Relevant bibliographies by topics / Multi-dimensional signals

Academic literature on the topic 'Multi-dimensional signals'

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Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Multi-dimensional signals"

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Sommer, Gerald, and Di Zang. "Parity symmetry in multi-dimensional signals." Communications on Pure & Applied Analysis 6, no. 3 (2007): 829–52. http://dx.doi.org/10.3934/cpaa.2007.6.829.

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Aiazzi, Bruno, Stefano Baronti, Leonardo Santurri, Massimo Selva, and Luciano Alparone. "Information-theoretic assessment of multi-dimensional signals." Signal Processing 85, no. 5 (May 2005): 903–16. http://dx.doi.org/10.1016/j.sigpro.2004.11.025.

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Heumann, Tibor. "An ascending auction with multi-dimensional signals." Journal of Economic Theory 184 (November 2019): 104938. http://dx.doi.org/10.1016/j.jet.2019.104938.

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KIDA, TAKURO. "THEORY OF GENERALIZED INTERPOLATORY APPROXIMATION OF MULTI-DIMENSIONAL SIGNALS." Journal of Circuits, Systems and Computers 03, no. 03 (September 1993): 673–99. http://dx.doi.org/10.1142/s0218126693000411.

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In this paper, we present a systematic theory of the optimum subband interpolation of a family of n-dimensional signals which are not necessarily band-limited. We assume that the Fourier spectrums of these signals have weighted L2 norms smaller than a given positive number. The proposed method minimizes the measure of error which is equal to the envelope of the approximation errors with respect to the signals. In the following discussion, we assume initially that the infinite number of interpolation functions with different functional forms are used in the corresponding approximation formula. However, the resultant optimum interpolation functions are expressed as the parallel shifts of the finite number of the n-dimensional functions. It should be noted that the optimum interpolation functions presented in this paper satisfy the generalized discrete orthogonality and, as a result, minimize the wide variety of measures of error at the same time. In the literature,6 a similar discussion is presented. However, it is assumed that the signal is band-limited and the interpolation functions are compulsorily time-limited. Hence, these interpolation functions cannot minimize other measures of error except the proposed one. Interesting reciprocal relation in the approximation, is also discussed. An equivalent expression of the approximation formula in the frequency domain is derived.
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Elvander, F., J. Swärd, and A. Jakobsson. "Multi-dimensional grid-less estimation of saturated signals." Signal Processing 145 (April 2018): 37–47. http://dx.doi.org/10.1016/j.sigpro.2017.11.008.

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Heumann, Tibor. "Efficiency in trading markets with multi-dimensional signals." Journal of Economic Theory 191 (January 2021): 105156. http://dx.doi.org/10.1016/j.jet.2020.105156.

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BUCHHOLZ, SVEN, and NICOLAS LE BIHAN. "POLARIZED SIGNAL CLASSIFICATION BY COMPLEX AND QUATERNIONIC MULTI-LAYER PERCEPTRONS." International Journal of Neural Systems 18, no. 02 (April 2008): 75–85. http://dx.doi.org/10.1142/s0129065708001403.

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For polarized signals, which arise in many application fields, a statistical framework in terms of quaternionic random processes is proposed. Based on it, the ability of real-, complex- and quaternionic-valued multi-layer perceptrons (MLPs) of performing classification tasks for such signals is evaluated. For the multi-dimensional neural networks the relevance of class label representations is discussed. For signal to noise separation it is shown that the quaternionic MLP yields an optimal solution. Results on the classification of two different polarized signals are also reported.
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Demuro, Angelo, and Ian Parker. "Multi-dimensional resolution of elementary Ca2+ signals by simultaneous multi-focal imaging." Cell Calcium 43, no. 4 (April 2008): 367–74. http://dx.doi.org/10.1016/j.ceca.2007.07.002.

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Wang, Guotai, Xingguang Geng, Lin Huang, Xiaoxiao Kang, Jun Zhang, Yitao Zhang, and Haiying Zhang. "Multi-Morphological Pulse Signal Feature Point Recognition Based on One-Dimensional Deep Convolutional Neural Network." Information 14, no. 2 (January 26, 2023): 70. http://dx.doi.org/10.3390/info14020070.

