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Artykuły w czasopismach na temat "Traitement du signal Tensor"
Ruiz-Alzola, J., i C. F. Westin. "Tensor signal processing". Signal Processing 87, nr 2 (luty 2007): 217–19. http://dx.doi.org/10.1016/j.sigpro.2006.05.002.
Pełny tekst źródłaJacobs, Bidhan. "Traitement du signal et abstraction". Vertigo 48, nr 1 (2015): 61. http://dx.doi.org/10.3917/ver.048.0061.
Pełny tekst źródłaWeichert, Gabriele, Magdalena Martinka i Jason K. Rivers. "Intravascular Lymphoma Presenting as Telangectasias: Response to Rituximab and Combination Chemotherapy". Journal of Cutaneous Medicine and Surgery 7, nr 6 (listopad 2003): 460–63. http://dx.doi.org/10.1177/120347540300700606.
Pełny tekst źródłaMiron, Sebastian, Yassine Zniyed, Rémy Boyer, André Lima Ferrer de Almeida, Gérard Favier, David Brie i Pierre Comon. "Tensor methods for multisensor signal processing". IET Signal Processing 14, nr 10 (grudzień 2020): 693–709. http://dx.doi.org/10.1049/iet-spr.2020.0373.
Pełny tekst źródłaMuti, Damien, i Salah Bourennane. "Survey on tensor signal algebraic filtering". Signal Processing 87, nr 2 (luty 2007): 237–49. http://dx.doi.org/10.1016/j.sigpro.2005.12.016.
Pełny tekst źródłaCzachor, Marek. "Hidden Tensor Structures". Entropy 26, nr 2 (7.02.2024): 145. http://dx.doi.org/10.3390/e26020145.
Pełny tekst źródłaGrounauer, P., M. Benard, D. Gartmann i Vo Van Toi. "Traitement automatique par computer du signal ERG". Klinische Monatsblätter für Augenheilkunde 192, nr 05 (maj 1988): 403–5. http://dx.doi.org/10.1055/s-2008-1050135.
Pełny tekst źródłaVingerhoeds, R. A. "Modelisation et identification en traitement du signal". Automatica 26, nr 1 (styczeń 1990): 185–86. http://dx.doi.org/10.1016/0005-1098(90)90171-d.
Pełny tekst źródłaMeyer, Yves. "Le traitement du signal et l'analyse mathématique". Annales de l’institut Fourier 50, nr 2 (2000): 593–632. http://dx.doi.org/10.5802/aif.1766.
Pełny tekst źródła-Le Chevalier, F. "Traitement physique du signal et de l'image". Revue de l'Electricité et de l'Electronique -, nr 02 (2001): 23. http://dx.doi.org/10.3845/ree.2001.015.
Pełny tekst źródłaRozprawy doktorskie na temat "Traitement du signal Tensor"
Sorensen, Mikael. "Tensor tools with application in signal processing". Nice, 2010. http://www.theses.fr/2010NICE4030.
Pełny tekst źródłaNombre de problèmes issus du traitement de signal peuvent être modélisés par des équations/problèmes tensoriels. La spécificité des problèmes de traitement de signal est qu’ils donnent lieu à des décompositions tensorielles structurées. L’objectif principal de cette thèse est, d’une part, le développement de méthodes numériques appliquées aux problèmes de décompositions de tenseurs structurés et, d’autre part, leur application en traitement de signal. Dans un premier temps, nous proposons des méthodes pour le calcul de la décomposition CANDECOMP/PARAFAC (CP) avec un facteur matriciel semi-unitaire. De plus, nous développons des méthodes pour résoudre des problèmes d’analyse en composantes indépendantes (ICA) pouvant être modélisés par une décomposition CP structurée, en les considérant comme des problèmes de décompositions CP sous contrainte de semi-unitarité. Ensuite, pour le calcul de décompositions CP avec symétries hermitiennes partielles, nous proposons des méthodes de décompositions simultanées de Schur généralisées. D’un point de vue numérique, nous développons le calcul de décompositions CP réelles par une méthode de Jacobi. En troisième lieu, nous nous attaquons à la décomposition de tenseurs ayant des facteurs matriciels bande ou Hankel/Toeplitz, conjointement (ou non) à des symétries hermitiennes partielles. Ces méthodes sont appliquées à la résolution de problèmes d’identification aveugle basés sur des cumulants. Enfin, nous proposons une méthode (plus) efficace pour l’égalisation aveugle de canaux paraunitaires basée sur les itérations de Jacobi. Dans le même esprit, nous dérivons une solution algébrique à la méthode de Jacobi pour effectuer la diagonalisation conjointe de matrices réelles définies positives
Silva, Alex Pereira da. "Techniques tensorielles pour le traitement du signal : algorithmes pour la décomposition polyadique canonique". Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT042/document.
