Relevant bibliographies by topics / Second-order autocorrelation

Academic literature on the topic 'Second-order autocorrelation'

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Published: 14 March 2023

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Journal articles on the topic "Second-order autocorrelation"

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Mashiko, H., A. Suda, and K. Midorikawa. "Second-order autocorrelation functions for all-reflective interferometric autocorrelator." Applied Physics B 87, no. 2 (February 17, 2007): 221–26. http://dx.doi.org/10.1007/s00340-006-2574-6.

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Ouyang Xiaoping, 欧阳小平, 张福领 Zhang Fuling, 张攀政 Zhang Panzheng, 谢兴龙 Xie Xinglong, and 朱健强 Zhu Jianqiang. "Second Order Autocorrelation Applied to Femtosecond Laser." Chinese Journal of Lasers 36, no. 3 (2009): 742–45. http://dx.doi.org/10.3788/cjl20093603.0742.

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Tzallas, P., D. Charalambidis, N. A. Papadogiannis, K. Witte, and G. D. Tsakiris. "Second-order autocorrelation measurements of attosecond XUV pulse trains." Journal of Modern Optics 52, no. 2-3 (January 20, 2005): 321–38. http://dx.doi.org/10.1080/09500340412331301533.

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COLLIER, J., C. HERNANDEZ-GOMEZ, R. ALLOTT, C. DANSON, and A. HALL. "A single-shot third-order autocorrelator for pulse contrast and pulse shape measurements." Laser and Particle Beams 19, no. 2 (April 2001): 231–35. http://dx.doi.org/10.1017/s0263034601192116.

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We present the design of a single-shot third-order autocorrelator that can be used to measure optical pulse lengths of ultrashort pulses within a fixed time window on a single-shot basis. It has a number of advantages over traditional second-order autocorrelation devices, namely a more direct and accurate measurement of pulse shape, the ability to differentiate temporal activity ahead and behind the pulse, and an increased dynamic range. The design is linear and is, in principle, no more difficult to construct and operate than a second-order autocorrelator.
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Dittrich, Dino, Roger Th A. J. Leenders, and Joris Mulder. "Network Autocorrelation Modeling: Bayesian Techniques for Estimating and Testing Multiple Network Autocorrelations." Sociological Methodology 50, no. 1 (May 29, 2020): 168–214. http://dx.doi.org/10.1177/0081175020913899.

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The network autocorrelation model has been the workhorse for estimating and testing the strength of theories of social influence in a network. In many network studies, different types of social influence are present simultaneously and can be modeled using various connectivity matrices. Often, researchers have expectations about the order of strength of these different influence mechanisms. However, currently available methods cannot be applied to test a specific order of social influence in a network. In this article, the authors first present flexible Bayesian techniques for estimating network autocorrelation models with multiple network autocorrelation parameters. Second, they develop new Bayes factors that allow researchers to test hypotheses with order constraints on the network autocorrelation parameters in a direct manner. Concomitantly, the authors give efficient algorithms for sampling from the posterior distributions and for computing the Bayes factors. Simulation results suggest that frequentist properties of Bayesian estimators on the basis of noninformative priors for the network autocorrelation parameters are overall slightly superior to those based on maximum likelihood estimation. Furthermore, when testing statistical hypotheses, the Bayes factors show consistent behavior with evidence for a true data-generating hypothesis increasing with the sample size. Finally, the authors illustrate their methods using a data set from economic growth theory.
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de Chatellus, H., S. Montant, E. Freysz, V. Bagnoud, and F. Salin. "Thermally poled fused silica as a second-order autocorrelation crystal." Applied Physics B 70, S1 (June 2000): S95—S98. http://dx.doi.org/10.1007/s003400000311.

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Ludkovski, Michael, and Guang Gong. "New Families of Ideal 2-Level Autocorrelation Ternary Sequences From Second Order DHT." Electronic Notes in Discrete Mathematics 6 (April 2001): 375–84. http://dx.doi.org/10.1016/s1571-0653(04)00189-1.

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Melamed, Benjamin. "The empirical TES methodology: modeling empirical time series." Journal of Applied Mathematics and Stochastic Analysis 10, no. 4 (January 1, 1997): 333–53. http://dx.doi.org/10.1155/s1048953397000403.

