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Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Signal processing- models"

1

Culver, R. Lee, and H. John Camin. "Sonar signal processing using probabilistic signal and ocean environmental models." Journal of the Acoustical Society of America 124, no. 6 (December 2008): 3619–31. http://dx.doi.org/10.1121/1.3006379.

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2

Pagès-Zamora, Alba, and Miguel A. Lagunas. "Fourier models for non-linear signal processing." Signal Processing 76, no. 1 (July 1999): 1–16. http://dx.doi.org/10.1016/s0165-1684(98)00243-6.

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3

Sottek, Roland, and Klaus Genuit. "Models of signal processing in human hearing." AEU - International Journal of Electronics and Communications 59, no. 3 (June 2005): 157–65. http://dx.doi.org/10.1016/j.aeue.2005.03.016.

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4

Liu, Keying, Rui Li, and Fasong Wang. "Blind Signal Processing models and methods for Foetal Electrocardiogram signals extraction." International Journal of Biomedical Engineering and Technology 7, no. 3 (2011): 225. http://dx.doi.org/10.1504/ijbet.2011.043296.

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5

Rogozinsky, G., M. Chesnokov, and A. Kutlyiarova. "Some New Mathematical Models of Synthesized Sound Signals." Proceedings of Telecommunication Universities 8, no. 2 (June 30, 2022): 76–81. http://dx.doi.org/10.31854/1813-324x-2022-8-2-76-81.

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Modern sound synthesis systems make it possible to implement various signal generation algorithms of higher complexity. The theory of sound synthesis actively uses the mathematical apparatus of analog and digital radio engineering and signal processing, however, it should be noted that the classical signal models used in acoustics are not adequate to real-world synthesized signals, mainly due to the significant complexity of the latter. This article presents some models of synthesized signals typical for practical use.
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6

Willsky, A. S. "Multiresolution Markov models for signal and image processing." Proceedings of the IEEE 90, no. 8 (August 2002): 1396–458. http://dx.doi.org/10.1109/jproc.2002.800717.

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7

Schooley, Larry C. "Charge-coupled device signal processing models and comparisons." Journal of Electronic Imaging 2, no. 2 (April 1, 1993): 100. http://dx.doi.org/10.1117/12.138355.

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8

Nakajima, Jouchi, and Mike West. "Dynamic network signal processing using latent threshold models." Digital Signal Processing 47 (December 2015): 5–16. http://dx.doi.org/10.1016/j.dsp.2015.04.008.

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9

Arik, Sercan O., Joseph M. Kahn, and Keang-Po Ho. "MIMO Signal Processing for Mode-Division Multiplexing: An overview of channel models and signal processing architectures." IEEE Signal Processing Magazine 31, no. 2 (March 2014): 25–34. http://dx.doi.org/10.1109/msp.2013.2290804.

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10

Fisher, B., and N. Bershad. "ALE behavior for two sinusoidal signal models." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 3 (June 1985): 658–65. http://dx.doi.org/10.1109/tassp.1985.1164590.

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Дисертації з теми "Signal processing- models"

1

Xu, Luzhou. "Growth curve models in signal processing applications." [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0015020.

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2

Lynch, Michael Richard. "Adaptive techniques in signal processing and connectionist models." Thesis, University of Cambridge, 1990. https://www.repository.cam.ac.uk/handle/1810/244884.

