Relevant bibliographies by topics / Signal approximation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Signal approximation'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Signal approximation"

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Cheng, Bing, and Xiaokun Zhu. "A Multiresolution Approximation Theory of Fractal Transform." Fractals 05, supp01 (1997): 173–86. http://dx.doi.org/10.1142/s0218348x97000747.

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In this paper, we show that the fractal transform (FT) constitutes a multiresolution approximation to the square-integrable space L2(Td) for d≥1, where T is the interval (-∞,∞). This provides a theoretical basis for the successful applications of the fractal transform algorithms in signal/image encoding. There are many similarities between fractal-based and wavelet-based approximations. However, they are undamentally different from each other in many aspects. Fractal-based multiresolution approximation to signals/images is by a way of self-increasing model complexity, and wavelet-based multire
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Hyvärinen, Aapo. "Optimal Approximation of Signal Priors." Neural Computation 20, no. 12 (2008): 3087–110. http://dx.doi.org/10.1162/neco.2008.10-06-384.

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In signal restoration by Bayesian inference, one typically uses a parametric model of the prior distribution of the signal. Here, we consider how the parameters of a prior model should be estimated from observations of uncorrupted signals. A lot of recent work has implicitly assumed that maximum likelihood estimation is the optimal estimation method. Our results imply that this is not the case. We first obtain an objective function that approximates the error occurred in signal restoration due to an imperfect prior model. Next, we show that in an important special case (small gaussian noise),
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Piltan, Farzin, and Jong-Myon Kim. "Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning." Applied Sciences 11, no. 10 (2021): 4602. http://dx.doi.org/10.3390/app11104602.

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In this study, the application of an intelligent digital twin integrated with machine learning for bearing anomaly detection and crack size identification will be observed. The intelligent digital twin has two main sections: signal approximation and intelligent signal estimation. The mathematical vibration bearing signal approximation is integrated with machine learning-based signal approximation to approximate the bearing vibration signal in normal conditions. After that, the combination of the Kalman filter, high-order variable structure technique, and adaptive neural-fuzzy technique is inte
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Mayorov, Boris. "Properties of Harmonic and Composite Half-Waves, Determination of the Uniform Time Sampling Interval of Digital Signal Processors." Informatics and Automation 21, no. 1 (2021): 95–125. http://dx.doi.org/10.15622/ia.2022.21.4.

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When building autonomous real-time systems (RTS), it is necessary to solve the problem of optimal multitasking loading of a number of parallel functioning digital signal processors. One of the reserves for achieving the desired result is the implementation of samples from the sensor signals of information about the magnitude of the signal most rarely in time. In this case, it is necessary to provide a linear or stepwise approximation of the signal by samples with an acceptable reconstruction error. One of the system tasks of these processors is filtering signals or limiting the spectrum to the
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SHANG, ZHAOWEI, YUAN YAN TANG, BIN FANG, JING WEN, and YAT ZHOU ONG. "MULTIRESOLUTION SIGNAL DECOMPOSITION AND APPROXIMATION BASED ON SUPPORT VECTOR MACHINES." International Journal of Wavelets, Multiresolution and Information Processing 06, no. 04 (2008): 593–607. http://dx.doi.org/10.1142/s0219691308002513.

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The fusion of wavelet technique and support vector machines (SVMs) has become an intensive study in recent years. Considering that the wavelet technique is the theoretical foundation of multiresolution analysis (MRA), it is valuable for us to investigate the problem of whether a good performance could be obtained if we combine the MRA with SVMs for signal approximation. Based on the fact that the feature space of SVM and the scale subspace in MRA can be viewed as the same Reproducing Kernel Hilbert Spaces (RKHS), a new algorithm named multiresolution signal decomposition and approximation base
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Srinivasan, Parthasarathy. "A Prony Method Variant which Surpasses the Adaptive LMS Filter in the Precision of the Output Signal’s Representation of the Input." Signal & Image Processing : An International Journal 15, no. 4 (2024): 01–09. http://dx.doi.org/10.5121/sipij.2024.15401.