Pełny tekst źródłaLow rank tensor decomposition has been playing for the last years an important rolein many applications such as blind source separation, telecommunications, sensor array processing,neuroscience, chemometrics, and data mining. The Canonical Polyadic tensor decomposition is veryattractive when compared to standard matrix-based tools, manly on system identification. In this thesis,we propose: (i) several algorithms to compute specific low rank-approximations: finite/iterativerank-1 approximations, iterative deflation approximations, and orthogonal tensor decompositions. (ii)A new strategy to solve multivariate quadratic systems, where this problem is reduced to a best rank-1 tensor approximation problem. (iii) Theoretical results to study and proof the performance or theconvergence of some algorithms. All performances are supported by numerical experiments
A aproximação tensorial de baixo posto desempenha nestes últimos anos um papel importanteem várias aplicações, tais como separação cega de fontes, telecomunicações, processamentode antenas, neurociênca, quimiometria e exploração de dados. A decomposição tensorial canônicaé bastante atrativa se comparada às técnicas matriciais clássicas, principalmente na identificação desistemas. Nesta tese, propõe-se (i) vários algoritmos para calcular alguns tipos de aproximação deposto: aproximação de posto-1 iterativa e em um número finito de operações, a aproximação pordeflação iterativa, e a decomposição tensorial ortogonal; (ii) uma nova estratégia para resolver sistemasquadráticos em várias variáveis, em que tal problema pode ser reduzido à melhor aproximaçãode posto-1 de um tensor; (iii) resultados teóricos visando estudar o desempenho ou demonstrar aconvergência de alguns algoritmos. Todas os desempenhos são ilustrados através de simulações computacionais
Marmin, Arthur. "Rational models optimized exactly for solving signal processing problems". Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG017.
Pełny tekst źródłaA wide class of nonconvex optimization problem is represented by rational optimization problems. The latter appear naturally in many areas such as signal processing or chemical engineering. However, finding the global optima of such problems is intricate. A recent approach called Lasserre's hierarchy provides a sequence of convex problems that has the theoretical guarantee to converge to the global optima. Nevertheless, this approach is computationally challenging due to the high dimensions of the convex relaxations. In this thesis, we tackle this challenge for various signal processing problems.First, we formulate the reconstruction of sparse signals as a rational optimization problem. We show that the latter has a structure that we wan exploit in order to reduce the complexity of the associated relaxations. We thus solve several practical problems such as the reconstruction of chromatography signals. We also extend our method to the reconstruction of various types of signal corrupted by different noise models.In a second part, we study the convex relaxations generated by our problems which take the form of high-dimensional semi-definite programming problems. We consider several algorithms mainly based on proximal operators to solve those high-dimensional problems efficiently.The last part of this thesis is dedicated to the link between polynomial optimization and symmetric tensor decomposition. Indeed, they both can be seen as an instance of the moment problem. We thereby propose a detection method as well as a decomposition algorithm for symmetric tensors based on the tools used in polynomial optimization. In parallel, we suggest a robust extraction method for polynomial optimization based on tensor decomposition algorithms. Those methods are illustrated on signal processing problems
Han, Xu. "Robust low-rank tensor approximations using group sparsity". Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S001/document.
