Bibliographies thématiques / Multi-dimensional graph signal processing

Littérature scientifique sur le sujet « Multi-dimensional graph signal processing »

Auteur : Grafiati

Publié le 14 mars 2023

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Articles de revues sur le sujet "Multi-dimensional graph signal processing"

Zheng, Xianwei, Yuan Yan Tang, Jiantao Zhou, Jianjia Pan, Shouzhi Yang, Youfa Li et Patrick S. P. Wang. « Multi-Level Downsampling of Graph Signals via Improved Maximum Spanning Trees ». International Journal of Pattern Recognition and Artificial Intelligence 33, n^o 03 (19 février 2019) : 1958005. http://dx.doi.org/10.1142/s0218001419580059.

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Graph signal processing (GSP) is an emerging field in the signal processing community. Novel GSP-based transforms, such as graph Fourier transform and graph wavelet filter banks, have been successfully utilized in image processing and pattern recognition. As a rapidly developing research area, graph signal processing aims to extend classical signal processing techniques to signals with irregular underlying structures. One of the hot topics in GSP is to develop multi-scale transforms such that novel GSP-based techniques can be applied in image processing or other related areas. For designing graph signal multi-scale frameworks, downsampling operations that ensuring multi-level downsampling should be specifically constructed. Among the existing downsampling methods in graph signal processing, the state-of-the-art method was constructed based on the maximum spanning tree (MST). However, when using this method for multi-level downsampling of graph signals defined on unweighted densely connected graphs, such as social network data, the sampling rates are not close to [Formula: see text]. This phenomenon is summarized as a new problem and called downsampling unbalance problem in this paper. Due to the unbalance, MST-based downsampling method cannot be applied to construct graph signal multi-scale transforms. In this paper, we propose a novel and efficient method to detect and reduce the downsampling unbalance generated by the MST-based method. For any given graph signal, we apply the graph density to construct a measurement of the downsampling unbalance generated by the MST-based method. If a graph signal has large unbalance possibility, the multi-level downsampling is conducted after the MST is improved. The experimental results on synthetic and real-world social network data show that downsampling unbalance can be efficiently detected and then reduced by our method.

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Liao, Kefei, Zerui Yu, Ningbo Xie et Junzheng Jiang. « Joint Estimation of Azimuth and Distance for Far-Field Multi Targets Based on Graph Signal Processing ». Remote Sensing 14, n^o 5 (24 février 2022) : 1110. http://dx.doi.org/10.3390/rs14051110.

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Target position estimation is one of the important research directions in array signal processing. In recent years, the research of target azimuth estimation based on graph signal processing (GSP) has sprung up, which provides new ideas for the Direction of Arrival (DoA) application. In this article, by extending GSP-based DOA to joint azimuth and distance estimation and constructing a fully connected graph signal model, a multi-target joint azimuth and distance estimation method based on GSP is proposed. Firstly, the fully connection graph model is established related to the phase information of a linear array. For the fully connection graph, the Fourier transform method is used to solve the estimated response function, and the one-dimensional estimation of azimuth and distance is completed, respectively. Finally, the azimuth and distance estimation information are combined, and the false points in the merging process are removed by using CLEAN algorithm to complete the two-dimensional estimation of targets. The simulation results show that the proposed method has a smaller mean square error than the Multiple Signal Classification (MUSIC) algorithm in azimuth estimation under the condition of a low signal-to-noise ratio and more accurate response values than the MUSIC algorithm in distance estimation under any signal-to-noise ratio in multi-target estimation.

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Yankelevsky, Yael, et Michael Elad. « Finding GEMS : Multi-Scale Dictionaries For High-Dimensional Graph Signals ». IEEE Transactions on Signal Processing 67, n^o 7 (1 avril 2019) : 1889–901. http://dx.doi.org/10.1109/tsp.2019.2899822.

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Jian, Xingchao, Feng Ji et Wee Peng Tay. « Generalizing Graph Signal Processing : High Dimensional Spaces, Models and Structures ». Foundations and Trends® in Signal Processing 17, n^o 3 (2023) : 209–90. http://dx.doi.org/10.1561/2000000119.

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Xiong, Chao, Wen Li, Yun Liu et Minghui Wang. « Multi-Dimensional Edge Features Graph Neural Network on Few-Shot Image Classification ». IEEE Signal Processing Letters 28 (2021) : 573–77. http://dx.doi.org/10.1109/lsp.2021.3061978.