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This thesis covers the development of a series of new methods and the application of adaptive filter theory which are combined to produce a generalised adaptive filter system which may be used to perform such tasks as pattern recognition. Firstly, the relevant background adaptive filter theory is discussed in Chapter 1 and methods and results which are important to the rest of the thesis are derived or referenced. Chapter 2 of this thesis covers the development of a new adaptive algorithm which is designed to give faster convergence than the LMS algorithm but unlike the Recursive Least Squares family of algorithms it does not require storage of a matrix with n2 elements, where n is the number of filter taps. In Chapter 3 a new extension of the LMS adaptive notch filter is derived and applied which gives an adaptive notch filter the ability to lock and track signals of varying pitch without sacrificing notch depth. This application of the LMS filter is of interest as it demonstrates a time varying filter solution to a stationary problem. The LMS filter is next extended to the multidimensional case which allows the application of LMS filters to image processing. The multidimensional filter is then applied to the problem of image registration and this new application of the LMS filter is shown to have significant advantages over current image registration methods. A consideration of the multidimensional LMS filter as a template matcher and pattern recogniser is given. In Chapter 5 a brief review of statistical pattern recognition is given, and in Chapter 6 a review of relevant connectionist models. In Chapter 7 the generalised adaptive filter is derived. This is an adaptive filter with the ability to model non-linear input-output relationships. The Volterra functional analysis of non-linear systems is given and this is combined with adaptive filter methods to give a generalised non-linear adaptive digital filter. This filter is then considered as a linear adaptive filter operating in a non-linearly extended vector space. This new filter is shown to have desirable properties as a pattern recognition system. The performance and properties of the new filter is compared with current connectionist models and results demonstrated in Chapter 8. In Chapter 9 further mathematical analysis of the networks leads to suggested methods to greatly reduce network complexity for a given problem by choosing suitable pattern classification indices and allowing it to define its own internal structure. In Chapter 10 robustness of the network to imperfections in its implementation is considered. Chapter 11 finishes the thesis with some conclusions and suggestions for future work.
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3

Rao, Tandhoni. "Noncausal methods and models for image." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/13344.

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4

Bengtsson, Mats. "Antenna array signal processing for high rank data models." Doctoral thesis, KTH, Signaler, sensorer och system, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-2903.

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5

Noland, Katy C. "Computational tonality estimation : signal processing and hidden Markov models." Thesis, Queen Mary, University of London, 2009. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8492.

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This thesis investigates computational musical tonality estimation from an audio signal. We present a hidden Markov model (HMM) in which relationships between chords and keys are expressed as probabilities of emitting observable chords from a hidden key sequence. The model is tested first using symbolic chord annotations as observations, and gives excellent global key recognition rates on a set of Beatles songs. The initial model is extended for audio input by using an existing chord recognition algorithm, which allows it to be tested on a much larger database. We show that a simple model of the upper partials in the signal improves percentage scores. We also present a variant of the HMM which has a continuous observation probability density, but show that the discrete version gives better performance. Then follows a detailed analysis of the effects on key estimation and computation time of changing the low level signal processing parameters. We find that much of the high frequency information can be omitted without loss of accuracy, and significant computational savings can be made by applying a threshold to the transform kernels. Results show that there is no single ideal set of parameters for all music, but that tuning the parameters can make a difference to accuracy. We discuss methods of evaluating more complex tonal changes than a single global key, and compare a metric that measures similarity to a ground truth to metrics that are rooted in music retrieval. We show that the two measures give different results, and so recommend that the choice of evaluation metric is determined by the intended application. Finally we draw together our conclusions and use them to suggest areas for continuation of this research, in the areas of tonality model development, feature extraction, evaluation methodology, and applications of computational tonality estimation.
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6

Said, Maya Rida 1976. "Signal processing in biological cells : proteins, networks, and models." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/30165.