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The Prony method for approximating signals comprising sinusoidal/exponential components is known through the pioneering work of Prony in his seminal dissertation in the year 1795. However, the Prony method saw the light of real world application only upon the advent of the computational era, which made feasible the extensive numerical intricacies and labor which the method demands inherently. The Adaptive LMS Filter which has been the most pervasive method for signal filtration and approximation since its inception in 1965 does not provide a consistently assured level of highly precise results
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Belyavsky, Grigory I., Nikita A. Mishin, and Konstantin E. Azhogin. "Signal Approximation Algorithm for Human Electroencephalograms Classification." UNIVERSITY NEWS. NORTH-CAUCASIAN REGION. NATURAL SCIENCES SERIES, no. 2 (222) (June 27, 2024): 12–20. http://dx.doi.org/10.18522/1026-2237-2024-2-12-20.

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The article examines the problem of classification of electroencephalograms (EEG), where noise in the signals, caused by various factors, prevents effective analysis and interpretation of the data. The main goal of the study is to analyze the effectiveness of a signal approximation algorithm using a wavelet technique with the next piece-wise approximation in order to effectively remove noise and subsequently solve the problem of signal classification using a convolutional neural network. The classification accuracy of the proposed algorithm with a low-pass filter is compared at different cutof
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Stoyanov, Stiliyan. "APPROXIMATION METHOD FOR DISCOVERY OF ANOMALOUS SIGNALS IN OPTICAL-ELECTRONIC DEVICES." Journal Scientific and Applied Research 17, no. 1 (2019): 9–12. http://dx.doi.org/10.46687/jsar.v17i1.266.

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The research is about registration of signals by optic-electronic devices. The subject of the current work is research of the approximative methods capability to discover anomalous signals in an impulse photometric device. The impulse photometric device ensures a high dimensional and timely resolution of the intensity distribution by natural optic emissions in the earth atmosphere and also light interference near the orbital station. The high spectral sensitivity and the dimensional resolution enable the research of fast processes, including pulsating polar lights, polar arcs, etc. When we dis
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Ray, Shashwati, and Vandana Chouhan. "Electrocardiogram reconstruction based on Hermite interpolating polynomial with Chebyshev nodes." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 2 (2024): 837. http://dx.doi.org/10.11591/ijeecs.v36.i2.pp837-845.

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Electrocardiogram (ECG) signals generate massive volume of digital data, so they need to be suitably compressed for efficient transmission and storage. Polynomial approximations and polynomial interpolation have been used for ECG data compression where the data signal is described by polynomial coefficients only. Here, we propose approximation using hermite polynomial interpolation with chebyshev nodes for compressing ECG signals that consequently denoises them too. Recommended algorithm is applied on various ECG signals taken from MIT-BIH arrhythmia database without any additional noise as th
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Shashwati, Ray Vandana Chouhan. "Electrocardiogram reconstruction based on Hermite interpolating polynomial with Chebyshev nodes." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 2 (2024): 837–45. https://doi.org/10.11591/ijeecs.v36.i2.pp837-845.

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Electrocardiogram (ECG) signals generate massive volume of digital data, so they need to be suitably compressed for efficient transmission and storage. Polynomial approximations and polynomial interpolation have been used for ECG data compression where the data signal is described by polynomial coefficients only. Here, we propose approximation using hermite polynomial interpolation with chebyshev nodes for compressing ECG signals that consequently denoises them too. Recommended algorithm is applied on various ECG signals taken from MIT-BIH arrhythmia database without any additional noise as th
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Dissertations / Theses on the topic "Signal approximation"

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Venkataraman, Archana Ph D. Massachusetts Institute of Technology. "Signal approximation using the bilinear transform." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/46466.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.<br>Includes bibliographical references (p. 117-118).<br>This thesis explores the approximation properties of a unique basis expansion. The expansion implements a nonlinear frequency warping between a continuous-time signal and its discrete-time representation according to the bilinear transform. Since there is a one-to-one mapping between the continuous-time and discrete-time frequency axes, the bilinear representation avoids any frequency aliasing distortions. We devote the fir
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Bergerhoff, Leif [Verfasser]. "Evolutionary Models for Signal Enhancement and Approximation / Leif Bergerhoff." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2020. http://d-nb.info/1237268753/34.

