Literatura académica sobre el tema "Multi-dimensional graph signal processing"
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Artículos de revistas sobre el tema "Multi-dimensional graph signal processing"
Zheng, Xianwei, Yuan Yan Tang, Jiantao Zhou, Jianjia Pan, Shouzhi Yang, Youfa Li y 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 de febrero de 2019): 1958005. http://dx.doi.org/10.1142/s0218001419580059.
Texto completoLiao, Kefei, Zerui Yu, Ningbo Xie y 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 de febrero de 2022): 1110. http://dx.doi.org/10.3390/rs14051110.
Texto completoYankelevsky, Yael y Michael Elad. "Finding GEMS: Multi-Scale Dictionaries For High-Dimensional Graph Signals". IEEE Transactions on Signal Processing 67, n.º 7 (1 de abril de 2019): 1889–901. http://dx.doi.org/10.1109/tsp.2019.2899822.
Texto completoJian, Xingchao, Feng Ji y 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.
Texto completoXiong, Chao, Wen Li, Yun Liu y 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.
Texto completoMathur, Priyanka y 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 de abril de 2022): 7971–78. http://dx.doi.org/10.1109/jsen.2022.3156152.
Texto completoPark, Han-Mu y Kuk-Jin Yoon. "Exploiting multi-layer graph factorization for multi-attributed graph matching". Pattern Recognition Letters 127 (noviembre de 2019): 85–93. http://dx.doi.org/10.1016/j.patrec.2018.09.024.
Texto completoRakhimberdina, Zarina, Xin Liu y Tsuyoshi Murata. "Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder". Sensors 20, n.º 21 (22 de octubre de 2020): 6001. http://dx.doi.org/10.3390/s20216001.
Texto completoLi, Shuang, Bing Liu y 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.
Texto completoOselio, Brandon, Alex Kulesza y Alfred O. Hero. "Multi-Layer Graph Analysis for Dynamic Social Networks". IEEE Journal of Selected Topics in Signal Processing 8, n.º 4 (agosto de 2014): 514–23. http://dx.doi.org/10.1109/jstsp.2014.2328312.
Texto completoTesis sobre el tema "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.
Texto completoLarkin, Kieran Gerard. "Topics in Multi dimensional Signal Demodulation". Thesis, The University of Sydney, 2000. http://hdl.handle.net/2123/367.
Texto completoLarkin, Kieran Gerard. "Topics in Multi dimensional Signal Demodulation". University of Sydney. Physics, 2001. http://hdl.handle.net/2123/367.
Texto completoLarkin, Kieran Gerard. "Topics in multi-dimensional signal demodulation". Connect to full text, 2000. http://hdl.handle.net/2123/367.
Texto completoTitle 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.
Costa, João Paulo Carvalho Lustosa da. "Parameter estimation techniques for multi-dimensional array signal processing". Aachen Shaker, 2010. http://d-nb.info/1000960765/04.
Texto completoRandeny, 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.
Texto completoAbewardana, 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.
Texto completoGianto, Gianto. "Multi-dimensional Teager-Kaiser signal processing for improved characterization using white light interferometry". Thesis, Strasbourg, 2018. http://www.theses.fr/2018STRAD026/document.
Texto completoThe 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
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.
Texto completoVorhies, John T. "Low-complexity Algorithms for Light Field Image Processing". University of Akron / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=akron1590771210097321.
Texto completoLibros sobre el tema "Multi-dimensional graph signal processing"
Jain, Lakhmi C., Roumen Kountchev y Junsheng Shi, eds. 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.
Texto completoJain, Lakhmi C., Roumen Kountchev y Junsheng Shi. 3D Imaging Technologies--Multi-Dimensional Signal Processing and Deep Learning: Mathematical Approaches and Applications, Volume 1. Springer, 2022.
Buscar texto completoCapítulos de libros sobre el tema "Multi-dimensional graph signal processing"
Deng, Yue. "Graph Structure for Visual Signal Sensing". En 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.
