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Open Access Article
1 - Presenting an Improved Approach in order to Optimum Mother Wavelet Identification for Earth Sciences Data Processing
فرناز قریشی Behzad tokhmechi امین روشندل کاهو حسین احمدی نوبریMore information can be extracted from signals, using signal transform tools. Among other tools, “wavelet transform” has an increasing fortune because of its good properties. The main issue is that choosing “different mother wavelet functions” results in diverse conclus MoreMore information can be extracted from signals, using signal transform tools. Among other tools, “wavelet transform” has an increasing fortune because of its good properties. The main issue is that choosing “different mother wavelet functions” results in diverse conclusions. There are various algorithms to build a suitable mother wavelet for the analyzed signal. Along with those algorithms, there are procedures too for choosing the optimum mother wavelet among existing functions. From the latter group, the “energy matching” algorithm was used in the present paper to find the optimum mother wavelet. During the use of this algorithm, its deficiency in two aspects was revealed. To solve the problem, “zero mean transform” was chose as an extendable solution to prepare data for the used energy matching algorithm. Applying this simple transform helped us not only finding the optimum mother wavelet but also a unique one. Manuscript profile -
Open Access Article
2 - بررسی کارایی مدل هیبریدی هالت-وینترز موجکی (WHW)در شبیه¬سازی تراز سطح ایستابی آبخوان ساحلی ارومیه
Mohammad Nakhaei Farshad Alijani Ali Mirarabi HamidReza NasseriFor management and planning valuable groundwater resources, it is very important to predict groundwater level and have a correct understanding about aquifer changes. In this paper for the first time, the wavelet Halt-Winters hybrid models (WHW) were used and tested for MoreFor management and planning valuable groundwater resources, it is very important to predict groundwater level and have a correct understanding about aquifer changes. In this paper for the first time, the wavelet Halt-Winters hybrid models (WHW) were used and tested for groundwater forecasting. A monthly data set of 16 years consisting of groundwater level fluctuations was used in two observation wells of Urmieh coastal aquifer. In the WHW, the dataset was converted into several sub-dataset with different time scales. Then, the sub-series were used in the HW model as inputs. Subsequently, the performance of the WHW model was compared with ARIMA, HW, and SARIMA as linear models and neural network models (ANN) and Support Vector Regression (SVR) as nonlinear models. The results showed that the NSE and RMSE values of the WHW model were upgraded up to 30% and 60% respectively, in comparison with linear models. The WHW hybrid model also has the same performance compared to nonlinear models. This research reflects that if there are multiple seasonal fluctuations in the groundwater time series, the performance of the WHW model compared with linear models will be more accurate. Manuscript profile -
Open Access Article
3 - Multimodal Biometric Recognition Using Particle Swarm Optimization-Based Selected Features
Sara Motamed Ali Broumandnia Azam sadat NourbakhshFeature selection is one of the best optimization problems in human recognition, which reduces the number of features, removes noise and redundant data in images, and results in high rate of recognition. This step affects on the performance of a human recognition system MoreFeature selection is one of the best optimization problems in human recognition, which reduces the number of features, removes noise and redundant data in images, and results in high rate of recognition. This step affects on the performance of a human recognition system. This paper presents a multimodal biometric verification system based on two features of palm and ear which has emerged as one of the most extensively studied research topics that spans multiple disciplines such as pattern recognition, signal processing and computer vision. Also, we present a novel Feature selection algorithm based on Particle Swarm Optimization (PSO). PSO is a computational paradigm based on the idea of collaborative behavior inspired by the social behavior of bird flocking or fish schooling. In this method, we used from two Feature selection techniques: the Discrete Cosine Transforms (DCT) and the Discrete Wavelet Transform (DWT). The identification process can be divided into the following phases: capturing the image; pre-processing; extracting and normalizing the palm and ear images; feature extraction; matching and fusion; and finally, a decision based on PSO and GA classifiers. The system was tested on a database of 60 people (240 palm and 180 ear images). Experimental results show that the PSO-based feature selection algorithm was found to generate excellent recognition results with the minimal set of selected features. Manuscript profile -
Open Access Article
4 - Camera Identification Algorithm Based on Sensor Pattern Noise Using Wavelet Transform, SVD / PCA and SVM Classifier
