Iterative Adaptive Algorithm based on the cross covariance matrix of acoustic pressure and particle velocity

2015 ◽  
pp. 279-284
Keyword(s):  
Covariance Matrix ◽  
Particle Velocity ◽  
Adaptive Algorithm ◽  
Acoustic Pressure ◽  
Cross Covariance ◽  
The Cross

Flow-Excited Acoustic Resonance of Isolated Cylinders in Cross-Flow

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Nadim Arafa ◽  
Atef Mohany
Keyword(s):  
Particle Velocity ◽  
Acoustic Pressure ◽  
Excited Level ◽  
Cross Flow ◽  
Acoustic Resonance ◽  
Excitation Level ◽  
Linear Superposition ◽  
The Cross ◽  
Particle Velocities ◽  
Mode A

The flow-excited acoustic resonance of isolated cylinders in cross-flow is investigated experimentally where the effect of the cylinder(s) proximity to the acoustic particle velocity nodes of the cross-modes is presented in this paper. For the case of a single cylinder, the cylinder's location does not significantly affect the vortex shedding process; however, it affects the excitation level of each acoustic cross-mode. When the cylinder is moved away from the acoustic particle velocity antinode of a specific acoustic cross-mode, a combination of the cross-modes is excited with intensities that seem to be proportional to the ratio of the acoustic particle velocities of these modes at the cylinder's location. For the cases of two and three hydrodynamically uncoupled cylinders positioned simultaneously side-by-side in the duct, it is observed that the first three acoustic cross-modes are excited. When one cylinder is positioned at the acoustic particle velocity antinode of a specific cross-mode and another cylinder is positioned at its acoustic particle velocity node, i.e., a cylinder that should excite the resonance and another one that should not excite it, respectively; the excitation always takes over and the resonance occurs at a further elevated levels. It is also observed that the acoustic pressure levels in the cases of multiple cylinders are not resulting from a linear superposition of the excited level obtained from each individual cylinder which indicates that the removal of cylinders at certain locations may not be a viable technique to eliminate the acoustic resonance in the case of tube bundles.

scholarly journals Perturbation theory approach to predict the covariance matrices of the galaxy power spectrum and bispectrum in redshift space

2020 ◽  
Vol 497 (2) ◽  
pp. 1684-1711 ◽  
Author(s):  
Naonori S Sugiyama ◽  
Shun Saito ◽  
Florian Beutler ◽  
Hee-Jong Seo
Keyword(s):  
Perturbation Theory ◽  
Power Spectrum ◽  
Shot Noise ◽  
Signal To Noise Ratio ◽  
Covariance Matrices ◽  
Theory Approach ◽  
Cross Covariance ◽  
The Galaxy ◽  
The Cross ◽  
Non Gaussian

ABSTRACT In this paper, we predict the covariance matrices of both the power spectrum and the bispectrum, including full non-Gaussian contributions, redshift space distortions, linear bias effects, and shot-noise corrections, using perturbation theory (PT). To quantify the redshift-space distortion effect, we focus mainly on the monopole and quadrupole components of both the power and bispectra. We, for the first time, compute the 5- and 6-point spectra to predict the cross-covariance between the power and bispectra, and the autocovariance of the bispectrum in redshift space. We test the validity of our calculations by comparing them with the covariance matrices measured from the MultiDark-Patchy mock catalogues that are designed to reproduce the galaxy clustering measured from the Baryon Oscillation Spectroscopic Survey Data Release 12. We argue that the simple, leading-order PT works because the shot-noise corrections for the Patchy mocks are more dominant than other higher order terms we ignore. In the meantime, we confirm some discrepancies in the comparison, especially of the cross-covariance. We discuss potential sources of such discrepancies. We also show that our PT model reproduces well the cumulative signal-to-noise ratio of the power spectrum and the bispectrum as a function of maximum wavenumber, implying that our PT model captures successfully essential contributions to the covariance matrices.

Cross-covariance matrix: Time-shifted correlations for 3D action recognition

2020 ◽  
Vol 171 ◽  
pp. 107499
Author(s):  
Jianhai Zhang ◽  
Zhiyong Feng ◽  
Yong Su ◽  
Meng Xing
Keyword(s):  
Covariance Matrix ◽  
Action Recognition ◽  
Cross Covariance

ENCODING STRUCTURAL SIMILARITY BY CROSS-COVARIANCE TENSORS FOR IMAGE CLASSIFICATION

2014 ◽  
Vol 28 (07) ◽  
pp. 1460008
Author(s):  
MARCO SAN BIAGIO ◽  
SAMUELE MARTELLI ◽  
MARCO CROCCO ◽  
MARCO CRISTANI ◽  
VITTORIO MURINO
Keyword(s):  
Computer Vision ◽  
Covariance Matrix ◽  
Image Representation ◽  
Structural Similarity ◽  
Covariance Matrices ◽  
Integral Image ◽  
Computational Burden ◽  
Feature Vectors ◽  
Single Region ◽  
Cross Covariance

In computer vision, an object can be modeled in two main ways: by explicitly measuring its characteristics in terms of feature vectors, and by capturing the relations which link an object with some exemplars, that is, in terms of similarities. In this paper, we propose a new similarity-based descriptor, dubbed structural similarity cross-covariance tensor (SS-CCT), where self-similarities come into play: Here the entity to be measured and the exemplar are regions of the same object, and their similarities are encoded in terms of cross-covariance matrices. These matrices are computed from a set of low-level feature vectors extracted from pairs of regions that cover the entire image. SS-CCT shares some similarities with the widely used covariance matrix descriptor, but extends its power focusing on structural similarities across multiple parts of an image, instead of capturing local similarities in a single region. The effectiveness of SS-CCT is tested on many diverse classification scenarios, considering objects and scenes on widely known benchmarks (Caltech-101, Caltech-256, PASCAL VOC 2007 and SenseCam). In all the cases, the results obtained demonstrate the superiority of our new descriptor against diverse competitors. Furthermore, we also reported an analysis on the reduced computational burden achieved by using and efficient implementation that takes advantage from the integral image representation.

scholarly journals Second-order properties and central limit theory for the vertex process of iteration infinitely divisible and iteration stable random tessellations in the plane

2010 ◽  
Vol 42 (4) ◽  
pp. 913-935 ◽  
Author(s):  
Tomasz Schreiber ◽  
Christoph Thäle
Keyword(s):  
Correlation Function ◽  
Point Process ◽  
Central Limit ◽  
Pair Correlation ◽  
Density Parameter ◽  
Infinitely Divisible ◽  
Limit Theory ◽  
Vertex Point ◽  
Cross Covariance ◽  
The Cross

The point process of vertices of an iteration infinitely divisible or, more specifically, of an iteration stable random tessellation in the Euclidean plane is considered. We explicitly determine its covariance measure and its pair-correlation function, as well as the cross-covariance measure and the cross-correlation function of the vertex point process and the random length measure in the general nonstationary regime. We also give special formulae in the stationary and isotropic setting. Exact formulae are given for vertex count variances in compact and convex sampling windows, and asymptotic relations are derived. Our results are then compared with those for a Poisson line tessellation having the same length density parameter. Moreover, a functional central limit theorem for the joint process of suitably rescaled total edge counts and edge lengths is established with the process (ξ, tξ), t > 0, arising in the limit, where ξ is a centered Gaussian variable with explicitly known variance.

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