تبدیل موجک گسسته

Open Access Article

1 - DWT-SVD based Semi-Blind Digital Image Watermarking
danyali danyali fardin akhlaghi morteza makhlooghi

With development of digital multimedia technology and rapid growth of the Internet, illegal copy and exchange of digital multimedia sources is also spread. In such environment, copyright protection plays an essential role. In this paper a new semi- blind image watermark More

With development of digital multimedia technology and rapid growth of the Internet, illegal copy and exchange of digital multimedia sources is also spread. In such environment, copyright protection plays an essential role. In this paper a new semi- blind image watermarking algorithm for proof of ownership is proposed. At first, the original image is transformed to transform domain and the low frequency sub-band is selected to make reference image. Then, 1-level wavelet decomposition is applied to reference and grey-scale watermark images. Finally, the embedding is done by modifying the singular values of reference image’s sub-bands with the equivalent singular values of watermark’s sub-bands. In the proposed method, the reference image is needed during the extraction process, so it is called semi blind method. Robustness of the proposed method against various attacks that are applied to the watermarked image is investigated. The experimental results show that the proposed method is more robust than previous works against different attacks and the watermarked image looks visually identical to the original image. Manuscript profile

Open Access Article

2 - Semi-blind labeling of images using SVD in the field of discrete wavelet transform
morteza makhlooghi danyali danyali fardin akhlaghi

With the rapid development of communication technology and the expansion of the Internet and the significant increase in forgery and unauthorized copying of data, the value of marking is increasing day by day. In this paper, a new semi-blind marking method using singula More

With the rapid development of communication technology and the expansion of the Internet and the significant increase in forgery and unauthorized copying of data, the value of marking is increasing day by day. In this paper, a new semi-blind marking method using singular value decomposition (Singular Value Decomposition) in the domain of discrete wavelet transform is presented to protect property rights. In the proposed method, first the discrete wavelet transform step is applied to the original image K and its lowest frequency subband is selected as the reference image. Then, by applying a discrete wavelet transformation step to the reference image and the reference image, the unique values of the frequency sub-bands of the converted signal image are embedded in the unique values of the corresponding sub-bands of the reference transformed image. Since in this method, a reference image is needed to recover the sign, and the original image is not needed, therefore it is called a Semi-Blind method. The resistance of the proposed method against different attacks was investigated. The results show that the proposed method is much more resistant to various attacks than similar works, and at the same time, the marked image is more transparent. Manuscript profile

Open Access Article

3 - 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

4 - 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

5 - 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

6 - 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

7 - 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

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