Pełny tekst źródłaLast decades, tensor decompositions have gained in popularity in several application domains. Most of the existing tensor decomposition methods require an estimating of the tensor rank in a preprocessing step to guarantee an outstanding decomposition results. Unfortunately, learning the exact rank of the tensor can be difficult in some particular cases, such as for low signal to noise ratio values. The objective of this thesis is to compute the best low-rank tensor approximation by a joint estimation of the rank and the loading matrices from the noisy tensor. Based on the low-rank property and an over estimation of the loading matrices or the core tensor, this joint estimation problem is solved by promoting group sparsity of over-estimated loading matrices and/or the core tensor. More particularly, three new methods are proposed to achieve efficient low rank estimation for three different tensors decomposition models, namely Canonical Polyadic Decomposition (CPD), Block Term Decomposition (BTD) and Multilinear Tensor Decomposition (MTD). All the proposed methods consist of two steps: the first step is designed to estimate the rank, and the second step uses the estimated rank to compute accurately the loading matrices. Numerical simulations with noisy tensor and results on real data the show effectiveness of the proposed methods compared to the state-of-the-art methods
Ionita, Razvan-Adrian. "Conception de circuits à signaux mixtes pour des communications portables à basse tension et haute fréquence en CMOS bulk et SOI". Evry-Val d'Essonne, 2005. http://www.theses.fr/2005EVRY0028.
Pełny tekst źródłaPoisson, Olivier. "Nouvelles techniques du traitement du signal et d'identification pour l'analyse des perturbations de la tension". Paris 6, 1998. http://www.theses.fr/1998PA066595.
Pełny tekst źródłaAndré, Rémi. "Algorithmes de diagonalisation conjointe par similitude pour la décomposition canonique polyadique de tenseurs : applications en séparation de sources". Electronic Thesis or Diss., Toulon, 2018. http://www.theses.fr/2018TOUL0011.
Pełny tekst źródłaThis thesis introduces new joint eigenvalue decomposition algorithms. These algorithms allowamongst others to solve the canonical polyadic decomposition problem. This decomposition iswidely used for blind source separation. Using the joint eigenvalue decomposition to solve thecanonical polyadic decomposition problem allows to avoid some problems whose the others canonicalpolyadic decomposition algorithms generally suffer, such as the convergence rate, theoverfactoring sensibility and the correlated factors sensibility. The joint eigenvalue decompositionalgorithms dealing with complex data give either good results when the noise power is low, orthey are robust to the noise power but have a high numerical cost. Therefore, we first proposealgorithms equally dealing with real and complex. Moreover, in some applications, factor matricesof the canonical polyadic decomposition contain only nonnegative values. Taking this constraintinto account makes the algorithms more robust to the overfactoring and to the correlated factors.Therefore, we also offer joint eigenvalue decomposition algorithms taking advantage of thisnonnegativity constraint. Suggested numerical simulations show that the first developed algorithmsimprove the estimation accuracy and reduce the numerical cost in the case of complexdata. Our numerical simulations also highlight the fact that our nonnegative joint eigenvaluedecomposition algorithms improve the factor matrices estimation when their columns have ahigh correlation degree. Eventually, we successfully applied our algorithms to two blind sourceseparation problems : one concerning numerical telecommunications and the other concerningfluorescence spectroscopy
André, Rémi. "Algorithmes de diagonalisation conjointe par similitude pour la décomposition canonique polyadique de tenseurs : applications en séparation de sources". Thesis, Toulon, 2018. http://www.theses.fr/2018TOUL0011/document.
