Periodicity of quasar and galaxy redshift

Context. An approach for studying the large-scale structure of the Universe lies in the detection and analysis of periodicity of redshift values of extragalactic objects, galaxies, and quasi stellar objects (QSO), in particular. Moreover, the hypothesis of the existence of multiple periodicities in the redshift distributions deserves exploration. The task is compounded by the presence of confounding effects and measurement noise. Aims. Studies of periodicity detection in redshift values of extragalactic objects obtained from the Sloan Digital Sky Survey (SDSS) have been conducted in the past, largely based on the Fourier transform. The present study aims to revisit the same thing using the singular value decomposition (SVD) as the primary tool. Methods. Periodicity detection and the determination of the fundamental period have been performed using a standard spectral approach as well as a SVD-based approach for a variety of simulated datasets. The analysis of the quasar redshift data from DR12 and galaxy redshift dataset of DR10 from SDSS data has been carried out. Results. A wide range of periodicities are observed in different redshift ranges of the quasar datasets. For redshifts greater than 0.03, a period length of 0.2094 was determined while periodicities of 0.1210 and 0.0654 were obtained for redshift ranges (0.03, 1) and (3, 4), respectively. Twin periodicities of 0.1153 and 0.0807 were obtained for the redshift range (1, 3). Determining the ranges to be examined has been done based on histogram computation; the binwidths of which have been obtained by employing a kernel density estimation. The redshift sequence for the galaxy samples exhibits a somewhat different nature, but still contains periodic components. Twin periodicities of 0.0056 and 0.0079 were observed for a redshift range greater than 0.03. Conclusions. Galaxy and quasar redshift values form sequences, which are not only discrete in amplitude but also contain periodic components. The superiority of the singular value decomposition method over the spectral estimation approach, in redshift periodicity analysis especially from the point of view of robustness, is demonstrated through simulations. The existence of periodicity for quasar and galaxy families is thus firmly established, lending further support to the Hoyle-Narlikar variable mass theory.

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Dynamic Response of the Manipulator Tip to a Random Excitation

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.2801192 ◽

1996 ◽

Vol 118 (3) ◽

pp. 629-632 ◽

Cited By ~ 1

Author(s):

Marek R. Kujath ◽

Unyime O. Akpan

Keyword(s):

Singular Value Decomposition ◽

Dynamic Response ◽

Random Excitation ◽

Singular Value ◽

Decomposition Technique ◽

Damping Coefficients ◽

Wide Range ◽

Value Decomposition

The paper describes the tip responses of a flexible articulated manipulator to a stationary random excitation of the base. The expressions for the covariance tensors of the manipulator tip motion are developed in the base and the inertia frame. The singular value decomposition technique is applied for the derivation of expressions for the principal variances. The principal variances are computed and discussed for a wide range of manipulator configurations and damping coefficients.

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Multilag Singular Value Decomposition Analysis of ENSO Convection with Tropical Stratospheric Temperature

Journal of Climate ◽

10.1175/2008jcli2298.1 ◽

2008 ◽

Vol 21 (24) ◽

pp. 6556-6568 ◽

Cited By ~ 7

Author(s):

Bryan C. Weare

Keyword(s):

Singular Value Decomposition ◽

Southern Oscillation ◽

Decomposition Analysis ◽

Singular Value ◽

Central Pacific ◽

Singular Value Decomposition Analysis ◽

Stratospheric Temperature ◽

Wide Range ◽

Value Decomposition ◽

The Impact

Abstract Multilag singular value decomposition (MLSVD) analysis is developed and applied to diagnosing the impact of interannual variations of outgoing longwave radiation (OLR) on tropical stratospheric temperature changes. MLSVD is designed to analyze simultaneously variations at multiple levels and for a large number of temporal lags and leads. The two dominant MLSVDs are strongly related to El Niño–Southern Oscillation (ENSO). The associated patterns of tropical OLR are similar to the canonical ENSO SST patterns with strong negative sign regions stretching along the equator in the eastern and central Pacific. These dominant modes are strongly linked to temperature perturbations at a wide range of lags. At the lowest analyzed level (200 hPa) and zero lag positive temperatures anomalies are in the region of low OLR. In the lower stratosphere near 100 hPa, strong negative temperature perturbations replace the positive values of the lowest level. Higher in the stratosphere near 20 hPa, equatorial temperature perturbations are again positive, but with a more zonally elongated spatial pattern. Overall, the equatorial temperature anomalies propagate slowly to the east, at a speed strongly related to ocean–atmosphere coupling of less than 1 m s−1, and vertically and westward into the stratosphere by Rossby waves with a speed in the range of 30 m s−1.