Texto completoLiu, Chan y Feiyan Cheng. "A Survey of Image Classification Algorithms Based on Graph Neural Networks". En 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.
Texto completoChelladurai, Xavier y Joseph Varghese Kureethara. "Parallel Algorithm to find Integer k where a given Well-Distributed Graph is k-Metric Dimensional". En 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.
Texto completoWang, Ling, Fu Tao Ma, Tie Hua Zhou y Xue Gao. "Multi-attributes Graph Algorithm for Association Rules Mining Over Energy Internet". En 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.
Texto completoBinh, Le Nguyen. "Multi-Dimensional Photonic Processing by Discrete-Domain Approach". En 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.
Texto completoDebusmann, Ralph, Denys Duchier y Marco Kuhlmann. "Multi-dimensional Graph Configuration for Natural Language Processing". En Constraint Solving and Language Processing, 104–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11424574_7.
Texto completoGhanbari, Shirin, John C. Woods y Simon M. Lucas. "Multi-dimensional BPTs for Content Retrieval". En 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.
Texto completoBillow, Thomas y Gerald Sommer. "Multi-dimensional signal processing using an algebraically extended signal representation". En Algebraic Frames for the Perception-Action Cycle, 148–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0017865.
Texto completoHanka, Rudolf y Thomas P. Harte. "Curse of Dimensionality: Classifying Large Multi-Dimensional Images with Neural Networks". En 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.
Texto completoLi, Fei, Yonghang Tai, Hongfei Yu, Hailing Zhou y Liqiang Zhang. "Research Status of Motor Imagery EEG Signal Based on Deep Learning". En 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.
Texto completoActas de conferencias sobre el tema "Multi-dimensional graph signal processing"
Venkitaraman, Arun, Saikat Chatterjee y Peter Handel. "Multi-Kernel Regression for Graph Signal Processing". En ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8461643.
Texto completoXu, Yao Lei y Danilo P. Mandic. "Recurrent Graph Tensor Networks: A Low-Complexity Framework for Modelling High-Dimensional Multi-Way Sequences". En 2021 29th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco54536.2021.9616314.
Texto completoKruzick, Stephen y Jose M. F. Moura. "Graph signal processing: Filter design and spectral statistics". En 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.
Texto completoNatali, Alberto, Elvin Isufi y Geert Leus. "Forecasting Multi-Dimensional Processes Over Graphs". En ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053522.
Texto completoYankelevsky, Yael y Michael Elad. "Dictionary Learning for High Dimensional Graph Signals". En ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8462609.
Texto completoThanou, Dorina y Pascal Frossard. "Multi-graph learning of spectral graph dictionaries". En ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178601.
Texto completoHidane, M., O. Lezoray y A. Elmoataz. "Graph signal decomposition for multi-scale detail manipulation". En 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025409.
Texto completoIsufi, Elvin, Geert Leus y Paolo Banelli. "2-Dimensional finite impulse response graph-temporal filters". En 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2016. http://dx.doi.org/10.1109/globalsip.2016.7905873.
Texto completoKim, Saehoon y Seungjin Choi. "Multi-view anchor graph hashing". En ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638233.
Texto completoTugnait, Jitendra K. "Graph Learning from Multi-Attribute Smooth Signals". En 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2020. http://dx.doi.org/10.1109/mlsp49062.2020.9231563.
Texto completoInformes sobre el tema "Multi-dimensional graph signal processing"
Stiles, James M. Multi-Dimensional Signal Processing for Sparse Radar Arrays. Fort Belvoir, VA: Defense Technical Information Center, noviembre de 2002. http://dx.doi.org/10.21236/ada419885.
Texto completoBhattacharya, Prabir. A New Multi-Dimensional Transform for Digital Signal Processing Using Generalized Association Schemes. Fort Belvoir, VA: Defense Technical Information Center, mayo de 1994. http://dx.doi.org/10.21236/ada284166.
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