMS with communication delays: Stability and performance analysis. Hua, F.; Nassif, R.; Richard, C.; Wang, H.; and Sayed, A. H. Signal Processing Letters, IEEE, 27: 730-734. 2020.
Diffusion LMS with communication delays: Stability and performance analysis [link] code   Diffusion LMS with communication delays: Stability and performance analysis [pdf] paper   link   bibtex   42 downloads  
Learning over multitask graphs – Part I: Stability analysis. Nassif, R.; Vlaski, S.; Richard, C.; and Sayed, A. H. Signal Processing, IEEE Open Journal on, 1: 28-45. 2020.
Learning over multitask graphs – Part I: Stability analysis [link] paper   link   bibtex   7 downloads  
Learning over multitask graphs – Part II: Performance analysis. Nassif, R.; Vlaski, S.; Richard, C.; and Sayed, A. H. Signal Processing, IEEE Open Journal on, 1(46-63). 2020.
Learning over multitask graphs – Part II: Performance analysis [link] paper   link   bibtex   7 downloads  
Kalman filtering and expectation maximization for multitemporal spectral unmixing. Borsoi, R.; Imbiriba, T.; Pau, C.; Bermudez, J. M.; and Richard, C. Geoscience and Remote Sensing, IEEE Letters on, 19: 1-5. 2020. Early Access
Kalman filtering and expectation maximization for multitemporal spectral unmixing [pdf] paper   link   bibtex   3 downloads  
Online graph topology inference with kernels for brain connectivity estimation. Moscu, M.; Borsoi, R.; and Richard, C. In Acoustics Speech and Signal Processing (ICASSP), 2020 IEEE International Conference on, 2020.
Online graph topology inference with kernels for brain connectivity estimation [pdf] paper   link   bibtex   7 downloads  
Non-parametric community change-points detection in streaming graph signals. Ferrari, A.; and Richard, C. In Acoustics, Speech and Signal Processing (ICASSP), 2020 IEEE International Conference on, 2020, 2020.
Non-parametric community change-points detection in streaming graph signals [pdf] paper   link   bibtex   17 downloads  
Proximal multitask learning over distributed networks with jointly sparse structure. Jin, D.; Chen, J.; Richard, C.; and Chen, J. In Acoustics Speech and Signal Processing (ICASSP), 2020 IEEE International Conference on, 2020.
Proximal multitask learning over distributed networks with jointly sparse structure [pdf] paper   link   bibtex   1 download  
A blind multiscale spatial regularization framework for kernel-based spectral unmixing. Borsoi, R.; Imbiriba, T.; Bermudez, J. M.; and Richard, C. Image Processing, IEEE Transactions on, 29: 4965-4979. 2020.
A blind multiscale spatial regularization framework for kernel-based spectral unmixing [pdf] arxiv   link   bibtex   1 download  
Learning spectral-spatial prior via 3DDnCNN for hyperspectral image deconvolution. Wang, X.; Chen, J.; Richard, C.; and Brie, D. In Acoustics, Speech and Signal Processing (ICASSP), 2020 IEEE International Conference on, 2020.
Learning spectral-spatial prior via 3DDnCNN for hyperspectral image deconvolution [pdf] paper   link   bibtex   4 downloads  
Online distributed learning over graphs with multitask graph-filter models. Hua, F.; Nassif, R.; Richard, C.; Wang, H.; and Sayed, A. H. Signal and Information Processing over Networks, IEEE Transactions on, 6(1): 63-77. 2020.
Online distributed learning over graphs with multitask graph-filter models [pdf] paper   link   bibtex   8 downloads  
Multitask learning over graphs: an approach for distributed, streaming machine learning. Nassif, R.; Richard, C.; Chen, J.; and Sayed, A. H. Signal Processing Magazine, IEEE, 37(3): 14-25. 2020.
Multitask learning over graphs: an approach for distributed, streaming machine learning [pdf] paper   link   bibtex   8 downloads  
Affine combination of diffusion strategies over networks. Jin, D.; Chen, J.; Richard, C.; and Sayed, A. H. Signal Processing, IEEE Transactions on, 68(1): 2087-2104. 2020.
Affine combination of diffusion strategies over networks [link] arxiv   link   bibtex   5 downloads  
  2019 (12)
Joint unmixing-deconvolution algorithms for hyperspectral images. Song, Y.; Djermoune, E.; Chen, J.; Richard, C.; and Brie, D. In Signal Processing (EUSIPCO), 26th European Conference on, 2019.
Joint unmixing-deconvolution algorithms for hyperspectral images [pdf] paper   link   bibtex   2 downloads  
Distributed change detection in streaming graph signals. Ferrari, A.; Richard, C.; and Verduci, L. In Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2019 IEEE International Workshop on, 2019.
Distributed change detection in streaming graph signals [pdf] paper   link   bibtex   15 downloads  
Learning combination of graph filters for graph signal modeling. Hua, F.; Richard, C.; Chen, J.; Wang, H.; Borgnat, P.; and Goncalves, P. Signal Processing Letters, IEEE, 26(12): 1912-1916. 2019.
Learning combination of graph filters for graph signal modeling [pdf] paper   link   bibtex   4 downloads  
Non-destructive prediction of pork meat degradation using a stacked autoencoder classifier on hyperspectral images. Almeida, S. J. M. d.; Bermudez, J. M.; Chen, J.; Richard, C.; and Gallo, B. In Signal Processing (EUSIPCO), 26th European Conference on, 2019.
Non-destructive prediction of pork meat degradation using a stacked autoencoder classifier on hyperspectral images [pdf] paper   link   bibtex   2 downloads  
Apprentissage distribué de la topologie d'un graphe à partir de signaux temporels sur graphe. Moscu, M.; Nassif, R.; Hua, F.; and Richard, C. In Actes du 27e Colloque GRETSI sur le Traitement du Signal et des Images, 2019.
Apprentissage distribué de la topologie d'un graphe à partir de signaux temporels sur graphe [pdf] paper   link   bibtex   7 downloads  
Séparation et déconvolution conjointes d'images hyperspectrales. Song, Y.; Djermoune, E.; Chen, J.; Richard, C.; and Brie, D. In Actes du 27e Colloque GRETSI sur le Traitement du Signal et des Images, 2019.
Séparation et déconvolution conjointes d'images hyperspectrales [pdf] paper   link   bibtex   5 downloads  
Learning causal networks topology from streaming graph signals. Moscu, M.; Nassif, R.; Hua, F.; and Richard, C. In Signal Processing (EUSIPCO), 26th European Conference on, 2019.
Learning causal networks topology from streaming graph signals [pdf] paper   link   bibtex   2 downloads  
A regularization framework for learning over multitask graphs. Nassif, R.; Vlaski, S.; Richard, C.; and Sayed, A. H. Signal Processing Letters, IEEE, 26(2): 297-301. 2019.
A regularization framework for learning over multitask graphs [pdf] paper   link   bibtex   2 downloads  
A fast multiscale spatial regularization for sparse hyperspectral unmixing. Borsoi, R.; Imbiriba, T.; Bermudez, J. M.; and Richard, C. IEEE Geoscience and Remote Sensing Letters, 16(4): 598-602. 2019.
A fast multiscale spatial regularization for sparse hyperspectral unmixing [link] code   A fast multiscale spatial regularization for sparse hyperspectral unmixing [pdf] paper   link   bibtex   10 downloads  
Online deconvolution for industrial hyperspectral imaging systems. Song, Y.; Djermoune, E.; Chen, J.; Richard, C.; and Brie, D. SIAM Journal on Imaging Sciences, 12(1): 54-86. 2019.
Online deconvolution for industrial hyperspectral imaging systems [pdf] paper   link   bibtex   7 downloads  
Kernel based online change point detection. Bouchikhi, I.; Ferrari, A.; Richard, C.; Bourrier, A.; and Bernot, M. In Signal Processing (EUSIPCO), 26th European Conference on, 2019.
Kernel based online change point detection [pdf] paper   link   bibtex   5 downloads  
On reducing the communication cost of the diffusion LMS algorithm. Harrane, I. E. K.; Flamary, R.; and Richard, C. Signal and Information Processing over Networks, IEEE Transactions on, 5(1): 100-112. 2019.
On reducing the communication cost of the diffusion LMS algorithm [pdf] paper   link   bibtex   5 downloads  
  2018 (13)
Non-parametric online change-point detection with kernel LMS by relative density ratio estimation. Bouchikhi, I.; Ferrari, A.; Richard, C.; Bourrier, A.; and Bernot, M. In Statistical Signal Processing Workshop (SSP), 2018 IEEE, 2018.
Non-parametric online change-point detection with kernel LMS by relative density ratio estimation [pdf] paper   link   bibtex  
Quantification of multispectral photoacoustic images: unsupervised unmixing methods comparison. Dolet, A.; Ammanouil, R.; Grenier, T.; Richard, C.; Tortoli, P.; Vray, D.; and Varray, F. In Ultrasonics, 2018 IEEE International Symposium, 2018.
link   bibtex  
Decentralized clustering for node-variant graph filtering with graph diffusion LMS. Hua, F.; Nassif, R.; Richard, C.; Wang, H.; and Sayed, A. H. In Signals, Systems and Computers (ASILOMAR), 2018 Fifty Second Asilomar Conference on, 2018.
Decentralized clustering for node-variant graph filtering with graph diffusion LMS [pdf] paper   link   bibtex   6 downloads  
Cooperative and Graph Signal Processing: Principles and Applications. Djuric, P. M.; and Richard, C. Academic Press, 2018.
Cooperative and Graph Signal Processing: Principles and Applications [link] paper   link   bibtex   3 downloads  
Random matrix theory for diffusion LMS analysis. Harrane, I. E. K.; Flamary, R.; Richard, C.; and Couillet, R. In Signals, Systems and Computers (ASILOMAR), 2018 Fifty Second Asilomar Conference on, 2018.
link   bibtex  
A preconditioned graph diffusion LMS for adaptive graph signal processing. Hua, F.; Nassif, R.; Richard, C.; Wang, H.; and Sayed, A. H. In Signal Processing (EUSIPCO), 25th European Conference on, 2018.
A preconditioned graph diffusion LMS for adaptive graph signal processing [pdf] paper   link   bibtex   1 download  
. Chen, J.; Richard, C.; Ting, S. K.; and Sayed, A. H. Multitask learning over adaptive networks with grouping strategies. Djuric, P. M.; and Richard, C., editor(s). Academic Press, 2018.
link   bibtex  
Decentralized online nonparametric learning. Koppel, A.; Paternain, S.; Richard, C.; and Ribeiro, A. In Signals, Systems and Computers (ASILOMAR), 2018 Fifty Second Asilomar Conference on, 2018.
Decentralized online nonparametric learning [pdf] paper   link   bibtex   1 download  
Ada-pt: an adaptive parameter tuning strategy based on weighted Stein unbiased risk estimator. Ammanouil, R.; Ferrari, A.; and Richard, C. In Acoustics, Speech and Signal Processing (ICASSP), 2018 IEEE International Conference on, 2018.
Ada-pt: an adaptive parameter tuning strategy based on weighted Stein unbiased risk estimator [pdf] paper   link   bibtex  
Distributed diffusion adaptation over graph signals. Nassif, R.; Richard, C.; Chen, J.; and Sayed, A. H. In Acoustics, Speech and Signal Processing (ICASSP), 2018 IEEE International Conference on, 2018.
Distributed diffusion adaptation over graph signals [pdf] paper   link   bibtex   7 downloads  
Adaptive parameters adjustment for group reweighted zero-attracting LMS. Jin, D.; Chen, J.; Richard, C.; and Chen, J. In Acoustics, Speech and Signal Processing (ICASSP), 2018 IEEE International Conference on, 2018.
Adaptive parameters adjustment for group reweighted zero-attracting LMS [pdf] paper   link   bibtex  
Model-driven online parameter adjustement for zero-attracting LMS. Jin, D.; Chen, J.; Richard, C.; and Chen, J. Signal Processing, 152: 373-383. 2018.
Model-driven online parameter adjustement for zero-attracting LMS [pdf] paper   link   bibtex   1 download  
Decentralized online learning with kernels. Koppel, A.; Paternain, S.; Richard, C.; and Ribeiro, A. Signal Processing, IEEE Transactions on, 66(12): 3240-3255. 2018.
Decentralized online learning with kernels [link] code   Decentralized online learning with kernels [pdf] paper   link   bibtex   13 downloads  
  2017 (16)
A graph diffusion LMS strategy for adaptive graph signal processing. Nassif, R.; Richard, C.; Chen, J.; and Sayed, A. H. In Signals, Systems and Computers (ASILOMAR), 2017 Fiftyth Oneth Asilomar Conference on, 2017.
A graph diffusion LMS strategy for adaptive graph signal processing [pdf] paper   link   bibtex   1 download  
Decentralized efficient nonparametric stochastic optimization. Koppel, A.; Paternain, S.; Richard, C.; and Ribeiro, A. In Signal and Information Processing (GlobalSIP), 2017 IEEE Global Conference on, 2017.