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Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
Includes bibliographical references (p. 202-210).
This thesis introduces systematic engineering principles to model, at different levels of abstraction the information processing in biological cells in order to understand the algorithms implemented by the signaling pathways that perform the processing. An example of how to emulate one of these algorithms in other signal processing contexts is also presented. At a high modeling level, the focus is on the network topology rather than the dynamical properties of the components of the signaling network. In this regime, we examine and analyze the distribution and properties of the network graph. Specifically, we present a global network investigation of the genotype/phenotype data-set recently developed for the yeast Saccharomyces cerevisiae from exposure to DNA damaging agents, enabling explicit study of how protein-protein interaction network characteristics may be associated with phenotypic functional effects. The properties of several functional yeast networks are also compared and a simple method to combine gene expression data with network information is proposed to better predict pathophysiological behavior. At a low level of modeling, the thesis introduces a new framework for modeling cellular signal processing based on interacting Markov chains. This framework provides a unified way to simultaneously capture the stochasticity of signaling networks in individual cells while computing a deterministic solution which provides average behavior. The use of this framework is demonstrated on two classical signaling networks: the mitogen activated protein kinase cascade and the bacterial chemotaxis pathway. The prospects of using cell biology as a metaphor for signal processing are also considered in a preliminary way by presenting a surface mapping algorithm based on bacterial chemotaxis.
by Maya Rida Said.
Sc.D.
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7

Marmin, Arthur. "Rational models optimized exactly for solving signal processing problems." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG017.

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Une vaste classe de problèmes d'optimisation non convexes est celle de l'optimisation rationnelle. Cette dernière apparaît naturellement dans de nombreux domaines tels que le traitement du signal ou le génie des procédés. Toutefois, trouver les optima globaux pour ces problèmes est difficile. Une approche récente, appelée la hiérarchie de Lasserre, fournit néanmoins une suite de problèmes convexes assurée de converger vers le minimum global. Cependant, cette approche représente un défi calculatoire du fait de la très grande dimension de ses relaxations. Dans cette thèse, nous abordons ce défi pour divers problèmes de traitement du signal.Dans un premier temps, nous formulons la reconstruction de signaux parcimonieux en un problème d'optimisation rationnelle. Nous montrons alors que ce dernier possède une structure que nous exploitons afin de réduire la complexité des relaxations associées. Nous pouvons ainsi résoudre plusieurs problèmes pratiques comme la restoration de signaux de chromatographie. Nous étendons également notre méthode à la restoration de signaux dans différents contextes en proposant plusieurs modèles de bruit et de signal. Dans une deuxième partie, nous étudions les relaxations convexes générées par nos problèmes et qui se présentent sous la forme de problèmes d'optimisation semi-définie positive de très grandes dimensions. Nous considérons plusieurs algorithmes basés sur les opérateurs proximaux pour les résoudre efficacement.La dernière partie de cette thèse est consacrée au lien entre les problèmes d'optimisation polynomiaux et la décomposition de tenseurs symétriques. En effet, ces derniers peuvent être tous deux vus comme une instance du problème des moments. Nous proposons ainsi une méthode de détection de rang et de décomposition pour les tenseurs symétriques basée sur les outils connus en optimisation polynomiale. Parallèlement, nous proposons une technique d'extraction robuste des solutions d'un problème d'optimisation poylnomiale basée sur les algorithmes de décomposition de tenseurs. Ces méthodes sont illustrées sur des problèmes de traitement du signal
A wide class of nonconvex optimization problem is represented by rational optimization problems. The latter appear naturally in many areas such as signal processing or chemical engineering. However, finding the global optima of such problems is intricate. A recent approach called Lasserre's hierarchy provides a sequence of convex problems that has the theoretical guarantee to converge to the global optima. Nevertheless, this approach is computationally challenging due to the high dimensions of the convex relaxations. In this thesis, we tackle this challenge for various signal processing problems.First, we formulate the reconstruction of sparse signals as a rational optimization problem. We show that the latter has a structure that we wan exploit in order to reduce the complexity of the associated relaxations. We thus solve several practical problems such as the reconstruction of chromatography signals. We also extend our method to the reconstruction of various types of signal corrupted by different noise models.In a second part, we study the convex relaxations generated by our problems which take the form of high-dimensional semi-definite programming problems. We consider several algorithms mainly based on proximal operators to solve those high-dimensional problems efficiently.The last part of this thesis is dedicated to the link between polynomial optimization and symmetric tensor decomposition. Indeed, they both can be seen as an instance of the moment problem. We thereby propose a detection method as well as a decomposition algorithm for symmetric tensors based on the tools used in polynomial optimization. In parallel, we suggest a robust extraction method for polynomial optimization based on tensor decomposition algorithms. Those methods are illustrated on signal processing problems
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8

Archer, Cynthia. "A framework for representing non-stationary data with mixtures of linear models /." Full text open access at:, 2002. http://content.ohsu.edu/u?/etd,585.

