Finite-Frequency Fault Detection for Two-Dimensional Roesser Systems

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Eberly College of Arts and Sciences


Physics and Astronomy


Rotating radio transients (RRATs) are sporadically emitting pulsars which are detected only through single pulse search. Detecting these single pulses in RRATs observation with high detection accuracy is a challenge due to the background noise. It is better to conduct the single pulse detection directly on the raw time-frequency observation than on the de-dispersed data, because de-dispersion process takes very intensive computation. In this paper, we propose to accomplish this idea by treating two-dimensional (2D) time-frequency data as images and develop a curvelet based denoising approach after studying the characteristics of the RRATs pulses and the noise. The denoising approach estimates the range of curvature (orientations) and width (scales) that describe the RRATs pulses and reconstructs cleaner images from the selected orientations and scales. The proposed denoising approach does not require prior knowledge of exact dispersion measures (DM) value. In addition, a framework of detecting the single pulses from the time-frequency data, named HOG-SVM, is also proposed to further evaluate the curvelet based denoising approach. Compared with the other four denoising approaches, the proposed curvelet based method leads to better detection results, with detection accuracy being increased to 98.7% by HOG-SVM.

Source Citation

M. Jiang, B. Cui, Y. Yu and Z. Cao, "DM-Free Curvelet Based Denoising for Astronomical Single Pulse Detection," in IEEE Access, vol. 7, pp. 107389-107399, 2019. doi: 10.1109/ACCESS.2019.2933387 keywords: {astronomical image processing;curvelet transforms;image denoising;object detection;pulsars;support vector machines;wavelet transforms;DM-free curvelet;astronomical single pulse detection;single pulse search;high detection accuracy;raw time-frequency observation;de-dispersion process;two-dimensional time-frequency data;denoising approach;exact dispersion measures value;curvelet based method;RRAT pulses;RRAT observation;Noise reduction;Time-frequency analysis;Dispersion;Image reconstruction;Transforms;Image denoising;Two dimensional displays;Astronomical single pulse;curvelet based denoising;DM-free;single pulse detection}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8788523&isnumber=8600701


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