Investigation on de-noising and demodulation of impulsive modulated signal using multi-scale morphological filters

This article presents a fast and accurate fault location approach for power transmission lines based on the theory of traveling waves. In fact, when faults occur, they give rise to transient voltages and currents that propagate at a speed close to that of light along the transmission line as traveling waves. Moreover, according to the superposition theorem, each of these transients is a combination of a steady-state quantity and an incremental quantity. These transient signals measured at both ends of the line are first transformed to the Clarke (0-α-β components) components in order to categorize the type of faults, and then multi-scale morphological gradient filters are used to extract equivalent quantities to the incremental quantities to form what are called characteristic signals. These latter will be used to identify the fault location according to the proposed algorithm.

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Multi–Scale Synthesized View Assessment Based on Morphological Pyramids

Journal of Electrical Engineering ◽

10.1515/jee-2016-0001 ◽

2016 ◽

Vol 67 (1) ◽

pp. 3-11 ◽

Cited By ~ 30

Author(s):

Dragana Sandić-Stanković ◽

Dragan Kukolj ◽

Patrick Le Callet

Keyword(s):

Image Quality Assessment ◽

Signal To Noise Ratio ◽

Depth Image ◽

Human Judgment ◽

Computationally Efficient ◽

Morphological Filters ◽

Geometric Information ◽

Multi Scale ◽

Geometric Distortions ◽

Depth Image Based Rendering

Abstract The Depth-Image-Based-Rendering (DIBR) algorithms used for 3D video applications introduce geometric distortions affecting the edge coherency in the synthesized images. In order to better deal with specific geometric distortions in the DIBR synthesized images, we propose full-reference metric based on multi-scale pyramid decompositions using morphological filters. The non-linear morphological filters used in multi-scale image decompositions maintain important geometric information such as edges across different resolution levels. We show that PSNR has particularly good agreement with human judgment when it is calculated between detailed images at higher scales of morphological pyramids. Consequently, we propose reduced morphological pyramid peak signal-to-noise ratio metric (MP-PSNR), taking into account only mean squared errors between pyramids’ images at higher scales. Proposed computationally efficient metric achieves significantly higher correlation with human judgment compared to the state-of-the-art image quality assessment metrics and compared to the tested metric dedicated to synthesis-related artifacts.

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