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9

Liu, Li. "Ground vehicle acoustic signal processing based on biological hearing models." College Park, Md. : University of Maryland, 1999. http://techreports.isr.umd.edu/reports/1999/MS%5F99-6.pdf.

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Анотація:
Thesis (M.S.) -- University of Maryland, College Park, 1999.
Thesis research directed by Institute for Systems Research. "M.S. 99-6." Includes bibliographical references (leaves 75-78). Available also online as a PDF file via the World Wide Web.
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10

Boman, Katarina. "Low-angle estimation : Models, methods and bounds." Licentiate thesis, Uppsala universitet, Avdelningen för systemteknik, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-85998.

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In this work we study the performance of elevation estimators and lower bounds on the estimation error variance for a low angle target in a smooth sea scenario using an array antenna. The article is structured around some key assumptions on multipath knowledge, signal parameterization and noise covariance, giving the reader a framework in which Maximum Likelihood estimators exploiting different á priori information can be found. The crucial factor that determines the estimator accuracy is the multipath modeling, and there are three alternative levels of knowledge that can be used: 1) two unknown target locations 2) the target and its corresponding sea-reflection are related via simple geometry 3) the sea-reflection coefficient is known as a function of grazing angle. A compact expression for the Cramér–Rao lower bound is derived, including all special cases of the key assumptions. We prove that the Cramér–Rao bound is highly dependent on the multipath model, while it is the same for the different signal parameterizations and that it is independent of the noise covariance. However, the Cramér–Rao bound is sometimes too optimistic and not achievable. The tighter Barankin bound is derived to predict the threshold behavior seen at low SNR. At high SNR the Barankin bound coincides with the Cramér–Rao bound. Simulations show that the Maximum Likelihood methods are statistically efficient and achieve the theoretical lower bound on error variance, in case of high enough SNR. The bounds are also useful tools to design an improved array structure that can give better performance than the standard uniform linear array structure. The influence of the number of sensors and the number of snapshots on the error variance is also studied, showing the rate of improvement with more sensors or snapshots. Finally we discuss the use of multiple frequencies, which is mainly a tool for suppressing ambiguities. We show for which signal models it provides improved performance.
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Книги з теми "Signal processing- models"

1

Biomedical signal processing. San Diego: Academic Press, 1994.

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2

Biomedical signal processing and signal modeling. New York: Wiley, 2001.

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3

Bruce, Eugene N. Biomedical signal processing and signal modeling. New York: Wiley, 2001.

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4

Darolles, Serge, Patrick Duvaut, and Emmanuelle Jay. Multi-Factor Models and Signal Processing Techniques. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118577387.

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5

Adaptive signal models: Theory, algorithms, and audio applications. Boston: Kluwer Academic Publishers, 1998.

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6

Statistical digital signal processing and modeling. New York: John Wiley & Sons, 1996.

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7

A, Gardner William, ed. Cyclostationarity in communications and signal processing. New York: IEEE Press, 1994.

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8

Signals and systems in biomedical engineering: Signal processing and physiological systems modeling. New York: Kluwer Academic/Plenum Publishers, 2000.

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9

Cerutti, Sergio, and Carlo Marchesi. Advanced methods of biomedical signal processing. Hoboken, N.J: Wiley, 2011.

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10

Naik, Ganesh R. Applications, challenges, and advancements in electromyography signal processing. Hershey PA: Medical Information Science Reference, 2014.