Kimia Bolouri Mehdi Javanmard Mohammad FirouzmandIdentifying the source camera of an image is one of the most important issues of digital court and is useful in many applications, such as images that are presented in court as evidence. In many methods, the image noise characteristics, extraction of Sensor Pattern Nois MoreIdentifying the source camera of an image is one of the most important issues of digital court and is useful in many applications, such as images that are presented in court as evidence. In many methods, the image noise characteristics, extraction of Sensor Pattern Noise and its correlation with non-uniformity of the light response (PNU) are used. In this paper we have presented a method based on photo response non uniformity (PRNU) that provides some features for classification by support vector machine (SVM). Because the noise model is affected by the complexity of the image, we used the wavelet transform to de-noise and reduce edge effects in PRNU noise pattern and also raise the detection accuracy. We also used the Precision processing theory to reduce the image size, then we simplified and summarized the data using the Single Value Decomposition (SVD) Or principal component analysis (PCA). The results show that using two-level wavelet transform and summarized data is more suitable using PCA. Manuscript profile -
Open Access Article
5 - A New Robust Digital Image Watermarking Algorithm Based on LWT-SVD and Fractal Images
Fardin Akhlaghian Tab Kayvan Ghaderi Parham MoradiThis paper presents a robust copyright protection scheme based on Lifting Wavelet Transform (LWT) and Singular Value Decomposition (SVD). We have used fractal decoding to make a very compact representation of watermark image. The fractal code is presented by a binary im MoreThis paper presents a robust copyright protection scheme based on Lifting Wavelet Transform (LWT) and Singular Value Decomposition (SVD). We have used fractal decoding to make a very compact representation of watermark image. The fractal code is presented by a binary image. In the embedding phase of watermarking scheme, at first, we perform decomposing of the host image with 2D-LWT transform, then SVD is applied to sub-bands of the transformed image, and then the watermark, “binary image,” is embedded by modifying the singular values. In the watermark extraction phase, after the reverse steps are applied, the embedded binary image and consequently the fractal code are extracted from the watermarked image. The original watermark image is rendered by running the code. To verify the validity of the proposed watermarking scheme, several experiments are carried out and the results are compared with the results of the other algorithms. In order to evaluate the quality of image, we use parameter peak value signal-to-noise ratio (PSNR). To measure the robustness of the proposed algorithm, the NC coefficient is evaluated. The experimental results indicate that, in addition to high transparency, the proposed scheme is strong enough to resist various signal processing operations, such as average filter, median filter, Jpeg compression, contrast adjustment, cropping, histogram equalization, rotation, etc. Manuscript profile -
Open Access Article
6 - Short Time Price Forecasting for Electricity Market Based on Hybrid Fuzzy Wavelet Transform and Bacteria Foraging Algorithm
keyvan Borna Sepideh PalizdarPredicting the price of electricity is very important because electricity can not be stored. To this end, parallel methods and adaptive regression have been used in the past. But because dependence on the ambient temperature, there was no good result. In this study, lin MorePredicting the price of electricity is very important because electricity can not be stored. To this end, parallel methods and adaptive regression have been used in the past. But because dependence on the ambient temperature, there was no good result. In this study, linear prediction methods and neural networks and fuzzy logic have been studied and emulated. An optimized fuzzy-wavelet prediction method is proposed to predict the price of electricity. In this method, in order to have a better prediction, the membership functions of the fuzzy regression along with the type of the wavelet transform filter have been optimized using the E.Coli Bacterial Foraging Optimization Algorithm. Then, to better compare this optimal method with other prediction methods including conventional linear prediction and neural network methods, they were analyzed with the same electricity price data. In fact, our fuzzy-wavelet method has a more desirable solution than previous methods. More precisely by choosing a suitable filter and a multiresolution processing method, the maximum error has improved by 13.6%, and the mean squared error has improved about 17.9%. In comparison with the fuzzy prediction method, our proposed method has a higher computational volume due to the use of wavelet transform as well as double use of fuzzy prediction. Due to the large number of layers and neurons used in it, the neural network method has a much higher computational volume than our fuzzy-wavelet method. Manuscript profile -
Open Access Article
7 - High-Resolution Fringe Pattern Phase Extraction, Placing a Focus on Real-Time 3D Imaging
