Wavelet transform

Open Access Article

1 - CDF (2,2) Wavelet Lossy Image Compression on CPLD
A. A. Lotfi-Neyestanak Mohammad Mohaghegh Hazrati Mohammad Mohaghegh Hazrati N. Ahmidi

20.1001.1.16823745.1385.4.8.2.8

This paper presents a hardware implementation of CDF(2,2) wavelet image compressor. The design demonstrates that high quality circuit implementation is possible through the use of suitable data organization (partitioned approach) and algorithm-to-architecture mappings ( More

This paper presents a hardware implementation of CDF(2,2) wavelet image compressor. The design demonstrates that high quality circuit implementation is possible through the use of suitable data organization (partitioned approach) and algorithm-to-architecture mappings (parallel-ism or pipelining). A VHDL code for CDF(2,2) was developed to satisfy our objective. Then it was synthesized in Foundation 5.1 software and downloaded to CPLD XC9572 by a JTAG ByteBlaster cable. The original image was transmitted through serial port. The AVR’s ATmega8535 was used to implement serial protocol to and back from the CPLD. The main goal is to reach a higher performance and throughput with a single CPLD. Details of the encoder design have been discussed and the results are presented. Manuscript profile

Open Access Article

2 - A Nonoblivious Watermarking Scheme for Embedding Spread Spectrum-like Watermarks in the Wavelet Domain Using HVS Characteristics
A. R. zolghadr asli S. Rezazadeh

20.1001.1.16823745.1386.5.12.6.7

In this paper, we introduce a multiresolution watermarking method for copyright protection of digital images. The method is based on the discrete wavelet transform. A noise type Gaussian sequence is used as watermark. To embed the watermark robustly and imperceptibly, w More

In this paper, we introduce a multiresolution watermarking method for copyright protection of digital images. The method is based on the discrete wavelet transform. A noise type Gaussian sequence is used as watermark. To embed the watermark robustly and imperceptibly, watermark components are added to the significant coefficients of each selected subband by considering the human visual system (HVS) characteristics. Some small modifications are performed to improve HVS model. The host image is needed in watermark extraction procedure and Normalized Correlation Function (NCF) is used to measure similarities of extracted watermarks. It is shown that this method is robust against wide variety of attacks such as: additive noise, low pass filtering, compression, chopping, histogram equalization, rotation. Comparison with other methods shows the better performance of this suggested method. Manuscript profile

Open Access Article

3 - An Adaptive Wavelet-Based Signal Denoising Schem
M. nasri H. Nezamabadi-pour S. Saryazdi

20.1001.1.16823745.1387.6.13.2.2

In this paper, a new class of nonlinear thresholding functions with a tunable shape parameter for wavelet-based signal denoising is presented. In addition, a new learning technique for training of thresholding neural network is introduced. Unlike to existing methods, bo More

In this paper, a new class of nonlinear thresholding functions with a tunable shape parameter for wavelet-based signal denoising is presented. In addition, a new learning technique for training of thresholding neural network is introduced. Unlike to existing methods, both the shape and the threshold parameters are tuned simultaneously using LMS rule. This permits us to consider the effects of both the threshold and the shape parameters on denoising. The proposed functions are tested in both universal-threshold and subband-adaptive denoising and compared with conventional functions. In addition, to evaluate the proposed training method, several numerical examples are performed. The experimental results obtained from denoising of several standard benchmark signals confirm the efficiency and effectiveness of the proposed methods. Manuscript profile

Open Access Article

4 - Improved Wavelet Spectral Subtraction Method Using LPC Analysis for Speech Enhancement
M. Heydari E. Nadernejad M. R. Karami

20.1001.1.16823745.1387.6.15.7.1

In this paper, we proposed a new method for speech enhancement. The method is based on wavelet spectral subtraction. We use linear predictive coding (LPC) for noise estimation and extraction. The proposed method was compared with the wavelet spectral subtraction method. More

In this paper, we proposed a new method for speech enhancement. The method is based on wavelet spectral subtraction. We use linear predictive coding (LPC) for noise estimation and extraction. The proposed method was compared with the wavelet spectral subtraction method. The new method increased signal to noise ratio of the noise contaminated speech signal more than wavelet spectral subtraction method. Also, good results have been achieved in auditory test (Mean Opinion Score). Manuscript profile

Open Access Article

5 - Adaptive Wavelet Thresholding for Denoising Speech Signals
F. sheikhalishahi H. R. abutalebi M. R. Taban