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Blind Digital Images Tampering Detection Based on Singular Value Decomposition

International Journal of Intelligent Engineering and Systems ◽

10.22266/ijies2020.1231.30 ◽

2020 ◽

Vol 13 (6) ◽

pp. 338-348

Author(s):

Nidhal Abbadi ◽

◽

Alyaa Mohsin ◽

Keyword(s):

Singular Value Decomposition ◽

Digital Images ◽

Singular Value ◽

Suggested Method ◽

Tampering Detection ◽

Gray Image ◽

Important Type ◽

Wide Range ◽

Object Rotation ◽

Value Decomposition

The growing use of digital images in a wide range of applications, and growing the availability of many editing photo software, cause to emerge a challenge to discover the images tampering. In this paper, we proposed a method to detect the most important type of forgery image (copy and move). We suggested many steps to classify the image as forgery or non-forgery image, started with preprocessing (included, convert image to gray image, de-noising, and image resize). Then, the image will be divided into several overlapping blocks. For each block, feature extracted (used it as a matching feature) by using the singular value decomposition (SVD) transformation. According to these features, the pixels were collected in many main groups, and then these groups clustered to many subgroups. The weight for each main group can be determined by comparing the subgroups with each other according to suggested conditions. The number of subgroups and weights are used to classify images to forgery or non-forgery images. The accuracy of detection and classified the forgery images were up to 97%. The suggested method is robust for tampered object rotation, scaling, and change of illumination.

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Image Denoising Using Low Rank Matrix Approximation in Singular Value Decomposition

Revista Gestão Inovação e Tecnologias ◽

10.47059/revistageintec.v11i2.1769 ◽

2021 ◽

Vol 11 (2) ◽

pp. 1430-1446

Author(s):

Satyanarayana Tallapragada V.V.

Keyword(s):

Singular Value Decomposition ◽

Image Denoising ◽

Singular Values ◽

Singular Value ◽

Low Rank ◽

Lower Rank ◽

Rank Matrix ◽

Wide Range ◽

Value Decomposition ◽

Low Rank Matrix

The factorization of a matrix into lower rank matrices give solutions to a wide range of computer vision and image processing tasks. The inherent patches or the atomic patches can completely describe the whole image. The lower rank matrices are obtained using different tools including Singular Value Decomposition (SVD), which is typically found in minimization problems of nuclear norms. The singular values obtained will generally be a thresholder to realize the nuclear norm minimization. However, soft-thresholding is performed uniformly on all the singular values that lead to a similar importance to all the patches whether it is principal/useful or not. Our observation is that the decision on a patch (to be principal/useful or not) can be taken only when the application of this minimization is taken into consideration. Thus, in this paper, we propose a new method for image denoising by choosing variable weights to different singular values with a deep noise effect. Experimental results illustrate that the proposed weighted scheme performs better than the state-of-the-art methods.

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COMBINING EXTREMAL OPTIMIZATION WITH SINGULAR VALUE DECOMPOSITION FOR EFFECTIVE POINT MATCHING

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001403002782 ◽

2003 ◽

Vol 17 (07) ◽

pp. 1111-1126 ◽

Cited By ~ 6

Author(s):

SOUHAM MESHOUL ◽

MOHAMED BATOUCHE

Keyword(s):

Singular Value Decomposition ◽

Real Data ◽

Singular Value ◽

Feature Point ◽

Point Matching ◽

Extremal Optimization ◽

Wide Range ◽

Feature Point Matching ◽

Value Decomposition

Feature point matching is a key step for most problems in computer vision. It is an ill-posed problem and suffers from combinatorial complexity which becomes even more critical with the increase in data and the presence of outliers. The work covered in this paper describes a new framework to solve this problem in order to achieve robust registration of two feature point sets assumed to be available. This framework combines the use of extremal optimization heuristic with a clever startup routine which exploits some properties of singular value decomposition. The role of the latter is to produce an interesting matching configuration whereas the role of the former is to refine the initial matching by generating hypothetical matches and outliers using a far-from-equilibrium based stochastic rule. Experiments on a wide range of real data have shown the effectiveness of the proposed method and its ability to achieve reliable feature point matching.

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