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      • Open Access Article

        1 - Multimodal Biometric Recognition Using Particle Swarm Optimization-Based Selected Features
        Sara Motamed Ali Broumandnia Azam sadat  Nourbakhsh
        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 - Camera Identification Algorithm Based on Sensor Pattern Noise Using Wavelet Transform, SVD / PCA and SVM Classifier
        Kimia Bolouri Mehdi Javanmard Mohammad Firouzmand
        Identifying 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 More
        Identifying 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

        3 - Image Retrieval Using Color-Texture Features Extracted From Gabor-Walsh Wavelet Pyramid
        Sajad Mohammadzadeh Hassan Farsi
        Image retrieval is one of the most applicable image processing techniques which have been extensively used. Feature extraction is one of the most important procedures used for interpretation and indexing images in Content-Based Image Retrieval (CBIR) systems. Effective More
        Image retrieval is one of the most applicable image processing techniques which have been extensively used. Feature extraction is one of the most important procedures used for interpretation and indexing images in Content-Based Image Retrieval (CBIR) systems. Effective storage, indexing and managing a large number of image collections are critical challenges in computer systems. There are many proposed methods to overcome these problems. However, the rate of image retrieval and speed of retrieval are still interesting fields of researches. In this paper, we propose a new method based on combination of Gabor filter and Walsh transform and Wavelet Pyramid (GWWP). The Crossover Point (CP) of precision and recall are considered as metrics to evaluate and compare different methods. The Obtained results show using GWWP provides better performance in compared to with other methods. Manuscript profile
      • Open Access Article

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

        5 - Early Detection of Pediatric Heart Disease by Automated Spectral Analysis of Phonocardiogram
        Azra Rasouli Kenari
        Early recognition of heart disease is an important goal in pediatrics. Developing countries have a large population of children living with undiagnosed heart murmurs. As a result of an accompanying skills shortage, most of these children will not get the necessary treat More
        Early recognition of heart disease is an important goal in pediatrics. Developing countries have a large population of children living with undiagnosed heart murmurs. As a result of an accompanying skills shortage, most of these children will not get the necessary treatment. Taking into account that heart auscultation remains the dominant method for heart examination in the small health centers of the rural areas and generally in primary healthcare setups, the enhancement of this technique would aid significantly in the diagnosis of heart diseases. The detection of murmurs from phonocardiographic recordings is an interesting problem that has been addressed before using a wide variety of techniques. We designed a system for automatically detecting systolic murmurs due to a variety of conditions. This could enable health care providers in developing countries with tools to screen large amounts of children without the need for expensive equipment or specialist skills. For this purpose an algorithm was designed and tested to detect heart murmurs in digitally recorded signals. Cardiac auscultatory examinations of 93 children were recorded, digitized, and stored along with corresponding echocardiographic diagnoses, and automated spectral analysis using discrete wavelet transforms was performed. Patients without heart disease and either no murmur or an innocent murmur (n = 40) were compared to patients with a variety of cardiac diagnoses and a pathologic systolic murmur present (n = 53). A specificity of 100% and a sensitivity of 90.57% were achieved using signal processing techniques and a k-nn as classifier. 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 Palizdar
        Predicting 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 More
        Predicting 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  Esmaeili
        The 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 More
        The 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 Ehsaeian
        In 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 More
        In 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 Sadeghzadeh
        The 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 More
        The 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 Brahim
        Speech 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 More
        Speech 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 Kheyrandish
        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

        12 - Study and Realization of an Alarm System by Coded Laser Barrier Analyzed by the Wavelet Transform
        meriane brahim Salah Rahmouni Issam Tifouti
        This 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 More
        This 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