Готові списки джерел за темами / Spectral Estimators

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Добірка наукової літератури з теми "Spectral Estimators"

Автор: Grafiati
Опубліковано: 10 березня 2023

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Статті в журналах з теми "Spectral Estimators"

1

Konofagou, Elisa E., Tomy Varghese, and Jonathan Ophir. "Spectral estimators in elastography." Ultrasonics 38, no. 1-8 (March 2000): 412–16. http://dx.doi.org/10.1016/s0041-624x(99)00116-x.

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2

Herment, A., and J. F. Giovannelli. "An Adaptive Approach to Computing the Spectrum and Mean Frequency of Doppler Signals." Ultrasonic Imaging 17, no. 1 (January 1995): 1–26. http://dx.doi.org/10.1177/016173469501700101.

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Modern ultrasound Doppler systems are facing the problem of processing increasingly shorter data sets. Spectral analysis of the strongly nonstationary Doppler signal needs to shorten the analysis window while maintaining a low variance and high resolution spectrum. Color flow imaging requires estimation of the Doppler mean frequency from even shorter Doppler data sets to obtain both a high frame rate and high spatial resolution. We reconsider these two estimation problems in light of adaptive methods. A regularized parametric method for spectral analysis as well as an adapted mean frequency estimator are developed. The choice of the adaptive criterion is then addressed and adaptive spectral and mean frequency estimators are developed to minimize the mean square error on estimation in the presence of noise. Two suboptimal spectral and mean-frequency estimators are then derived for real-time applications. Finally, their performance is compared to that of both the FFT based periodogram and the AR parametric spectral analysis for the spectral estimator, and, to both the correlation angle and the Kristoffersen's [8] estimators for the mean frequency estimator using Doppler data recorded in vitro.
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3

Stine, Robert A., and Paul Shaman. "Bias of Autoregressive Spectral Estimators." Journal of the American Statistical Association 85, no. 412 (December 1990): 1091–98. http://dx.doi.org/10.1080/01621459.1990.10474980.

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4

Gerard, David, and Peter Hoff. "Adaptive higher-order spectral estimators." Electronic Journal of Statistics 11, no. 2 (2017): 3703–37. http://dx.doi.org/10.1214/17-ejs1330.

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5

Chang, Christopher, and Dimitris Politis. "Aggregation of spectral density estimators." Statistics & Probability Letters 94 (November 2014): 204–13. http://dx.doi.org/10.1016/j.spl.2014.07.017.

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6

Bakar, M. A. A., D. A. Green, and A. V. Metcalfe. "Comparison of Spectral and Wavelet Estimators of Transfer Function for Linear Systems." East Asian Journal on Applied Mathematics 2, no. 3 (August 2012): 214–37. http://dx.doi.org/10.4208/eajam.170512.270712a.

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AbstractWe compare spectral and wavelet estimators of the response amplitude operator (RAO) of a linear system, with various input signals and added noise scenarios. The comparison is based on a model of a heaving buoy wave energy device (HBWED), which oscillates vertically as a single mode of vibration linear system. HBWEDs and other single degree of freedom wave energy devices such as oscillating wave surge convertors (OWSC) are currently deployed in the ocean, making such devices important systems to both model and analyse in some detail. The results of the comparison relate to any linear system. It was found that the wavelet estimator of the RAO offers no advantage over the spectral estimators if both input and response time series data are noise free and long time series are available. If there is noise on only the response time series, only the wavelet estimator or the spectral estimator that uses the cross-spectrum of the input and response signals in the numerator should be used. For the case of noise on only the input time series, only the spectral estimator that uses the cross-spectrum in the denominator gives a sensible estimate of the RAO. If both the input and response signals are corrupted with noise, a modification to both the input and response spectrum estimates can provide a good estimator of the RAO. A combination of wavelet and spectral methods is introduced as an alternative RAO estimator. The conclusions apply for autoregressive emulators of sea surface elevation, impulse, and pseudorandom binary sequences (PRBS) inputs. However, a wavelet estimator is needed in the special case of a chirp input where the signal has a continuously varying frequency.
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7

Politis, Dimitris N. "HIGHER-ORDER ACCURATE, POSITIVE SEMIDEFINITE ESTIMATION OF LARGE-SAMPLE COVARIANCE AND SPECTRAL DENSITY MATRICES." Econometric Theory 27, no. 4 (March 3, 2011): 703–44. http://dx.doi.org/10.1017/s0266466610000484.