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Частини книг з теми "Signal processing- models"

1

Au, Whitlow W. L. "Signal Processing and Signal Processing Models." In The Sonar of Dolphins, 216–41. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4612-4356-4_10.

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2

Nandi, Swagata, and Debasis Kundu. "Related Models." In Statistical Signal Processing, 239–57. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6280-8_11.

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3

Nandi, Swagata, and Debasis Kundu. "Multidimensional Models." In Statistical Signal Processing, 163–77. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6280-8_8.

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4

Kundu, Debasis, and Swagata Nandi. "Multidimensional Models." In Statistical Signal Processing, 101–12. India: Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-0628-6_7.

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5

Kundu, Debasis, and Swagata Nandi. "Related Models." In Statistical Signal Processing, 113–27. India: Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-0628-6_8.

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6

Dau, Torsten. "Auditory Processing Models." In Handbook of Signal Processing in Acoustics, 175–96. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-30441-0_12.

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7

Colburn, H. Steven, Yi Zhou, and Vasant Dasika. "Inhibition in models of coincidence detection." In Auditory Signal Processing, 354–60. New York, NY: Springer New York, 2005. http://dx.doi.org/10.1007/0-387-27045-0_44.

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8

Nandi, Swagata, and Debasis Kundu. "Real Data Example Using Sinusoidal-Like Models." In Statistical Signal Processing, 143–61. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6280-8_7.

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9

Butler, John L., and Charles H. Sherman. "Transducer Models." In Modern Acoustics and Signal Processing, 91–152. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39044-4_3.

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10

Chaigne, Antoine, and Jean Kergomard. "Continuous Models." In Modern Acoustics and Signal Processing, 3–75. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3679-3_1.

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Тези доповідей конференцій з теми "Signal processing- models"

1

Barnes, C. W., E. J. Pisa, and O. Ishrak. "Signal Processing Models In Medical Ultrasound." In Pattern Recognition and Acoustical Imaging, edited by Leonard A. Ferrari. SPIE, 1987. http://dx.doi.org/10.1117/12.940242.

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2

Modi, Kirtan N., Eul-Shik Hong, and Bhaskar Bhattacharya. "Interactive models for teaching digital signal processing." In 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop. IEEE, 2009. http://dx.doi.org/10.1109/dsp.2009.4785928.

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3

Nihtila, Markku. "Discrete Signal Processing with Flat System Models." In 2007 IEEE International Conference on Signal Processing and Communications. IEEE, 2007. http://dx.doi.org/10.1109/icspc.2007.4728399.

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4

Otomanski, Przemyslaw. "Signal processing models of the laser diode." In Laser Technology V, edited by Wieslaw L. Wolinski and Michal Malinowski. SPIE, 1997. http://dx.doi.org/10.1117/12.280515.

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5

Bestugin, A. R., A. F. Kryachko, S. S. Poddubniy, and V. N. Kayatkin. "Radiated signal models." In 2018 Systems of Signals Generating and Processing in the Field of on Board Communications. IEEE, 2018. http://dx.doi.org/10.1109/sosg.2018.8350572.

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6

Thang, Nguyen Duc, Chen Lihui, and Chan Chee Keong. "An outlier-aware data clustering algorithm in mixture models." In Signal Processing (ICICS). IEEE, 2009. http://dx.doi.org/10.1109/icics.2009.5397571.

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7

Wang, Chensheng, Joris S. M. Vergeest, Pieter J. Stappers, and Willem F. Bronsvoort. "Freeform Feature Retrieval by Signal Processing." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57061.