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Mathur, Priyanka, et Vijay Kumar Chakka. « Graph Signal Processing Based Cross-Subject Mental Task Classification Using Multi-Channel EEG Signals ». IEEE Sensors Journal 22, n^o 8 (15 avril 2022) : 7971–78. http://dx.doi.org/10.1109/jsen.2022.3156152.

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Park, Han-Mu, et Kuk-Jin Yoon. « Exploiting multi-layer graph factorization for multi-attributed graph matching ». Pattern Recognition Letters 127 (novembre 2019) : 85–93. http://dx.doi.org/10.1016/j.patrec.2018.09.024.

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Rakhimberdina, Zarina, Xin Liu et Tsuyoshi Murata. « Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder ». Sensors 20, n^o 21 (22 octobre 2020) : 6001. http://dx.doi.org/10.3390/s20216001.

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With the advancement of brain imaging techniques and a variety of machine learning methods, significant progress has been made in brain disorder diagnosis, in particular Autism Spectrum Disorder. The development of machine learning models that can differentiate between healthy subjects and patients is of great importance. Recently, graph neural networks have found increasing application in domains where the population’s structure is modeled as a graph. The application of graphs for analyzing brain imaging datasets helps to discover clusters of individuals with a specific diagnosis. However, the choice of the appropriate population graph becomes a challenge in practice, as no systematic way exists for defining it. To solve this problem, we propose a population graph-based multi-model ensemble, which improves the prediction, regardless of the choice of the underlying graph. First, we construct a set of population graphs using different combinations of imaging and phenotypic features and evaluate them using Graph Signal Processing tools. Subsequently, we utilize a neural network architecture to combine multiple graph-based models. The results demonstrate that the proposed model outperforms the state-of-the-art methods on Autism Brain Imaging Data Exchange (ABIDE) dataset.

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Li, Shuang, Bing Liu et Chen Zhang. « Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing ». Computational Intelligence and Neuroscience 2016 (2016) : 1–12. http://dx.doi.org/10.1155/2016/4920670.

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Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption. But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a negative influence on the performance of multiple kernel learning. In addition, some models might be ill-posed if the rank of matrices in their objective functions was not high enough. To address these issues, we extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method. Different from the conventional convex relaxation technique, the proposed algorithm directly takes advantage of a binary search and an alternative optimization scheme to obtain optimal solutions efficiently. The experimental results demonstrate the effectiveness of the proposed method for supervised, unsupervised, and semisupervised scenarios.

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Oselio, Brandon, Alex Kulesza et Alfred O. Hero. « Multi-Layer Graph Analysis for Dynamic Social Networks ». IEEE Journal of Selected Topics in Signal Processing 8, n^o 4 (août 2014) : 514–23. http://dx.doi.org/10.1109/jstsp.2014.2328312.

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Thèses sur le sujet "Multi-dimensional graph signal processing"

GRASSI, FRANCESCO. « Statistical and Graph-Based Signal Processing : Fundamental Results and Application to Cardiac Electrophysiology ». Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2710580.