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A new class of large-sample covariance and spectral density matrix estimators is proposed based on the notion of flat-top kernels. The new estimators are shown to be higher-order accurate when higher-order accuracy is possible. A discussion on kernel choice is presented as well as a supporting finite-sample simulation. The problem of spectral estimation under a potential lack of finite fourth moments is also addressed. The higher-order accuracy of flat-top kernel estimators typically comes at the sacrifice of the positive semidefinite property. Nevertheless, we show how a flat-top estimator can be modified to become positive semidefinite (even strictly positive definite) while maintaining its higher-order accuracy. In addition, an easy (and consistent) procedure for optimal bandwidth choice is given; this procedure estimates the optimal bandwidth associated with each individual element of the target matrix, automatically sensing (and adapting to) the underlying correlation structure.
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8

Candes, Emmanuel J., Carlos A. Sing-Long, and Joshua D. Trzasko. "Unbiased Risk Estimates for Singular Value Thresholding and Spectral Estimators." IEEE Transactions on Signal Processing 61, no. 19 (October 2013): 4643–57. http://dx.doi.org/10.1109/tsp.2013.2270464.

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9

Wen, Li, Changxue Wang, and Peter Sherman. "Using Variability Related to Families of Spectral Estimators for Mixed Random Processes." Journal of Dynamic Systems, Measurement, and Control 123, no. 4 (February 21, 2001): 572–84. http://dx.doi.org/10.1115/1.1409257.

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Traditionally, characterization of spectral information for wide sense stationary processes has been addressed by identifying a single best spectral estimator from a given family. If one were to observe significant variability in neighboring spectral estimators then the level of confidence in the chosen estimator would naturally be lessened. Such variability naturally occurs in the case of a mixed random process, since the influence of the point spectrum in a spectral density characterization arises in the form of approximations of Dirac delta functions. In this work we investigate the nature of the variability of the point spectrum related to three families of spectral estimators: Fourier transform of the truncated unbiased correlation estimator, the truncated periodogram, and the autoregressive estimator. We show that tones are a significant source of bias and variability. This is done in the context of Dirichlet and Fejer kernels, and with respect to order rates. We offer some expressions for estimating statistical and arithmetic variability. Finally, we include an example concerning helicopter vibration. These results are especially pertinent to mechanical systems settings wherein harmonic content is prevalent.
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10

Teimouri, Mahdi, Saeid Rezakhah, and Adel Mohammadpour. "U-Statistic for Multivariate Stable Distributions." Journal of Probability and Statistics 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/3483827.

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A U-statistic for the tail index of a multivariate stable random vector is given as an extension of the univariate case introduced by Fan (2006). Asymptotic normality and consistency of the proposed U-statistic for the tail index are proved theoretically. The proposed estimator is used to estimate the spectral measure. The performance of both introduced tail index and spectral measure estimators is compared with the known estimators by comprehensive simulations and real datasets.
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Дисертації з теми "Spectral Estimators"

1

Völcker, Björn. "Performance Analysis of Parametric Spectral Estimators." Doctoral thesis, KTH, Signals, Sensors and Systems, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3323.

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2

Plourde, Eric. "Bayesian short-time spectral amplitude estimators for single-channel speech enhancement." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66864.