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Radial pulse signals are produced by the periodic ejection of blood from the heart, and physiological and pathological information of the human body can be analyzed by extracting the time-domain characteristics of pulse waves. However, since pulse signals are weak physiological signals on the body surface and complex, the acquisition of pulse characteristics using the traditional curvature method will produce a large error, which cannot meet the needs of pulse wave analysis in current clinical practice. To solve this problem, a multi-morphological pulse signal feature recognition algorithm based on the one-dimensional deep convolutional neural network (1D-DCNN) model is proposed. We used the multi-channel pulse diagnosis instrument independently developed by the team to collect radial pulse signals under continuous pressure of the test subjects and collected 115 subjects and extracted a total of 1300 single-cycle pulse signals and then divided these pulse signals into 6 different forms. Five types of pulse signal time-domain feature points were labeled, and five independent feature point datasets were labeled and formed five customized neural network models that were generated to train and identify the pulse feature point datasets independently. The results show that the correction coefficient () of the multi-class pulse signal processing algorithm proposed in this paper for each type of feature point recognition reaches more than 0.92. The performance is significantly better than that of the traditional curvature method, which shows the accuracy and superiority of the proposed method. Therefore, the multi-class pulse signal characteristic parameter recognition model based on the 1D-DCNN model proposed in this paper can efficiently and accurately identify pulse time-domain characteristic parameters, which can be applied to discriminate time-domain pulse information in clinical practice and assist doctors in diagnosis.
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Xie, Shengkun. "Wavelet Power Spectral Domain Functional Principal Component Analysis for Feature Extraction of Epileptic EEGs." Computation 9, no. 7 (July 7, 2021): 78. http://dx.doi.org/10.3390/computation9070078.

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Feature extraction plays an important role in machine learning for signal processing, particularly for low-dimensional data visualization and predictive analytics. Data from real-world complex systems are often high-dimensional, multi-scale, and non-stationary. Extracting key features of this type of data is challenging. This work proposes a novel approach to analyze Epileptic EEG signals using both wavelet power spectra and functional principal component analysis. We focus on how the feature extraction method can help improve the separation of signals in a low-dimensional feature subspace. By transforming EEG signals into wavelet power spectra, the functionality of signals is significantly enhanced. Furthermore, the power spectra transformation makes functional principal component analysis suitable for extracting key signal features. Therefore, we refer to this approach as a double feature extraction method since both wavelet transform and functional PCA are feature extractors. To demonstrate the applicability of the proposed method, we have tested it using a set of publicly available epileptic EEGs and patient-specific, multi-channel EEG signals, for both ictal signals and pre-ictal signals. The obtained results demonstrate that combining wavelet power spectra and functional principal component analysis is promising for feature extraction of epileptic EEGs. Therefore, they can be useful in computer-based medical systems for epilepsy diagnosis and epileptic seizure detection problems.
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Dissertations / Theses on the topic "Multi-dimensional signals"

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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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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." 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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Khandani, Amir K. (Amir Keyvan). "Shaping multi-dimensional signal spaces." Thesis, McGill University, 1992. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=70268.

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In selecting the boundary of a signal constellation used for data transmission, the objective is to minimize the average energy of the set for a given number of points from a given packing. Reduction in the average energy because of using the region ${ cal C}$ as the boundary instead of a hypercube is called the shape gain of ${ cal C}$. The price to be paid for shaping is: (i) an increase in the factor CER$ sb{s}$ (Constellation-Expansion-Ratio), (ii) an increase in the factor PAR (Peak-to-Average-power-Ratio), and (iii) an increase in the addressing complexity. In this thesis, the structure of the region which optimizes the tradeoff between the shape gain and the CER$ sb{s}$ and also between the shape gain and the PAR in a finite dimensional space is found. Analytical expressions are derived for the optimum tradeoff. The optimum shaping region can be mapped to a hypercube truncated within a simplex. This mapping has properties which facilitate the addressing of the signal points. We introduce several addressing schemes with low complexity and good performance. The concept of the unsymmetrical shaping is discussed. This is the selection of the boundary of a constellation which has different values of power along different dimensions. The rate of the constellation is maximized subject to some constraints on its power spectrum. This spectral shaping also involves the selection of an appropriate basis (modulating waveform) for the space. Finally, we discuss the selection a signal constellation of signaling over a partial-response channel. In the continuous approximation, we introduce a method to select the nonempty dimensions. This method is based on minimizing the degradation caused by the channel memory. In the discrete case, shaping and coding depend on each other. In this case, a combined shaping and coding method is used. This concerns the joint selection of the shaping and coding to minimize the probability of the symbol error.
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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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Son, Kyung-Im. "A multi-class, multi-dimensional classifier as a topology selector for analog circuit design / by Kyung-Im Son." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/5919.

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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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Books on the topic "Multi-dimensional signals"

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Jain, Lakhmi C., Roumen Kountchev, and 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.

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

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Cina, Jeffrey A. Getting Started on Time-Resolved Molecular Spectroscopy. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780199590315.001.0001.

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This textbook details the basic theory of ultrafast molecular spectroscopy starting from time-dependent quantum mechanical perturbation theory in Hilbert space. The emphasis is on the dynamics of nuclear and electronic motion initiated and monitored by femtosecond laser pulses that underlies nonlinear optical signal formation and interpretation. Topics include short-pulse optical absorption, the molecular adiabatic approximation, transient-absorption spectroscopy, vibrational adiabaticity during conformational change, femtosecond stimulated Raman spectroscopy, multi-dimensional electronic spectroscopy and wave-packet interferometry, and two-dimensional wave-packet interferometry of electronic excitation-transfer systems. Numerous exercises embedded in the text explore and expand upon the physical concepts encountered in this important research field.
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Book chapters on the topic "Multi-dimensional signals"

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Schmidt, Michael. "Towards a Multi-Scale Representation of Multi-Dimensional Signals." In International Association of Geodesy Symposia, 119–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22078-4_18.