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TES (Transform-Expand-Sample) is a versatile class of stochastic sequences defined via an autoregressive scheme with modulo-1 reduction and additional transformations. The scope of TES encompasses a wide variety of sample path behaviors, which in turn give rise to autocorrelation functions with diverse functional forms - monotone, oscillatory, alternating, and others. TES sequences are readily generated on a computer, and their autocorrelation functions can be numerically computed from accurate analytical formulas at a modest computational cost.This paper presents the empirical TES modeling methodology which uses TES process theory to model empirical records. The novel feature of the TES methodology is that it expressly aims to simultaneously capture the empirical marginal distribution (histogram) and autocorrelation function. We draw attention to the non-parametric nature of TES modeling in that it always guarantees an exact match to the empirical marginal distribution. However, fitting the corresponding autocorrelation function calls for a heuristic search for a TES model over a large parametric space. Consequently, practical TES modeling of empirical records must currently rely on software assistance. A visual interactive software environment, called TEStool, has been designed and implemented to support TES modeling. The paper describes the empirical TES modeling methodology as implemented in TEStool and provides numerically-computable formulas for TES autocorrelations. Two examples illustrate the efficacy of the TES modeling approach. These examples serve to highlight the ability of TES models to capture first-order and second-order properties of empirical sample paths and to mimic their qualitative appearance.
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Floris, Claudio. "Stochastic Stability Criteria for Second-Order Oscillator Parametrically Excited by Colored Noise." Fluctuation and Noise Letters 16, no. 01 (February 2017): 1750003. http://dx.doi.org/10.1142/s0219477517500031.

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A second-order oscillator is considered having a random perturbation in its stiffness. This is given by a colored Gaussian or non-Gaussian process. In this way, the oscillator may be stochastically stable or unstable according to the intensity of the excitation. The almost sure (sample) stochastic stability and the stability in the first three response statistical moments are compared for different excitation processes: process with exponential autocorrelation, second-order Gaussian process, bounded noise process. Notable differences in the stability boundaries are found either according to the stability criteria or to the type of excitation. These comparisons are lacking in literature.
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Chesi, Giovanni, Alessia Allevi, and Maria Bondani. "Autocorrelation functions: a useful tool for both state and detector characterisation." Quantum Measurements and Quantum Metrology 6, no. 1 (January 1, 2019): 1–6. http://dx.doi.org/10.1515/qmetro-2019-0001.

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Abstract The calculation of autocorrelation functions represents a routinely used tool to characterise quantum states of light. In this paper, we evaluate the g(2) function for detected photons in the case of mesoscopic multi-mode twin-beam states in order to fully investigate their statistical properties starting from measurable quantities. Moreover, we show that the second-order autocorrelation function is also useful to estimate the spurious effects affecting the employed Silicon-photomultiplier detectors.
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Books on the topic "Second-order autocorrelation"

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Boudreau, Joseph F., and Eric S. Swanson. Classical spin systems. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0020.

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The thermodynamic properties of spin systems are evaluated with Monte Carlo methods. A review of classical thermodynamics is followed by a discussion of critical exponents. The Monte Carlo method is then applied to the two-dimensional Ising model with the goal of determining the phase diagram for magnetization. Boundary conditions, the reweighting method, autocorrelation, and critical slowing down are all explored. Cluster algorithms for overcoming critical slowing down are developed next and shown to dramatically reduce autocorrelation. A variety of spin systems that illustrate first, second, and infinite order (topological) phase transitions are explored. Finally, applications to random systems called spin glasses and to neural networks are briefly reviewed.
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Book chapters on the topic "Second-order autocorrelation"

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Shang, Yajun, and Tianran Lin. "Fault Diagnosis of Rolling Element Bearings Based on a Second Order Cyclic Autocorrelation and a Deep Auto-encoder." In Mechanisms and Machine Science, 518–27. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26193-0_45.

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Polyak, Ilya. "Variability of ARMA Processes." In Computational Statistics in Climatology. Oxford University Press, 1996. http://dx.doi.org/10.1093/oso/9780195099997.003.0006.

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In this chapter, the numerical and pictorial interpretation of the dependence of the standard deviation of the forecast error for the different types and orders of univariate autoregressive-moving average (ARMA) processes on the lead time and on the autocorrelations (in the domains of the permissible autocorrelations) are given. While the convenience of fitting a stochastic model enables us to estimate its accuracy for the only time series under consideration, the graphs in this chapter demonstrate such accuracy for all possible models of the first and second order. Such a study can help in evaluating the appropriateness of the presupposed model, in earring out the model identification procedure, in designing an experiment, and in optimally organizing computations (or electing not to do so). A priori knowledge of the theoretical values of a forecast’s accuracy indicates the reasonable limits of complicating the model and facilitates evaluation of the consequences of certain preliminary decisions concerning its application. The approach applied is similar to the methodology developed in Chapters 1 and 2. Because the linear process theory has been thoroughly discussed in the statistical literature (see, for example, Box and Jenkins, 1976; Kashyap and Rao, 1976; and so on), its principal concepts are presented in recipe form with the minimum of details necessary for understanding the computational aspects of the subject. Consider a discrete stationary random process zt with null expected value [E(zt) = 0] and autocovariance function . . . M(T) = σ2 ρ(T), (4.1) . . . where σ2 is the variance and ρ(T) is the autocorrelation function of zt. Let at be a discrete white noise process with a zero mean and a variance σ2a. Let us assume that processes zt and at are normally distributed and that their cross-covariance function Mza(T) = 0 if T > 0.
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Karadimas, Petros. "Stochastic Modeling of Narrowband Fading Channels with Three Dimensional Diffuse Scattering." In Handbook of Research on Heterogeneous Next Generation Networking, 361–81. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-108-7.ch016.