Pełny tekst źródłaThis thesis introduces new joint eigenvalue decomposition algorithms. These algorithms allowamongst others to solve the canonical polyadic decomposition problem. This decomposition iswidely used for blind source separation. Using the joint eigenvalue decomposition to solve thecanonical polyadic decomposition problem allows to avoid some problems whose the others canonicalpolyadic decomposition algorithms generally suffer, such as the convergence rate, theoverfactoring sensibility and the correlated factors sensibility. The joint eigenvalue decompositionalgorithms dealing with complex data give either good results when the noise power is low, orthey are robust to the noise power but have a high numerical cost. Therefore, we first proposealgorithms equally dealing with real and complex. Moreover, in some applications, factor matricesof the canonical polyadic decomposition contain only nonnegative values. Taking this constraintinto account makes the algorithms more robust to the overfactoring and to the correlated factors.Therefore, we also offer joint eigenvalue decomposition algorithms taking advantage of thisnonnegativity constraint. Suggested numerical simulations show that the first developed algorithmsimprove the estimation accuracy and reduce the numerical cost in the case of complexdata. Our numerical simulations also highlight the fact that our nonnegative joint eigenvaluedecomposition algorithms improve the factor matrices estimation when their columns have ahigh correlation degree. Eventually, we successfully applied our algorithms to two blind sourceseparation problems : one concerning numerical telecommunications and the other concerningfluorescence spectroscopy
Boudehane, Abdelhak. "Structured-joint factor estimation for high-order and large-scale tensors". Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG085.
Pełny tekst źródłaMultidimensional data sets and signals occupy an important place in recent application fields. Tensor decomposition represents a powerful mathematical tool for modeling multidimensional data and signals, without losing the interdimensional relations. The Canonical Polyadic (CP) model, a widely used tensor decomposition model, is unique up to scale and permutation indeterminacies. This property facilitates the physical interpretation, which has led the integration of the CP model in various contexts. The main challenge facing the tensor modeling is the computational complexity and memory requirements. High-order tensors represent a important issue, since the computational complexity and the required memory space increase exponentially with respect to the order. Another issue is the size of the tensor in the case of large-scale problems, which adds another burden to the complexity and memory. Tensor Networks (TN) theory is a promising framework, allowing to reduce high-order problems into a set of lower order problems. In particular, the Tensor-Train (TT) model, one of the TN models, is an interesting ground for dimensionality reduction. However, respresenting a CP tensor using a TT model, is extremely expensive in the case of large-scale tensors, since it requires full matricization of the tensor, which may exceed the memory capacity.In this thesis, we study the dimensionality reduction in the context of sparse-coding and high-order coupled tensor decomposition. Based on the results of Joint dImensionality Reduction And Factor rEtrieval (JIRAFE) scheme, we use the flexibility of the TT model to integrate the physical driven constraints and the prior knowledge on the factors, with the aim to reduce the computation time. For large-scale problems, we propose a scheme allowing to parallelize and randomize the different steps, i.e., the dimensionality reduction and the factor estimation. We also propose a grid-based strategy, allowing a full parallel processing for the case of very large scales and dynamic tensor decomposition
Cipriano, Francesco. "Recherche de matière noire scalaire légère avec des détecteurs d'ondes gravitationnelles". Thesis, Université Côte d'Azur, 2020. http://www.theses.fr/2020COAZ4040.