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Boulbry, Jean-Claude. "Approximation de signaux et modélisation de systèmes linéaires." Brest, 1989. http://www.theses.fr/1989BRES2017.

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L'approximation temporelle elaboree sous la forme d'une combinaison lineaire de fonctions exponentielles reelles ou complexes est optimale au sens de la minimisation d'un critere d'erreur quadratique et est bien adaptee a la representation du signal.
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Chillan, José. "Déformation d'un signal acoustique dans un milieu aléatoire stratifié : (approximation diffusion et phases stationnaires)." Palaiseau, Ecole polytechnique, 1996. http://www.theses.fr/1996EPXX0015.

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Ce travail étudie les déformations que subit un signal acoustique en traversant une tranche de milieu aléatoire stratifie, l'aléa ayant une grandeur caractéristique petite devant la longueur d'onde, elle-même petite devant l'épaisseur du milieu traverse. Un résultat limite montre que le signal est décale d'un retard aléatoire gaussien et est convolue avec la dérivée d'une gaussienne. Les variances de cette variable aléatoire et de la gaussienne sont d'autant plus grandes que l'aléa dans le milieu est corrélé et que le signal a parcouru une grande distance
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Kowalski, Matthieu. "Approximation des signaux: approches variationnelles et modèles aléatoires." Phd thesis, Université de Provence - Aix-Marseille I, 2008. http://tel.archives-ouvertes.fr/tel-00347441.

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Deux approches pour la décomposition parcimonieuse des signaux sont étudiées.<br /> L'une utilise des méthodes variationnelles avec une attache aux données l2 pénalisée par une norme mixte permettant de structurer la parcimonie. Les fonctionnelles sont minimisées par des algorithmes itératifs dont la convergence est prouvée. Les normes mixtes donnent des estimations par des opérateurs de seuillage généralisés, qui ont été modifiés pour les localiser ou introduire de la persistance.<br />L'autre modélise les signaux comme combinaisons linéaires parcimonieuses d'atomes temps-fréquence choisis da
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Cano, William. "Identification d'invariants de système - étude statistique pour la détection de défauts." Poitiers, 1997. http://www.theses.fr/1997POIT2275.

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Ce travail propose une methode de detection de defauts basee sur l'identification des moments ponderes de la fonction de transfert d'un systeme. Nous validons l'idee selon laquelle le deplacement de la ponderation vers un pole en rupture modifie les proprietes du comportement du modele et donc d'un detecteur lors de la rupture. Nous avons mis en evidence, dans les cas continu et discret, que le developpement en serie de taylor d'une fonction de transfert avec un nombre fini n de moments, permet une modelisation sans avoir une connaissance tres precise du procede. Leurs proprietes et leur utili
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Blu, Thierry. "Bancs de filtres itérés en fraction d'octave : application au codage de son /." Paris : École nationale supérieure des télécommunications, 1996. http://catalogue.bnf.fr/ark:/12148/cb361562294.

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BHOOPATHY, MANIVANNAN. "EXPLOITING A MULTI-LEVEL MODELING TECHNIQUE WITH APPLICATION TO THE ANALYSIS OF A SUCCESSIVE APPROXIMATION ANALOG-TO-DIGITAL CONVERTER." University of Cincinnati / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1132016200.

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Harris, Bradley William. "Anomaly detection in rolling element bearings via two-dimensional Symbolic Aggregate Approximation." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/23103.