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This chapter studies a composite stochastic model, in which the diffuse component arises from three dimensional (3-D) multipath scattering. That case occurs especially in dense scattering environments, in which the tall obstacles cause arrival of multipath power in the elevation plane, besides that arriving in the azimuth one. Also the multipath components are assumed to arrive at the mobile receiver in specific angular sectors at the azimuth receiver’s plane. The last is physically justified by multipath power blocking due to the channel obstacles (shadow fading), or/and lack of scattering objects at specific angular directions, or/and directional antennas utilization. An extended Suzuki model, where the Rician process for the diffuse scattering component is multiplied by a lognormal one, is considered as an appropriate composite model. The most important metrics of the model are presented, according to its assumptions. More specifically, from the closed form autocorrelation function, the Doppler power spectral density (PSD) of the diffuse component can be analytically derived. Afterwards exact solutions for the envelope and phase probability density functions (PDF’s) are presented. Exact solutions are also derived for the second order statistics, i.e. the level crossing rate (LCR) and the average duration of fades (ADF’s). An efficient deterministic simulation scheme will be presented, which implements the analytical model on a digital computer. Finally a curve fitting of the LCR to real world data, drawn from channel measurements, will demonstrate the flexibility and usefulness of the extended Suzuki model.
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Conference papers on the topic "Second-order autocorrelation"

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BENIS, E. P., L. A. A. NIKOLOPOULOS, P. TZALLAS, D. CHARALAMBIDIS, K. WITTE, and G. D. TSAKIRIS. "ON THE SECOND ORDER AUTOCORRELATION OF AN XUV ATTOSECOND PULSE TRAIN." In Proceedings of the XXIV International Conference. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812772442_0019.

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Ahmad, Mohammad Shukri, Osman Kukrer, and Aykut Hocanin. "Recursive inverse adaptive filter with second order estimation of autocorrelation matrix." In 2010 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2010). IEEE, 2010. http://dx.doi.org/10.1109/isspit.2010.5711771.

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Khoury, Jehad, Anne Marie Biernacki, Mark Cronin-Golomb, and Charles L. Woods. "Photorefractive adaptive autocorrelation measurements of surface roughness." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.mtt4.

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We show that adaptive optical autocorrelation can be used to measure the typical grain size of a rough surface. According to second-order statistics,1 the autocorrelation of the optical image of a surface provides a measurement of its grain size. This grain size may be found from the ratio of the peak (de) intensity to the broad background intensity in the autocorrelation. A photorefractive autocorrelator may be made by modifying a four-wave mixing correlator.2 A beam bearing an image of the rough surface is presented as one input to a correlator using bismuth germanate (BGO) in the Fourier plane. After passage through the BGO, this beam is phase conjugated by a barium titanate crystal. The resulting complex conjugate of the image beam is presented automatically self-aligned as the second input. The third input is a plane wave in the Fourier plane. The output of the device is the autocorrelation of the constant amplitude, phase modulated surface image. This autocorrelation consists of a sharp (de component) surrounded by a broad low-intensity background of the cross correlation of the rough surface.3 The ratio of the de component to the cross correlation component is used to determine the surface roughness properties. Care was taken to capture all of the diffracted light from the surface to avoid finite aperture effects.4 The autocorrelator measurements agree with microscopic surface measurements.
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Wei, Yizhen, Scott Howard, Ji Cheng, Zinan Wang, Adam Straub, and Chris Xu. "High sensitivity, simultaneous second- and third-order autocorrelation measurement in a GaAsP photomultiplier tube." In CLEO: Applications and Technology. Washington, D.C.: OSA, 2011. http://dx.doi.org/10.1364/cleo_at.2011.jwa83.

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Chenyu, Ding, Yue Ruihua, and Li Yuandong. "ARL Study of Second Order Autocorrelation Residual Control Chart and its Application in MAP." In 2018 37th Chinese Control Conference (CCC). IEEE, 2018. http://dx.doi.org/10.23919/chicc.2018.8483040.

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Klein, Stanley A., and Christopher W. Tyler. "Nth-order autocorrelation functions and peripheral phase discrimination." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.thh6.