Decentralized efficient nonparametric stochastic optimization [pdf] paper   link   bibtex   2 downloads  
Unmixing of multispectral photoacoustic images. Dolet, A.; Ammanouil, R.; Varray, F.; Liu, Y.; Yuan, Z.; Tortoli, P.; Ferrari, A.; Richard, C.; and Vray, D. In Actes du 26e Colloque GRETSI sur le Traitement du Signal et des Images, 2017.
Unmixing of multispectral photoacoustic images [pdf] paper   link   bibtex   1 download  
An adaptive distributed asynchronous algorithm with application to target localization. Mourya, R.; Bianchi, P.; Salim, A.; and Richard, C. In Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017 IEEE International Workshop on, 2017.
An adaptive distributed asynchronous algorithm with application to target localization [pdf] paper   link   bibtex   1 download  
Penalty-based multitask estimation with non-local linear equality constraints. Hua, F.; Nassif, R.; Richard, C.; and Wang, H. In Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017 IEEE International Workshop on, pages 433-437, Curacao, 2017.
Penalty-based multitask estimation with non-local linear equality constraints [pdf] paper   link   bibtex   1 download  
Filtrage LMS sur graphe. Algorithme et analyse. Nassif, R.; Richard, C.; Chen, J.; Couillet, R.; and Borgnat, P. In Actes du 26e Colloque GRETSI sur le Traitement du Signal et des Images, 2017.
Filtrage LMS sur graphe. Algorithme et analyse [pdf] paper   link   bibtex   1 download  
Déconvolution en ligne d’images hyperspectrales pour les imageurs de type pushbroom. Song, Y.; Djermoune, E.; Chen, J.; Richard, C.; and Brie, D. In Actes du 26e Colloque GRETSI sur le Traitement du Signal et des Images, 2017.
Déconvolution en ligne d’images hyperspectrales pour les imageurs de type pushbroom [pdf] paper   link   bibtex  
Penalty-based multitask distributed adaptation over networks with constraints. Hua, F.; Nassif, R.; Richard, C.; Wang, H.; and Huang, J. In Signals, Systems and Computers (ASILOMAR), 2017 Fifty Oneth Asilomar Conference on, 2017.
Penalty-based multitask distributed adaptation over networks with constraints [pdf] paper   link   bibtex   2 downloads  
A design methodology for the Gaussian KLMS algorithm. Pedrosa, P.; Bermudez, J. M.; and Richard, C. In Signal Processing (EUSIPCO), 25th European Conference on, 2017.
A design methodology for the Gaussian KLMS algorithm [pdf] paper   link   bibtex   2 downloads  
Distributed approach for deblurring large images with shift-variant blur. Mourya, R.; Ferrari, A.; Flamary, R.; Bianchi, P.; and Richard, C. In Signal Processing (EUSIPCO), 25th European Conference on, 2017.
Distributed approach for deblurring large images with shift-variant blur [pdf] paper   link   bibtex   1 download  
Online deconvolution for pushbroom hyperspectral imaging systems. Song, Y.; Djermoune, E.; Chen, J.; Richard, C.; and Brie, D. In Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017 IEEE International Workshop on, 2017.
Online deconvolution for pushbroom hyperspectral imaging systems [pdf] paper   link   bibtex  
Adaptation and learning over networks for nonlinear system modeling. Scardapane, S.; Chen, J.; and Richard, C. Adaptive learning methods for nonlinear system modeling. Comminiello, D.; and Principe, J. C., editor(s). Elsevier Publishing, 2017.
Adaptive learning methods for nonlinear system modeling [pdf] paper   link   bibtex   1 download  
Diffusion LMS for multitask problems with local linear equality constraints. Nassif, R.; Richard, C.; Ferrari, A.; and Sayed, A. H. Signal Processing, IEEE Transactions on, 65(19): 4979 - 4993. 2017.
Diffusion LMS for multitask problems with local linear equality constraints [pdf] paper   link   bibtex   8 downloads  
Multitask diffusion adaptation over networks with common latent representations. Chen, J.; Richard, C.; and Sayed, A. H. Signal Processing, IEEE Journal of Selected Topics in, 11(3): 563-579. 2017.
Multitask diffusion adaptation over networks with common latent representations [pdf] paper   link   bibtex   2 downloads  
Nonlinear unmixing of hyperspectral data with vector-valued kernel functions. Ammanouil, R.; Ferrari, A.; Richard, C.; and Mathieu, S. Image Processing, IEEE Transactions on, 26(1): 340-354. 2017.
Nonlinear unmixing of hyperspectral data with vector-valued kernel functions [link] code   Nonlinear unmixing of hyperspectral data with vector-valued kernel functions [pdf] paper   link   bibtex   4 downloads  
Band selection for nonlinear unmixing of hyperspectral images as maximal click problem. Imbiriba, T.; Bermudez, J. M.; and Richard, C. Image Processing, IEEE Transactions on, 26(5): 2179-2191. 2017.
Band selection for nonlinear unmixing of hyperspectral images as maximal click problem [link] code   Band selection for nonlinear unmixing of hyperspectral images as maximal click problem [pdf] paper   link   bibtex   4 downloads  
  2016 (15)
Transient performance analysis of zero-attracting LMS. Chen, J.; Richard, C.; Song, Y.; and Brie, D. Signal Processing Letters, IEEE, 23(12): 1786-1790. 2016.
Transient performance analysis of zero-attracting LMS [link] code   Transient performance analysis of zero-attracting LMS [pdf] paper   link   bibtex   5 downloads  
Robust nonlinear unmixing of hyperspectral images with a linear-mixture/nonlinear-fluctuation model. Chen, J.; and Richard, C. In Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International, 2016.
Robust nonlinear unmixing of hyperspectral images with a linear-mixture/nonlinear-fluctuation model [pdf] paper   link   bibtex   1 download  
Doubly compressed diffusion LMS over adaptive networks. Harrane, I. E. K.; Flamary, R.; and Richard, C. In Signals, Systems and Computers (ASILOMAR), 2016 Fiftyth Asilomar Conference on, 2016.
Doubly compressed diffusion LMS over adaptive networks [link] code   Doubly compressed diffusion LMS over adaptive networks [pdf] paper   link   bibtex   13 downloads  
Distributed learning over multitask networks with linearly related tasks. Nassif, R.; Richard, C.; Ferrari, A.; and Sayed, A. H. In Signals, Systems and Computers (ASILOMAR), 2016 Fiftyth Asilomar Conference on, 2016.
Distributed learning over multitask networks with linearly related tasks [pdf] paper   link   bibtex  
Toward privacy-preserving diffusion strategies for adaptation and learning over networks. Harrane, I. E. K.; Flamary, R.; and Richard, C. In Signal Processing (EUSIPCO), 24th European Conference on, 2016.
Toward privacy-preserving diffusion strategies for adaptation and learning over networks [pdf] paper   link   bibtex  
Spatial regularization for nonlinear unmixing of hyperspectral data with vector-valued kernel functions. Ammanouil, R.; Ferrari, A.; Richard, C.; and Tourneret, J. In Statistical Signal Processing Workshop (SSP), 2016 IEEE, 2016.
Spatial regularization for nonlinear unmixing of hyperspectral data with vector-valued kernel functions [pdf] paper   link   bibtex  
Unsupervised neighbor dependent nonlinear unmixing. Ammanouil, R.; Ferrari, A.; and Richard, C. In Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on, 2016.
Unsupervised neighbor dependent nonlinear unmixing [pdf] paper   link   bibtex  
Group diffusion LMS. Chen, J.; Ting, S. K.; Richard, C.; and Sayed, A. H. In Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on, 2016.
Group diffusion LMS [pdf] paper   link   bibtex  
Proximal multitask learning over networks with sparsity-inducing coregularization. Nassif, R.; Richard, C.; Ferrari, A.; and Sayed, A. H. Signal Processing, IEEE Transactions on, 64(23): 6329-6344. 2016.
Proximal multitask learning over networks with sparsity-inducing coregularization [pdf] paper   link   bibtex   2 downloads  
Reweighted nonnegative least-mean-square algorithm. Chen, J.; Richard, C.; and Bermudez, J. M. Signal processing, 128: 131-141. 2016.
Reweighted nonnegative least-mean-square algorithm [link] code   Reweighted nonnegative least-mean-square algorithm [pdf] paper   link   bibtex   4 downloads  
Nonparametric detection of nonlinearly mixed pixels and endmember estimation in hyperspectral images. Imbiriba, T.; Bermudez, J. M.; Richard, C.; and Tourneret, J. Image Processing, IEEE Transactions on, 25(3): 1136-1151. 2016.
Nonparametric detection of nonlinearly mixed pixels and endmember estimation in hyperspectral images [link] code   Nonparametric detection of nonlinearly mixed pixels and endmember estimation in hyperspectral images [pdf] paper   link   bibtex   5 downloads  
Estimating the intrinsic dimension of hyperspectral images using a noise-whitened eigengap approach. Halimi, A.; Honeine, P.; Kharouf, M.; Richard, C.; and Tourneret, J. Geoscience and Remote Sensing, IEEE Transactions on, 54(7): 3811-3821. 2016.
Estimating the intrinsic dimension of hyperspectral images using a noise-whitened eigengap approach [link] code   Estimating the intrinsic dimension of hyperspectral images using a noise-whitened eigengap approach [link] paper   link   bibtex  
Multitask diffusion adaptation over asynchronous networks. Nassif, R.; Richard, C.; Ferrari, A.; and Sayed, A. H. Signal Processing, IEEE Transactions on, 64(11): 2835-2850. 2016.
Multitask diffusion adaptation over asynchronous networks [link] paper   link   bibtex   1 download  
Stochastic behavior of the nonnegative least mean fourth algorithm for stationary gaussian inputs and slow learning. Ni, J.; Yang, J.; Chen, J.; Richard, C.; and Bermudez, J. M. Signal Processing, 128: 18-27. 2016.
Stochastic behavior of the nonnegative least mean fourth algorithm for stationary gaussian inputs and slow learning [pdf] paper   link   bibtex   1 download  
  2015 (15)
Diffusion adaptation over networks with kernel least-mean-square. Gao, W.; Chen, J.; Richard, C.; and Huang, J. In Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015 IEEE International Workshop on, 2015.
Diffusion adaptation over networks with kernel least-mean-square [pdf] paper   link   bibtex   4 downloads  
Modèle non-linéaire et semi-paramétrique pour le démélange non-supervisé d'images hyperspectrales. Ammanouil, R.; Ferrari, A.; and Richard, C. In Actes du 25e Colloque GRETSI sur le Traitement du Signal et des Images, 2015.
Modèle non-linéaire et semi-paramétrique pour le démélange non-supervisé d'images hyperspectrales [pdf] paper   link   bibtex  
Estimation distribuée sur les réseaux multitâches en présence d'échanges d'informations bruitées. Nassif, R.; Ferrari, A.; and Richard, C. In Actes du 25e Colloque GRETSI sur le Traitement du Signal et des Images, 2015.
Estimation distribuée sur les réseaux multitâches en présence d'échanges d'informations bruitées [pdf] paper   link   bibtex  
Estimation de la dimension intrinsèque des images hyperspectrales à l'aide d'un modèle à variances isolées. Halimi, A.; Honeine, P.; Kharouf, M.; Richard, C.; and Tourneret, J. In Actes du 25e Colloque GRETSI sur le Traitement du Signal et des Images, 2015.
Estimation de la dimension intrinsèque des images hyperspectrales à l'aide d'un modèle à variances isolées [pdf] paper   link   bibtex  
Band selection in RKHS for fast nonlinear unmixing of hyperspectral images. Imbiriba, T.; Bermudez, J. M.; Richard, C.; and Tourneret, J. In Signal Processing (EUSIPCO), 23rd European Conference on, 2015.
Band selection in RKHS for fast nonlinear unmixing of hyperspectral images [link] paper   link   bibtex  
Hyperspectral data unmixing with graph-based regularization. Ammanouil, R.; Ferrari, A.; and Richard, C. In Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2015 7th Workshop on, pages 1-4, 2015.
Hyperspectral data unmixing with graph-based regularization [pdf] paper   link   bibtex   1 download  
. Bermudez, J. M.; Honeine, P.; Tourneret, J.; and Richard, C. Kernel-based nonlinear signal processing, pages 1-23. Nascimento, V. H., editor(s). CRC Press, 2015.
Kernel-based nonlinear signal processing [pdf] paper   link   bibtex   3 downloads  
A stochastic behavior analysis of stochastic restricted-gradient descent algorithm in reproducing kernel Hilbert spaces. Takizawa, M.; Yukawa, M.; and Richard, C. In Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on, 2015.
A stochastic behavior analysis of stochastic restricted-gradient descent algorithm in reproducing kernel Hilbert spaces [pdf] paper   link   bibtex  
A graph Laplacian regularization for hyperspectral data unmixing. Ammanouil, R.; Ferrari, A.; and Richard, C. In Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on, 2015.
A graph Laplacian regularization for hyperspectral data unmixing [link] code   A graph Laplacian regularization for hyperspectral data unmixing [pdf] paper   link   bibtex   2 downloads  
Steady-state performance of non-negative least-mean-square algorithm and its variants. Chen, J.; Bermudez, J. M.; and Richard, C. In Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on, 2015.
link   bibtex  
Convergence analysis of the augmented complex KLMS algorithm with pre-tuned dictionary. Gao, W.; Chen, J.; Richard, C.; Bermudez, J. M.; and Huang, J. In Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on, 2015.