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Cyganek, Bogusław. "Modern Approaches to Multi-dimensional Visual Signals Analysis." In Cryptology and Network Security, 5–6. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98678-4_2.

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Ramirez, Geovany A., Tadas Baltrušaitis, and Louis-Philippe Morency. "Modeling Latent Discriminative Dynamic of Multi-dimensional Affective Signals." In Affective Computing and Intelligent Interaction, 396–406. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24571-8_51.

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Cyganek, Bogusław. "Overview of Tensor Methods for Multi-dimensional Signals Change Detection and Compression." In Image Processing and Communications, 3–5. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31254-1_1.

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Rocha, C., J. L. Dillenseger, and J. L. Coatrieux. "Multi-array EEG signals mapped with three dimensional images for clinical epilepsy studies." In Lecture Notes in Computer Science, 467–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0046987.

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Binh, Le Nguyen. "Multi-Dimensional Photonic Processing by Discrete-Domain Approach." In 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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Ghanbari, Shirin, John C. Woods, and Simon M. Lucas. "Multi-dimensional BPTs for Content Retrieval." In 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, and Gerald Sommer. "Multi-dimensional signal processing using an algebraically extended signal representation." In 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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Li, Fei, Yonghang Tai, Hongfei Yu, Hailing Zhou, and Liqiang Zhang. "Research Status of Motor Imagery EEG Signal Based on Deep Learning." In 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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Theis, Fabian J., Anke Meyer-Bäse, and Elmar W. Lang. "Second-Order Blind Source Separation Based on Multi-dimensional Autocovariances." In Independent Component Analysis and Blind Signal Separation, 726–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30110-3_92.

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Conference papers on the topic "Multi-dimensional signals"

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Zhu, Hongwei, Ilie I. Luican, and Florin Balasa. "Mapping Multi-Dimensional Signals into Hierarchical Memory Organizations." In Design, Automation & Test in Europe Conference. IEEE, 2007. http://dx.doi.org/10.1109/date.2007.364622.

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Schniter, Phil. "Session TP3a: Multi-dimensional compressive inference." In 2011 45th Asilomar Conference on Signals, Systems and Computers. IEEE, 2011. http://dx.doi.org/10.1109/acssc.2011.6190253.

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Feng, Yong, Juan Li, and Xinghuo Yu. "Multi-dimensional signals transmission via single channel for chaos synchronization." In IECON 2010 - 36th Annual Conference of IEEE Industrial Electronics. IEEE, 2010. http://dx.doi.org/10.1109/iecon.2010.5675531.

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Friedlander, Benjamin. "Estimating homeomorphic deformations of multi-dimensional signals - An accuracy analysis." In 2008 42nd Asilomar Conference on Signals, Systems and Computers. IEEE, 2008. http://dx.doi.org/10.1109/acssc.2008.5074706.

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Sward, Johan, Filip Elvander, and Andreas Jakobsson. "Designing optimal sampling schemes for multi-dimensional data." In 2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, 2017. http://dx.doi.org/10.1109/acssc.2017.8335468.

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Qiao, Heng, Mehmet Can Hucumenoglu, and Piya Pal. "Compressive Kriging Using Multi-Dimensional Generalized Nested Sampling." In 2018 52nd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2018. http://dx.doi.org/10.1109/acssc.2018.8645258.

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Forero, Pedro A., and Georgios B. Giannakis. "Robust multi-dimensional scaling via outlier-sparsity control." In 2011 45th Asilomar Conference on Signals, Systems and Computers. IEEE, 2011. http://dx.doi.org/10.1109/acssc.2011.6190202.

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Alkhateeb, Ahmed, Geert Leus, and Robert W. Heath. "Multi-layer precoding for full-dimensional massive MIMO systems." In 2014 48th Asilomar Conference on Signals, Systems and Computers. IEEE, 2014. http://dx.doi.org/10.1109/acssc.2014.7094563.

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Tenneti, Srikanth V., and P. P. Vaidyanathan. "Minimal Non-Uniform Sampling For Multi-Dimensional Period Identification." In 2018 52nd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2018. http://dx.doi.org/10.1109/acssc.2018.8645347.

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Stuke, Ingo, Erhardt Barth, and Cicero Mota. "Estimation of Multiple Orientations and Multiple Motions in Multi-Dimensional Signals." In 2006 19th Brazilian Symposium on Computer Graphics and Image Processing. IEEE, 2006. http://dx.doi.org/10.1109/sibgrapi.2006.15.

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Reports on the topic "Multi-dimensional signals"

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Stiles, James M. Multi-Dimensional Signal Processing for Sparse Radar Arrays. Fort Belvoir, VA: Defense Technical Information Center, November 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, May 1994. http://dx.doi.org/10.21236/ada284166.

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