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Single-channel speech enhancement algorithms are used to remove background noise in speech. They are present in many common devices such as cell phones and hearing aids. In the Bayesian short-time spectral amplitude (STSA) approach for speech enhancement, an estimate of the clean speech STSA is derived by minimizing the statistical expectation of a chosen cost function. Examples of such estimators are the minimum mean square error (MMSE) STSA, the β-order MMSE STSA (β-SA), which includes a power law parameter, and the weighted Euclidian (WE), which includes a weighting parameter. This thesis analyzes single-channel Bayesian STSA estimators for speech enhancement with the aim of, firstly, gaining a better understanding of their properties and, secondly, proposing new cost functions and statistical models to improve their performance. In addition to a novel analysis of the β-SA estimator for parameter β ≤ 0, three new families of estimators are developed in this thesis: the Weighted β-SA (Wβ-SA), the Generalized Weighted family of STSA estimators (GWSA) and a family of multi-dimensional Bayesian STSA estimators. The Wβ-SA combines the power law of the β-SA and the weighting factor of the WE. Its parameters are chosen based on the characteristics of the human auditory system which is found to have the advantage of improving the noise reduction at high frequencies while limiting the speech distortions at low frequencies. An analytical generalization of a cost function structure found in many existing Bayesian STSA estimators is proposed through the GWSA family of estimators. This allows a unification of Bayesian STSA estimators and, moreover, provides a better understanding of this general class of estimators. Finally, we propose a multi-dimensional family of estimators that accounts for the correlated frequency components in a digitized speech signal. In fact, the spectral components of the clean
Les algorithmes de rehaussement de la parole à voie unique sont utilisés afin de réduire le bruit de fond d'un signal de parole bruité. Ils sont présents dans plusieurs appareils tels que les téléphones sans fil et les prothèses auditives. Dans l'approche bayésienne d'estimation de l'amplitude spectrale locale (Short-Time Spectral Amplitude - STSA) pour le rehaussement de la parole, un estimé de la STSA non bruitée est déterminé en minimisant l'espérance statistique d'une fonction de coût. Ce type d'estimateurs incluent le MMSE STSA, le β-SA, qui intègre un exposant comme paramètre de la fonction de coût, et le WE, qui possède un paramètre de pondération.Cette thèse étudie les estimateurs bayésiens du STSA avec pour objectifs d'approfondir la compréhension de leurs propriétés et de proposer de nouvelles fonctions de coût ainsi que de nouveaux modèles statistiques afin d'améliorer leurs performances. En plus d'une étude approfondie de l'estimateur β-SA pour les valeurs de β ≤ 0, trois nouvelles familles d'estimateur sont dévelopées dans cette thèse: le β-SA pondéré (Weighted β-SA - Wβ-SA), une famille d'estimateur du STSA généralisé et pondéré (Generalized Weighted STSA - GWSA) ainsi qu'une famille d'estimateur du STSA multi-dimensionnel.Le Wβ-SA combine l'exposant présent dans le β-SA et le paramètre de pondération du WE. Ses paramètres sont choisis en considérant certaines caractéristiques du système auditif humain ce qui a pour avantage d'améliorer la réduction du bruit de fond à hautes fréquences tout en limitant les distorsions de la parole à basses fréquences. Une généralisation de la structure commune des fonctions de coût de plusieurs estimateurs bayésiens du STSA est proposée à l'aide de la famille d'estimateur GWSA. Cette dernière permet une unification des estimateurs bayésiens du STSA et apporte une meilleure compréhensio
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3

Blanchette, Jonathan. "Short-time Multichannel Noise Power Spectral Density Estimators for Acoustic Signals." Thèse, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/30964.

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The estimation of power spectral densities is a critical step in many speech enhancement algorithms. The demand for multi-channel speech enhancement systems is high with applications in teleconferencing, cellular phones, and hearing aids. The first objective of the thesis is to develop a general multi-channel framework to solve for the diffuse noise power spectral densities whenever the spatial correlation or coherence matrix is pre-estimated and the number of speakers is less than the number of microphones. The second objective is to develop closed-form analytical solutions. The performance of the developed algorithms is evaluated with pre-existing algorithms using prescribed performance measures.
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4

Mavriplis, Cathy. "Nonconforming discretizations and a posteriori error estimators for adaptive spectral element techniques." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/14526.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1989.
Includes bibliographical references (leaves 151-157).
by Catherine Andria Mavriplis.
Ph.D.
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5

Tamburello, Philip Michael. "Iterative Memoryless Non-linear Estimators of Correlation for Complex-Valued Gaussian Processes that Exhibit Robustness to Impulsive Noise." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/64785.