Amir Hooshang Mazinan Ali EsmaeiliThe idea behind the research is to deal with real-time 3D imaging that may extensively be referred to the fields of medical science and engineering in general. It is to note that most effective non-contact measurement techniques can include the structured light patterns MoreThe idea behind the research is to deal with real-time 3D imaging that may extensively be referred to the fields of medical science and engineering in general. It is to note that most effective non-contact measurement techniques can include the structured light patterns, provided in the surface of object for the purpose of acquiring its 3D depth. The traditional structured light pattern can now be known as the fringe pattern. In this study, the conventional approaches, realized in the fringe pattern analysis with applications to 3D imaging such as wavelet and Fourier transform are efficiently investigated. In addition to the frequency estimation algorithm in most of these approaches, additional unwrapping algorithm is needed to extract the phase, coherently. Considering problems regarding phase unwrapping of fringe algorithm surveyed in the literatures, a state-of-the-art approach is here organized to be proposed. In the aforementioned proposed approach, the key characteristics of the same conventional algorithms such as the frequency estimation and the Itoh algorithm are synchronously realized. At the end, the results carried out through the simulation programs have revealed that the proposed approach is able to extract image phase of simulated fringe patterns and correspondingly realistic patterns with high quality. Another advantage of this investigated approach is considered as its real-time application, while a significant part of operations might be executed in parallel. Manuscript profile -
Open Access Article
8 - An Efficient Noise Removal Edge Detection Algorithm Based on Wavelet Transform
Ehsan EhsaeianIn this paper, we propose an efficient noise robust edge detection technique based on odd Gaussian derivations in the wavelet transform domain. At first, new basis wavelet functions are introduced and the proposed algorithm is explained. The algorithm consists of two st MoreIn this paper, we propose an efficient noise robust edge detection technique based on odd Gaussian derivations in the wavelet transform domain. At first, new basis wavelet functions are introduced and the proposed algorithm is explained. The algorithm consists of two stage. The first idea comes from the response multiplication across the derivation and the second one is pruning algorithm which improves fake edges. Our method is applied to the binary and the natural grayscale image in the noise-free and the noisy condition with the different power density. The results are compared with the traditional wavelet edge detection method in the visual and the statistical data in the relevant tables. With the proper selection of the wavelet basis function, an admissible edge response to the significant inhibited noise without the smoothing technique is obtained, and some of the edge detection criteria are improved. The experimental visual and statistical results of studying images show that our method is feasibly strong and has good edge detection performances, in particular, in the high noise contaminated condition. Moreover, to have a better result and improve edge detection criteria, a pruning algorithm as a post processing stage is introduced and applied to the binary and grayscale images. The obtained results, verify that the proposed scheme can detect reasonable edge features and dilute the noise effect properly. Manuscript profile -
Open Access Article
9 - Wavelet-based Bayesian Algorithm for Distributed Compressed Sensing
Razieh Torkamani Ramezan Ali SadeghzadehThe emerging field of compressive sensing (CS) enables the reconstruction of the signal from a small set of linear projections. Traditional CS deals with a single signal; while one can jointly reconstruct multiple signals via distributed CS (DCS) algorithm. DCS inversio MoreThe emerging field of compressive sensing (CS) enables the reconstruction of the signal from a small set of linear projections. Traditional CS deals with a single signal; while one can jointly reconstruct multiple signals via distributed CS (DCS) algorithm. DCS inversion method exploits both the inter- and intra-signal correlations via joint sparsity models (JSM). Since the wavelet coefficients of many signals is sparse, in this paper, the wavelet transform is used as sparsifying transform, and a new wavelet-based Bayesian DCS algorithm (WB-DCS) is proposed, which takes into account the inter-scale dependencies among the wavelet coefficients via hidden Markov tree model (HMT), as well as the inter-signal correlations. This paper uses the Bayesian procedure to statistically model this correlations via the prior distributions. Also, in this work, a type-1 JSM (JSM-1) signal model is used for jointly sparse signals, in which every sparse coefficient vector is considered as the sum of a common component and an innovation component. In order to jointly reconstruct multiple sparse signals, the centralized approach is used in DCS, in which all the data is processed in the fusion center (FC). Also, variational Bayes (VB) procedure is used to infer the posterior distributions of unknown variables. Simulation results demonstrate that the structure exploited within the wavelet coefficients provides superior performance in terms of average reconstruction error and structural similarity index. Manuscript profile -
Open Access Article
10 - Denoising and Enhancement Speech Signal Using Wavelet