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Feature retrieval is of great importance in shape modelling, in terms of supporting design reuse by obtaining reusable geometric entities. However, conventional techniques for feature retrieval are generally limited to the extraction of feature lines, curve segments, or surfaces, and the feature distortion imposed by feature interaction remains unconsidered. This paper investigates approaches for freeform feature retrieval by means of signal processing techniques. By treating features or regions of interest as surface signals, we employ digital filters to separate the feature signal from that of the domain surface, retrieving the “pure” feature from an existing shape model. Strategies for different model types are elaborated, for instance, the exact feature retrieval method designed for shape models with explicit data structure, such as B-Rep, or other accessible representations; and the signal filtering method for models with structured or unstructured data sets, such as that in mesh or point cloud models. Specifically, in the signal filtering method feature retrieval is implemented by the convolving operator in the frequency domain. By transforming the problem of shape decomposition from geometric extraction in the spatial domain to computation in the frequency domain, the proposed methods not only brings in significant computational efficiency, but also reduces the complexity of problem solving for feature retrieval. Provided examples show that the proposed approaches can achieve satisfactory results for simple geometries, whereas for sophisticated shapes guidelines for the design of dedicated filters are elaborated.
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8

Santhanam, Balu. "Session MA8b1: Models for signal and image processing." In 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers. IEEE, 2009. http://dx.doi.org/10.1109/acssc.2009.5470085.

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9

Bayati, Mohsen. "Session MA3b: Graphical models in signal processing I." In 2011 45th Asilomar Conference on Signals, Systems and Computers. IEEE, 2011. http://dx.doi.org/10.1109/acssc.2011.6189950.

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Ihler, Alex. "Session MP3a: Graphical models in signal processing II." In 2011 45th Asilomar Conference on Signals, Systems and Computers. IEEE, 2011. http://dx.doi.org/10.1109/acssc.2011.6190026.

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Звіти організацій з теми "Signal processing- models"

1

Baraniuk, Richard G. Multiscale Statistical Models for Signal and Image Processing. Fort Belvoir, VA: Defense Technical Information Center, June 2004. http://dx.doi.org/10.21236/ada425177.

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2

Shubitidze, Fridon. A Complex Approach to UXO Discrimination: Combining Advanced EMI Forward Models and Statistical Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada578937.

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3

Burnett, G. C. Damage Detection and Identification of Finite Element Models Using State-Space Based Signal Processing a Summation of Work Completed at the Lawrence Livermore National Laboratory February 1999 to April 2000. Office of Scientific and Technical Information (OSTI), April 2000. http://dx.doi.org/10.2172/793960.

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4

Chambers, D. Signal Processing Model for Radiation Transport. Office of Scientific and Technical Information (OSTI), July 2008. http://dx.doi.org/10.2172/945821.

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5

Bai, Z. D., P. R. Krishnaiah, and L. C. Zhao. Signal Processing Using Model Selection Methods,. Fort Belvoir, VA: Defense Technical Information Center, January 1986. http://dx.doi.org/10.21236/ada167318.

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6

Rodgers, A., D. Harris, and M. Pasyanos. A Model-Based Signal Processing Approach to Nuclear Explosion Monitoring. Office of Scientific and Technical Information (OSTI), March 2007. http://dx.doi.org/10.2172/908120.

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7

Edelblute, David J. Array Processing That Uses a Normal-Mode Model for Signal Representation. Fort Belvoir, VA: Defense Technical Information Center, May 1988. http://dx.doi.org/10.21236/ada198031.

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8

Ling, Hao. Application of Model-Based Signal Processing Methods to Computational Electromagnetics Simulators. Fort Belvoir, VA: Defense Technical Information Center, December 2000. http://dx.doi.org/10.21236/ada389286.

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9

Candy, J. V., B. R. Illingworth, K. W. Craft, and J. E. Case. Real-time Signal Processing for Sounding Rocket Modal Frequency Estimation. Office of Scientific and Technical Information (OSTI), March 2019. http://dx.doi.org/10.2172/1548320.

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10

Candy, J., K. Fisher, and B. Fix. Model-Based Ultrasonic Signal Processing for the Nondestructive Evaluation of Additive Manufacturing Components. Office of Scientific and Technical Information (OSTI), January 2021. http://dx.doi.org/10.2172/1762858.

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