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The autocorrelation function is a commonly used tool in statistical time series analysis. Under the assumption of Gaussianity, the sample autocorrelation function is the standard method used to estimate this function given a finite number of observations. Non-Gaussian, impulsive observation noise following probability density functions with thick tails, which often occurs in practice, can bias this estimator, rendering classical time series analysis methods ineffective. This work examines the robustness of two estimators of correlation based on memoryless nonlinear functions of observations, the Phase-Phase Correlator (PPC) and the Median- of-Ratios Estimator (MRE), which are applicable to complex-valued Gaussian random pro- cesses. These estimators are very fast and easy to implement in current processors. We show that these estimators are robust from a bias perspective when complex-valued Gaussian pro- cesses are contaminated with impulsive noise at the expense of statistical efficiency at the assumed Gaussian distribution. Additionally, iterative versions of these estimators named the IMRE and IPPC are developed, realizing an improved bias performance over their non- iterative counterparts and the well-known robust Schweppe-type Generalized M-estimator utilizing a Huber cost function (SHGM). An impulsive noise suppression technique is developed using basis pursuit and a priori atom weighting derived from the newly developed iterative estimators. This new technique is proposed as an alternative to the robust filter cleaner, a Kalman filter-like approach that relies on linear prediction residuals to identity and replace corrupted observations. It does not have the same initialization issues as the robust filter cleaner. Robust spectral estimation methods are developed using these new estimators and impulsive noise suppression techniques. Results are obtained for synthetic complex-valued Guassian processes and real-world digital television signals collected using a software defined radio.
Ph. D.
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6

Liang, Hongkang. "Statistics of nonlinear averaging spectral estimators and a novel distance measure for HMMs with application to speech quality estimation." Laramie, Wyo. : University of Wyoming, 2005. http://proquest.umi.com/pqdweb?did=1031050291&sid=1&Fmt=2&clientId=18949&RQT=309&VName=PQD.

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7

Offermans, Nicolas. "Towards adaptive mesh refinement in Nek5000." Licentiate thesis, KTH, Mekanik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217501.

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The development of adaptive mesh refinement capabilities in the field of computational fluid dynamics is an essential tool for enabling the simulation of larger and more complex physical problems. While such techniques have been known for a long time, most simulations do not make use of them because of the lack of a robust implementation. In this work, we present recent progresses that have been made to develop adaptive mesh refinement features in Nek5000, a code based on the spectral element method. These developments are driven by the algorithmic challenges posed by future exascale supercomputers. First, we perform the study of the strong scaling of Nek5000 on three petascale machines in order to assess the scalability of the code and identify the current bottlenecks. It is found that strong scaling limit ranges between 5, 000 and 220, 000 degrees of freedom per core depending on the machine and the case. The need for synchronized and low latency communication for efficient computational fluid dynamics simulation is also confirmed. Additionally, we present how Hypre, a library for linear algebra, is used to develop a new and efficient code for performing the setup step required prior to the use of an algebraic multigrid solver for preconditioning the pressure equation in Nek5000. Finally, the main objective of this work is to develop new methods for estimating the error on a numerical solution of the Navier–Stokes equations via the resolution of an adjoint problem. These new estimators are compared to existing ones, which are based on the decay of the spectral coefficients. Then, the estimators are combined with newly implemented capabilities in Nek5000 for automatic grid refinement and adaptive mesh adaptation is carried out. The applications considered so far are steady and two-dimensional, namely the lid-driven cavity at Re = 7, 500 and the flow past a cylinder at Re = 40. The use of adaptive mesh refinement techniques makes mesh generation easier and it is shown that a similar accuracy as with a static mesh can be reached with a significant reduction in the number of degrees of freedom.

QC 20171114

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8

Hieber, Matthias, та Elmar Schrohe. "Lρ spectral independence of elliptic operators via commutator estimates". Universität Potsdam, 1997. http://opus.kobv.de/ubp/volltexte/2008/2504/.

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Let {Tsub(p) : q1 ≤ p ≤ q2} be a family of consistent Csub(0) semigroups on Lφ(Ω) with q1, q2 ∈ [1, ∞)and Ω ⊆ IRn open. We show that certain commutator conditions on Tφ and on the resolvent of its generator Aφ ensure the φ independence of the spectrum of Aφ for φ ∈ [q1, q2]. Applications include the case of Petrovskij correct systems with Hölder continuous coeffcients, Schrödinger operators, and certain elliptic operators in divergence form with real, but not necessarily symmetric, or complex coeffcients.
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9

Shirley, Christopher. "Statistiques spectrales d'opérateurs de Schrödinger aléatoires unidimensionnels." Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066434/document.