Meriane BrahimSpeech enhancement aims to improve the quality and intelligibility of speech using various techniques and algorithms. The speech signal is always accompanied by background noise. The speech and communication processing systems must apply effective noise reduction techni MoreSpeech enhancement aims to improve the quality and intelligibility of speech using various techniques and algorithms. The speech signal is always accompanied by background noise. The speech and communication processing systems must apply effective noise reduction techniques in order to extract the desired speech signal from its corrupted speech signal. In this project we study wavelet and wavelet transform, and the possibility of its employment in the processing and analysis of the speech signal in order to enhance the signal and remove noise of it. We will present different algorithms that depend on the wavelet transform and the mechanism to apply them in order to get rid of noise in the speech, and compare the results of the application of these algorithms with some traditional algorithms that are used to enhance the speech. The basic principles of the wavelike transform are presented as an alternative to the Fourier transform. Or immediate switching of the window The practical results obtained are based on processing a large database dedicated to speech bookmarks polluted with various noises in many SNRs. This article tends to be an extension of practical research to improve speech signal for hearing aid purposes. Also learn about the main frequency of letters and their uses in intelligent systems, such as voice control systems. Manuscript profile -
Open Access Article
11 - A New High-Capacity Audio Watermarking Based on Wavelet Transform using the Golden Ratio and TLBO Algorithm
Ali Zeidi joudaki Marjan Abdeyazdan Mohammad Mosleh Mohammad KheyrandishDigital watermarking is one of the best solutions for copyright infringement, copying, data verification, and illegal distribution of digital media. Recently, the protection of digital audio signals has received much attention as one of the fascinating topics for resear MoreDigital watermarking is one of the best solutions for copyright infringement, copying, data verification, and illegal distribution of digital media. Recently, the protection of digital audio signals has received much attention as one of the fascinating topics for researchers and scholars. In this paper, we presented a new high-capacity, clear, and robust audio signaling scheme based on the DWT conversion synergy and golden ratio advantages using the TLBO algorithm. We used the TLBO algorithm to determine the effective frame length and embedded range, and the golden ratio to determine the appropriate embedded locations for each frame. First, the main audio signal was broken down into several sub-bands using a DWT in a specific frequency range. Since the human auditory system is not sensitive to changes in high-frequency bands, to increase the clarity and capacity of these sub-bands to embed bits we used the watermark signal. Moreover, to increase the resistance to common attacks, we framed the high-frequency bandwidth and then used the average of the frames as a key value. Our main idea was to embed an 8-bit signal simultaneously in the host signal. Experimental results showed that the proposed method is free from significant noticeable distortion (SNR about 29.68dB) and increases the resistance to common signal processing attacks such as high pass filter, echo, resampling, MPEG (MP3), etc. Manuscript profile -
Open Access Article
12 - Study and Realization of an Alarm System by Coded Laser Barrier Analyzed by the Wavelet Transform
meriane brahim Salah Rahmouni Issam TifoutiThis article introduces the study and realization of the laser barrier alarm system, after the laser is obtained by an electronic card, the wireless control system is connected to the control room to announce the application in real time, and the laser is used in many a MoreThis article introduces the study and realization of the laser barrier alarm system, after the laser is obtained by an electronic card, the wireless control system is connected to the control room to announce the application in real time, and the laser is used in many applications fields, from industry to medicine, in this article on the basis of Industrial applications such as laser barrier. It uses an alarm system to detect and deter intruders. Basic security includes protecting the perimeter of a military base or a safety distance in unsafe locations or near a government location. The first stage secures surrounding access points such as doors and windows; The second stage consists of internal detection with motion detectors that monitor movements, In this article, we adopt the embodiment of a coded laser barrier that is transmitted between two units, processes the signal, compares the agreed conditions, and to be high accuracy, we suggest using wavelet transmission to process the received signal and find out the frequencies that achieve alarm activation considering that the transmitted signal They are pulses, but after analysis with a proposed algorithm, we can separate the unwanted frequencies generated by the differential vibrations in order to arrive at a practically efficient system. Manuscript profile -
Open Access Article
13 - CDF (2,2) Wavelet Lossy Image Compression on CPLD
A. A. Lotfi-Neyestanak Mohammad Mohaghegh Hazrati Mohammad Mohaghegh Hazrati N. AhmidiThis 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 ( MoreThis 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
14 - A Nonoblivious Watermarking Scheme for Embedding Spread Spectrum-like Watermarks in the Wavelet Domain Using HVS Characteristics
A. R. zolghadr asli S. RezazadehIn 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 MoreIn 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
15 - An Adaptive Wavelet-Based Signal Denoising Schem
M. nasri H. Nezamabadi-pour S. SaryazdiIn 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 MoreIn 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
16 - Improved Wavelet Spectral Subtraction Method Using LPC Analysis for Speech Enhancement
