ISSN: 1813-0410

Keywords : DWT


Comprehensive Faults Classification Method for Unbalanced Power Distribution Systems

Shamam Fadhil Alwash

journal of kerbala university, Volume 14, Issue 1, Pages 396-406

Unbalanced power distribution systems experience single faults and compound faults types. The classification of these faults is considered as one of the most important requirements for the fault analysis and the fault location techniques. However, existing methods for fault classification have been formulated to consider only single-fault types. This paper presents a comprehensive faults classification method for unbalanced power distribution systems. In this method, new fault classification indices are derived to consider all fault types including the compound-fault ones. The values of these indices are determined based on the transient analysis of the current signals using discrete wavelet transform (DWT). These indices are utilized in conjunction with adaptive neural-fuzzy inference systems (ANFIS) to classify all fault types. In order to verify the accuracy of the proposed method, a practical distribution system is used to test the method under different fault conditions.

Wavelet-Based Video Compression System Using Diamond Search (DS) Matching Algorithm

Marwa Kamel Hussien; Hameed Abdul-Kareem Younis

journal of kerbala university, Volume 1, Issue 0, Pages 249-258

Currently, multimedia technology is widely used. Using the video encoding compression technology can save storage space, and also can improve the transmission efficiency of network communications. In video compression methods, the first frame of video is independently compressed as a still image, this is called intra coded frame. The remaining successive frames are compressed by estimating the disparity between two adjacent frames, which is called inter coded frame. In this paper, intra frame was transformed using Discrete Wavelet Transform (DWT).
The disparity between each two frames was estimated by Diamond Search (DS) Algorithm. The result of the Motion Vector (MV) was encoded into a bit stream by Huffman encoding while the remaining part is compressed like the compression was used in intra frame. Experimental results showed good results in terms of Peak Signal-to-Noise Ratio (PSNR), Compression Ratio (CR), and processing time.