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Dans cette thèse, nous allons prouver des estimations de décorrelation des valeurs propres pour plusieurs modèles d'opérateurs de Schrödinger aléatoires en dimension un, dans le régime localisé, tant que nous avons des estimations de Wegner. Ceci permet l'étude des statistiques spectrales.Nous commencerons donc par présenter les hypothèses sur lesquelles nous nous appuyons et les différents modèles considérés.Nous étudierons ensuite les estimations de Minami, qui peuvent être vues comme des estimations de décorrélation des valeurs propres proches. Nous montrerons qu'en dimension un, elles sont conséquences des estimations de Wegner et de l'hypothèse de localisation. Les estimations prouvées ici ont un domaine de validité plus restreint que les estimations de Minami classiques, mais sont suffisantes pour notre étude.Nous étudierons ensuite les estimations de décorrélation des valeurs propres éloignées pour les différents modèles présentés. Nous montrerons qu'elles sont conséquences des estimations de Minami, des estimations de Wegner et de l'hypothèse de localisation. Les preuves données seront différentes selon les modèles étudiés.Enfin, nous montrerons que ces résultats permettent d'étudier les statistiques spectrales, dans le régime localisé. Par exemple, les estimations de décorrélation permettent de montrer que les statistiques locales des niveaux d'énergies, prises à deux énergies différentes, convergent faiblement vers deux processus de Poisson indépendants sur $\R$ d'intensité la mesure de Lebesgue
In this thesis, we will prove decorrelation estimates of eigenvalues for several models of random Schrödinger operators in dimension one, in the localized regime, provided we have Wegner estimates. This will allow us to study spectral statistics.We will begin with the presentation of the hypotheses needed in our proofs and the models under consideration.We will continue with the study of the Minami estimates, which can be seen as decorrelation estimates of close eigenvalues. We will show that, in dimension one and in the localized regime, they are the consequences of the Wegner estimates. The results proven here have a area of validity smaller than the usual Minami estimates, but it will suffice for our study.Next, we will study the decorrelation estimates of distant eigenvalues for the models under consideration. We will show that they are consequences of the Minami estimates and the Wegner estimates, in the localized regime. The proofs will be different from one model to another.Eventually, we will show that these results allow us to study spectral statistics in the localized regime. For instance, the decorrelation estimates will be used to prove that the local energy level statistics, taken at two distincts energy levels, converge weakly to two independent Poisson processes on $\R$ with intensity the Lebesgue measure
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10

Rodríguez, Alexandra Lemus. "Spectral estimates for Schrödinger operators." Thesis, Connect to online version, 2008. http://proquest.umi.com/pqdweb?did=1675127001&sid=3&Fmt=2&clientId=10306&RQT=309&VName=PQD.

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Книги з теми "Spectral Estimators"

1

Center, Langley Research, ed. An overdetermined system for improved autocorrelation based spectral moment estimator performance. Hampton, VA: National Aeronautics and Space Administration, Langley Research Center, 1996.

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2

Francis, Nier, ed. Hypoelliptic estimates and spectral theory for Fokker-Planck operators and Witten Laplacians. Berlin: Springer, 2005.

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3

Weber, Andreas. Heat kernel estimates and L_1hnp spectral theory of locally symmetric spaces. Karlsruhe: Univ.-Verl. Karlsruhe, 2006.

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4

Helffer, Bernard, and Francis Nier. Hypoelliptic Estimates and Spectral Theory for Fokker-Planck Operators and Witten Laplacians. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b104762.

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5

Birman, M., ed. Estimates and Asymptotics for Discrete Spectra of Integral and Differential Equations. Providence, Rhode Island: American Mathematical Society, 1991. http://dx.doi.org/10.1090/advsov/007.

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6

Elsner, Guido. Distributions of values of indefinite forms and higher-order spectral estimates for finite Markov chains. Bielefeld: [s.n.], 2007.

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Elsner, Guido. Distributions of values of indefinite forms and higher-order spectral estimates for finite Markov chains. Bielefeld: [s.n.], 2007.