Convergence analysis of the augmented complex KLMS algorithm with pre-tuned dictionary [pdf] paper   link   bibtex   4 downloads  
Multitask diffusion LMS with sparsity-based regularization. Nassif, R.; Richard, C.; Ferrari, A.; and Sayed, A. H. In Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on, 2015.
Multitask diffusion LMS with sparsity-based regularization [pdf] paper   link   bibtex  
Diffusion LMS over multitask networks. Chen, J.; Richard, C.; and Sayed, A. H. Signal Processing, IEEE Transactions on, 63(11): 2733-2748. 2015.
Diffusion LMS over multitask networks [pdf] paper   link   bibtex   2 downloads  
Adaptive clustering for multitask diffusion networks. Chen, J.; Richard, C.; and Sayed, A. H. In Signal Processing (EUSIPCO), 23rd European Conference on, 2015.
Adaptive clustering for multitask diffusion networks [pdf] paper   link   bibtex   1 download  
Kernel variational approach for target tracking in a wireless sensor network. Snoussi, H.; Honeine, P.; and Richard, C. Regularization and bayesian methods for inverse problems in signal and image processing. Giovannelli, J.; and Idier, J., editor(s). Wiley, 2015.
link   bibtex  
  2014 (17)
Diffusion LMS for clustered multitask networks. Chen, J.; Richard, C.; and Sayed, A. H. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, 2014.
Diffusion LMS for clustered multitask networks [pdf] paper   link   bibtex  
Diffusion LMS for multitask problems with overlapping hypothesis subspaces. Chen, J.; Richard, C.; Hero, A. O.; and Sayed, A. H. In Machine Learning for Signal Processing (MLSP), 2014 IEEE International Workshop on, 2014.
Diffusion LMS for multitask problems with overlapping hypothesis subspaces [pdf] paper   link   bibtex  
Performance analysis of multitask diffusion adaptation over asynchronous networks. Nassif, R.; Richard, C.; Ferrari, A.; and Sayed, A. H. In Signals, Systems and Computers (ASILOMAR), 2014 Forty Eighth Asilomar Conference on, 2014.
Performance analysis of multitask diffusion adaptation over asynchronous networks [pdf] paper   link   bibtex   1 download  
Convex combination of kernel adaptive filters. Gao, W.; Richard, C.; Bermudez, J. M.; and Huang, J. In Machine Learning for Signal Processing (MLSP), 2014 IEEE International Workshop on, 2014.
Convex combination of kernel adaptive filters [pdf] paper   link   bibtex   2 downloads  
Distributed image reconstruction for very large arrays in radio astronomy. Ferrari, A.; Mary, D.; Flamary, R.; and Richard, C. In Sensor Array and Multichannel Signal Processing (SAM), 2014 IEEE Workshop on, 2014.
Distributed image reconstruction for very large arrays in radio astronomy [pdf] paper   link   bibtex  
GLUP: Yet another algorithm for blind unmixing of hyperspectral data. Ammanouil, R.; Ferrari, A.; Richard, C.; and Mary, D. In Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2014 6th Workshop on, pages 1-4, 2014.
GLUP: Yet another algorithm for blind unmixing of hyperspectral data [link] code   GLUP: Yet another algorithm for blind unmixing of hyperspectral data [pdf] paper   link   bibtex   1 download  
Blind and fully constrained unmixing of hyperspectral images. Ammanouil, R.; Ferrari, A.; Richard, C.; and Mary, D. Image Processing, IEEE Transactions on, 23(12): 5510-5518. 2014.
Blind and fully constrained unmixing of hyperspectral images [pdf] paper   link   bibtex   3 downloads  
Decentralized positioning algorithm for relative nodes localization in wireless body area networks. Hamie, J.; Denis, B.; and Richard, C. Journal on Mobile Networks and Applications, 19(6): 698-706. 2014.
Decentralized positioning algorithm for relative nodes localization in wireless body area networks [pdf] paper   link   bibtex  
Detection of nonlinear mixtures using Gaussian processes: Application to hyperspectral imaging. Imbiriba, T.; Bermudez, J. M.; Tourneret, J.; and Richard, C. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, 2014.
Detection of nonlinear mixtures using Gaussian processes: Application to hyperspectral imaging [pdf] paper   link   bibtex  
Steady-state performance of non-negative least-mean-square algorithm and its variants. Chen, J.; Bermudez, J. M.; and Richard, C. Signal Processing Letters, IEEE, 21(8): 928-932. 2014.
Steady-state performance of non-negative least-mean-square algorithm and its variants [link] code   Steady-state performance of non-negative least-mean-square algorithm and its variants [pdf] paper   link   bibtex  
Nonlinear unmixing of hyperspectral images using a semiparametric model and spatial regularization. Chen, J.; Richard, C.; and Hero, A. O. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, 2014.
Nonlinear unmixing of hyperspectral images using a semiparametric model and spatial regularization [pdf] paper   link   bibtex  
Multitask diffusion adaptation over networks. Chen, J.; Richard, C.; and Sayed, A. H. Signal Processing, IEEE Transactions on, 62(16): 4129-4144. 2014.
Multitask diffusion adaptation over networks [link] paper   link   bibtex   3 downloads  
Online dictionary learning for kernel LMS. Gao, W.; Chen, J.; Richard, C.; and Huang, J. Signal Processing, IEEE Transactions on, 62(11): 2765-2777. 2014.
Online dictionary learning for kernel LMS [pdf] paper   link   bibtex   3 downloads  
Variants of non-negative least-mean-square algorithm and convergence analysis. Chen, J.; Richard, C.; Bermudez, J. M.; and Honeine, P. Signal Processing, IEEE Transactions on, 62(15): 3990-4005. 2014.
Variants of non-negative least-mean-square algorithm and convergence analysis [link] code   Variants of non-negative least-mean-square algorithm and convergence analysis [pdf] paper   link   bibtex   5 downloads  
Nonlinear estimation of material abundances in hyperspectral images with L1-norm spatial regularization. Chen, J.; Richard, C.; and Honeine, P. Geoscience and Remote Sensing, IEEE Transactions on, 52(5): 2654-2665. 2014.
Nonlinear estimation of material abundances in hyperspectral images with L1-norm spatial regularization [link] code   Nonlinear estimation of material abundances in hyperspectral images with L1-norm spatial regularization [pdf] paper   link   bibtex  
Nonlinear unmixing of hyperspectral images: Models and algorithms. Dobigeon, N.; Tourneret, J.; Richard, C.; Bermudez, J. M.; McLaughlin, S.; and Hero, A. O. Signal Processing Magazine, IEEE, 31(1): 82-94. 2014.
Nonlinear unmixing of hyperspectral images: Models and algorithms [pdf] paper   link   bibtex   2 downloads  
Convergence analysis of kernel LMS algorithm with pre-tuned dictionary. Chen, J.; Gao, W.; Richard, C.; and Bermudez, J. M. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, 2014.
Convergence analysis of kernel LMS algorithm with pre-tuned dictionary [link] code   Convergence analysis of kernel LMS algorithm with pre-tuned dictionary [pdf] paper   link   bibtex   7 downloads  
  2013 (20)
Approche variationnelle à noyau pour le suivi de cibles dans un réseau de capteurs sans fil. Snoussi, H.; Honeine, P.; and Richard, C. Méthodes d'inversion appliquées au traitement du signal et de l'image. Giovannelli, J.; and Idier, J., editor(s). Hermès, Lavoisier edition, 2013.
Méthodes d'inversion appliquées au traitement du signal et de l'image [pdf] paper   link   bibtex  
Joint motion capture and navigation in heterogeneous body area networks with distance estimation over neighborhood graph. Hamie, J.; Denis, B.; and Richard, C. In Positioning Navigation and Communication (WPNC), 2013 10th Workshop on, 2013.
link   bibtex  
On-body TOA-based ranging error model for motion capture. Applications within wearable UWB networks. Hamie, J.; Denis, B.; D’Errico, R.; and Richard, C. Journal of Ambient Intelligence and Humanized Computing. 2013.
On-body TOA-based ranging error model for motion capture. Applications within wearable UWB networks [pdf] paper   link   bibtex  
Kernel LMS algorithm with forward-backward splitting for dictionary learning. Gao, W.; Chen, J.; Richard, C.; Huang, J.; and Flamary, R. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, 2013.
Kernel LMS algorithm with forward-backward splitting for dictionary learning [link] code   Kernel LMS algorithm with forward-backward splitting for dictionary learning [pdf] paper   link   bibtex   3 downloads  
Learning a common dictionary over a sensor network. Chainais, P.; and Richard, C. In Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013 IEEE International Workshop on, 2013.
Learning a common dictionary over a sensor network [pdf] paper   link   bibtex   1 download  
Performance analysis of diffusion LMS in multitask networks. Chen, J.; and Richard, C. In Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013 IEEE International Workshop on, 2013.
Performance analysis of diffusion LMS in multitask networks [pdf] paper   link   bibtex  
One-class SVM sans biais. Lecomte, S.; Lengelle, R.; Richard, C.; Capman, F.; and Ravera, B. In Actes du 24e Colloque GRETSI sur le Traitement du Signal et des Images, 2013.
One-class SVM sans biais [pdf] paper   link   bibtex  
Adaptation en ligne d'un dictionnaire pour les méthodes à noyau. Saidé, C.; Honeine, P.; Lengellé, R.; Richard, C.; and Achkar, R. In Actes du 24e Colloque GRETSI sur le Traitement du Signal et des Images, 2013.
Adaptation en ligne d'un dictionnaire pour les méthodes à noyau [pdf] paper   link   bibtex  
Identification en ligne avec régularisation L1. Algorithme et analyse de convergence en environnement non-stationnaire. Chen, J.; Richard, C.; Bermudez, J. M.; and Honeine, P. In Actes du 24e Colloque GRETSI sur le Traitement du Signal et des Images, 2013.
Identification en ligne avec régularisation L1. Algorithme et analyse de convergence en environnement non-stationnaire [pdf] paper   link   bibtex  
Group Lasso avec contraintes de positivité et de somme unité pour le démélange non-supervisé des images hyperspectrales. Ferrari, A.; Richard, C.; and Mary, D. In Actes du 24e Colloque GRETSI sur le Traitement du Signal et des Images, 2013.
Group Lasso avec contraintes de positivité et de somme unité pour le démélange non-supervisé des images hyperspectrales [pdf] paper   link   bibtex  
Distributed dictionary learning over a sensor network. Chainais, P.; and Richard, C. In Apprentissage Automatique (CAP), Conférence Francophone sur, 2013.
Distributed dictionary learning over a sensor network [link] paper   link   bibtex  
Non-stationary analysis of the convergence of the non-negative least-mean-square algorithm. Chen, J.; Richard, C.; and Bermudez, J. M. In Signal Processing (EUSIPCO), 21th European Conference on, 2013.
Non-stationary analysis of the convergence of the non-negative least-mean-square algorithm [pdf] paper   link   bibtex   1 download  
Constrained Kaczmarz's cyclic projections for unmixing hyperspectral data. Honeine, P.; Lantéri, H.; and Richard, C. In Signal Processing (EUSIPCO), 21th European Conference on, 2013.
Constrained Kaczmarz's cyclic projections for unmixing hyperspectral data [pdf] paper   link   bibtex  
Estimating abundance fractions of materials in hyperspectral images by fitting a post-nonlinear mixing model. Chen, J.; Richard, C.; and Honeine, P. In Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013 5rd Workshop on, pages 1-4, 2013.
Estimating abundance fractions of materials in hyperspectral images by fitting a post-nonlinear mixing model [pdf] paper   link   bibtex  
Nonlinear unmixing of hyperspectral data with partially linear least-squares support vector regression. Chen, J.; Richard, C.; Ferrari, A.; and Honeine, P. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, 2013. IEEE
Nonlinear unmixing of hyperspectral data with partially linear least-squares support vector regression [pdf] paper   link   bibtex  
Nonlinear unmixing of hyperspectral data based on a linear-mixture/nonlinear-fluctuation model. Chen, J.; Richard, C.; and Honeine, P. Signal Processing, IEEE Transactions on, 61(2): 480-492. 2013.
Nonlinear unmixing of hyperspectral data based on a linear-mixture/nonlinear-fluctuation model [link] code   Nonlinear unmixing of hyperspectral data based on a linear-mixture/nonlinear-fluctuation model [pdf] paper   link   bibtex   11 downloads  
Approches géométriques pour l'estimation des fractions d'abondance en traitement de données hyperspectales. Extensions aux modèles de mélange non-linéaires. Honeine, P.; Richard, C.; and Nguyen, N. H. Traitement du Signal, 30(1-2): 61-86. 2013.
Approches géométriques pour l'estimation des fractions d'abondance en traitement de données hyperspectales. Extensions aux modèles de mélange non-linéaires [pdf] paper   link   bibtex  
. Nguyen, N. H.; Chen, J.; Richard, C.; Honeine, P.; and Theys, C. Volume 59 . Supervised nonlinear unmixing of hyperspectral images using a pre-image method, pages 417-437. Mary, D.; Theys, C.; and Aime, C., editor(s). EDP Sciences, 2013.