20.1001.1.16823745.1388.7.17.7.2

This paper addresses the problem of speech enhancement in wavelet domain. After decomposition of noisy signal into wavelet sub-bands, an adaptive thresholding process is applied on wavelet coefficients. In the proposed technique, small threshold value and hard thrsholdi More

This paper addresses the problem of speech enhancement in wavelet domain. After decomposition of noisy signal into wavelet sub-bands, an adaptive thresholding process is applied on wavelet coefficients. In the proposed technique, small threshold value and hard thrsholding function are used in sub-bands with high speech energy; vice versa, in sub-bands with low speech energy, large threshold value and soft thresholding function are employed. For other sub-bands (between above two extreme cases for speech energy), we use an adaptive thresholding function that is actually between soft- and hard-thresholding functions. The threshold value and thresholding function are determined by a parameter related to the ratio of speech and noise powers in each sub-band. Our extensive experiments show the superiority of proposed method in removing the background noise and reduction of speech distortion. It was also shown that both wavelet tree structure and wavelet type affect on the performance of speech de-noising system. Manuscript profile

Open Access Article

6 - A Novel Wavelet Based Method for Improvement of Power Transformer Differential Relay against Magnetizing Inrush Current and CT Saturation
A. Rahmati M. Sanaye-Pasand

20.1001.1.16823745.1388.7.18.4.1

Differential protective relay serves as the main protection of transformers against faults in the windings for many years. Unremitting tries for more smart of this protection relay have been done by different creative manufactures and up to now different methods and inv More

Differential protective relay serves as the main protection of transformers against faults in the windings for many years. Unremitting tries for more smart of this protection relay have been done by different creative manufactures and up to now different methods and inventions have been proposed for better operational of the differential relay. However, a differential protection for differential currents would be handicapped by difficulties due to several natural phenomena which are the cause of false differential currents and misoperate of the relay. Some of these phenomena, such as the inrush current and saturation of instrument current transformers, are the main concern in designing an efficient differential protection algorithm. This paper at first, proposes a new algorithm based on the Wavelet Transform (WT) to identify internal fault from magnetizing inrush in three phase power transformers. The internal faults can be accurately discriminated from inrush current less than a quarter a cycle after the disturbance. At following with definition of an index which it is extracted from the high frequencies by WT, the restrain current in bias-current characteristic of differential relay has been improved. Obtained results demonstrate that the proposed algorithm can provide the desired response and can be used as a very fast and accurate method. Manuscript profile

Open Access Article

7 - A Pseudo Covariance Wavelet-based Feature Extraction Method to Biomarker Selection from Ovarian Cancer Proteomic Patterns
H. Montazery Kordy M. H. Miran-Baygi M. H. Moradi

20.1001.1.16823745.1388.7.18.6.3

Pathological changes within an organ can be reflected as proteomic patterns in blood. The mass spectrometry has been used as powerful tools to generate proteomic patterns from serum. The produced profiles can be viewed as high dimensional and correlation data for which More

Pathological changes within an organ can be reflected as proteomic patterns in blood. The mass spectrometry has been used as powerful tools to generate proteomic patterns from serum. The produced profiles can be viewed as high dimensional and correlation data for which the features of scientific interest are the peaks. Due to this complexity of data, an appropriate analysis method is needed such as wavelet transform. In this study, we proposed a pseudo-covariance wavelet-based feature extraction method for dimension reduction and de-correlation between mass spectra data. Our algorithm was applied to datasets of ovarian cancer obtained from the National Cancer Institute of USA. The proposed algorithm was used to extract the set of proteins as potential biomarkers in each dataset from reconstructed mass spectra. The selected biomarkers were able to diagnose ovarian cancer patients from non-cancer with high accurate results using standard diagnosis criteria. Using different classification algorithms, our approach yielded an accuracy of 98%, specificity of 97%, and sensitivity of 98%. Manuscript profile

Open Access Article

8 - Using Minimum Mean Squared Error Estimator for Quality Improvement of Abdominal Computerized Tomography Images Based on a Bivariate Laplacian Mixture Model for Complex Wavelet Coefficient
H. Rabbani M. Vafadust

20.1001.1.16823745.1388.7.18.7.4

One of the important subjects in the wavelet-based image denoising based on the Bayes theorem is choosing the appropriate density function for modeling the wavelet coefficients. The interscale dependency between parent and child coefficients is one of the statistical p More