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8

Andrew, Michael E. Methods for computing confidence intervals for spectral estimates in monthly reports of the California Coastal Data Information Program. Vicksburg, Miss: Dept. of the Army, Waterways Experiment Station, Corps of Engineers, 1985.

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9

Jonggil, Lee, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., eds. The pulse-pair algorithm as a robust estimator of turbulent weather spectral parameters using airborne pulse Doppler radar. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1991.

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Jonggil, Lee, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., eds. The pulse-pair algorithm as a robust estimator of turbulent weather spectral parameters using airborne pulse Doppler radar. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1991.

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Частини книг з теми "Spectral Estimators"

1

Reisen, Valdério Anselmo, Céline Lévy-Leduc, Higor Henrique Aranda Cotta, Pascal Bondon, Marton Ispany, and Paulo Roberto Prezotti Filho. "An Overview of Robust Spectral Estimators." In Applied Condition Monitoring, 204–24. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22529-2_12.

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2

Zhurbenko, I. G. "Estimators of Spectral Functions of Random Processes." In Selected Works of A. N. Kolmogorov, 505–14. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2260-3_52.

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3

Mavriplis, Catherine. "A Posteriori Error Estimators for Adaptive Spectral Element Techniques." In Proceedings of the Eighth GAMM-Conference on Numerical Methods in Fluid Mechanics, 333–42. Wiesbaden: Vieweg+Teubner Verlag, 1990. http://dx.doi.org/10.1007/978-3-663-13975-1_34.

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4

Qin, Yingli, and Weiming Li. "Bias-Reduced Moment Estimators of Population Spectral Distribution and Their Applications." In Contributions to Statistics, 103–19. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-41573-4_6.

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5

Duchesne, Pierre, and Yongmiao Hong. "On Diagnostic Checking Autoregressive Conditional Duration Models with Wavelet-Based Spectral Density Estimators." In Advances in Time Series Methods and Applications, 47–106. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6568-7_3.

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6

Biagi, E., S. Granchi, A. Luddi, L. Breschi, R. Facchini, A. Ricci, and L. Masotti. "Carotid Plaque Tissue Differentiation Based on Radiofrequency Echographic Signal Local Spectral Content (RULES: Radiofrequency Ultrasonic Local Estimators)." In Acoustical Imaging, 93–99. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8823-0_13.

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7

Rosenblatt, Murray. "Spectral Density Estimates." In Stationary Sequences and Random Fields, 125–61. Boston, MA: Birkhäuser Boston, 1985. http://dx.doi.org/10.1007/978-1-4612-5156-9_5.

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Rosenblatt, Murray. "Cumulant Spectral Estimates." In Stationary Sequences and Random Fields, 163–90. Boston, MA: Birkhäuser Boston, 1985. http://dx.doi.org/10.1007/978-1-4612-5156-9_6.

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9

Fournais, Søren, and Bernard Helffer. "Decay Estimates." In Spectral Methods in Surface Superconductivity, 179–92. Boston: Birkhäuser Boston, 2009. http://dx.doi.org/10.1007/978-0-8176-4797-1_12.

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10

Hislop, P. D., and I. M. Sigal. "Quantum Nontrapping Estimates." In Introduction to Spectral Theory, 235–50. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-0741-2_21.

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Тези доповідей конференцій з теми "Spectral Estimators"

1

Schober, Steffen, and Martin Bossert. "On spectral estimators of Boolean functions." In 2010 IEEE International Symposium on Information Theory - ISIT. IEEE, 2010. http://dx.doi.org/10.1109/isit.2010.5513332.

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2

Ricci, S., F. Guidi, and P. Tortoli. "Velocity profile detection through adaptive spectral estimators." In 2010 IEEE Ultrasonics Symposium (IUS). IEEE, 2010. http://dx.doi.org/10.1109/ultsym.2010.5935437.

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3

Hardesty, R. M., and Barry J. Rye. "Simulations of discrete spectral peak estimators for single and multiple lidar pulse returns." In Coherent Laser Radar. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/clr.1991.tha2.