Supervised nonlinear unmixing of hyperspectral images using a pre-image method [pdf] paper   link   bibtex  
. Theys, C.; Lantéri, H.; and Richard, C. Volume 59 . SGM to solve NMF. Application to hyperspectral data, pages 357-379. Mary, D.; Theys, C.; and Aime, C., editor(s). EDP Sciences, 2013.
SGM to solve NMF. Application to hyperspectral data [pdf] paper   link   bibtex  
. Lantéri, H.; Theys, C.; and Richard, C. Volume 59 . Constrained minimization algorithms, pages 303-324. Mary, D.; Theys, C.; and Aime, C., editor(s). EDP Sciences, 2013.
Constrained minimization algorithms [pdf] paper   link   bibtex  
  2012 (30)
Robust interval-based localization algorithms for mobile sensor networks. Mourad, F.; Snoussi, H.; Kieffer, M.; and Richard, C. International Journal of Distributed Sensor Networks,1-7. 2012.
Robust interval-based localization algorithms for mobile sensor networks [link] paper   link   bibtex  
Stochastic behavior analysis of the Gaussian kernel least-mean-square algorithm. Parreira, W. D.; Bermudez, J. M.; Richard, C.; and Tourneret, J. Signal Processing, IEEE Transactions on, 60(5): 2208-2222. 2012.
Stochastic behavior analysis of the Gaussian kernel least-mean-square algorithm [link] code   Stochastic behavior analysis of the Gaussian kernel least-mean-square algorithm [pdf] paper   link   bibtex   16 downloads  
Nonlinear unmixing of hyperspectral images with multi-kernel learning. Chen, J.; Richard, C.; and Honeine, P. In Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2012 4rd Workshop on, pages 1-4, 2012.
Nonlinear unmixing of hyperspectral images with multi-kernel learning [pdf] paper   link   bibtex  
Controlled mobility sensor networks for target tracking using ant colony optimization. Mourad, F.; Chehade, H.; Snoussi, H.; Yalaoui, F.; Amodeo, L.; and Richard, C. Mobile Computing, IEEE Transactions on, 11(8): 1261-1273. 2012.
Controlled mobility sensor networks for target tracking using ant colony optimization [pdf] paper   link   bibtex  
A Gaussian process regression approach for testing Granger causality between time series data. Amblard, P.; Michel, O. J. J.; Richard, C.; and Honeine, P. In Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, pages 3357-3360, 2012. IEEE
A Gaussian process regression approach for testing Granger causality between time series data [pdf] paper   link   bibtex  
Geometric unmixing of large hyperspectral images: A barycentric coordinate approach. Honeine, P.; and Richard, C. Geoscience and Remote Sensing, IEEE Transactions on, 50(6): 2185-2195. 2012.
Geometric unmixing of large hyperspectral images: A barycentric coordinate approach [pdf] paper   link   bibtex  
Kernelizing Geweke's measures of granger causality. Amblard, P.; Vincent, R.; Michel, O. J. J.; and Richard, C In Machine Learning for Signal Processing (MLSP), 2012 IEEE International Workshop on, pages 1-6, 2012. IEEE
Kernelizing Geweke's measures of granger causality [pdf] paper   link   bibtex   1 download  
Prediction-based cluster management for target tracking in wireless sensor networks. Teng, J.; Snoussi, H.; and Richard, C. Wireless Communications and Mobile Computing, 12(9): 797-812. 2012.
Prediction-based cluster management for target tracking in wireless sensor networks [pdf] paper   link   bibtex  
One-class machines based on the coherence criterion. Noumir, Z.; Honeine, P.; and Richard, C. In Statistical Signal Processing Workshop (SSP), 2012 IEEE, pages 600-603, 2012. IEEE
One-class machines based on the coherence criterion [pdf] paper   link   bibtex   1 download  
Robust bounded-error tracking in wireless sensor networks. Mourad, F.; Snoussi, H.; Kieffer, M.; and Richard, C. In 16th IFAC Symposium on System Identification, pages 1097-1102, 2012.
Robust bounded-error tracking in wireless sensor networks [pdf] paper   link   bibtex  
Multiclass classification machines with the complexity of a single binary classifier. Honeine, P.; Noumir, Z.; and Richard, C. Signal Processing, 93(5): 1013-1026. 2012.
Multiclass classification machines with the complexity of a single binary classifier [pdf] paper   link   bibtex  
Prediction of time series using Yule-Walker equations with kernels. Kallas, M.; Honeine, P.; Richard, C.; Francis, C.; and Amoud, H. In Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, pages 2185-2188, 2012. IEEE
Prediction of time series using Yule-Walker equations with kernels [pdf] paper   link   bibtex  
Adaptive inverse control using kernel identification. Abelli, A.; Ferrari, A.; Monaco, S.; and Richard, C. In American Control Conference (ACC), 2012, pages 202-207, 2012. IEEE
Adaptive inverse control using kernel identification [pdf] paper   link   bibtex  
Kernels for time series of exponential decay/growth processes. Noumir, Z.; Honeine, P.; and Richard, C. In Machine Learning for Signal Processing (MLSP), 2012 IEEE International Workshop on, pages 1-6, 2012. IEEE
Kernels for time series of exponential decay/growth processes [pdf] paper   link   bibtex  
Hyperspectral image unmixing using manifold learning methods derivations and comparative tests. Nguyen, H. N.; Richard, C.; Honeine, P.; and Theys, C. In Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International, pages 3086-3089, 2012. IEEE
Hyperspectral image unmixing using manifold learning methods derivations and comparative tests [pdf] paper   link   bibtex  
System and method for locating a target using a tranceiver array. Snoussi, H.; Richard, C.; and Honeine, P. 2012. EP Patent 2,419,754
link   bibtex  
On simple one-class classification methods. Noumir, Z.; Honeine, P.; and Richard, C. In Information Theory Proceedings (ISIT), 2012 IEEE International Symposium on, pages 2022-2026, 2012. IEEE
On simple one-class classification methods [pdf] paper   link   bibtex  
Time-frequency learning machines for nonstationarity detection using surrogates. Borgnat, P.; Flandrin, P.; Richard, C.; Ferrari, A.; Amoud, H.; and Honeine, P. Volume 1 . Advances in Machine Learning and Data Mining for Astronomy, pages 487-503. Way, M. J.; Scargle, J. D.; Ali, K. M.; and Srivastava, A. N., editor(s). CRC Press, Taylor and Francis Group, 2012.
Advances in Machine Learning and Data Mining for Astronomy [pdf] paper   link   bibtex   1 download  
Modeling electrocardiogram using Yule-Walker equations and kernel machines. Kallas, M.; Francis, C.; Honeine, P.; Amoud, H.; and Richard, C. In Telecommunications (ICT), 2012 19th International Conference on, pages 1-5, 2012. IEEE
Modeling electrocardiogram using Yule-Walker equations and kernel machines [pdf] paper   link   bibtex  
Routing optimization and secure target tracking in distributed wireless sensor networks. Mansouri, M.; Lyes, K.; Snoussi, H.; and Richard, C. Wireless Sensor Networks and Energy Efficiency: Protocols, Routing, and Management,396–417. 2012.
Routing optimization and secure target tracking in distributed wireless sensor networks [pdf] paper   link   bibtex   1 download  
Multi-objective optimization for target tracking in quantized sensor networks. Mansouri, M.; Hnaien, F.; Nounou, H.; Nounou, M.; Snoussi, H.; and Richard, C. Journal of Communication and Computer, 9: 1195-1205. 2012.
link   bibtex  
Prediction of rain attenuation series based on discretized spectral model. Chen, J.; Richard, C.; Honeine, P.; and Tourneret, J. In Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International, pages 2407-2410, 2012. IEEE
Prediction of rain attenuation series based on discretized spectral model [pdf] paper   link   bibtex  
Dictionary adaptation for online prediction of time series data with kernels. Saidé, C.; Lengellé, R.; Honeine, P.; Richard, C.; and Achkar, R. In Statistical Signal Processing Workshop (SSP), 2012 IEEE, pages 604-607, 2012. IEEE
Dictionary adaptation for online prediction of time series data with kernels [pdf] paper   link   bibtex   4 downloads  
Distributed variational filtering for simultaneous sensor localization and target tracking in wireless sensor networks. Teng, J.; Snoussi, H.; Richard, C.; and Zhou, R. Vehicular Technology, IEEE Transactions on, 61(5): 2305-2318. 2012.
Distributed variational filtering for simultaneous sensor localization and target tracking in wireless sensor networks [pdf] paper   link   bibtex   2 downloads  
Online one-class machines based on the coherence criterion. Noumir, Z.; Honeine, P.; and Richard, C. In Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European, pages 664-668, 2012. IEEE
Online one-class machines based on the coherence criterion [pdf] paper   link   bibtex  
Constrained decentralized algorithm for the relative localization of wearable wireless sensor nodes. Hamie, J.; Denis, B.; and Richard, C. In Sensors, 2012 IEEE, pages 1-4, 2012. IEEE
Constrained decentralized algorithm for the relative localization of wearable wireless sensor nodes [pdf] paper   link   bibtex  
Multi-channel differencing adaptive noise cancellation based on kernel-based normalized least-mean-square algorithm. Huang, J.; Gao, W.; and Richard, C. In OCEANS, 2012, pages 1-5, 2012. IEEE
link   bibtex  
Empirical modeling of intra-ban ranging errors based on ir-uwb toa estimation. Hamie, J.; Denis, B.; D'Errico, R.; and Richard, C. In 7th International Conference on Body Area Networks (BodyNets), pages 139-144, 2012. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)
Empirical modeling of intra-ban ranging errors based on ir-uwb toa estimation [pdf] paper   link   bibtex  
Nodes updates censoring and scheduling in constrained decentralized positioning for large-scale motion capture based on wireless body area networks. Hamie, J.; Denis, B.; and Richard, C. In 7th International Conference on Body Area Networks (BodyNets), pages 100-105, 2012. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)
Nodes updates censoring and scheduling in constrained decentralized positioning for large-scale motion capture based on wireless body area networks [pdf] paper   link   bibtex  
Closed-form conditions for convergence of the Gaussian kernel-least-mean-square algorithm. Richard, C.; and Bermudez, J. M. In Signals, Systems and Computers (ASILOMAR), 2012 Conference Record of the Forty Sixth Asilomar Conference on, 2012.
Closed-form conditions for convergence of the Gaussian kernel-least-mean-square algorithm [link] code   Closed-form conditions for convergence of the Gaussian kernel-least-mean-square algorithm [pdf] paper   link   bibtex   2 downloads  
  2011 (39)
Minimisation d'une forme générale de divergence sous contrainte de non-négativité Application à la factorisation en matrices non-négatives. Lantéri, H.; Theys, C.; and Richard, C. In Actes du 23e Colloque GRETSI sur le Traitement du Signal et des Images, 2011.
Minimisation d'une forme générale de divergence sous contrainte de non-négativité Application à la factorisation en matrices non-négatives [pdf] paper   link   bibtex  
Stationnarité relative et approches connexes. Flandrin, P.; Richard, C.; Amblard, P.; Borgnat, P.; Honeine, P.; Amoud, H.; Ferrari, A.; Xiao, J.; Moghtaderi, A.; and Ramirez-Cobo, P. Traitement du signal, 28(6): 691-716. 2011.
Stationnarité relative et approches connexes [pdf] paper   link   bibtex  
Preimage problem in kernel-based machine learning. Honeine, P.; and Richard, C. Signal Processing Magazine, IEEE, 28(2): 77-88. 2011.
Preimage problem in kernel-based machine learning [pdf] paper   link   bibtex  
Stochastic behavior analysis of the Gaussian kernel least-mean-square algorithm. Parreira, W. D.; Bermudez, J. M.; Richard, C.; and Tourneret, J. In Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on, pages 4116-4119, 2011. IEEE
Stochastic behavior analysis of the Gaussian kernel least-mean-square algorithm [pdf] paper   link   bibtex   1 download  
Channel estimation and multiple target tracking in wireless sensor networks based on quantised proximity sensors. Mansouri, M; Snoussi, H; and Richard, C Wireless Sensor Systems, IET, 1(1): 7-14. 2011.
link   bibtex  
A robust localization algorithm for mobile sensors using belief functions. Mourad, F.; Snoussi, H.; Abdallah, F.; and Richard, C. Vehicular Technology, IEEE Transactions on, 60(4): 1799-1811. 2011.
A robust localization algorithm for mobile sensors using belief functions [pdf] paper   link   bibtex  
Nonnegative least-mean-square algorithm. Chen, J.; Richard, C.; Bermudez, J. M.; and Honeine, P. Signal Processing, IEEE Transactions on, 59(11): 5225-5235. 2011.
Nonnegative least-mean-square algorithm [link] code   Nonnegative least-mean-square algorithm [pdf] paper   link   bibtex   2 downloads  
Collaborative multi-target tracking in wireless sensor networks. Teng, J.; Snoussi, H.; and Richard, C. International Journal of Systems Science, 42(9): 1427-1443. 2011.