Pełny tekst źródłaThe existence of the dark matter and the truth beyond its nature has been one of the greatest puzzles of the twentieth century and still it is nowadays. In the last decades several hypotheses, such as the WIMPs model, have been proposed to solve such puzzle but none of them has been able so far to succeed.In this thesis work we will focus on another very appealing model in which dark matter is successfully described by an ultra-light scalar field whose origin can be sought in the low-energy limit of one of the most promising unification theories: the String Theory.In this work we show how such scalar field, if present, interacts with standard matter and in particular with the optical apparatus that is at the core of gravitational waves antennas. We derive and discuss the signal produced by this interaction through different approaches deriving both approximated and exact solutions. Special attention is paid to the second-order term of the signal approximate series expansion whose contribution ends up to be not negligible when one factors in the specific geometrical dimensions and frequency range of gravitational waves detectors like Advanced LIGO and Advanced Virgo.A suggested by recent surveys we assume presence of a dark matter stream in the local neighborhood of the solar system and show its effect on the signal.We then propose and discuss a hierarchical statistical analysis aimed to the signal detection. In case of no detection a limit curve for the coupling parameter dg* is derived. Such curve is then analyzed in detail showing the magnitude of the contribution of the first-order and second-order terms of the signal series expansion. We analyze the modification of the constraint curve due to the variation of the fraction of local dark matter belonging to the stream. We show finally how the constraint curve responds to variations of the search parameter and discuss the optimal choices
Książki na temat "Traitement du signal Tensor"
Jean-Louis, Lacoume, Durrani Tariq S, Stora Raymond 1930-, Université scientifique et médicale de Grenoble. i NATO Advanced Study Institute, red. Traitement du signal =: Signal processing. Amsterdam: North-Holland, 1987.
Znajdź pełny tekst źródłaCoulon, Frédéric de. Théorie et traitement des signaux. Lausanne: Presses Polytechniques Romandes, 1990.
Znajdź pełny tekst źródłaVanderLugt, Anthony. Optical signal processing. New York: Wiley, 1992.
Znajdź pełny tekst źródłaCheng, Lei, Zhongtao Chen i Yik-Chung Wu. Bayesian Tensor Decomposition for Signal Processing and Machine Learning. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22438-6.
Pełny tekst źródła1945-, Kunt M., red. Traitement de l'information. Lausanne: Presses polytechniques et universitaires romandes, 1991.
Znajdź pełny tekst źródłaBellanger, Maurice. Traitement numérique du signal: Théorie et pratique. Wyd. 8. Paris: Dunod, 2006.
Znajdź pełny tekst źródłaL, Mari J., Glangeaud F i Coppens Françoise, red. Traitement du signal pour géologues et géophysiciens. Paris: Editions Technip, 2001.
Znajdź pełny tekst źródłaBellanger, Maurice. Traitement numérique du signal: Théorie et pratique. Wyd. 3. Paris: Masson, 1987.
Znajdź pełny tekst źródłaBlanchet, Gérard. Traitement numérique du signal: Simulation sous Matlab. Paris: Hermès, 1998.
Znajdź pełny tekst źródłaMari, J. L. Traitement du signal pour géologues et géophysiciens. Paris: Editions Technip, 1997.
Znajdź pełny tekst źródłaCzęści książek na temat "Traitement du signal Tensor"
Del Moral, Pierre, i Christelle Vergé. "Traitement du signal". W Mathématiques et Applications, 347–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54616-7_12.
Pełny tekst źródłaMartaj, Dr Nadia, i Dr Mohand Mokhtari. "Traitement du signal". W MATLAB R2009, SIMULINK et STATEFLOW pour Ingénieurs, Chercheurs et Etudiants, 587–672. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11764-0_14.
Pełny tekst źródłaMartaj, Nadia, i Mohand Mokhtari. "Traitement du signal déterministe". W Apprendre et maîtriser LabVIEW par ses applications, 785–834. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-45335-9_21.
Pełny tekst źródłaTolimieri, Richard, Myoung An i Chao Lu. "Tensor Product". W Signal Processing and Digital Filtering, 1–23. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-1948-4_1.
Pełny tekst źródłaTolimieri, Richard, Myoung An i Chao Lu. "Tensor Product". W Signal Processing and Digital Filtering, 1–28. New York, NY: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4684-0205-6_1.
Pełny tekst źródłaYılmaz, Y. Kenan, i A. Taylan Cemgil. "Probabilistic Latent Tensor Factorization". W Latent Variable Analysis and Signal Separation, 346–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15995-4_43.
Pełny tekst źródłaGranlund, Gösta H., i Hans Knutsson. "Vector and Tensor Field Filtering". W Signal Processing for Computer Vision, 343–65. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-2377-9_11.