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Given an estimate of the spectrum of a time series sample drawn from a single pulse lidar return, there are several ways in which the signal frequency can be estimated. Texts on weather radar stress use of the first moment expression for estimation of the mean Doppler frequency shift, with or without threshold techniques to remove contributions from the spectrum of the noise.1,2 For lidar, there is increasing interest in discrete spectral peak estimation (DSPE) techniques,3 where the frequency at which the spectral maximum occurs is accepted as the estimate. Indeed, the weighting algorithm proposed for use with radar wind profilers4 uses a DSPE as a ’first guess’ of the mean frequency, prior to formation of a weighted average of spectral elements in the neighborhood of this value that have power levels above a selected threshold. It evidently has more in common with the DSPE method than earlier moment algorithms.
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4

Adam, O., J. L. Zarader, A. Dabas, and P. H. Flamant. "Autoregressive Moving Average Spectral Estimation of Coherent Lidar Signal." In Coherent Laser Radar. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/clr.1995.wb2.

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The frequency estimators that perform the best on Doppler lidar signals are somehow matched to the signal characteristics like spectral width or SNR. In the Levin estimator for instance, the fitted filter is tuned in height and width to be as close as possible to the expected signal power spectrum. In practical applications, the tunable estimator parameters are set to values corresponding to the atmospheric conditions that are most likely to be met (clear air stable conditions without significant shear and low turbulence level). Other conditions are however often met that unpredictably broaden or alter the returned spectrum. This may be particularly true for dedicated applications like wake vortex or wind shear detections. The performance of the estimation may then be highly degraded. The coupled estimation of both the signal mean frequency and width could then improve the radial velocity retrieval. Such a co-estimation is possible using an autoregressive moving average (ARMA) type of filter.
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5

Su, Zhigang, Renbiao Wu, and Yingning Peng. "Two Amplitude Spectral Estimators with Rank-Deficient Covariance Matrix." In 2006 8th international Conference on Signal Processing. IEEE, 2006. http://dx.doi.org/10.1109/icosp.2006.344483.

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6

Stoica, P., and T. Soderstrom. "On spectral and root forms of sinusoidal frequency estimators." In [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1991. http://dx.doi.org/10.1109/icassp.1991.150148.

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7

Lee, Chong-Won, Yun-Sik Han, and Jong-Po Park. "Use of Directional AR and ML Spectra for Detection of Misfired Engine Cylinder." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0387.

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Abstract A powerful diagnostic method of cylinder power faults, which uses the two-sided directional power spectra of complex-valued engine vibration signals, is presented and tested with an automobile engine. As the spectral estimators, the AR, ML and FFT methods are compared and the multi-layer neural network is employed for pattern recognition. Experimental results show that the success rate for identifying the misfired cylinder is higher with the combined AR and ML estimator than the FFT method.
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8

Parchami, Mahdi, Wei-Ping Zhu, and Benoit Champagne. "Microphone array based speech spectral amplitude estimators with phase estimation." In 2014 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2014. http://dx.doi.org/10.1109/iscas.2014.6865083.

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9

Yildirim, I., N. S. Tezel, I. Erer, and B. Yazgan. "A comparison of non-parametric spectral estimators for SAR imaging." In Proceedings of International Conference on Recent Advances in Space Technologies. IEEE, 2003. http://dx.doi.org/10.1109/rast.2003.1303945.

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10

Ferro-Famil, Laurent, Yue Huang, and Andreas Reigber. "High-Resolution SAR Tomography using full rank Polarimetric spectral estimators." In IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012. http://dx.doi.org/10.1109/igarss.2012.6352439.

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Звіти організацій з теми "Spectral Estimators"

1

Orzech, Mark, Jay Veeramony, and Stylianos Flampouris. Optimizing Spectral Wave Estimates with Adjoint-Based Sensitivity Maps. Fort Belvoir, VA: Defense Technical Information Center, February 2014. http://dx.doi.org/10.21236/ada603141.

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2

Nuttall, Albert H. Second-Order Statistics of Spectral and Correlation Estimates Obtained by Means of Weighted Overlapped FFT Processing. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada439705.

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3

Nguyen, Bao V., and David R. Russell. Noise Power Spectral Density and Coherency Estimates at Sites in Nevada, USA: Mina, Excelsior Mountains, Gabbs Valleys, Garfield (GAR) Seismic Stations, and East of Garfield. Fort Belvoir, VA: Defense Technical Information Center, July 1998. http://dx.doi.org/10.21236/ada353609.