Collaborative multi-target tracking in wireless sensor networks [pdf] paper   link   bibtex  
A closed-form solution for the pre-image problem in kernel-based machines. Honeine, P.; and Richard, C. Journal of Signal Processing Systems, 65(3): 289-299. 2011.
A closed-form solution for the pre-image problem in kernel-based machines [pdf] paper   link   bibtex  
A novel kernel-based nonlinear unmixing scheme of hyperspectral images. Chen, J.; Richard, C.; and Honeine, P. In Signals, Systems and Computers (ASILOMAR), 2011 Conference Record of the Forty Fifth Asilomar Conference on, pages 1898-1902, 2011. IEEE
A novel kernel-based nonlinear unmixing scheme of hyperspectral images [link] code   A novel kernel-based nonlinear unmixing scheme of hyperspectral images [pdf] paper   link   bibtex   5 downloads  
Interval-based localization using RSSI comparison in MANETs. Mourad, F.; Snoussi, H.; and Richard, C. Aerospace and Electronic Systems, IEEE Transactions on, 47(4): 2897-2910. 2011.
Interval-based localization using RSSI comparison in MANETs [pdf] paper   link   bibtex  
Abnormal events detection using unsupervised One-Class SVM-Application to audio surveillance and evaluation. Lecomte, S.; Lengellé, R.; Richard, C.; Capman, F; and Ravera, B In Advanced Video and Signal-Based Surveillance (AVSS), 2011 8th IEEE International Conference on, pages 124-129, 2011. IEEE
Abnormal events detection using unsupervised One-Class SVM-Application to audio surveillance and evaluation [pdf] paper   link   bibtex  
Non-negative pre-image in machine learning for pattern recognition. Kallas, M.; Honeine, P.; Richard, C.; Francis, C.; and Amoud, H. In Signal Processing (EUSIPCO), European Conference on, 2011.
Non-negative pre-image in machine learning for pattern recognition [pdf] paper   link   bibtex  
Multi-class least squares classification at binary-classification complexity. Noumir, Z.; Honeine, P.; and Richard, C. In Statistical Signal Processing Workshop (SSP), 2011 IEEE, pages 277-280, 2011. IEEE
Multi-class least squares classification at binary-classification complexity [pdf] paper   link   bibtex  
Quantized variational filtering for target tracking and relay localization in sensor networks. Mansouri, M.; Khoukhi, L.; Snoussi, H.; and Richard, C. In Wireless Communications and Mobile Computing Conference (IWCMC), 2011 7th International, pages 1837-1842, 2011. IEEE
link   bibtex  
Classification multi-classes au prix d'un classifieur binaire. Noumir, Z.; Honeine, P.; and Richard, C. In Actes du 23e Colloque GRETSI sur le Traitement du Signal et des Images, 2011.
Classification multi-classes au prix d'un classifieur binaire [pdf] paper   link   bibtex  
A single-target tracking algorithm in controlled mobility sensor networks. Mourad, F.; Chehade, H.; Snoussi, H.; Yalaoui, F.; Amodéo, L.; and Richard, C. In Systems, Signal Processing and their Applications (WOSSPA), 2011 7th International Workshop on, pages 371-374, 2011. IEEE
A single-target tracking algorithm in controlled mobility sensor networks [pdf] paper   link   bibtex   1 download  
Learning general Gaussian kernel hyperparameters of SVMs using optimization on symmetric positive-definite matrices manifold. Laanaya, H.; Abdallah, F.; Snoussi, H.; and Richard, C. Pattern Recognition Letters, 32(13): 1511-1515. 2011.
Learning general Gaussian kernel hyperparameters of SVMs using optimization on symmetric positive-definite matrices manifold [pdf] paper   link   bibtex  
Robust interval-based localization algorithms for mobile sensor networks. Mourad, F.; Snoussi, H.; Kieffer, M.; and Richard, C. International Journal of Distributed Sensor Networks, 2012. 2011.
Robust interval-based localization algorithms for mobile sensor networks [pdf] paper   link   bibtex  
Regularized split gradient method for nonnegative matrix factorization. Lantéri, H.; Theys, C.; Richard, C.; and Mary, D. In Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on, pages 1133-1136, 2011. IEEE
Regularized split gradient method for nonnegative matrix factorization [pdf] paper   link   bibtex  
Kernel-based autoregressive modeling with a pre-image technique. Kallas, M.; Honeine, P.; Richard, C.; Francis, C.; and Amoud, H. In Statistical Signal Processing Workshop (SSP), 2011 IEEE, pages 281-284, 2011. IEEE
Kernel-based autoregressive modeling with a pre-image technique [pdf] paper   link   bibtex  
A gradient based method for fully constrained least-squares unmixing of hyperspectral images. Chen, J.; Richard, C.; Lantéri, H.; Theys, C.; and Honeine, P. In Statistical Signal Processing Workshop (SSP), 2011 IEEE, pages 301-304, 2011. IEEE
A gradient based method for fully constrained least-squares unmixing of hyperspectral images [pdf] paper   link   bibtex  
Machines à vecteur de support 1-classe (OC-SVM) pour la détection non supervisée d'événements sonores anormaux. Lecomte, S.; Richard, C.; Lengellé, R.; Capman, F.; and Ravera, B. In Actes du 23e Colloque GRETSI sur le Traitement du Signal et des Images, 2011.
Machines à vecteur de support 1-classe (OC-SVM) pour la détection non supervisée d'événements sonores anormaux [pdf] paper   link   bibtex  
Self-localization and target tracking in mobile sensor networks. Mourad, F.; Snoussi, H.; and Richard, C. In Small Workshop on Interval Methods (SWIM), 2011, 2011.
Self-localization and target tracking in mobile sensor networks [pdf] paper   link   bibtex  
Robust distributed target tracking in Wireless Sensor Networks based on multi-objective optimization. Mansouri, M.; Hnaien, F.; Snoussi, H.; and Richard, C. In Statistical Signal Processing Workshop (SSP), 2011 IEEE, pages 69-72, 2011. IEEE
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A modified non-negative LMS algorithm and its stochastic behavior analysis. Chen, J.; Richard, C.; Bermudez, J. M.; and Honeine, P. In Signals, Systems and Computers (ASILOMAR), 2011 Conference Record of the Forty Fifth Asilomar Conference on, pages 542-546, 2011. IEEE
A modified non-negative LMS algorithm and its stochastic behavior analysis [link] code   A modified non-negative LMS algorithm and its stochastic behavior analysis [pdf] paper   link   bibtex   3 downloads  
Quantized variational filtering for bayesian inference in wireless sensor networks. Mansouri, M.; Snoussi, H.; Teng, J.; Ilham, O.; and Richard, C. Visual Information Processing in Wireless Sensor Networks: Technology, Trends, and Applications,224. 2011.
Quantized variational filtering for bayesian inference in wireless sensor networks [pdf] paper   link   bibtex  
Interval-based localization for mobile sensors in low-anchors density networks. Mourad, F.; Snoussi, H.; and Richard, C. In Wireless Communications and Mobile Computing Conference (IWCMC), 2011 7th International, pages 1843-1848, 2011. IEEE
Interval-based localization for mobile sensors in low-anchors density networks [pdf] paper   link   bibtex  
Nonnegative matrix factorization with regularization and sparsity-enforcing terms. Lantéri, H.; Theys, C.; and Richard, C. In Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2011 IEEE International Workshop on, pages 97-100, 2011. IEEE
Nonnegative matrix factorization with regularization and sparsity-enforcing terms [pdf] paper   link   bibtex  
Transitional surrogates. Borgnat, P.; Flandrin, P.; Ferrari, A.; and Richard, C. In Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on, pages 3600-3603, 2011. IEEE
Transitional surrogates [pdf] paper   link   bibtex  
Approches géométriques pour l'estimation des fractions d'abondance en traitement de données hyperspectales. Honeine, P.; and Richard, C. In Actes du 23e Colloque GRETSI sur le Traitement du Signal et des Images, 2011.
Approches géométriques pour l'estimation des fractions d'abondance en traitement de données hyperspectales [pdf] paper   link   bibtex  
Filtrage adaptatif avec contrainte de non-négativité. Principes de l'algorithme NN-LMS et modèle de convergence. Richard, C.; Chen, J.; Bermudez, J. M.; and Honeine, P. In Actes du 23e Colloque GRETSI sur le Traitement du Signal et des Images, 2011.
Filtrage adaptatif avec contrainte de non-négativité. Principes de l'algorithme NN-LMS et modèle de convergence [pdf] paper   link   bibtex  
Un nouveau paradigme pour le démélange non-linéaire des images hyperspectrales. Chen, J.; Richard, C.; and Honeine, P. In Actes du 23e Colloque GRETSI sur le Traitement du Signal et des Images, 2011.
Un nouveau paradigme pour le démélange non-linéaire des images hyperspectrales [pdf] paper   link   bibtex  
Optimal path selection for quantized target tracking in distributed sensor networks. Mansouri, M.; Snoussi, H.; and Richard, C. In Wireless Communications and Mobile Computing Conference (IWCMC), 2011 7th International, pages 1849-1853, 2011. IEEE
Optimal path selection for quantized target tracking in distributed sensor networks [pdf] paper   link   bibtex  
One Class Support Vector Machines for audio abnormal events detection. Lecomte, S.; Lengellé, R.; Richard, C.; and Capman, F. In Statistical Signal Processing Workshop (SSP), 2011 IEEE, pages 489-492, 2011. IEEE
One Class Support Vector Machines for audio abnormal events detection [pdf] paper   link   bibtex  
Online system identification under non-negativity and l1-norm constraints. Algorithm and weight behavior analysis. Chen, J.; Richard, C.; Lantéri, H.; Theys, C.; and Honeine, P. In Signal Processing (EUSIPCO), 19th European Conference on, 2011.
Online system identification under non-negativity and l1-norm constraints. Algorithm and weight behavior analysis [pdf] paper   link   bibtex  
Adaptive quantized target tracking in wireless sensor networks. Mansouri, M.; Ilham, O.; Snoussi, H.; and Richard, C. Wireless Networks, 17(7): 1625-1639. 2011.
Adaptive quantized target tracking in wireless sensor networks [pdf] paper   link   bibtex  
Modèle autorégressif non-linéaire à noyau : une première approche. Kallas, M.; Honeine, P.; Richard, C.; Francis, C.; and Amoud, H. In Actes du 23e Colloque GRETSI sur le Traitement du Signal et des Images, 2011.
Modèle autorégressif non-linéaire à noyau : une première approche [pdf] paper   link   bibtex  
Steady-state behavior and design of the gaussian KLMS algorithm. Parreira, W. D.; Bermudez, J. M.; Richard, C.; and Tourneret, J. In Signal Processing (EUSIPCO), 19th European Conference on, pages 121-125, 2011.
Steady-state behavior and design of the gaussian KLMS algorithm [pdf] paper   link   bibtex  
  2010 (20)
Optimal path and sensor selection for target tracking in wireless sensor networks. Mansouri, M.; Snoussi, H.; and Richard, C. In Maxent, 2010.
link   bibtex  
Transductive kernel matrix learning with hierarchic Bayesian model. Application to hyperspectral Imaging. Ferrari, A.; Richard, C.; Smith, I.; and Theys, C. In Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International, 2010.
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Non-linear adaptive filtering with kernel functions: an overview. Liu, W.; Richard, C.; Principe, J.; and Haykin, S. In Signals, Systems and Computers (ASILOMAR), 2010 Forty Fourth Asilomar Conference on, 2010.
Non-linear adaptive filtering with kernel functions: an overview [pdf] paper   link   bibtex   1 download  
A decentralized approach for nonlinear prediction of time series data in sensor networks. Honeine, P.; Richard, C.; Bermudez, J. M.; Chen, J.; and Snoussi, H. EURASIP Journal on Wireless Communications and Networking, 2010(1): 1687-1499. 2010.
A decentralized approach for nonlinear prediction of time series data in sensor networks [pdf] paper   doi   link   bibtex   1 download  
Testing stationarity with surrogates: A time-frequency approach. Borgnat, P.; Flandrin, P.; Honeine, P.; Richard, C.; and Xiao, J. Signal Processing, IEEE Transactions on, 58(7): 3459-3470. 2010.
Testing stationarity with surrogates: A time-frequency approach [pdf] paper   link   bibtex   1 download  
Decentralized variational filtering for target tracking in binary sensor networks. Teng, J.; Snoussi, H.; and Richard, C. Mobile Computing, IEEE Transactions on, 9(10): 1465-1477. 2010.
Decentralized variational filtering for target tracking in binary sensor networks [pdf] paper   link   bibtex  
Statistical hypothesis testing with time-frequency surrogates to check signal stationarity. Richard, C.; Ferrari, A.; Amoud, H.; Honeine, P.; Flandrin, P.; and Borgnat, P. In Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on, pages 3666-3669, 2010. IEEE
Statistical hypothesis testing with time-frequency surrogates to check signal stationarity [pdf] paper   link   bibtex  
Split gradient method for nonnegative matrix factorization. Lantéri, H.; Theys, C.; Richard, C.; and Févotte, C. In Signal Processing (EUSIPCO), 18th European Conference on, 2010. EURASIP
Split gradient method for nonnegative matrix factorization [pdf] paper   link   bibtex  
System identification under non-negativity constraints. Chen, J.; Richard, C.; Honeine, P.; Lantéri, H.; and Theys, C. In Signal Processing (EUSIPCO), 18th European Conference on, 2010.