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Symbolic dynamics is a current interest in the area of anomaly detection, especially in mechanical systems.  Symbolic dynamics reduces the overall dimensionality of system responses while maintaining a high level of robustness to noise.  Rolling element bearings are particularly common mechanical components where anomaly detection is of high importance.  Harsh operating conditions and manufacturing imperfections increase vibration innately reducing component life and increasing downtime and costly repairs.  This thesis presents a novel way to detect bearing vibrational anomalies through Symbol
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Lee, Dong-Wook. "Extracting multiple frequencies from phase-only data." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/15031.

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Books on the topic "Signal approximation"

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Casey, Stephen D., M. Maurice Dodson, Paulo J. S. G. Ferreira, and Ahmed Zayed, eds. Sampling, Approximation, and Signal Analysis. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-41130-4.

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Katkovnik, V. I︠A︡. Local approximation techniques in signal and image processing. SPIE Press, 2006.

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Farrell, Jay. Adaptive Approximation Based Control. John Wiley & Sons, Ltd., 2006.

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Šmídl, Václav. The variational Bayes method in signal processing. Springer, 2006.

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1931-, Métivier Michel, and Priouret P. 1939-, eds. Algorithmes adaptatifs et approximations stochastiques: Théorie et applications à l'identification, au traitement du signal et à la reconnaissance des formes. New York, 1987.

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Alyukov, Sergey. Approximation of piecewise linear and generalized functions. INFRA-M Academic Publishing LLC., 2024. http://dx.doi.org/10.12737/2104876.

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The monograph is devoted to piecewise linear and generalized functions. They are widely used in various fields of research: in the theory of signal transmission and transformation, quantum field theory, control theory, problems of nonlinear dynamics, structural mechanics, semiconductor theory, economic applications, medicine, description of impulse effects and many others. When creating mathematical models, in some cases it is necessary to approximate these functions using analytical expressions, but not in the form of linear combinations, as in known methods, but in the form of attachments, c
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Britanak, Vladimir. Discrete cosine and sine transforms: General properties, fast algorithms and integer approximations. Academic, 2007.

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Jørgensen, Palle E. T., 1947-, ed. Wavelets through a looking glass: The world of the spectrum. Birkhäuser, 2002.

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Casazza, Peter G. Finite Frames: Theory and Applications. Birkhäuser Boston, 2013.

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Katkovnik, Vladimir, Karen Egiazarian, and Jaakko Astola. Local Approximation Techniques in Signal and Image Processing. SPIE, 2006. http://dx.doi.org/10.1117/3.660178.

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Book chapters on the topic "Signal approximation"

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Iske, Armin. "Basiskonzepte zur Signal-Approximation." In Approximation. Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-55465-4_7.

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Shenoi, Belle A. "Classical Methods of Magnitude Approximation." In Digital Signal Processing. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58573-9_1.

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Ylöstalo, Jyri. "Function Approximation Using Polynomials." In Streamlining Digital Signal Processing. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118316948.ch26.

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Louis, Alfred K. "The Eikonal Approximation in Ultrasound Computer Tomography." In Signal Processing. Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4684-7095-6_14.

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Chui, Charles K., and Guanrong Chen. "Approximation in Hardy Spaces." In Signal Processing and Systems Theory. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-97406-9_3.

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Azghani, Masoumeh, and Farokh Marvasti. "Sparse Signal Processing." In New Perspectives on Approximation and Sampling Theory. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08801-3_8.

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Iske, Armin. "Basic Concepts of Signal Approximation." In Approximation Theory and Algorithms for Data Analysis. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05228-7_7.

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Shenoi, Belle A. "Magnitude Approximation of 2-D IIR Filters." In Digital Signal Processing. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58573-9_3.

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Helton, J. W., and N. J. Young. "Approximation of Hankel Operators: Truncation Error in an H ∞ Design Method." In Signal Processing. Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4684-7095-6_6.

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Shenoi, Belle A. "Magnitude and Delay Approximation of 1-D IIR Filters." In Digital Signal Processing. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58573-9_2.