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4

García Zaballos, Antonio, Pau Puig Gabarró, and Enrique Iglesias Rodriguez. Digital Infrastructure in Trinidad and Tobago: Analysis, Challenges, and Action Plan. Inter-American Development Bank, February 2022. http://dx.doi.org/10.18235/0003997.

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This document presents an analysis of the state of digital connectivity in Trinidad and Tobago and an action plan to close the existing gap between the countries of Latin America and the Caribbean and those of the Organisation for Economic Co-operation and Development. There is also a large gap within the country between urban, densely populated regions and rural, remote, or difficult-to-access regions. Among the impediments to closing the gap are: lack of investment in infrastructure in the most remote areas; limited bandwidth of citizens, institutions and companies that are far from this infrastructure; and lack of competition among internet companies. The government is making efforts in the areas of a universal service fund, spectrum management, and the formulation of the national ICT plan to improve access conditions in the country. Finally, the document estimates the investment gap in the region and in Trinidad and Tobago specifically.
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5

Schubmehl, M. An analysis of the uncertainty in temperature and density estimates from fitting model spectra to data. 1998 summer research program for high school juniors at the University of Rochester`s Laboratory for Laser Energetics: Student research reports. Office of Scientific and Technical Information (OSTI), March 1999. http://dx.doi.org/10.2172/362529.

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6

Mäkelä, Antti, Tapio Tourula, Heikki Tuomenvirta, Pauli Jokinen, Terhi Laurila, Ari-Juhani Punkka, Minna Huuskonen, Tuomo Brgman, and Hannu Valta. Climate change impacts to the security of supply. Finnish Meteorological Institute, 2023. http://dx.doi.org/10.35614/isbn.9789523361645.

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Serious disruptions and exceptional circumstances for society, that the society tries to prepare for and act in them are at the center of security of supply. Current examples are the COVID pandemic and the ongoing energy crisis for which Finland's security of supply has also been strongly highlighted. Disturbances can also be caused by weather phenomena: in Finland, such examples are windstorms, severe thunderstorms, floods, and droughts, which can, at least in principle, paralyze the society. It is possible to prepare for the impacts of weather phenomena, but the ongoing rapid climate change makes it more complicated. Some of the weather phenomena that cause impacts are fast and violent (e.g. intense thunderstorms) and some occur more slowly (e.g. long heat waves), and climate change affects the phenomena in different ways. In this work, the estimated impacts of climate change on Finland's security of supply were investigated. The starting point was to gain an understanding of which weather phenomena and weather situations are central to security of supply and which sectors of security of supply are the most vulnerable. The work constituted of workshops and expert interviews organized with the National Emergency Supply Agency. In addition to the interviews, the work covered past significant weather situations in Finland that are known to have had significant societal impacts. Information was also extracted from recent literature, especially regarding the vulnerabilities and adaptability of different sectors in Finland. Estimates of the climate change impacts on the identified phenomena were combined with the collected information, resulting in a first understanding of how climate change affects Finland's security of supply. Based on the results, it can be concluded that the impacts of climate change on security of supply are quite complex, especially due to the wide spectrum of weather phenomena and their different impact mechanisms. In addition, the matter becomes more complicated by the fact that there is no clear distinction of what weather phenomenon actually is critical to security of supply and what is not. For example, could the increasing adverse impacts on health care due to the increasingly common heat conditions reach a serious societal disturbance situation at some point, if it is not sufficiently prepared in advance? Another key result is that in terms of security of supply, the direct effects of climate change are very small in Finland compared to many other countries. Although the climate in Finland has already changed considerably and will continue to change in the future, the biggest impacts to security of supply seem to be reflected from elsewhere: the experts of the National Emergency Supply Agency consider the worst situation to be a lack of food, water and habitable living environment in the world, which would also be reflected to Finland. Among the sectors, food/water and energy supply and logistics are perceived as the most vulnerable. The work mainly focused on the direct effects of climate change, i.e. the effects of climate change on the occurrence of various weather phenomena. However, the work also considers to some extent indirect effects, i.e. those reflected from other parts of the world, and transitional effects that result from climate change mitigation measures, especially from the rapid energy transition.
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