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The goal of cardiac electrophysiology is to obtain information about the mechanism, function, and performance of the electrical activities of the heart, the identification of deviation from normal pattern and the design of treatments. Offering a better insight into cardiac arrhythmias comprehension and management, signal processing can help the physician to enhance the treatment strategies, in particular in case of atrial fibrillation (AF), a very common atrial arrhythmia which is associated to significant morbidities, such as increased risk of mortality, heart failure, and thromboembolic events. Catheter ablation of AF is a therapeutic technique which uses radiofrequency energy to destroy atrial tissue involved in the arrhythmia sustenance, typically aiming at the electrical disconnection of the of the pulmonary veins triggers. However, recurrence rate is still very high, showing that the very complex and heterogeneous nature of AF still represents a challenging problem. Leveraging the tools of non-stationary and statistical signal processing, the first part of our work has a twofold focus: firstly, we compare the performance of two different ablation technologies, based on contact force sensing or remote magnetic controlled, using signal-based criteria as surrogates for lesion assessment. Furthermore, we investigate the role of ablation parameters in lesion formation using the late-gadolinium enhanced magnetic resonance imaging. Secondly, we hypothesized that in human atria the frequency content of the bipolar signal is directly related to the local conduction velocity (CV), a key parameter characterizing the substrate abnormality and influencing atrial arrhythmias. Comparing the degree of spectral compression among signals recorded at different points of the endocardial surface in response to decreasing pacing rate, our experimental data demonstrate a significant correlation between CV and the corresponding spectral centroids. However, complex spatio-temporal propagation pattern characterizing AF spurred the need for new signals acquisition and processing methods. Multi-electrode catheters allow whole-chamber panoramic mapping of electrical activity but produce an amount of data which need to be preprocessed and analyzed to provide clinically relevant support to the physician. Graph signal processing has shown its potential on a variety of applications involving high-dimensional data on irregular domains and complex network. Nevertheless, though state-of-the-art graph-based methods have been successful for many tasks, so far they predominantly ignore the time-dimension of data. To address this shortcoming, in the second part of this dissertation, we put forth a Time-Vertex Signal Processing Framework, as a particular case of the multi-dimensional graph signal processing. Linking together the time-domain signal processing techniques with the tools of GSP, the Time-Vertex Signal Processing facilitates the analysis of graph structured data which also evolve in time. We motivate our framework leveraging the notion of partial differential equations on graphs. We introduce joint operators, such as time-vertex localization and we present a novel approach to significantly improve the accuracy of fast joint filtering. We also illustrate how to build time-vertex dictionaries, providing conditions for efficient invertibility and examples of constructions. The experimental results on a variety of datasets suggest that the proposed tools can bring significant benefits in various signal processing and learning tasks involving time-series on graphs. We close the gap between the two parts illustrating the application of graph and time-vertex signal processing to the challenging case of multi-channels intracardiac signals.

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Larkin, Kieran Gerard. « Topics in Multi dimensional Signal Demodulation ». Thesis, The University of Sydney, 2000. http://hdl.handle.net/2123/367.

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Problems in the demodulation of one, two, and three-dimensional signals are investigated. In one-dimensional linear systems the analytic signal and the Hilbert transform are central to the understanding of both modulation and demodulation. However, it is shown that an efficient nonlinear algorithm exists which is not explicable purely in terms of an approximation to the Hilbert transform. The algorithm is applied to the problem of finding the envelope peak of a white light interferogram. The accuracy of peak location is then shown to compare favourably with conventional, but less efficient, techniques. In two dimensions (2-D) the intensity of a wavefield yields to a phase demodulation technique equivalent to direct phase retrieval. The special symmetry of a Helmholtz wavefield allows a unique inversion of an autocorrelation. More generally, a 2-D (non-Helmholtz) fringe pattern can be demodulated by an isotropic 2-D extension of the Hilbert transform that uses a spiral phase signum function. The range of validity of the new transform is established using the asymptotic method of stationary phase. Simulations of the algorithm confirm that deviations from the ideal occur where the fringe pattern curvature is larger than the fringe frequency. A new self-calibrating algorithm for arbitrary sequences of phase-shifted interferograms is developed using the aforementioned spiral phase transform. The algorithm is shown to work even with discontinuous fringe patterns, which are known to seriously hamper other methods. Initial simulations of the algorithm indicate an accuracy of 5 milliradians is achievable. Previously undocumented connections between the demodulation techniques are uncovered and discussed.

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Larkin, Kieran Gerard. « Topics in Multi dimensional Signal Demodulation ». University of Sydney. Physics, 2001. http://hdl.handle.net/2123/367.

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Larkin, Kieran Gerard. « Topics in multi-dimensional signal demodulation ». Connect to full text, 2000. http://hdl.handle.net/2123/367.

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Thesis (Ph. D.)--University of Sydney, 2000.
Title from title screen (viewed Apr. 23, 2008). Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the School of Physics, Faculty of Science. Includes bibliography. Also available in print form.

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Costa, João Paulo Carvalho Lustosa da. « Parameter estimation techniques for multi-dimensional array signal processing ». Aachen Shaker, 2010. http://d-nb.info/1000960765/04.

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Randeny, Tharindu D. « Multi-Dimensional Digital Signal Processing in Radar Signature Extraction ». University of Akron / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=akron1451944778.

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Abewardana, Wijenayake Chamith K. « Multi-dimensional Signal Processing And Circuits For Advanced Electronically Scanned Antenna Arrays ». University of Akron / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=akron1415358304.