Pełny tekst źródłaTolimieri, Richard, Chao Lu i Myoung An. "Tensor Product and Stride Permutation". W Signal Processing and Digital Filtering, 27–54. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2767-8_2.
Pełny tekst źródłaTolimieri, Richard, Myoung An i Chao Lu. "Multidimensional Tensor Product and FFT". W Signal Processing and Digital Filtering, 25–36. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-1948-4_2.
Pełny tekst źródłaTolimieri, R., Myoung An i Chao Lu. "Tensor Product and Stride Permutation". W Signal Processing and Digital Filtering, 36–71. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4757-3854-4_2.
Pełny tekst źródłaStreszczenia konferencji na temat "Traitement du signal Tensor"
ZHANG, Jianfu, ZERUI TAO, LIQING ZHANG i QIBIN ZHAO. "Tensor Decomposition Via Core Tensor Networks". W ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9413637.
Pełny tekst źródłaThanh, Le Trung, Karim Abed-Meraim, Nguyen Linh Trung i Adel Hafiane. "Robust Tensor Tracking With Missing Data Under Tensor-Train Format". W 2022 30th European Signal Processing Conference (EUSIPCO). IEEE, 2022. http://dx.doi.org/10.23919/eusipco55093.2022.9909702.
Pełny tekst źródła"Session WA5: Tensor signal processing". W 2016 50th Asilomar Conference on Signals, Systems and Computers. IEEE, 2016. http://dx.doi.org/10.1109/acssc.2016.7869676.
Pełny tekst źródłaHinrich, Jesper L., i Morten Morup. "Probabilistic Tensor Train Decomposition". W 2019 27th European Signal Processing Conference (EUSIPCO). IEEE, 2019. http://dx.doi.org/10.23919/eusipco.2019.8903177.
Pełny tekst źródłaZniyed, Yassine, Remy Boyer, Andre L. F. de Almeida i Gerard Favier. "Tensor-Train Modeling for Mimo-OFDM Tensor Coding-and-Forwarding Relay Systems". W 2019 27th European Signal Processing Conference (EUSIPCO). IEEE, 2019. http://dx.doi.org/10.23919/eusipco.2019.8902770.
Pełny tekst źródłaMorison, Gordon. "SURE Based Truncated Tensor Nuclear Norm Regularization for Low Rank Tensor Completion". W 2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco47968.2020.9287726.
Pełny tekst źródłaVermeylen, Charlotte, Guillaume Olikier, P. A. Absil i Marc Van Barel. "Rank Estimation for Third-Order Tensor Completion in the Tensor-Train Format". W 2023 31st European Signal Processing Conference (EUSIPCO). IEEE, 2023. http://dx.doi.org/10.23919/eusipco58844.2023.10289827.
Pełny tekst źródłaChen, Zhongtao, Lei Cheng i Yik-Chung Wu. "Enhanced Tensor Rank Learning in Bayesian PARAFAC2 for Noisy Irregular Tensor Data". W 2023 31st European Signal Processing Conference (EUSIPCO). IEEE, 2023. http://dx.doi.org/10.23919/eusipco58844.2023.10289945.
Pełny tekst źródłaBao, Yi-Ting, i Jen-Tzung Chien. "Tensor classification network". W 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2015. http://dx.doi.org/10.1109/mlsp.2015.7324360.
Pełny tekst źródła"Session MP1b: Tensor-based signal processing". W 2014 48th Asilomar Conference on Signals, Systems and Computers. IEEE, 2014. http://dx.doi.org/10.1109/acssc.2014.7094511.
Pełny tekst źródłaRaporty organizacyjne na temat "Traitement du signal Tensor"
Jury, William A., i David Russo. Characterization of Field-Scale Solute Transport in Spatially Variable Unsaturated Field Soils. United States Department of Agriculture, styczeń 1994. http://dx.doi.org/10.32747/1994.7568772.bard.
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