Sharif Digital Repository

Management and treatment of subjective tinnitus is an ongoing focus of research activities. One of the most viable assessments of such treatment is the evaluation of brain activity in addition to patient response and clinical assessment. This study focuses on sound therapy and evaluation of patients’ electroencephalogram (EEG) in order to verify the potency of this approach. Broadband sound therapy was applied to nineteen participants aging from 25 to 64 and suffering from chronic subjective tinnitus to study the difference of brain activity, a) before fake treatment, b) after fake treatment and c) after the main treatment, using EEG and Visual Analog Scale (VAS) for evaluating Residual... 

This research presents a robust method for P300 component recognition and classification in EEG signals for a P300 Speller Brain-Computer Interface (BCI). The multiresolution wavelet decomposition technique was used for feature extraction. The feature selection was done using an improved t-test method. For feature classification the Quadratic Discriminant Analysis was employed. No any particular specification is previously assumed in the proposed algorithm and all the constants of the system are optimized to generate the highest accuracy on a validation set. The method is first verified in offline experiments on 'BCI competition 2003' data set IIb and data recorded by Emotiv Neuroheadset and... 

A new steganalysis system for JPG-based image data hiding is proposed in this paper. We use features extracted from both wavelet and DCT domains that are refined later in the sense of utmost discrimination between the clear and stego images in the classification system. Statistical properties of the SVD of wavelet sub-bands are combined with the extended DCT-Markov features, and the features that are most sensitive to the data embedding are chosen through a SVM-RFE based selection algorithm. Experimental results show significant improvement over baseline methods, especially for steganalysis of Perturbed Quantization (PQ), which is known to be one of most secure JPG-based steganography... 

In this paper, we present a spatio-temporal event-based approach to video signal analysis and abstraction employing wavelet transform features. The video signal is assumed to be a sequence of overlapping independent visual components called events, which typically are temporally overlapping compact functions that describe temporal evolution of a given set of the spatial parameters of the video signal. We utilize event-based temporal decomposition technique to resolve the overlapping arrangement of the video signal that is known to be one of the main concerns in video analysis via conventional frame-based schemes. In our method, a set of spatial parameters, extracted from the video, is... 

The multidimensional Discrete Wavelet Transform (DWT) has been widely used in signal and image processing for regularly sampled data. For irregularly sampled data, however, other techniques should be used including the Least Square Wavelet Decomposition (LSWD). The commonly used level by level (sequential) wavelet decomposition, which calculates the wavelet coefficients in each resolution separately, may result in a gross interpolation error. To overcome this drawback, a different approach called the Simultaneous Least Square Wavelet Decomposition, which computes all wavelet coefficients simultaneously, have been proposed by the authors. In this paper, we extend the simultaneous LSWD... 

A passive universal image steganalysis method is proposed that is shown to be of higher detection accuracy than existing truly blind steganalysis methods including Farid's and the WAM. This is achieved by improving some weaknesses of Farid's steganalysis scheme in feature extraction, that is, instead of deriving an over-determined equation system for each sub band of the wavelet decomposition, the sub bands are divided into overlapping blocks and an over-determined equation system is constructed for each block. To guarantee the existence of finite answers, the over-determined equation systems are solved in a way different from Farid's by using Moore-Penrose pseudo-inverse concept. Further... 

The growth of data communication networks has made digital watermarking an important issue for copyright and content protection. Achieving high level of robustness and good transparency are the main objectives of developing every digital watermarking algorithm. From among transform domains as the basis of watermark hiding place, Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) are the most commonly used transforms in literature. In this paper we propose a new hybrid DWT-SVD domain watermarking scheme taking into account the energy content of every chosen block of the selected DWT sub-band coefficients. Before embedding, we append a... 

In this paper, we present a novel speech steganography method using discrete wavelet transform and sparse decomposition to address the undetectability concern in speech steganography. The proposed speech steganography method exploits the sparse representation to embed secret messages into higher semantic levels of the cover signal, resulting in increased undetectability. The proposed method also yields improvements on both stego signal quality and embedding capacity, which are the two major requirements of a steganography algorithm. Our experimental results illustrate that the stego signals generated by the proposed method are perceptually indistinguishable from the original cover signals,... 

Sparse decomposition of wavelet coefficients of cover image blocks for data hiding is addressed in this paper. By using the proposed algorithm, the embedded secret message can be reliably extracted without resorting to the original image. We use all four sub-images (LL, LH, HL and HH) of the 2D wavelet transform for data embedding without losing the image imperceptibility. An over-complete dictionary matrix is estimated by using the KSVD dictionary learning algorithm, and then the secret message bits are inserted in the sparse representation of the wavelet coefficients over the estimated dictionary. This is believed to be one of the first approaches to the image data hiding that uses the... 

