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About 10,000 papers for “Spectral density”

Spectral Density

Khan M. Iftekharuddin, Abdul Ahad S. Awwal · SPIE eBooks · 2012

Cited by 0

Spectral Density Estimates

Murray Rosenblatt · Birkhäuser Boston eBooks · 1985

The periodogram was introduced in Chapter III. Here we shall consider it in greater detail. It was originally introduced by Schuster [1898] as a tool to detect hidden periodicities. Suppose that $${Y_kappa} = {X_kappa} + {m_kappa}$$ where X κ is a weakly stationary process with mean zero and a continuous spectral density f (with f(π) = f(− π)), and m κ is a trigonometric series with a finite number of frequencies μ j 1 $${m_kappa} = \sum\limits_i {{a_j} {e^{ik\mu j}}.}$$

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Analysis Spectral Density

Thomas Mikosch, Yuwei Zhao, T. Mikosch et al. · 2013

• A sequence (Xt) is regularly varying if (X0,..., Xh) is regularly varying, that is, nP(a −1 n (X0,..., Xh) ∈ C) → µh(C) , h ≥ 0. Then it follows µh(tC) = t −α µh(C), t> 0. P(|X0 |> an) = 1/n. • For a d-dimensional strictly stationary time series (Xt), the extremogram at lag h ≥ 1 ρ(h) = lim x→∞ P(x −1 Xh ∈ A|x −1 X0 ∈ A) = µh(A × R h−1 × A)

Cited by 0

Power Spectral Density

Abhey Ram Bansal, V. P. Dimri · Encyclopedia of earth sciences series/Encyclopedia of earth sciences · 2023

Cited by 3

Power Spectral Density

2011

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Cited by 2

Spectral Density Measurements

2011

State of the art supercontinuum white light lasers are now able to produce several Watts of average power in a spectral range covering more than 2000nm. For such systems, stating the total average power in the entire supercontinuum spectrum is a poor specification of system performance, as significant power can be concentrated in discrete spectral bands. Specification of spectral density reveals such pitfalls and shows the true optical performance of the source. Precise characterization of the spectral density is thus very important for evaluation of a given light source. This note will show how spectral density from high power supercontinuum lasers can be correctly quantified.

Cited by 0

Power Spectral Density

Juan Llácer, Leandro · Digital Repository (Polytechnic University of Cartagena) · 2018

Cited by 0

Online Spectral Density Estimation

Shahriar Hasnat Kazi, Niall M. Adams, Edward A. K. Cohen · Spiral (Imperial College London) · 2025

This paper develops the first online algorithms for estimating the spectral density function -- a fundamental object of interest in time series analysis -- that satisfies the three core requirements of streaming inference: fixed memory, fixed computational complexity, and temporal adaptivity. Our method builds on the concept of forgetting factors, allowing the estimator to adapt to gradual or abrupt changes in the data-generating process without prior knowledge of its dynamics. We introduce a novel online forgetting-factor periodogram and show that, under stationarity, it asymptotically recovers the properties of its offline counterpart. Leveraging this, we construct an online Whittle estimator, and further develop an adaptive online spectral estimator that dynamically tunes its forgetting factor using the Whittle likelihood as a loss. Through extensive simulation studies and an application to ocean drifter velocity data, we demonstrate the method's ability to track time-varying spectral properties in real-time with strong empirical performance.

Cited by 0Open access

Example spectral density plots.

Louise T. Kregting (464490), Anna L. Bass (464491), Òscar Guadayol (464492) et al. · Figshare · 2015

<p>Spectral density plots calculated from vertical velocity components representative from each of the rms(<i>u</i><sub>w</sub>) categories: LOW [< 0.03 m s<sup>−1</sup>, blue], MEDIUM [> 0.03<0.06 m s<sup>−1</sup>, green], HIGH [> 0.06 m s<sup>−1</sup>, red]</p>

Cited by 0Open access

Power Spectral Density (PSD).