M. Heydari E. Nadernejad M. R. KaramiIn 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. MoreIn 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
17 - Adaptive Wavelet Thresholding for Denoising Speech Signals
F. sheikhalishahi H. R. abutalebi M. R. TabanThis 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 MoreThis 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
18 - A Novel Wavelet Based Method for Improvement of Power Transformer Differential Relay against Magnetizing Inrush Current and CT Saturation
A. Rahmati M. Sanaye-PasandDifferential 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 MoreDifferential 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
19 - 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. MoradiPathological 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 MorePathological 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
20 - 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. VafadustOne 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 MoreOne 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
21 - Geodesic Path Based Image Inpainting Using Wavelet Transform
M. Jahangard S. Saryazdi H. Nezamabadi-pour عصمت راشدیIn 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 MoreIn 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
22 - Online Signature Verification in Stationary Wavelet Transform Domain
M. Valizadeh E. KabirIn 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 MoreIn 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
23 - 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. RazazIn 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 MoreIn 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
24 - A New Scheme for Automatic Classification of Power Quality Disturbances Based on Signal Processing and Machine Learning
M. Hajian A. Akbari ForodIdentification 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 MoreIdentification 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
25 - A Multi-Resolution Learning Based Method for Multimodal Medical Image Registration
S. S. Alehojat Khasmakhi M. R. KeyvanpourThe 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 MoreThe 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
26 - Detection of Wind Aerodynamic Turbulence and Gear Tooth Breaks in Wind Turbine Gearboxes Using Wavelet Function
A. Ghabel A. Akbari ForodIn 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 MoreIn 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
27 - Detection of Wind Aerodynamic Turbulence and Gear Tooth Breaks in Wind Turbine Gearboxes Using Wavelet Function
A. Ghabel A. Akbari ForodIn 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 MoreIn 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
28 - 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. ZareIn 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 MoreIn 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
29 - The Extraction of Fetal ECG from Abdominal Recordings Using Sparse Representation of ECG Signals
Parya Tavoosi قاسم عازمی پگاه زرجامone 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 Moreone 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
30 - A Content-Based Image Retrieval System Using Semi-Supervised Learning and Frequent Patterns Mining
Maral KolahkajContent-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 MoreContent-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 -
Open Access Article
31 - Reservoir Fluid Contact Detection Using Continues Wavelet Transform of Resistivity Log
امیر ملا جان مصطفی جاوید حسین معماریان بهزاد تخم چیExact assessment of reservoir fluid contacts and distribution is an important part of reservoir characterization. Reservoir fluid contacts may be detected by petrophysical interpretations, well testing, special core analysis and seismic inverse modeling techniques. In p MoreExact assessment of reservoir fluid contacts and distribution is an important part of reservoir characterization. Reservoir fluid contacts may be detected by petrophysical interpretations, well testing, special core analysis and seismic inverse modeling techniques. In practice, due to noneconomic and unavailability of well test and seismic data, wire line log data are commonly used. Since these contacts affected by complexity of reservoir properties, thickness of reservoir rocks, and some factors such as vug effect, fractures and mud filtrate invasion, it is essential to find a way for reducing such these factors. The present study uses data related to three wells of an oil field in southwestern Iran to detect oil-water contact by continues wavelet transform of resistivity log. The results obtained from this method are compared with well test responses to validate the proposed algorithm. The results show that this method is capable to detect fluids contact accurately Manuscript profile -
Open Access Article
32 - Automatic Lung Diseases Identification using Discrete Cosine Transform-based Features in Radiography Images