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Gianto, Gianto. « Multi-dimensional Teager-Kaiser signal processing for improved characterization using white light interferometry ». Thesis, Strasbourg, 2018. http://www.theses.fr/2018STRAD026/document.

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L'utilisation de franges d'interférence en lumière blanche comme une sonde optique en microscopie interférométrique est d'une importance croissante dans la caractérisation des matériaux, la métrologie de surface et de l'imagerie médicale. L'Interférométrie en lumière blanche est une technique basée sur la détection de l'enveloppe de franges d'interférence. Il a été démontré antérieurement, la capacité des approches 2D à rivaliser avec certaines méthodes classiques utilisées dans le domaine de l'interférométrie, en termes de robustesse et de temps de calcul. En outre, alors que la plupart des méthodes tiennent compte seulement des données 1 D, il semblerait avantageux de prendre en compte le voisinage spatial utilisant des approches multidimensionnelles (2D/3D), y compris le paramètre de temps afin d'améliorer les mesures. Le but de ce projet de thèse est de développer de nouvelles approches n-D qui sont appropriées pour une meilleure caractérisation des surfaces plus complexes et des couches transparentes
The use of white light interference fringes as an optical probe in microscopy is of growing importance in materials characterization, surface metrology and medical imaging. Coherence Scanning Interferometry (CSI, also known as White Light Scanning Interferometry, WSLI) is well known for surface roughness and topology measurement [1]. Full-Field Optical Coherence Tomography (FF-OCT) is the version used for the tomographic analysis of complex transparent layers. Both techniques generally make use of some sort of fringe scanning along the optical axis and the acquisition of a stack of xyz images. Image processing is then used to identify the fringe envelopes along z at each pixel in order to measure the positions of either a single surface or of multiple scattering objects within a layer.In CSI, the measurement of surface shape generally requires peak or phase extraction of the mono dimensional fringe signal. Most of the methods are based on an AM-FM signal model, which represents the variation in light intensity measured along the optical axis of an interference microscope [2]. We have demonstrated earlier [3, 4] the ability of 2D approaches to compete with some classical methods used in the field of interferometry, in terms of robustness and computing time. In addition, whereas most methods only take into account the 1D data, it would seem advantageous to take into account the spatial neighborhood using multidimensional approaches (2D, 3D, 4D), including the time parameter in order to improve the measurements.The purpose of this PhD project is to develop new n-D approaches that are suitable for improved characterization of more complex surfaces and transparent layers. In addition, we will enrich the field of study by means of heterogeneous image processing from multiple sensor sources (heterogeneous data fusion). Applications considered will be in the fields of materials metrology, biomaterials and medical imaging

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Carvalho, Lustosa da Costa Joao P. [Verfasser]. « Parameter Estimation Techniques for Multi-Dimensional Array Signal Processing / Joao P Carvalho Lustosa da Costa ». Aachen : Shaker, 2010. http://d-nb.info/112254653X/34.

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Vorhies, John T. « Low-complexity Algorithms for Light Field Image Processing ». University of Akron / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=akron1590771210097321.

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Livres sur le sujet "Multi-dimensional graph signal processing"

Jain, Lakhmi C., Roumen Kountchev et Junsheng Shi, dir. 3D Imaging Technologies—Multi-dimensional Signal Processing and Deep Learning. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3391-1.

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Jain, Lakhmi C., Roumen Kountchev et Junsheng Shi. 3D Imaging Technologies--Multi-Dimensional Signal Processing and Deep Learning : Mathematical Approaches and Applications, Volume 1. Springer, 2022.

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Chapitres de livres sur le sujet "Multi-dimensional graph signal processing"

Deng, Yue. « Graph Structure for Visual Signal Sensing ». Dans High-Dimensional and Low-Quality Visual Information Processing, 45–62. Berlin, Heidelberg : Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44526-6_4.

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Liu, Chan, et Feiyan Cheng. « A Survey of Image Classification Algorithms Based on Graph Neural Networks ». Dans 3D Imaging Technologies—Multi-dimensional Signal Processing and Deep Learning, 203–12. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3391-1_22.

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Chelladurai, Xavier, et Joseph Varghese Kureethara. « Parallel Algorithm to find Integer k where a given Well-Distributed Graph is k-Metric Dimensional ». Dans Recent Trends in Signal and Image Processing, 145–53. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6966-5_15.

