Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 8

Search results for: wavelet transform

8 Robust Minutiae Watermarking in Wavelet Domain for Fingerprint Security

Authors: Rajlaxmi Chouhan, Pritee Khanna

Abstract:

In this manuscript, a wavelet-based blind watermarking scheme has been proposed as a means to provide security to authenticity of a fingerprint. The information used for identification or verification of a fingerprint mainly lies in its minutiae. By robust watermarking of the minutiae in the fingerprint image itself, the useful information can be extracted accurately even if the fingerprint is severely degraded. The minutiae are converted in a binary watermark and embedding these watermarks in the detail regions increases the robustness of watermarking, at little to no additional impact on image quality. It has been experimentally shown that when the minutiae is embedded into wavelet detail coefficients of a fingerprint image in spread spectrum fashion using a pseudorandom sequence, the robustness is observed to have a proportional response while perceptual invisibility has an inversely proportional response to amplification factor “K". The DWT-based technique has been found to be very robust against noises, geometrical distortions filtering and JPEG compression attacks and is also found to give remarkably better performance than DCT-based technique in terms of correlation coefficient and number of erroneous minutiae.

Keywords: PN sequence, Minutiae, discrete wavelet transform, Fingerprint watermarking

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7 Detection of Power Quality Disturbances using Wavelet Transform

Authors: Sudipta Nath, Arindam Dey, Abhijit Chakrabarti

Abstract:

This paper presents features that characterize power quality disturbances from recorded voltage waveforms using wavelet transform. The discrete wavelet transform has been used to detect and analyze power quality disturbances. The disturbances of interest include sag, swell, outage and transient. A power system network has been simulated by Electromagnetic Transients Program. Voltage waveforms at strategic points have been obtained for analysis, which includes different power quality disturbances. Then wavelet has been chosen to perform feature extraction. The outputs of the feature extraction are the wavelet coefficients representing the power quality disturbance signal. Wavelet coefficients at different levels reveal the time localizing information about the variation of the signal.

Keywords: Power Quality, detection of disturbance, wavelet transform, multiresolution signal decomposition

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6 A Parallel Quadtree Approach for Image Compression using Wavelets

Authors: Hamed Vahdat Nejad, Hossein Deldari

Abstract:

Wavelet transforms are multiresolution decompositions that can be used to analyze signals and images. Image compression is one of major applications of wavelet transforms in image processing. It is considered as one of the most powerful methods that provides a high compression ratio. However, its implementation is very time-consuming. At the other hand, parallel computing technologies are an efficient method for image compression using wavelets. In this paper, we propose a parallel wavelet compression algorithm based on quadtrees. We implement the algorithm using MatlabMPI (a parallel, message passing version of Matlab), and compute its isoefficiency function, and show that it is scalable. Our experimental results confirm the efficiency of the algorithm also.

Keywords: Parallel Computing, Image Compression, Wavelets, MPI

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5 A Comparative Study of SVM Classifiers and Artificial Neural Networks Application for Rolling Element Bearing Fault Diagnosis using Wavelet Transform Preprocessing

Authors: Commander Sunil Tyagi

Abstract:

Effectiveness of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) classifiers for fault diagnosis of rolling element bearings are presented in this paper. The characteristic features of vibration signals of rotating driveline that was run in its normal condition and with faults introduced were used as input to ANN and SVM classifiers. Simple statistical features such as standard deviation, skewness, kurtosis etc. of the time-domain vibration signal segments along with peaks of the signal and peak of power spectral density (PSD) are used as features to input the ANN and SVM classifier. The effect of preprocessing of the vibration signal by Discreet Wavelet Transform (DWT) prior to feature extraction is also studied. It is shown from the experimental results that the performance of SVM classifier in identification of bearing condition is better then ANN and pre-processing of vibration signal by DWT enhances the effectiveness of both ANN and SVM classifier

Keywords: Pattern Recognition, Artificial Intelligence, Fault diagnosis, ANN, rolling element bearing, SVM. Wavelet Transform

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4 An Improved Algorithm of SPIHT based on the Human Visual Characteristics

Authors: Meng Wang, Qi-rui Han

Abstract:

Because of excellent properties, people has paid more attention to SPIHI algorithm, which is based on the traditional wavelet transformation theory, but it also has its shortcomings. Combined the progress in the present wavelet domain and the human's visual characteristics, we propose an improved algorithm based on human visual characteristics of SPIHT in the base of analysis of SPIHI algorithm. The experiment indicated that the coding speed and quality has been enhanced well compared to the original SPIHT algorithm, moreover improved the quality of the transmission cut off.

Keywords: SPIHT, lifted wavelet transform, Human Visual Characteristics

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3 Modified Fast and Exact Algorithm for Fast Haar Transform

Authors: Phang Chang, Phang Piau

Abstract:

Wavelet transform or wavelet analysis is a recently developed mathematical tool in applied mathematics. In numerical analysis, wavelets also serve as a Galerkin basis to solve partial differential equations. Haar transform or Haar wavelet transform has been used as a simplest and earliest example for orthonormal wavelet transform. Since its popularity in wavelet analysis, there are several definitions and various generalizations or algorithms for calculating Haar transform. Fast Haar transform, FHT, is one of the algorithms which can reduce the tedious calculation works in Haar transform. In this paper, we present a modified fast and exact algorithm for FHT, namely Modified Fast Haar Transform, MFHT. The algorithm or procedure proposed allows certain calculation in the process decomposition be ignored without affecting the results.

Keywords: Fast Haar Transform, Haar transform, Wavelet analysis

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2 Harmonic Parameters with HHT and Wavelet Transform for Automatic Sleep Stages Scoring

Authors: Wei-Chih Tang, Shih-Wei Lu, Chih-Mong Tsai, Cheng-Yan Kao, Hsiu-Hui Lee

Abstract:

Previously, harmonic parameters (HPs) have been selected as features extracted from EEG signals for automatic sleep scoring. However, in previous studies, only one HP parameter was used, which were directly extracted from the whole epoch of EEG signal. In this study, two different transformations were applied to extract HPs from EEG signals: Hilbert-Huang transform (HHT) and wavelet transform (WT). EEG signals are decomposed by the two transformations; and features were extracted from different components. Twelve parameters (four sets of HPs) were extracted. Some of the parameters are highly diverse among different stages. Afterward, HPs from two transformations were used to building a rough sleep stages scoring model using the classifier SVM. The performance of this model is about 78% using the features obtained by our proposed extractions. Our results suggest that these features may be useful for automatic sleep stages scoring.

Keywords: eeg, Wavelet Transform, harmonic parameter, Hilbert-Huang transform, sleep stages

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1 High Impedance Faults Detection Technique Based on Wavelet Transform

Authors: Ming-Ta Yang, Jin-Lung Guan, Jhy-Cherng Gu

Abstract:

The purpose of this paper is to solve the problem of protecting aerial lines from high impedance faults (HIFs) in distribution systems. This investigation successfully applies 3I0 zero sequence current to solve HIF problems. The feature extraction system based on discrete wavelet transform (DWT) and the feature identification technique found on statistical confidence are then applied to discriminate effectively between the HIFs and the switch operations. Based on continuous wavelet transform (CWT) pattern recognition of HIFs is proposed, also. Staged fault testing results demonstrate that the proposed wavelet based algorithm is feasible performance well.

Keywords: continuous wavelet transform, discrete wavelet transform, high impedance faults, statistical confidence

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