System identification under non-negativity constraints [pdf] paper   link   bibtex  
Nonlinear feature extraction using kernel principal component analysis with non-negative pre-image. Kallas, M.; Honeine, P.; Richard, C.; Amoud, H.; and Francis, C. In Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, pages 3642-3645, 2010. IEEE
Nonlinear feature extraction using kernel principal component analysis with non-negative pre-image [pdf] paper   link   bibtex  
The angular kernel in machine learning for hyperspectral data classification. Honeine, P.; and Richard, C. In Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2010 2nd Workshop on, pages 1-4, 2010. IEEE
The angular kernel in machine learning for hyperspectral data classification [pdf] paper   link   bibtex  
Factors that may influence the performance of wireless sensor networks. Mansouri, M.; Sardouk, A.; Merghem-Boulahia, L.; Gaiti, D.; Snoussi, H.; Rahim-Amoud, R.; and Richard, C. Smart wireless sensor networks. Chinh, H. D.; and Tan, Y. K., editor(s). INTECH, 2010.
Smart wireless sensor networks [pdf] paper   link   bibtex  
A simple scheme for unmixing hyperspectral data based on the geometry of the N-dimensional simplex. Honeine, P.; and Richard, C. In Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International, pages 2271-2274, 2010. IEEE
A simple scheme for unmixing hyperspectral data based on the geometry of the N-dimensional simplex [pdf] paper   link   bibtex  
Distributed learning with kernels in wireless sensor networks for physical phenomena modeling and tracking. Richard, C.; Honeine, P.; Snoussi, H.; Ferrari, A.; and Theys, C. In Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International, 2010.
Distributed learning with kernels in wireless sensor networks for physical phenomena modeling and tracking [pdf] paper   link   bibtex  
Robust target tracking with quantized proximity sensors. Mansouri, M.; Snoussi, H.; and Richard, C. In Wireless Pervasive Computing (ISWPC), 2010 5th IEEE International Symposium on, pages 278-282, 2010. IEEE
link   bibtex  
Techniques d'apprentissage non-linéaires en ligne avec contraintes de positivité. Chen, J.; Richard, C.; Honeine, P.; Snoussi, H.; Lantéri, H.; and Theys, C. In Actes de la 5e Conférence Internationale Francophone d'Automatique (CIFA), 2010.
Techniques d'apprentissage non-linéaires en ligne avec contraintes de positivité [pdf] paper   link   bibtex  
Non-negative distributed regression for data inference in wireless sensor networks. Chen, J.; Richard, C.; Honeine, P.; and Bermudez, J. M. In Signals, Systems and Computers (ASILOMAR), 2010 Forty Fourth Asilomar Conference on, pages 451-455, 2010.
Non-negative distributed regression for data inference in wireless sensor networks [pdf] paper   link   bibtex  
Joint multiple target tracking and channel estimation in wireless sensor networks. Mansouri, M.; Snoussi, H.; and Richard, C. In Global Telecommunications Conference (GLOBECOM 2010), 2010 IEEE, pages 1-5, 2010. IEEE
link   bibtex  
System and method for locating a target using a tranceiver array. Snoussi, H.; Richard, C.; and Honeine, P. 2010. US Patent App. 13/264,719
link   bibtex  
A sensor selection method for target tracking in wireless sensor networks using quantized variational filtering. Mansouri, M.; Snoussi, H.; and Richard, C. In Vehicular Technology Conference Fall (VTC 2010-Fall), 2010 IEEE 72nd, pages 1-5, 2010. IEEE
link   bibtex  
  2009 (22)
Adaptive quantized target tracking in wireless sensor networks. Mansouri, M.; Snoussi, H.; Ouachani, I.; and Richard, C. In Cyber-Physical and Sensor Networks (SensorNets), The first International School on, 2009.
Adaptive quantized target tracking in wireless sensor networks [pdf] paper   link   bibtex  
Online prediction of time series data with kernels. Richard, C.; Bermudez, J. M.; and Honeine, P. Signal Processing, IEEE Transactions on, 57(3): 1058-1067. 2009.
Online prediction of time series data with kernels [link] code   Online prediction of time series data with kernels [pdf] paper   link   bibtex   19 downloads  
Cramer-Rao bound-based adaptive quantization for target tracking in wireless sensor networks. Mansouri, M.; Ouachani, I.; Snoussi, H.; and Richard, C. In Statistical Signal Processing Workshop (SSP), 2009 IEEE, pages 693-696, 2009. IEEE
Cramer-Rao bound-based adaptive quantization for target tracking in wireless sensor networks [pdf] paper   link   bibtex  
Solving the pre-image problem in kernel machines: A direct method. Honeine, P.; and Richard, C. In Machine Learning for Signal Processing (MLSP), 2009 IEEE International Workshop on, pages 1-6, 2009. IEEE
Solving the pre-image problem in kernel machines: A direct method [pdf] paper   link   bibtex   4 downloads  
Functional estimation in Hilbert space for distributed learning in wireless sensor networks. Honeine, P.; Richard, C.; Bermudez, J. M.; Snoussi, H.; Essoloh, M.; and Vincent, F. In Acoustics, Speech and Signal Processing (ICASSP), 2009 IEEE International Conference on, pages 2861-2864, 2009. IEEE
Functional estimation in Hilbert space for distributed learning in wireless sensor networks [pdf] paper   link   bibtex  
Decentralized variational filtering for simultaneous sensor localization and target tracking in binary sensor networks. Teng, J.; Snoussi, H.; and Richard, C. In Acoustics Speech and Signal Processing (ICASSP), 2009 IEEE International Conference on, pages 2233-2236, 2009. IEEE
Decentralized variational filtering for simultaneous sensor localization and target tracking in binary sensor networks [pdf] paper   link   bibtex  
Monte Carlo tracking on the Riemannian manifold of multivariate normal distributions. Snoussi, H.; and Richard, C. In Digital Signal Processing Workshop, 2009. DSP/SPE 2009. IEEE 13th, pages 280-285, 2009. IEEE
Monte Carlo tracking on the Riemannian manifold of multivariate normal distributions [pdf] paper   link   bibtex  
Model-free interval-based localization in manets. Mourad, F.; Snoussi, H.; Abdallah, F.; and Richard, C. In Digital Signal Processing Workshop, 2009. DSP/SPE 2009. IEEE 13th, pages 474-479, 2009. IEEE
Model-free interval-based localization in manets [pdf] paper   link   bibtex  
Time-frequency learning machines for nonstationarity detection using surrogates. Amoud, H.; Honeine, P.; Richard, C.; Borgnat, P.; and Flandrin, P. In Statistical Signal Processing Workshop (SSP), 2009 IEEE, pages 565-568, 2009. IEEE
Time-frequency learning machines for nonstationarity detection using surrogates [pdf] paper   link   bibtex  
Auto-localisation dans les réseaux de capteurs sans fil par régression de matrices de Gram. Honeine, P.; Richard, C.; and Snoussi, H. In Actes du 22e Colloque GRETSI sur le Traitement du Signal et des Images, 2009.
Auto-localisation dans les réseaux de capteurs sans fil par régression de matrices de Gram [pdf] paper   link   bibtex  
A nonlinear estimation for target tracking in wireless sensor networks using quantized variational filtering. Mansouri, M.; Snoussi, H.; and Richard, C. In Signals, Circuits and Systems (SCS), 2009 3rd International Conference on, pages 1-4, 2009. IEEE
A nonlinear estimation for target tracking in wireless sensor networks using quantized variational filtering [pdf] paper   link   bibtex  
Localisation par intervalles basée sur les ancres et les non-ancres dans les réseaux de capteurs mobiles. Mourad, F.; Snoussi, H.; Abdallah, F.; and Richard, C. In Actes du 22e Colloque GRETSI sur le Traitement du Signal et des Images, 2009.
Localisation par intervalles basée sur les ancres et les non-ancres dans les réseaux de capteurs mobiles [pdf] paper   link   bibtex  
Joint adaptive quantization and fading channel estimation for target tracking in wireless sensor networks. Mansouri, M.; Snoussi, H.; and Richard, C. In Signal Processing and Information Technology (ISSPIT), 2009 IEEE International Symposium on, pages 612-615, 2009. IEEE
Joint adaptive quantization and fading channel estimation for target tracking in wireless sensor networks [pdf] paper   link   bibtex  
Signal detection with noise power variation between the hypotheses. Vincent, F.; Besson, O.; and Richard, C. In Digital Signal Processing Workshop, 2009. DSP/SPE 2009. IEEE 13th, pages 588-593, 2009. IEEE
Signal detection with noise power variation between the hypotheses [pdf] paper   link   bibtex  
Sur la caractérisation de non-stationnarités par la méthode des substituts. Amoud, H.; Richard, C.; Honeine, P.; Flandrin, P.; and Borgnat, P. In Actes du 22e Colloque GRETSI sur le Traitement du Signal et des Images, 2009.
Sur la caractérisation de non-stationnarités par la méthode des substituts [pdf] paper   link   bibtex  
Auto-localisation et suivi de cible simultanés dans les réseaux de capteurs sans fil. Teng, J.; Snoussi, H.; and Richard, C. In Actes du 22e Colloque GRETSI sur le Traitement du Signal et des Images, 2009.
Auto-localisation et suivi de cible simultanés dans les réseaux de capteurs sans fil [pdf] paper   link   bibtex  
Nonstationary signal analysis with kernel machines. Honeine, P.; Richard, C.; and Flandrin, P. Nonstationary signal analysis with kernel machines, pages 223. Information Science Publishing, 2009.
Nonstationary signal analysis with kernel machines [pdf] paper   link   bibtex  
Anchor-based localization via interval analysis for mobile ad-hoc sensor networks. Mourad, F.; Snoussi, H.; Abdallah, F.; and Richard, C. Signal Processing, IEEE Transactions on, 57(8): 3226-3239. 2009.
Anchor-based localization via interval analysis for mobile ad-hoc sensor networks [pdf] paper   link   bibtex  
Apprentissage non-linéaire en ligne dans les réseaux de capteurs sans fil. Richard, C.; Honeine, P.; Essoloh, M.; and Snoussi, H. In Actes du 22e Colloque GRETSI sur le Traitement du Signal et des Images, 2009.
Apprentissage non-linéaire en ligne dans les réseaux de capteurs sans fil [pdf] paper   link   bibtex  
Data-driven online variational filtering in wireless sensor networks. Snoussi, H.; Tourneret, J.; Djuric, P. M.; and Richard, C. In Acoustics Speech and Signal Processing (ICASSP), 2009 IEEE International Conference on, pages 2413-2416, 2009. IEEE
Data-driven online variational filtering in wireless sensor networks [pdf] paper   link   bibtex   1 download  
Hybrid probabilistic data association and variational filtering for multi-target tracking in wireless sensor networks. Teng, J.; Snoussi, H.; Richard, C.; and Zhou, Y. In Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2009 3rd IEEE International Workshop on, pages 368-371, 2009. IEEE
Hybrid probabilistic data association and variational filtering for multi-target tracking in wireless sensor networks [pdf] paper   link   bibtex  
Fragment-based variational visual tracking. Zhou, Y.; Snoussi, H.; Zheng, S.; Richard, C.; and Teng, J. In Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2009 3rd IEEE International Workshop on, pages 376-379, 2009. IEEE
Fragment-based variational visual tracking [pdf] paper   link   bibtex  
  2008 (9)
Distributed regression in sensor networks with a reduced-order kernel model. Honeine, P.; Essoloh, M.; Richard, C.; and Snoussi, H. In Global Telecommunications Conference, 2008. IEEE GLOBECOM 2008. IEEE, pages 1-5, 2008. IEEE
Distributed regression in sensor networks with a reduced-order kernel model [pdf] paper   link   bibtex  
Matched subspace detection with hypothesis dependent noise power. Vincent, F.; Besson, O.; and Richard, C. Signal Processing, IEEE Transactions on, 56(11): 5713-5718. 2008.
Matched subspace detection with hypothesis dependent noise power [pdf] paper   link   bibtex  
Guaranteed boxed localization in manets by interval analysis and constraints propagation techniques. Mourad, F.; Snoussi, H.; Abdallah, F.; and Richard, C. In Global Telecommunications Conference, 2008. IEEE GLOBECOM 2008. IEEE, pages 1-5, 2008. IEEE
Guaranteed boxed localization in manets by interval analysis and constraints propagation techniques [pdf] paper   link   bibtex  
Localization in sensor networks. A matrix regression approach. Honeine, P.; Richard, C.; Essoloh, M.; and Snoussi, H. In Sensor Array and Multichannel Signal Processing Workshop, 2008. SAM 2008. 5th IEEE, pages 284-287, 2008. IEEE
Localization in sensor networks. A matrix regression approach [pdf] paper   link   bibtex  
Optimizing kernel alignment by data translation in feature space. Pothin, J.; and Richard, C. In Acoustics Speech and Signal Processing (ICASSP), 2008 IEEE International Conference on, pages 3345-3348, 2008. IEEE
Optimizing kernel alignment by data translation in feature space [pdf] paper   link   bibtex  
Distributed localization in wireless sensor networks as a pre-image problem in a reproducing kernel Hilbert space. Essoloh, M.; Richard, C.; Snoussi, H.; and Honeine, P. In Signal Processing (EUSIPCO), European Conference on, 2008.