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Wang, Ling, Fu Tao Ma, Tie Hua Zhou et Xue Gao. « Multi-attributes Graph Algorithm for Association Rules Mining Over Energy Internet ». Dans Recent Advances in Intelligent Information Hiding and Multimedia Signal Processing, 11–18. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03745-1_2.

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Binh, Le Nguyen. « Multi-Dimensional Photonic Processing by Discrete-Domain Approach ». Dans Photonic Signal Processing, 341–404. Second edition. | Boca Raton : Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa, plc, [2019] : CRC Press, 2019. http://dx.doi.org/10.1201/9780429436994-8.

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Debusmann, Ralph, Denys Duchier et Marco Kuhlmann. « Multi-dimensional Graph Configuration for Natural Language Processing ». Dans Constraint Solving and Language Processing, 104–20. Berlin, Heidelberg : Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11424574_7.

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Ghanbari, Shirin, John C. Woods et Simon M. Lucas. « Multi-dimensional BPTs for Content Retrieval ». Dans Recent Advances in Multimedia Signal Processing and Communications, 73–90. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02900-4_4.

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Billow, Thomas, et Gerald Sommer. « Multi-dimensional signal processing using an algebraically extended signal representation ». Dans Algebraic Frames for the Perception-Action Cycle, 148–63. Berlin, Heidelberg : Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0017865.

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Hanka, Rudolf, et Thomas P. Harte. « Curse of Dimensionality : Classifying Large Multi-Dimensional Images with Neural Networks ». Dans Computer Intensive Methods in Control and Signal Processing, 249–60. Boston, MA : Birkhäuser Boston, 1997. http://dx.doi.org/10.1007/978-1-4612-1996-5_15.

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Li, Fei, Yonghang Tai, Hongfei Yu, Hailing Zhou et Liqiang Zhang. « Research Status of Motor Imagery EEG Signal Based on Deep Learning ». Dans 3D Imaging Technologies—Multi-dimensional Signal Processing and Deep Learning, 11–17. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3391-1_2.

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Actes de conférences sur le sujet "Multi-dimensional graph signal processing"

Venkitaraman, Arun, Saikat Chatterjee et Peter Handel. « Multi-Kernel Regression for Graph Signal Processing ». Dans ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8461643.

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Xu, Yao Lei, et Danilo P. Mandic. « Recurrent Graph Tensor Networks : A Low-Complexity Framework for Modelling High-Dimensional Multi-Way Sequences ». Dans 2021 29th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco54536.2021.9616314.

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Kruzick, Stephen, et Jose M. F. Moura. « Graph signal processing : Filter design and spectral statistics ». Dans 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2017. http://dx.doi.org/10.1109/camsap.2017.8313101.

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Natali, Alberto, Elvin Isufi et Geert Leus. « Forecasting Multi-Dimensional Processes Over Graphs ». Dans ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053522.

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Yankelevsky, Yael, et Michael Elad. « Dictionary Learning for High Dimensional Graph Signals ». Dans ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8462609.

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Thanou, Dorina, et Pascal Frossard. « Multi-graph learning of spectral graph dictionaries ». Dans ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178601.

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Hidane, M., O. Lezoray et A. Elmoataz. « Graph signal decomposition for multi-scale detail manipulation ». Dans 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025409.

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Isufi, Elvin, Geert Leus et Paolo Banelli. « 2-Dimensional finite impulse response graph-temporal filters ». Dans 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2016. http://dx.doi.org/10.1109/globalsip.2016.7905873.

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Kim, Saehoon, et Seungjin Choi. « Multi-view anchor graph hashing ». Dans ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638233.

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Tugnait, Jitendra K. « Graph Learning from Multi-Attribute Smooth Signals ». Dans 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2020. http://dx.doi.org/10.1109/mlsp49062.2020.9231563.

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Rapports d'organisations sur le sujet "Multi-dimensional graph signal processing"

Stiles, James M. Multi-Dimensional Signal Processing for Sparse Radar Arrays. Fort Belvoir, VA : Defense Technical Information Center, novembre 2002. http://dx.doi.org/10.21236/ada419885.

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Bhattacharya, Prabir. A New Multi-Dimensional Transform for Digital Signal Processing Using Generalized Association Schemes. Fort Belvoir, VA : Defense Technical Information Center, mai 1994. http://dx.doi.org/10.21236/ada284166.

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