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A pair of repetitive textures with the same Fourier spectra can be discriminated based on the relative phases of the components. We have found that for some pairs of textures phase discrimination is easy while for other pairs phase discrimination is impossible. A scheme for classifying textures based on autocorrelation functions greater than second order is able to account for these results. The autocorrelation scheme allows one to generate patterns with statistical constraints of any order, in one and two dimensions. We show that without scrutiny by foveal attention, discrimination fails at about the level of fourth-order constraints. This autocorrelation analysis also can account for why certain phases are easier to discriminate than others. For example, Rentschler and Treutwein1 showed that in the periphery the threshold phase ϕ for discriminating cos(fx) + cos(3fx + ϕ) vs cos(fx) + cos(3fx − ϕ) is much larger than the phase threshold of sin(fx) + sin(3fx + ϕ) vs sin(fx) + sin(3fx − ϕ). The autocorrelation analysis is in agreement with this result. The autocorrelation approach may provide a general metric for phase discrimination of repetitive textures in peripheral vision.
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Jennings, Andrew, Paul Horan, and John Hegarty. "Quantum well asymmetric Fabry-Perot array for second-order neural nets." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.mv4.

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Multiple quantum well electrooptic (EO) devices based on the quantum confined Stark effect have been studied widely. Asymmetric Fabry-Perot (AFP)1 devices in these materials have shown promise as normal incidence devices allowing freedom for generating device arrays. We report on an array of EO AFP reflection devices that has been designed and fabricated specifically for use in a second-order neural network system. The device structure consists of 70 gallium arsenide wells 90 Å in width and 60-Å aluminum/gallium arsenide barriers within a PIN structure and an AFP resonator with back and front reflectivities of 98% and 30%, respectively. A linear array of twenty-five devices each 100 mm wide and 2.5 mm long form a 2.5-mm square of active device area, representing a binary vector with twenty-five elements. By having a double pass on the device array and rotating the device image by 90° between passes, all the terms of the autocorrelation matrix (vector cross product with itself) are generated.2 This is a basic requirement for any second-order neural network.
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Papadimitriou, Dimitrios, Zissimos P. Mourelatos, and Zhen Hu. "Non-Linear Random Vibrations Using Second-Order Adjoint and Projected Differentiation Methods." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-69685.

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Abstract This paper proposes a new computationally efficient methodology for random vibrations of nonlinear vibratory systems using a time-dependent second-order adjoint variable (AV2) method, and a second-order projected differentiation (PD2) method. The proposed approach is called AV2-PD2. The vibratory system can be excited by stationary Gaussian or non-Gaussian random processes. A Karhunen-Loeve (KL) expansion expresses each input random process in terms of standard normal random variables. A second-order adjoint approach is used to obtain the required first and second-order output derivatives accurately by solving as many sets of equations of motion (EOMs) as the number of KL random variables. These derivatives are used to compute the marginal CDF of the output process with second-order accuracy. Then, a second-order projected differentiation method calculates the autocorrelation function of each output process with second-order accuracy, at an additional cost of solving as many sets of EOM as the number of outputs of interest, independently of the time horizon (simulation time). The total number of solutions of the EOM scales linearly with the number of input KL random variables and the number of output processes. The efficiency and accuracy of the proposed approach is demonstrated using a non-linear Duffing oscillator problem under a quadratic random excitation.
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Fischer, R., C. Rempel, J. Gauger, and J. Tilgner. "Ultrashort pulse chirp parameter determination by interferometric methods." In International Laser Science Conference. Washington, D.C.: Optica Publishing Group, 1986. http://dx.doi.org/10.1364/ils.1986.thj5.

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For pulses shorter than 1 ps, only indirect methods can be used. Besides conventional second-order autocorrelations and the detection of the pulse-bandwidth product, two interferometric techniques have been proposed: fringe resolved second-order autocorrelation (FRACF) and linear autocorrelation (LA), i.e., the Fourier spectrum of the pulse. Especially interesting is the measurement of the phase dependence on the length of the pulse duration, the so-called chirp. We have performed calculations of the FRACF and the LA with a fast Fourier transform algorithm for different pulse shapes and chirp functions. The results show for both the FRACF and the LA the typical narrowing of the main peak when the chirp increases. It was already known that FRACF shows side maxima in the presence of chirp, but we have shown that such side maxima also occur in the case of LA if the chirp is nonlinear. Because of the simpler mathematical expression, the LA is easier to interpret. We compare the results of both methods for two different pulses: a hyperbolic secant pulse with a chirp function which is typical of colliding pulse-mode locking of dye lasers and a Gaussian pulse shape where the time dependence of the phase is caused by self-phase modulation.
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PALICOT, Jacques, Ziad KHALAF, and Amor NAFKHA. "Blind Spectrum Detector for Cognitive Radio Using Compressed Sensing and Symmetry Property of the Second Order Cyclic Autocorrelation." In 7th International Conference on Cognitive Radio Oriented Wireless Networks. IEEE, 2012. http://dx.doi.org/10.4108/icst.crowncom.2012.248133.

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