Distributed localization in wireless sensor networks as a pre-image problem in a reproducing kernel Hilbert space [pdf] paper   link   bibtex  
Variational filtering algorithm for interdependent target tracking and sensor localization in wireless sensor network. Teng, J.; Snoussi, H.; and Richard, C. In Signal Processing (EUSIPCO), 16th European Conference on, 2008.
Variational filtering algorithm for interdependent target tracking and sensor localization in wireless sensor network [pdf] paper   link   bibtex  
Distributed prediction of time series data with kernels and adaptive filtering techniques in sensor networks. Honeine, P.; Richard, C.; Bermudez, J. M.; and Snoussi, H. In Signals, Systems and Computers (ASILOMAR), 2008 Conference Record of the Forty Fourth Asilomar Conference on, pages 246-250, 2008.
Distributed prediction of time series data with kernels and adaptive filtering techniques in sensor networks [pdf] paper   link   bibtex  
Distribution temps-fréquence à paramétrisation radialement Gaussienne optimisée pour la classification. Honeine, P.; and Richard, C. Traitement du signal, 25(6): 469-479. 2008.
Distribution temps-fréquence à paramétrisation radialement Gaussienne optimisée pour la classification [pdf] paper   link   bibtex  
  2007 (14)
Signal-dependent time-frequency representations for classification using a radially gaussian kernel and the alignment criterion. Honeine, P.; and Richard, C. In Statistical Signal Processing, 2007. SSP'07. IEEE/SP 14th Workshop on, pages 735-739, 2007. IEEE
Signal-dependent time-frequency representations for classification using a radially gaussian kernel and the alignment criterion [pdf] paper   link   bibtex  
Binary variational filtering for target tracking in sensor networks. Teng, J.; Snoussi, H.; and Richard, C. In Statistical Signal Processing, 2007. SSP'07. IEEE/SP 14th Workshop on, pages 685-689, 2007. IEEE
Binary variational filtering for target tracking in sensor networks [pdf] paper   link   bibtex  
On-line nonlinear sparse approximation of functions. Honeine, P.; Richard, C.; and Bermudez, J. M. In Information Theory, 2007. ISIT 2007. IEEE International Symposium on, pages 956-960, 2007. IEEE
On-line nonlinear sparse approximation of functions [pdf] paper   link   bibtex  
Testing stationarity with surrogates. A one-class SVM approach. Xiao, J.; Borgnat, P.; Flandrin, P.; and Richard, C. In Statistical Signal Processing, 2007. SSP'07. IEEE/SP 14th Workshop on, pages 720-724, 2007. IEEE
Testing stationarity with surrogates. A one-class SVM approach [pdf] paper   link   bibtex  
Prediction-based proactive cluster target tracking protocol for binary sensor networks. Teng, J.; Snoussi, H.; and Richard, C. In Signal Processing and Information Technology, 2007 IEEE International Symposium on, pages 234-239, 2007. IEEE
Prediction-based proactive cluster target tracking protocol for binary sensor networks [pdf] paper   link   bibtex  
Time-frequency learning machines. Honeine, P.; Richard, C.; and Flandrin, P. Signal Processing, IEEE Transactions on, 55(7): 3930-3936. 2007.
Time-frequency learning machines [pdf] paper   link   bibtex  
Distributed bayesian fault diagnosis of jump Markov systems in wireless sensor networks. Snoussi, H.; and Richard, C. International Journal of Sensor Networks, 2(1): 118-127. 2007.
Distributed bayesian fault diagnosis of jump Markov systems in wireless sensor networks [pdf] paper   link   bibtex  
Optimal feature representation for kernel machines using kernel-target alignment criterion. Pothin, J.; and Richard, C. In Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on, volume 3, pages III-1065, 2007. IEEE
Optimal feature representation for kernel machines using kernel-target alignment criterion [pdf] paper   link   bibtex   1 download  
Anchor-based distributed localization in wireless sensor networks. Essoloh, M.; Richard, C.; and Snoussi, H. In Statistical Signal Processing, 2007. SSP'07. IEEE/SP 14th Workshop on, pages 393-397, 2007. IEEE
Anchor-based distributed localization in wireless sensor networks [pdf] paper   link   bibtex  
Information-theoretic node scheduling in collaborative sensor networks. Snoussi, H.; and Richard, C. In Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on, pages 1-4, 2007. IEEE
Information-theoretic node scheduling in collaborative sensor networks [pdf] paper   link   bibtex  
Localisation distribuée dans les réseaux de capteurs sans fil par résolution d'un problème quadratique. Essoloh, M.; Richard, C.; and Snoussi, H. In Actes du 21e Colloque GRETSI sur le Traitement du Signal et des Images, 2007.
Localisation distribuée dans les réseaux de capteurs sans fil par résolution d'un problème quadratique [pdf] paper   link   bibtex  
Distribution temps-fréquence à noyau radialement Gaussien: optimisation pour la classification par le critère d'alignement noyau-cible. Honeine, P.; and Richard, C. In Actes du 21e Colloque GRETSI sur le Traitement du Signal et des Images, 2007.
Distribution temps-fréquence à noyau radialement Gaussien: optimisation pour la classification par le critère d'alignement noyau-cible [pdf] paper   link   bibtex  
Apprentissage de métrique appliqué à la classification de textes par méthodes à noyaux. Pothin, J.; and Richard, C. In Actes du 21e Colloque GRETSI sur le Traitement du Signal et des Images, pages 1317-1320, 2007.
Apprentissage de métrique appliqué à la classification de textes par méthodes à noyaux [pdf] paper   link   bibtex  
Modélisation parcimonieuse non linéaire en ligne par une méthode à noyau reproduisant et un critère de cohérence. Honeine, P.; Richard, C.; and Bermudez, J. M. In Actes du 21e Colloque GRETSI sur le Traitement du Signal et des Images, pages 1257-1260, 2007.
Modélisation parcimonieuse non linéaire en ligne par une méthode à noyau reproduisant et un critère de cohérence [pdf] paper   link   bibtex   2 downloads  
  2006 (8)
A kernel-based RLS algorithm for nonlinear adaptive filtering using sparse approximation theory. Richard, C. In Signals, Systems and Computers, 2006. Conference Record of the Asilomar Conference on, 2006. IEEE
A kernel-based RLS algorithm for nonlinear adaptive filtering using sparse approximation theory [pdf] paper   link   bibtex  
Ensemble learning online filtering in wireless sensor networks. Snoussi, H.; and Richard, C. In Communication systems, 2006. ICCS 2006. 10th IEEE Singapore International Conference on, pages 1-5, 2006. IEEE
Ensemble learning online filtering in wireless sensor networks [pdf] paper   link   bibtex  
A greedy algorithm for optimizing the kernel alignment and the performance of kernel machines. Pothin, J.; and Richard, C. In Signal Processing (EUSIPCO), 14th European Conference on, volume 6, pages 4-8, 2006.
A greedy algorithm for optimizing the kernel alignment and the performance of kernel machines [pdf] paper   link   bibtex  
Distributed bayesian fault diagnosis in collaborative wireless sensor networks. Snoussi, H.; and Richard, C. In Global Telecommunications Conference, 2006. GLOBECOM '06. IEEE, 2006.
Distributed bayesian fault diagnosis in collaborative wireless sensor networks [pdf] paper   link   bibtex  
Optimal selection of time-frequency representations for signal classification: A kernel-target alignment approach. Honeine, P.; Richard, C.; Flandrin, P.; and Pothin, J. In Acoustics Speech and Signal Processing (ICASSP), 2006 IEEE International Conference on, volume 3, pages III-III, 2006. IEEE
Optimal selection of time-frequency representations for signal classification: A kernel-target alignment approach [pdf] paper   link   bibtex   1 download  
Nonlinear regularized Wiener filtering with kernels: Application in denoising MEG data corrupted by ECG. Constantin, I.; Richard, C.; Lengellé, R.; and Soufflet, L. Signal Processing, IEEE Transactions on, 54(12): 4796-4806. 2006.
Nonlinear regularized Wiener filtering with kernels: Application in denoising MEG data corrupted by ECG [pdf] paper   link   bibtex  
Incorporating prior information into support vector machines in the form of ellipsoidal knowledge sets. Pothin, J.; and Richard, C. In Signal Processing (EUSIPCO), 14th European Conference on, pages 2173-2183, 2006.
Incorporating prior information into support vector machines in the form of ellipsoidal knowledge sets [pdf] paper   link   bibtex  
Online learning with kernels. A new approach for sparsity control based on a coherence criterion. Pothin, J.; and Richard, C. In Machine Learning for Signal Processing (MLSP), 2006 IEEE International Workshop on, pages 241-245, 2006. IEEE
Online learning with kernels. A new approach for sparsity control based on a coherence criterion [pdf] paper   link   bibtex  
  2005 (7)
Regularized kernel-based Wiener filtering. Application to magnetoencephalographic signals denoising. Constantin, I.; Richard, C.; Lengelle, R.; and Soufflet, L. In Acoustics Speech and Signal Processing (ICASSP), 2005 IEEE International Conference on, volume 4, pages iv-289, 2005. IEEE
Regularized kernel-based Wiener filtering. Application to magnetoencephalographic signals denoising [pdf] paper   link   bibtex  
Adaptive diffusion as a versatile tool for time-frequency and time-scale representations processing: a review. Gosme, J.; Richard, C.; and Goncalves, P. Signal Processing, IEEE Transactions on, 53(11): 4136-4146. 2005.
Adaptive diffusion as a versatile tool for time-frequency and time-scale representations processing: a review [pdf] paper   link   bibtex  
Kernel machines : une nouvelle méthode pour l'optimisation de l'alignement des noyaux et l'amélioration des performances. Pothin, J.; and Richard, C. In Actes du 20e Colloque GRETSI sur le Traitement du Signal et des Images, 2005.
Kernel machines : une nouvelle méthode pour l'optimisation de l'alignement des noyaux et l'amélioration des performances [pdf] paper   link   bibtex  
Reconnaissance des formes par méthodes à noyau dans le domaine temps-fréquence. Honeine, P.; Richard, C.; and Flandrin, P. In Actes du 20e Colloque GRETSI sur le Traitement du Signal et des Images, 2005.
Reconnaissance des formes par méthodes à noyau dans le domaine temps-fréquence [pdf] paper   link   bibtex  
Diffused distribution reassignment. Gosme, J.; and Richard, C. In Statistical Signal Processing, 2005 IEEE/SP 13th Workshop on, pages 877-882, 2005. IEEE
Diffused distribution reassignment [pdf] paper   link   bibtex  
Filtrage adaptatif non-linéaire par méthodes de gradient stochastique court-terme à noyau. Richard, C. In Actes du 20e Colloque GRETSI sur le Traitement du Signal et des Images, 2005.
Filtrage adaptatif non-linéaire par méthodes de gradient stochastique court-terme à noyau [pdf] paper   link   bibtex  
Beyond standard classes of generalized joint signal representations of arbitrary variables: Mercer kernel-based representations. Gosme, J.; and Richard, C. Signal Processing Letters, IEEE, 12(1): 25-28. 2005.
Beyond standard classes of generalized joint signal representations of arbitrary variables: Mercer kernel-based representations [pdf] paper   link   bibtex  
  2004 (6)
Détection par représentations temps-fréquence discrètes. Richard, C. Décision dans le plan temps-fréquence. Martin, N.; and Doncarli, C., editor(s). Hermès, Lavoisier edition, 2004.
Décision dans le plan temps-fréquence [pdf] paper   link   bibtex   2 downloads  
An improved training algorithm for nonlinear kernel discriminants. Abdallah, F.; Richard, C.; and Lengellé, R. Signal Processing, IEEE Transactions on, 52(10): 2798-2806. 2004.
An improved training algorithm for nonlinear kernel discriminants [pdf] paper   link   bibtex  
Diffusion equations for adaptive affine distributions. Gosme, J.; Richard, C.; and Goncalves, P. In Acoustics Speech and Signal Processing (ICASSP), 2004 IEEE International Conference on, volume 2, pages ii-629, 2004. IEEE
Diffusion equations for adaptive affine distributions [pdf] paper   link   bibtex  
A sequential approach for multi-class discriminant analysis with kernels. Abdallah, F.; Richard, C.; and Lengelle, R. In Acoustics Speech and Signal Processing (ICASSP), 2004 IEEE International Conference on, volume 5, pages V-453, 2004. IEEE
A sequential approach for multi-class discriminant analysis with kernels [pdf] paper   link   bibtex  
Anisotropic diffusion equations for adaptive quadratic representations. Gosme, J.; Richard, C.; and Goncalves, P. In Signal Processing (EUSIPCO), 12th European Conference on, 2004.