Rong-Hua Wang (182474), Zhen Jin (124805), Quan-Xing Liu (182477) et al. · Figshare · 2015

<p>(a) Comparisons between the theoretical prediction of the PSD (Eq. (7)) and the average PSD calculated from full stochastic simulation as the one shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0028873#pone-0028873-g001" target="_blank">Fig. 1(b)</a>, for the fluctuations of the total number of the susceptible, the infected and the virus. The black lines represent the power spectra of time series obtained from stochastic simulations, and red lines represent the analytical prediction. The parameters are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0028873#pone-0028873-t002" target="_blank">Table 2</a> and , where is equal to 2.387, and a main oscillatory period about 7 years. (b) Changes in the PSD as a function of an increasing environmental transmission rate with . (c) Three-dimensional representation of the PSD for the variable , (Eq. (7)), for a continuum of values of on the axis, with the restriction . (d) Dominant period and persistence of the disease as a function of parameters and . Here we divide the domain of the parameter space where sthochastic fluctuations occur in three different regions characterized by periods less than 1, from 1 to 5, from 5 to 10 years, respectively. We also represent the hyperbolic-shaped instability boundary, separating the domain of disease persistence () from the region of disease extinction (), which is determined by the basic reproductive number in the deterministic system (5). The same boundary can be calculated through simulation for the full stochastic model. It corresponds approximately to . Symbols () represent 100-year-long simulations, where the transient dynamics have been discarded (the first 50 years).</p>

Cited by 0Open access

Spectral Density Matrix Transformations

Michael T. Hale · Journal of the IEST · 2017

Abstract Reference criteria for a multiple degree-of-freedom (MDOF) random vibration test is generally provided in terms of an acceleration-based spectral density matrix (SDM). This SDM may be developed in terms of the auto-spectral densities (ASDs) and cross-spectral densities (CSDs) computed from the time histories acquired from an appropriately placed and oriented set of linear accelerometers. Such a direct measurement linear accelerometer based reference criteria will be denoted as SDM meas . A second technique for defining a reference SDM is in terms of the accelerations associated with the six classical motion degrees-of-freedom as defined at an arbitrary point of origin. Such a reference will be denoted as SDM motion . The objective at hand is to demonstrate the method of transforming between these two reference criteria spaces.

Cited by 3

Power-Spectral-Density: v1.0

Podshivaylov, Eduard A, Frantsuzov, Pavel A · Zenodo (CERN European Organization for Nuclear Research) · 2023

A MATLAB function to calculate the power spectral density of the random signal using the modifed Welch's alghorithm

Cited by 0Open access

Online Spectral Density Estimation

Shahriar Hasnat Kazi, Niall M. Adams, Edward A. K. Cohen · Journal of Computational and Graphical Statistics · 2025

Cited by 0Open access

Reduced spectral density functions.

Per Rogne (147042), Przemysław Ozdowy (147046), Christian Richter (147050) et al. · Figshare · 2015

<p>(<b>A</b>) , (<b>B</b>) and (<b>C</b>) for 6 M urea-denatured TC5b derived from the 60.25 MHz <i>R</i><sub>1</sub>, <i>R</i><sub>2</sub>, and heteronuclear NOE relaxation data from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041301#pone-0041301-g003" target="_blank">Figure 3</a>. The average errors are 2.5%, 1.2% and 2.7%, for , and , respectively.</p>

Cited by 0Open access

Absorbed (Spectral) Radiant Density

Silvia E. Braslavsky, André M. Braun, Alberto E. Cassano et al. · IUPAC Standards Online · 2016

This glossary of terms covers phenomena considered under the very wide terms photocatalysis and radiation catalysis. A clear distinction is made between phenomena related to either photochemistry and photocatalysis or radiation chemistry and radiation catalysis. The term “radiation” is used here as embracing electromagnetic radiation of all wavelengths, but in general excluding fast-moving particles. Consistent definitions are given of terms in the areas mentioned above, as well as definitions of the most important parameters used for the quantitative description of the phenomena. Terms related to the up-scaling of photocatalytic processes for industrial applications have been included. This Glossary should be used together with the Glossary of terms used in photochemistry, 3rd edition, IUPAC Recommendations 2006: (doi:10.1351/pac200779030293) as well as with the IUPAC Compendium of Chemical Terminology, 2nd ed. (the “Gold Book” 2006–; doi:10.1351/goldbook), because many terms used in photocatalysis are defined in these documents.

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