Shamim Yousefi Samad Najjar-GhabelThe use of raw radiography results in lung disease identification has not acceptable performance. Machine learning can help identify diseases more accurately. Extensive studies were performed in classical and deep learning-based disease identification, but these methods MoreThe use of raw radiography results in lung disease identification has not acceptable performance. Machine learning can help identify diseases more accurately. Extensive studies were performed in classical and deep learning-based disease identification, but these methods do not have acceptable accuracy and efficiency or require high learning data. In this paper, a new method is presented for automatic interstitial lung disease identification on radiography images to address these challenges. In the first step, patient information is removed from the images; the remaining pixels are standardized for more precise processing. In the second step, the reliability of the proposed method is improved by Radon transform, extra data is removed using the Top-hat filter, and the detection rate is increased by Discrete Wavelet Transform and Discrete Cosine Transform. Then, the number of final features is reduced with Locality Sensitive Discriminant Analysis. The processed images are divided into learning and test categories in the third step to create different models using learning data. Finally, the best model is selected using test data. Simulation results on the NIH dataset show that the decision tree provides the most accurate model by improving the harmonic mean of sensitivity and accuracy by up to 1.09times compared to similar approaches. Manuscript profile -
Open Access Article
33 - Detection and Analysis of Acoustic Signals of Power Transformers On-Load Tap Changers for Assessment of Their Faults
adel younesi Abbas Ghayebloo Hasanreza Mirzaei<p><span style="font-size: 10.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">On load tap changers are very important equipment of the power transfor More<p><span style="font-size: 10.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">On load tap changers are very important equipment of the power transformers. Due to the strongly mechanical movements and high-energy arcs, this equipment has a much higher failure rate with respect to other internal transformer equipment. Online and accurate evaluation of well operation of these equipment by indicators with no interfere on the normal operation of the transformer, is very important issue. In this paper, various faults detecting methods in the tap changer have been discussed an investigated by some extracted features of acoustic signals. These signals have been captured experimentally in various tap changing periods by an accelerometer sensor mounted on a power transformer body. In this paper, in addition to common features, two new feathers entitled time and frequency indicators have been introduced. Finally, for selecting the proper features to faults detection and proposing an effective classification method, some available experimental data were randomly defected by results in the references, and classified successfully as healthy and defective data by support vector machine (SVM) method.</span></p> Manuscript profile -
Open Access Article
34 - Fault Detection and Localization in Hybrid Shipboard Electrical Power Grids Using Wavelet Transform
Mohsen Aliyani Arash Dehestani Kolagar Mohammad Reza Alizadeh Pahlavani<p>In the hybrid shipboard power networks, the possibility of occurrence of various types of faults is very high. According to the studies conducted in the field of fault detection and localization in hybrid microgrids, the lack of comprehensive fault management techniq More<p>In the hybrid shipboard power networks, the possibility of occurrence of various types of faults is very high. According to the studies conducted in the field of fault detection and localization in hybrid microgrids, the lack of comprehensive fault management techniques to protect the microgrid against short-circuit faults is the main obstacle to the use of hybrid microgrids in vessels for vital marine missions. Considering the restrictions and limitations in marine vessels, the design of an electrical protection system for hybrid microgrids requires high attention to special requirements. In this paper, an appropriate protection scheme for fault detection and localization in hybrid shipboard microgrids is presented. In this regard, fault detection, classification and localization in a period of 0.034 to 0.54 seconds are performed using an algorithm based on Daubechies order 4 wavelet transform (db4). Observing the results and analyzing them shows that the proposed algorithm can detect, classify and localize all types of faults both in the AC and DC parts of the shipboard microgrids.</p> Manuscript profile -
Open Access Article
35 - Identification of gas in carbonate rock using wavelet transform
Hassan Omrani Hashem omraniGas can be diagnosed in clean sand rock by petrophysical log. It is not easy to determine the gas in carbonate rock by petrophysical log. The R.F.T. tool is used to determine the gas in carbonate rock. The fluid density in the rock is determined by calculating the press MoreGas can be diagnosed in clean sand rock by petrophysical log. It is not easy to determine the gas in carbonate rock by petrophysical log. The R.F.T. tool is used to determine the gas in carbonate rock. The fluid density in the rock is determined by calculating the pressure difference related to depth. The R.F.T. tool has some disadvantages, such as being expensive, taking much time to run, and rock having a neutron porosity of about 15%, and sometimes the R.F.T. tool is stuck in well. This study applies the wavelet transformation, a recent advance in signal analysis technique, to detect reservoir rock fluid. The porosity and water saturation are denoised using the "demy" mother wavelet. At last, the pore hydrocarbon saturation, porosity denoise by the "demy" wavelet, pore volume plot and R.F.T. tool are plotted together in one figure to identify the kind of fluid in sand and carbonate rocks. Manuscript profile
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