One of the important subjects in the wavelet-based image denoising based on the Bayes theorem is choosing the appropriate density function for modeling the wavelet coefficients. The interscale dependency between parent and child coefficients is one of the statistical properties of wavelets. So, in the recent years instead of univariate distribution, bivariate density functions have been suggested by the researchers and in this paper we use a mixture of bivariate Laplacian densities for this reason. Using this distribution we are able to model both heavy-tailed property and interscale dependency of wavelets. Using the mentioned density function for a minimum mean squared error estimator, we obtain a new shrinkage function for denoising. Applying this function to each subband of discrete complex wavelet transform of abdominal computerized tomography images, we will be able to improve the quality of these images better than some reported methods. Manuscript profile

Open Access Article

9 - Geodesic Path Based Image Inpainting Using Wavelet Transform
M. Jahangard S. Saryazdi H. Nezamabadi-pour عصمت راشدی

20.1001.1.16823745.1389.8.1.3.3

In image inpainting, distorted and damaged parts of image or selected objects are removed or replaced with the appropriate information. In this article, image inpainting is performed by using frequency information of wavelet transform. The fill-in is done by diffusion o More

In image inpainting, distorted and damaged parts of image or selected objects are removed or replaced with the appropriate information. In this article, image inpainting is performed by using frequency information of wavelet transform. The fill-in is done by diffusion of information of intact pixels into the damaged regions, which is begun from the outermost pixels and gradually the damaged region is reconstructed. To determine direction and the amount of diffusion, the geodesic path based image inpainting method is generalized by incorporating information of wavelet domain. The experimental results confirm superiority of the proposed method over the geodesic path based image inpainting method. Manuscript profile

Open Access Article

10 - Online Signature Verification in Stationary Wavelet Transform Domain
M. Valizadeh E. Kabir

20.1001.1.16823745.1389.8.1.4.4

In this paper, an online signature verification method using extended regression in stationary wavelet domain is presented. To calculate the similarity between two signatures by extended regression, we should equalize the time length of the corresponding signals in two More

In this paper, an online signature verification method using extended regression in stationary wavelet domain is presented. To calculate the similarity between two signatures by extended regression, we should equalize the time length of the corresponding signals in two signatures. Using all points of the signals to equalize their time length will decrease the difference between a genuine signature and its forgery. Here a new approach based on the extreme points warping of the signals is presented. This approach equalizes the time length of two signals without degrading the differences between them. Also we calculated the similarity of signatures by using the details of the signals in stationary wavelet transform, SWT, domain, which showed very good results. The proposed system was tested on SVC2004 signature database. The results were compared with the results of participant teams in the first international signature verification competition. We have gained EER=6% for skilled forgery signatures. Comparing the result, it shows that we stand in the second rank between all the participants. This system has no verification error for random forgery signatures and stands in the first rank. Our experimental results show that using SWT domain instead of time domain decreases the verification error rate by 35%. Manuscript profile

Open Access Article

11 - Electrical Islanding Detection in Electrical Distribution Networks with Distributed Generation Using Discrete Wavelet Transform and Artificial Neural Network
M. Heidari Orejloo S. Gh. Seifossadat M. Razaz

20.1001.1.16823745.1392.11.1.3.4

In this paper a new algorithm is provided for detecting of electrical islands, based on analysis of transient signals using discrete wavelet transform (DWT) and artificial neural network (ANN). The neural network is taught for Classification of events to the "islands" o More

In this paper a new algorithm is provided for detecting of electrical islands, based on analysis of transient signals using discrete wavelet transform (DWT) and artificial neural network (ANN). The neural network is taught for Classification of events to the "islands" or "non-islands". Needed features for classification are extracted by DWT of DG transient voltage signal. DIgSILENT, MATLAB and WEKA softwares are used for simulation. Proposed method is tested on a CIGRE medium voltage distribution system with two different types of DGs. The final method is chosen from among 162 relay projects with respect to different criteria, including accuracy, speed, simplicity and cost efficiency is the best. With The analysis done in the best relay selection for DGs, the voltage signal, the mother wavelet db4 and seventh level wavelet transform are used. Simulation results show that this method in compared with existing methods, can detect the electrical islands, with a shorter time and higher accuracy. Manuscript profile

Open Access Article

12 - A New Scheme for Automatic Classification of Power Quality Disturbances Based on Signal Processing and Machine Learning
M. Hajian A. Akbari Forod

