wavelet transform

Open Access Article

1 - Multimodal Biometric Recognition Using Particle Swarm Optimization-Based Selected Features
Sara Motamed Ali Broumandnia Azam sadat Nourbakhsh

10.7508/jist.2013.02.002

Feature 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 More

Feature 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

2 - A New Robust Digital Image Watermarking Algorithm Based on LWT-SVD and Fractal Images
Fardin Akhlaghian Tab Kayvan Ghaderi Parham Moradi

10.7508/jist.2015.01.008

This 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 More

This 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

3 - 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 Kheyrandish

10.52547/jist.15957.10.38.151

Digital 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 More

Digital 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

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

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

5 - 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

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

6 - 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

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

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

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

8 - 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

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

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

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

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

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

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

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

12 - Reservoir Fluid Contact Detection Using Continues Wavelet Transform of Resistivity Log
امیر ملا جان مصطفی جاوید حسین معماریان بهزاد تخم چی

Exact 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 More

Exact 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

13 - Automatic Lung Diseases Identification using Discrete Cosine Transform-based Features in Radiography Images
Shamim Yousefi Samad Najjar-Ghabel

The 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 More

The 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

14 - Detection and Analysis of Acoustic Signals of Power Transformers On-Load Tap Changers for Assessment of Their Faults
adel younesi Abbas Ghayebloo Hasanreza Mirzaei

On load tap changers are very important equipment of the power transfor More

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. Manuscript profile

Open Access Article

15 - Fault Detection and Localization in Hybrid Shipboard Electrical Power Grids Using Wavelet Transform
Mohsen Aliyani Arash Dehestani Kolagar Mohammad Reza Alizadeh Pahlavani

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

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. Manuscript profile

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