Anisotropic diffusion equations for adaptive quadratic representations [pdf] paper   link   bibtex  
Algorithmes séquentiels pour l'analyse de données par méthodes à noyau. Richard, C.; Abdallah, F.; and Lengellé, R. Traitement du Signal, 21: 97-107. 2004.
Algorithmes séquentiels pour l'analyse de données par méthodes à noyau [pdf] paper   link   bibtex  
  2003 (5)
Kernel second-order discriminants versus support vector machines. Abdallah, F.; Richard, C.; and Lengellé, R. In Acoustics Speech and Signal Processing (ICASSP), 2003 IEEE International Conference on, volume 6, pages VI-149, 2003. IEEE
Kernel second-order discriminants versus support vector machines [pdf] paper   link   bibtex  
Algorithme d'apprentissage séquentiel pour la méthode KFD Relations avec la méthode KPCA. Richard, C.; and Abdallah, F. In Actes du 19e Colloque GRETSI sur le Traitement du Signal et des Images, 2003.
Algorithme d'apprentissage séquentiel pour la méthode KFD Relations avec la méthode KPCA [pdf] paper   link   bibtex  
Structures à noyau reproduisant pour le filtrage adaptatif. Richard, C.; Lengellé, R.; Constantine, I.; and Soufflet, L. In Actes du 19e Colloque GRETSI sur le Traitement du Signal et des Images, 2003.
Structures à noyau reproduisant pour le filtrage adaptatif [pdf] paper   link   bibtex  
Une nouvelle approche pour la construction de classes de représentations non-linéaires de signaux. Gosme, J.; and Richard, C. In Actes du 19e Colloque GRETSI sur le Traitement du Signal et des Images, 2003.
Une nouvelle approche pour la construction de classes de représentations non-linéaires de signaux [pdf] paper   link   bibtex  
Second-order measures of quality for binary classification: A critical overview and their use for nonlinear receiver design. Abdallah, F.; Richard, C.; and Lengellé, R. International Journal of Smart Engineering System Design, 5(4): 401-408. 2003.
Second-order measures of quality for binary classification: A critical overview and their use for nonlinear receiver design [pdf] paper   link   bibtex  
  2002 (11)
Autres applications. Brie, D.; Lengellé, R.; Nikiforov, I.; and Richard, C. Décision et reconnaissance des formes en signal. Lengellé, R., editor(s). Hermès, Lavoisier edition, 2002.
Décision et reconnaissance des formes en signal [pdf] paper   link   bibtex   1 download  
Apprentissage de règles de décision à structure imposée et contrôle de la complexité. Lengellé, R.; and Richard, C. Décision et reconnaissance des formes en signal. Lengellé, R., editor(s). Hermès, Lavoisier edition, 2002.
Décision et reconnaissance des formes en signal [pdf] paper   link   bibtex  
Time-frequency-based detection using discrete-time discrete-frequency Wigner distributions. Richard, C. Signal Processing, IEEE Transactions on, 50(9): 2170-2176. 2002.
Time-frequency-based detection using discrete-time discrete-frequency Wigner distributions [pdf] paper   link   bibtex  
Méthodes à noyau et critères de contraste pour la détection à structure imposée (HDR). Richard, C. Ph.D. Thesis, Université de Technologie de Compiègne, 2002.
Méthodes à noyau et critères de contraste pour la détection à structure imposée (HDR) [pdf] paper   link   bibtex   1 download  
Bayes-optimal detectors design using relevant second-order criteria. Richard, C.; Lengelle, R.; and Abdallah, F. Signal Processing Letters, IEEE, 9(1): 32-33. 2002.
Bayes-optimal detectors design using relevant second-order criteria [pdf] paper   link   bibtex  
On equivalence between detectors obtained from second-order measures of performance. Abdallah, F.; Richard, C.; and Lengellé, R. In Signal Processing (EUSIPCO), 11th European Conference on, pages 3-6, 2002.
On equivalence between detectors obtained from second-order measures of performance [pdf] paper   link   bibtex  
Adaptive diffusion and discriminant analysis for complexity control of time-frequency detectors. Gosme, J.; Goncalves, P.; Richard, C.; and Lengellé, R. In Signal Processing (EUSIPCO), 11th European Conference on, 2002.
Adaptive diffusion and discriminant analysis for complexity control of time-frequency detectors [pdf] paper   link   bibtex  
On virtues and vices of second-order measures of quality for binary classification. Abdallah, F.; Richard, C.; and Lengellé, R. In ANNIE, pages 10-13, 2002.
On virtues and vices of second-order measures of quality for binary classification [pdf] paper   link   bibtex  
Classification en temps-fréquence à partir de données expertisées. Coatanhay, A.; Richard, C.; and Staner, L. Extraction des connaissances et apprentissage, 4(1): 293-304. 2002.
Classification en temps-fréquence à partir de données expertisées [pdf] paper   link   bibtex  
Mixture of experts for automated detection of phasic arousals in sleep signals. Viardot, G.; Lengellé, R.; and Richard, C. In Systems, Man and Cybernetics, 2002 IEEE International Conference on, volume 1, pages 551-555, 2002. IEEE
Mixture of experts for automated detection of phasic arousals in sleep signals [pdf] paper   link   bibtex  
A method for designing nonlinear kernel-based discriminant functions from the class of second-order criteria. Abdallah, F.; Richard, C.; and Lengellé, R. In Signals, Systems and Computers, 2002. Conference Record of the Thirty-Sixth Asilomar Conference on, volume 1, pages 939-942, 2002. IEEE
A method for designing nonlinear kernel-based discriminant functions from the class of second-order criteria [pdf] paper   link   bibtex  
  2001 (5)
Linear redundancy of information carried by the discrete Wigner distribution. Richard, C. Signal Processing, IEEE Transactions on, 49(11): 2536-2544. 2001.
Linear redundancy of information carried by the discrete Wigner distribution [pdf] paper   link   bibtex  
Optimisation de critères de contraste et des performances en détection. Richard, C.; Lengellé, R.; and Abdallah, F. In Actes du 18e Colloque GRETSI sur le Traitement du Signal et des Images, 2001.
Optimisation de critères de contraste et des performances en détection [pdf] paper   link   bibtex  
Discrétisation des détecteurs temps-fréquence Problèmes en découlant et éléments de solution. Richard, C. In Actes du 18e Colloque GRETSI sur le Traitement du Signal et des Images, 2001.
Discrétisation des détecteurs temps-fréquence Problèmes en découlant et éléments de solution [pdf] paper   link   bibtex  
Fusion d'avis d'experts et caractérisation de l'expertise. Application à la détection de transitoires dans les signaux physiologiques. Viardot, G.; Lengellé, R.; Richard, C.; and Coatanhay, A. In Actes du 18e Colloque GRETSI sur le Traitement du Signal et des Images, 2001.
Fusion d'avis d'experts et caractérisation de l'expertise. Application à la détection de transitoires dans les signaux physiologiques [pdf] paper   link   bibtex  
Time-frequency analysis of near-field optical data for extracting local attributes. Barchiesi, D.; and Richard, C. In Acoustics Speech and Signal Processing (ICASSP), 2001 IEEE International Conference on, volume 6, pages 3565-3568, 2001. IEEE
Time-frequency analysis of near-field optical data for extracting local attributes [pdf] paper   link   bibtex  
  2000 (4)
Neural network based fuzzy membership function estimation. Application to uncertain time-varying systems supervision. Lengellé, R.; Richard, C.; and Milleman, S. In Intelligent Signal Processing and Communication Systems (ISPACS), 2000 International Symposium on, 2000. IEEE
Neural network based fuzzy membership function estimation. Application to uncertain time-varying systems supervision [pdf] paper   link   bibtex   1 download  
Automated detection of K-complexes: characterization of benzodiazepine effects. Coatanhay, A.; Richard, C.; Lengellé, R.; Muzet, A.; and Macher, J. In European Sleep Research Society, Congress of, 2000.
Automated detection of K-complexes: characterization of benzodiazepine effects [pdf] paper   link   bibtex   1 download  
Optical near-field data analysis through time-frequency distributions: application to the characterization and separation of the image spectral content by reassignment. Gharbi, T.; Barchiesi, D.; Bergossi, O.; Wioland, H.; and Richard, C. JOSA A, 17(12): 2513-2519. 2000.
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On the linear relations connecting the components of the discrete Wigner distribution in the case of real-valued signals. Richard, C.; and Lengellé, R. In Acoustics Speech and Signal Processing (ICASSP), 2000 IEEE International Conference on, volume 1, pages 85-88, 2000. IEEE
On the linear relations connecting the components of the discrete Wigner distribution in the case of real-valued signals [pdf] paper   link   bibtex  
  1999 (3)
A new approach to the K-complexes automated detection. Coatanhay, A.; Richard, C.; Lengelle, R.; Muzet, A.; and Macher, J. In World Federation of Sleep Research and Sleep Medecine Societies, 1999 Congress of, 1999.
A new approach to the K-complexes automated detection [pdf] paper   link   bibtex  
Data-driven design and complexity control of time-frequency detectors. Richard, C.; and Lengellé, R. Signal processing, 77(1): 37-48. 1999.
Data-driven design and complexity control of time-frequency detectors [pdf] paper   link   bibtex  
Sur le contrôle de la complexité des détecteurs opérant dans le domaine temps-fréquence par le biais de la fonction de paramétrisation. Richard, C.; and Lengellé, R. In Actes du 17e Colloque GRETSI sur le Traitement du Signal et des Images, 1999.
Sur le contrôle de la complexité des détecteurs opérant dans le domaine temps-fréquence par le biais de la fonction de paramétrisation [pdf] paper   link   bibtex  
  1998 (6)
Une méthodologie pour la détection à structure imposée. Applications au plan temps-fréquence. Richard, C. Ph.D. Thesis, Université de Technologie de Compiègne, 1998.
Une méthodologie pour la détection à structure imposée. Applications au plan temps-fréquence [pdf] paper   link   bibtex   8 downloads  
Joint time and time-frequency optimal detection of K-complexes in sleep EEG. Richard, C.; and Lengellé, R. Computers and Biomedical Research, 31(3): 209-229. 1998.
Joint time and time-frequency optimal detection of K-complexes in sleep EEG [pdf] paper   link   bibtex   2 downloads  
Two algorithms for designing optimal reduced-bias data-driven time-frequency detectors. Richard, C.; and Lengellé, R. In Time-Frequency and Time-Scale Analysis, 1998. Proceedings of the IEEE-SP International Symposium on, pages 601-604, 1998. IEEE
Two algorithms for designing optimal reduced-bias data-driven time-frequency detectors [pdf] paper   link   bibtex  
Structural risk minimization for reduced-bias time-frequency-based detectors design. Richard, C.; and Lengellé, R. In Acoustics Speech and Signal Processing (ICASSP), 1998 IEEE International Conference on, volume 4, pages 2397-2400, 1998. IEEE
Structural risk minimization for reduced-bias time-frequency-based detectors design [pdf] paper   link   bibtex  
On the dimension of the discrete Wigner-Ville transform range space: application to time-frequency based detectors design. Richard, C.; and Lengellé, R. In Time-Frequency and Time-Scale Analysis, 1998. Proceedings of the IEEE-SP International Symposium on, pages 5-8, 1998. IEEE
On the dimension of the discrete Wigner-Ville transform range space: application to time-frequency based detectors design [pdf] paper   link   bibtex   1 download  
Détection automatique des phénomènes transitoires de l'EEG par analyse temps-fréquence. Richard, C.; and Lengellé, R. Innovation et technologie en biologie et médecine, 19(3): 167-177. 1998.
Détection automatique des phénomènes transitoires de l'EEG par analyse temps-fréquence [pdf] paper   link   bibtex   1 download  
  1997 (2)
Joint recursive implementation of time-frequency representations and their modified version by the reassignment method. Richard, C.; and Lengellé, R. Signal Processing, 60(2): 163-179. 1997.
Joint recursive implementation of time-frequency representations and their modified version by the reassignment method [pdf] paper   link   bibtex  
Une nouvelle approche pour la détection linéaire optimale dans le plan temps-fréquence. Richard, C.; and Lengellé, R. In Actes du 16e Colloque GRETSI sur le Traitement du Signal et des Images, 1997.
Une nouvelle approche pour la détection linéaire optimale dans le plan temps-fréquence [pdf] paper   link   bibtex  
  1996 (2)
Recursive implementation of some time-frequency representations. Richard, C.; and Lengellé, R. In Time-Frequency and Time-Scale Analysis, 1996., Proceedings of the IEEE-SP International Symposium on, pages 313-316, 1996. IEEE
Recursive implementation of some time-frequency representations [pdf] paper   link   bibtex   1 download  
Fast implementation of time-frequency representations modified by the reassignment method. Richard, C.; and Lengellé, R. In Signal Processing, 1996., 3rd International Conference on, volume 1, pages 343-346, 1996. IEEE
Fast implementation of time-frequency representations modified by the reassignment method [pdf] paper   link   bibtex   2 downloads  
  1995 (1)
Analyse de la perception acoustique de véhicules. Bardot, A.; Richard, C.; and Masson, M. In Société des Ingénieurs de l'Automobile, Colloque de, 1995.
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