20.1001.1.16823745.1393.12.1.1.9

Identification and classification of power quality disturbances (PQDs) are one of the most important functions of monitoring and protection of modern power systems. One of the most important issues in PQ analysis is automatic diagnosis of waveforms using an effective al More

Identification and classification of power quality disturbances (PQDs) are one of the most important functions of monitoring and protection of modern power systems. One of the most important issues in PQ analysis is automatic diagnosis of waveforms using an effective algorithm. This paper presents an effective method, for extracting features, using integration of discrete wavelet transform (DWT) and hyperbolic S transform (HST). Moreover, an efficient feature selection method namely Orthogonal Forward Selection (OFS) by incorporating Gram Schmidt (GS) procedure and forward selection is applied for selection of the best subset features. Multi support vector machines (MSVM), as famous classifier, is applied. Also, the variable parameters of the classifier are optimized using a powerful method namely particle swarm optimization (PSO). Six single disturbances and two complex disturbances as well pure sine (normal) selected as reference are considered for the classification. Sensitivity of the proposed expert system under different noisy conditions is investigated. Also, efficiency of the proposed methods by comparing the results of this study with the results of other papers is examined. Manuscript profile

Open Access Article

13 - A Multi-Resolution Learning Based Method for Multimodal Medical Image Registration
S. S. Alehojat Khasmakhi M. R. Keyvanpour

20.1001.1.16823745.1393.12.4.2.6

The main purpose in various methods of image registration is to find the transformation parameters for accurate mapping an image onto another image coordinates. In medical sciences creating a precise mapping between medical images data is very important in application More

The main purpose in various methods of image registration is to find the transformation parameters for accurate mapping an image onto another image coordinates. In medical sciences creating a precise mapping between medical images data is very important in application such as diagnosis and treatment. Accordingly, several approaches have been proposed for image registration. The compression of results and performance between different image registration algorithms was the main motivation for this research to design and implement a new hybrid algorithm so that provide high accuracy in multimodal image registration. Automating the image registration process by using machine learning approach is the innovation of this method compared to previous ones. To this end, the proposed method which is named multi resolution learning is composed of multi resolution decomposition and a hierarchical neural network which it learn the transformation parameters by using global properties of the image and uses learned transformation parameter for image registration. The proposed method is implemented and tested on the medical images of Vanderbilt university database. Experiment result show acceptable accuracy for the proposed method compared with other methods. Manuscript profile

Open Access Article

14 - Detection of Wind Aerodynamic Turbulence and Gear Tooth Breaks in Wind Turbine Gearboxes Using Wavelet Function
A. Ghabel A. Akbari Forod

20.1001.1.16823745.1395.14.2.2.6

In order to improve power quality, the detection and identification of factors involved in reducing power quality are paramount. One of the main factors in creating flicker and harmonics in the system connected to the wind turbines are wind aerodynamic turbulences and w More

In order to improve power quality, the detection and identification of factors involved in reducing power quality are paramount. One of the main factors in creating flicker and harmonics in the system connected to the wind turbines are wind aerodynamic turbulences and wind turbines mechanical errors. In this paper the mathematical equations of turbulence wind shadow and wind shear and mechanical equations of Gear Tooth Breaks in Wind Turbine Gearboxes have been evaluated accurately using the MATLAB simulation. The continued impact of the disturbances on the output parameters of the network is observed. Also, it is shown that these disturbances can be identified and classified properly by wavelet function. Manuscript profile

Open Access Article

15 - Detection of Wind Aerodynamic Turbulence and Gear Tooth Breaks in Wind Turbine Gearboxes Using Wavelet Function
A. Ghabel A. Akbari Forod

In order to improve power quality, the detection and identification of factors involved in reducing power quality are paramount. One of the main factors in creating flicker and harmonics in the system connected to the wind turbines are wind aerodynamic turbulences and w More

In order to improve power quality, the detection and identification of factors involved in reducing power quality are paramount. One of the main factors in creating flicker and harmonics in the system connected to the wind turbines are wind aerodynamic turbulences and wind turbines mechanical errors. In this paper the mathematical equations of turbulence wind shadow and wind shear and mechanical equations of Gear Tooth Breaks in Wind Turbine Gearboxes have been evaluated accurately using the MATLAB simulation. The continued impact of the disturbances on the output parameters of the network is observed. Also, it is shown that these disturbances can be identified and classified properly by wavelet function. Manuscript profile

