Fringe analysis software: Application to velocity measurement in dynamic compression experiment

Sur, Amit; Rav, Amit; Joshi, K. D.; Gupta, Satish C.; Roy, Kallol

doi:10.1109/indicon.2014.7030619

Cited by 3 publications

(4 citation statements)

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“…Phase unwrapping algorithms are developed to detect phase jumps and convert them into an accurate total number of fringes [16,17]. The presence of noise (low SNR) in the recorded signal makes it difficult to detect the phase jump thereby causing phase unwrapping errors [16][17][18][19][20]. Apart from that, as mentioned earlier, the increasing time of the signals in dynamic compression experiments are of the order of a few tens of nanoseconds, hence the dynamic range (in terms of time as well as amplitude response) of a SRS has to be sufficient to record the fringe signal faithfully.…”

Section: Visar Output and Effect Of The Srs On Output Signalmentioning

confidence: 99%

Characterization of a signal recording system for accurate velocity estimation using a VISAR

Rav

Joshi

Singh

et al. 2018

Meas. Sci. Technol.

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The linearity of a signal recording system (SRS) in time as well as in amplitude are important for the accurate estimation of the free surface velocity history of a moving target during shock loading and unloading when measured using optical interferometers such as a velocity interferometer system for any reflector (VISAR). Signal recording being the first step in a long sequence of signal processes, the incorporation of errors due to nonlinearity, and low signal-to-noise ratio (SNR) affects the overall accuracy and precision of the estimation of velocity history. In shock experiments the small duration (a few µs) of loading/unloading, the reflectivity of moving target surface, and the properties of optical components, control the amount of input of light to the SRS of a VISAR and this in turn affects the linearity and SNR of the overall measurement. These factors make it essential to develop in situ procedures for (i) minimizing the effect of signal induced noise and (ii) determine the linear region of operation for the SRS. Here we report on a procedure for the optimization of SRS parameters such as photodetector gain, optical power, aperture etc, so as to achieve a linear region of operation with a high SNR. The linear region of operation so determined has been utilized successfully to estimate the temporal history of the free surface velocity of the moving target in shock experiments.

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Section: Visar Output and Effect Of The Srs On Output Signalmentioning

confidence: 99%

Characterization of a signal recording system for accurate velocity estimation using a VISAR

Rav

Joshi

Singh

et al. 2018

Meas. Sci. Technol.

View full text Add to dashboard Cite

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“…Φ(t) is the time profile of the unwrapped phase of the recorded signal. Equation (1), which is recorded in the quadrature, is solved for F(t)/Φ(t) using the different phase unwrapping methods provided in the literature [12,13,15,19].…”

Section: Visarmentioning

confidence: 99%

“…The bior6.8 [14] and db8 mother wavelets were selected. Further details of the wavelet shrinkage method for de-noising are beyond the scope of this communication and can be obtained in [13,14,16,17].…”

Section: Comparison Of the Proposed Filter With Other Commonly Used F...mentioning

confidence: 99%

“…Phase unwrapping algorithms have been developed to detect phase jumps and convert them into an accurate total number of fringes [12]. The presence of noise (a low SNR) in the recorded signal makes it difficult to detect phase jumps correctly, meaning false jumps are detected [13][14][15]. Figures 1(a) and (b) represent two situations: the first one with a clean signal, and the second with a noisy signal.…”

Section: Introductionmentioning

confidence: 99%

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Velocity interferometer signal de-noising using modified Wiener filter

Rav

Joshi

Roy

et al. 2017

Meas. Sci. Technol.

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The accuracy and precision of the non-contact velocity interferometer system for any reflector (VISAR) depends not only on the good optical design and linear optical-to- electrical conversion system, but also on accurate and robust post-processing techniques. The performance of these techniques, such as the phase unwrapping algorithm, depends on the signal-to-noise ratio (SNR) of the recorded signal. In the present work, a novel method of improving the SNR of the recorded VISAR signal, based on the knowledge of the noise characteristic of the signal conversion and recording system, is presented. The proposed method uses a modified Wiener filter, for which the signal power spectrum estimation is obtained using a spectral subtraction method (SSM), and the noise power spectrum estimation is obtained by taking the average of the recorded signal during the period when no target movement is expected. Since the noise power spectrum estimate is dynamic in nature, and obtained for each experimental record individually, the improved signal quality is high. The proposed method is applied to the simulated standard signals, and is not only found to be better than the SSM, but is also less sensitive to the selection of the noise floor during signal power spectrum estimation. Finally, the proposed method is applied to the recorded experimental signal and an improvement in the SNR is reported.

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