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Conference papers on the topic "Signal approximation"

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Athanasakos, Emmanouil M., Nicholas Kalouptsidis, and Hariprasad Manjunath. "Local Approximation of Secrecy Capacity." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715107.

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Tichavský, Petr, and Ondřej Straka. "Tensor Train Approximation of Multivariate Functions." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715191.

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Wu, Chi-hsin, and Peter C. Doerschuk. "Markov random fields as a priori information for image restoration." In Signal Recovery and Synthesis. Optica Publishing Group, 1995. http://dx.doi.org/10.1364/srs.1995.rwc2.

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Markov random fields (MRFs) [1, 2, 3, 4] provide attractive statistical models for multidimensional signals. However, unfortunately, optimal Bayesian estimators tend to require large amounts of computation. We present an approximation to a particular Bayesian estimator which requires much reduced computation and an example illustrating low-light unknown-blur imaging. See [7] for an alternative approximation based on approximating the MRF lattice by a system of trees and for an alternative cost function.
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Venkataraman, Archana, and Alan V. Oppenheim. "Signal approximation using the bilinear transform." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518463.

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Laptiev, Oleksandr, Ivan Parkhomenko, Andrii Musienko, Andriy Makarchuk, Anton Mishchuk, and Denys Shapovalov. "Weierstrass Method of Analogue Signal Approximation." In 2023 IEEE 4th KhPI Week on Advanced Technology (KhPIWeek). IEEE, 2023. http://dx.doi.org/10.1109/khpiweek61412.2023.10311583.

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Oltean, Gabriel, Laura-Nicoleta Ivanciu, and Botond Kirei. "Signal approximation using GA guided wavelet decomposition." In 2015 International Symposium on Signals, Circuits and Systems (ISSCS). IEEE, 2015. http://dx.doi.org/10.1109/isscs.2015.7203996.

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Chen, Lei, Xinghuo Yu, and Jinhu Lu. "Signal approximation with Pascal’s triangle and sampling." In 2020 Chinese Control And Decision Conference (CCDC). IEEE, 2020. http://dx.doi.org/10.1109/ccdc49329.2020.9164011.

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Su, Mengyue, Xizi Song, Yijie Zhou, Jiajia Yang, Yufeng Ke, and Dong Ming. "Acoustoelectric Signal Decoding Based on Fourier Approximation." In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020. http://dx.doi.org/10.1109/embc44109.2020.9176330.

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Shuman, David I., Pierre Vandergheynst, and Pascal Frossard. "Chebyshev polynomial approximation for distributed signal processing." In 2011 International Conference on Distributed Computing in Sensor Systems (DCOSS). IEEE, 2011. http://dx.doi.org/10.1109/dcoss.2011.5982158.

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Zhou, Ya-tong, Tai-yi Zhang, and Xiao-he Li. "Support Vector Machine Based Multiresolution Signal Approximation." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258579.

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Reports on the topic "Signal approximation"

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Raup, Richard C. Best Approximation of Signal Amplitude and Delay in a Narrowband Radar. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada165700.

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Shah, Rajiv R. High-Level Adaptive Signal Processing Architecture with Applications to Radar Non-Gaussian Clutter. Volume 2. A New Technique for Distribution Approximation of Random Data. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada300902.

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Wilson, D., Vladimir Ostashev, and Max Krackow. Phase-modulated Rice model for statistical distributions of complex signals. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47379.

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The basic Rice model is commonly used to describe complex signal statistics from randomly scattered waves. It correctly describes weak (Born) scattering, as well as fully saturated scattering, and smoothly interpolates between these extremes. However, the basic Rice model is unsuitable for situations involving scattering by random inhomogeneities spanning a broad range of spatial scales, as commonly occurs for sound scattering by turbulence in the atmospheric boundary layer and other scenarios. In such scenarios, the phase variations are often considerably stronger than those predicted by the
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