Open Access Article

16 - Grid Impedance Estimation of Low Voltage Grids Using Signal Processing Techniques for Frequency Range of 2 kHz – 150 kHz
M. M. AlyanNezhadi H. Hassanpour F. Zare

20.1001.1.16823745.1398.17.3.5.2

In this paper, the impedance of low voltage grids in frequency range of 2 kHz - 150 kHz is estimated using rectangular pulse injections and signal processing techniques. The grid impedance is defined as division of voltage signal to current signal in frequency domain. I More

In this paper, the impedance of low voltage grids in frequency range of 2 kHz - 150 kHz is estimated using rectangular pulse injections and signal processing techniques. The grid impedance is defined as division of voltage signal to current signal in frequency domain. In noisy condition, the accuracy of impedance estimation is directly dependent to energy of injected signal. The injection signal must has sufficient energy in the frequency range of estimation for an accurate impedance estimation. In the proposed method, several injection signals with different widths are selected with the Genetic algorithm. The grid responses to the injected signals are measured and then denoised for an accurate impedance estimation. When the measurement duration is low, the whole transient state of the grid is not measured; hence the impedance estimation is not accurate. Therefore, in this paper a method is proposed for determining the best measurement duration for impedance estimation using Time-Frequency distributions. The proposed method is applied on several simulated grids and the results show the ability and accuracy of the proposed method in grid impedance estimation. Manuscript profile

Open Access Article

17 - The Extraction of Fetal ECG from Abdominal Recordings Using Sparse Representation of ECG Signals
Parya Tavoosi قاسم عازمی پگاه زرجام

20.1001.1.16823745.1398.17.4.15.4

one of the most prevalent causes for mortality of infants is cardiac failure. Recordings of heart electrical activities by Electrocardiogram (ECG) are a safe method to detect abnormal arrhythmia in time and reduce cardiac failure in newborns. However, the non-invasive e More

one of the most prevalent causes for mortality of infants is cardiac failure. Recordings of heart electrical activities by Electrocardiogram (ECG) are a safe method to detect abnormal arrhythmia in time and reduce cardiac failure in newborns. However, the non-invasive extraction of fetal ECG (fECG) from the maternal abdominal is quite challenging, since the fECG signals are often corrupted by some electrical noises from other sources such as: maternal heart activity, uterine contractions, and respiration, in addition to instrumental noises. Among such signals, the maternal heart signal (due to high amplitude) has the most disruptive effect and the fetal brain signal (due to low amplitude) has the least effect on distortion of the fetal heart signal. In this paper, a new method for extracting fECG signals from multichannel abdominal recordings is proposed. The proposed method uses Compressive Sensing (CS)to reduce the computational complexity and fast Independent Component Analysis (fICA) algorithm to estimate the sources. Also, for finding sparse representations of the acquired ECG signals, two dictionaries namely: discrete cosine transformation and discrete wavelet transform are deployed here. The proposed method is then implemented and its performance is tested using the well-known and publicly available database used in 2013 Physionet Challenge. The performance results are compared with that of the best performing existing methods. The results show that the proposed method based on CS and ICA outperforms the existing detection methods with a Mean Minimum Square Error (MMSE) of 171.65, and therefore can be used for non-invasive and reliable extraction fECG from abdominal recordings. Manuscript profile

Open Access Article

18 - A Content-Based Image Retrieval System Using Semi-Supervised Learning and Frequent Patterns Mining
Maral Kolahkaj

20.1001.1.16823745.1401.20.3.7.5

Content-based image retrieval, which is also known as query based on image content, is one of the sub-branches of machine vision, which is used to organize and recognize the content of digital images using visual features. This technology automatically searches the imag More

Content-based image retrieval, which is also known as query based on image content, is one of the sub-branches of machine vision, which is used to organize and recognize the content of digital images using visual features. This technology automatically searches the images similar to the query image from huge image database and it provides the most similar images to the users by directly extracting visual features from image data; not keywords and textual annotations. Therefore, in this paper, a method is proposed that utilizes wavelet transformation and combining features with color histogram to reduce the semantic gap between low-level visual features and high-level meanings of images. In this regard, the final output will be presented using the feature extraction method from the input images. In the next step, when the query images are given to the system by the target user, the most similar images are retrieved by using semi-supervised learning that results from the combination of clustering and classification based on frequent patterns mining. The experimental results show that the proposed system has provided the highest level of effectiveness compared to other methods. Manuscript profile

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