Cross-correlation image processing applications in time-resolved thermography of near-wall flows

Super-Resolution Defect Characterization Using Microwave Near-Field Resonance Reflectometry and Cross-correlation Image Processing

Sensing and Imaging ◽

10.1007/s11220-017-0157-8 ◽

2017 ◽

Vol 18 (1) ◽

Cited By ~ 6

Author(s):

Oleksandr Malyuskin ◽

Vincent Fusco

Keyword(s):

Image Processing ◽

Cross Correlation ◽

Near Field ◽

Super Resolution ◽

Defect Characterization ◽

Correlation Image

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Real-Time Measurement of Microvascular Dimensions Using Digital Cross-Correlation Image Processing

Journal of Vascular Research ◽

10.1159/000158938 ◽

1992 ◽

Vol 29 (3) ◽

pp. 241-247 ◽

Cited By ~ 8

Author(s):

S. Magers ◽

J.E. Faber

Keyword(s):

Image Processing ◽

Real Time ◽

Cross Correlation ◽

Time Measurement ◽

Real Time Measurement ◽

Correlation Image

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Time-resolved fractal dimension analysis in ferroelectric copolymer thin films using R-based image processing

Materials Letters ◽

10.1016/j.matlet.2018.07.125 ◽

2018 ◽

Vol 230 ◽

pp. 195-198 ◽

Cited By ~ 1

Author(s):

Seonhyoung Kim ◽

Kwang-Won Park ◽

Ho-young Woo ◽

Jongin Hong

Keyword(s):

Thin Films ◽

Image Processing ◽

Fractal Dimension ◽

Time Resolved ◽

Dimension Analysis ◽

Fractal Dimension Analysis

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RECOGNIZING DIFFERENT TYPES OF STOCHASTIC PROCESSES

Fluctuation and Noise Letters ◽

10.1142/s0219477506003082 ◽

2006 ◽

Vol 06 (01) ◽

pp. L1-L6

Author(s):

JONG U. KIM ◽

LASZLO B. KISH

Keyword(s):

Image Processing ◽

Stochastic Processes ◽

Cross Correlation ◽

Correlation Method ◽

Correlation Coefficients ◽

Power Density Spectrum ◽

Density Spectrum ◽

Random Telegraph Signals ◽

Different Types ◽

Coefficient Method

We propose a new cross-correlation method that can recognize independent realizations of the same type of stochastic processes and can be used as a new kind of pattern recognition tool in biometrics, sensing, forensic, security and image processing applications. The method, which we call bispectrum correlation coefficient method, makes use of the cross-correlation of the bispectra. Three kinds of cross-correlation coefficients are introduced. To demonstrate the new method, six different random telegraph signals are tested, where four of them have the same power density spectrum. It is shown that the three coefficients can map the different stochastic processes to specific sub-volumes in a cube.

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Assessing Convergence in Predictions of Periodic-Unsteady Flowfields

Volume 6: Turbomachinery, Parts A and B ◽

10.1115/gt2006-90735 ◽

2006 ◽

Cited By ~ 4

Author(s):

J. P. Clark ◽

E. A. Grover

Keyword(s):

Cross Correlation ◽

Unsteady Flows ◽

Digital Signal ◽

Design Decisions ◽

Time Resolved ◽

Design Engineers ◽

Cross Correlations ◽

Parseval’S Theorem ◽

Hot Streak

Predictions of time-resolved flowfields are now commonplace within the gas-turbine industry, and the results of such simulations are often used to make design decisions during the development of new products. Hence it is necessary for design engineers to have a robust method to determine the level of convergence in design predictions. Here we report on a method developed to determine the level of convergence in a predicted flowfield that is characterized by periodic-unsteadiness. The method relies on fundamental concepts from digital signal processing including the discrete Fourier transform, cross-correlation, and Parseval’s theorem. Often in predictions of vane-blade interaction in turbomachines, the period of the unsteady fluctuations is expected. In this method, the development of time-mean quantities. Fourier components (both magnitude and phase), cross-correlations, and integrated signal power are tracked at locations of interest from one period to the next as the solution progresses. Each of these separate quantities yields some relative measure of convergence that is subsequently processed to form a fuzzy set. Thus the overall level of convergence in the solution is given by the intersection of these sets. Examples of the application of this technique to several predictions of unsteady flows from two separate solvers are given. These include a prediction of hot-streak migration as well as more typical cases. It is shown that the method yields a robust determination of convergence. Also, the results of the technique can guide further analysis and/or post-processing of the flowfield. Finally, the method is useful for the detection of inherent unsteadiness in the flowfield, and as such it can be used to prevent design escapes.

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