This study presents a new method for epilepsy detection based on autoregressive (AR) estimation of EEG signals. In this method, optimum order for AR model is determined by Bayesian Information Criterion (BIC) and then AR parameters of EEG signals (from EEG data set of epilepsy center of the University of Bonn, Germany) and their sub-bands (created with the help of wavelet decomposition) are extracted based on it. These parameters are used as a feature to classify the EEG signals into Healthy, Interictal (seizure free) and Ictal (during a seizure) groups using multilayer perceptron (MLP) classifier. Correct classification scores at the range of 91% to 96% reveals the potential of our approach... 

One of the main properties of semi-fragile image watermarking methods is robustness against lossy compressions, e.g. JPEG conversion. In this paper, robustness of a semi-fragile watermarking scheme based on DWT (Discrete Wavelet Transform), coupled with SVD (Singular Value Decomposition), is improved. To achieve this goal a traditional, critically sampled wavelet transform is replaced by a redundant wavelet transform. The proposed approach is compared to the DWT-SVD based watermarking, introduced earlier, and significantly greater robustness under the JPEG compression is presented. ©2008 IEEE  

This paper presents a lossless data hiding method using coefficients of integer wavelet domain. The modification of selected small coefficients of the high frequency subbands are used to embed data. We use the histogram modification to intelligently select the proper coefficients for data hiding. Data embedding is done by processing these selected coefficients. We show that at low payload data hiding our method has comparable PSNR than the best known reversible data hiding techniques, while at higher payloads it has significant superiority on image quality. ©2007 IEEE  

This study presents applying recurrence quantification analysis (RQA) on EEG recordings and their subbands: delta, theta, alpha, beta, and gamma for epileptic seizure detection. RQA is adopted since it does not require assumptions about stationarity, length of signal, and noise. The decomposition of the original EEG into its five constituent subbands helps better identification of the dynamical system of EEG signal. This leads to better classification of the database into three groups: Healthy subjects, epileptic subjects during a seizure-free interval (Interictal) and epileptic subjects during a seizure course (Ictal). The proposed algorithm is applied to an epileptic EEG dataset provided... 

Partial Discharge (PD) measurements in Ultra High Frequency (UHF) band requires modern techniques for monitoring of power transformer. This is a topic of interest investigated in this field, especially in recent years. Different experimental models, such as: defect models, and winding models are used in PD studies of power transformer. The purpose in application of these models is to represent and simulate the real transformer performance using a simple model. The proposed model needs to be a simplified one which can be implemented of them. However, the applied simplifications may result in some changes (inaccuracy) into the recorded PD current signals captured by UHF sensors. These changes... 

Accurate Partial Discharge (PD) measurement requires the expansion of the measurement frequency range to Ultra High Frequency (UHF) band. For this it is needed to implement modern techniques when monitoring an important element of power system, such as power transformer. Different kind of experimental models, such as insulation defect models, and the complete transformer windings models are used to simulate the partial insulation failure situations which occur in a real transformer. However, since in these models, some kind of simplifications are employed which may result in an electromagnetic waves (EM) propagation pattern different from what exists in a real transformer and encounter... 

The modulation transfer function (MTF) is well known as a crucial parameter in quality assurance of computed tomography (CT) scanners, which provides detailed information of both contrast and resolution of CT images. Different methods have been introduced and developed to calculate the MTF of CT scanners. However, a robust methodology which accurately estimates the MTF of CT scanners under the use of every range of object electron density and tube current-time product (mAs) has not been reported so far. To this aim, a new wavelet-based circular edge method for MTF measurement has been presented in this work. Owning to the edge spread function (ESF) susceptibility to noise, the approach was... 

This article presents a novel identification approach which can deal with nonlinear and time-varying characteristics of complex dynamic systems, especially an aerial vehicle in the entire flight envelope. A set of local sub-models are first developed at different operating points of the system, and subsequently a self-organizing multi-model ensemble is introduced to aggregate the outputs of the local models as a single model. The number of employed local models in the proposed multi-model ensemble is optimized using a novel self-organizing approach. Also, wavelet neural networks, which combine both the universal approximation property of neural networks and the wavelet decomposition... 

Accelerograms recorded near active faults have some important characteristics that make them different from those recorded in far-fault regions. High-frequency components in acceleration records and long-period velocity pulses are among notable specifications of such ground motions. In this paper, a moving average filtering with appropriate cut-off frequency has been used to decompose the near-fault ground motions into two components having different frequency contents: first, Pulse-Type Record (PTR) that possesses long-period pulses; second, the relatively high-frequency BackGround Record (BGR), which does not include large velocity pulses. Comparing the results with those extracted through... 

In this article, a high-accuracy hybrid approach for short-term wind power forecasting is proposed using historical data of wind farm and Numerical Weather Prediction (NWP) data. The power forecasting is carried out in three stages: wind direction forecasting, wind speed forecasting, and wind power forecasting. In all three phases, the same hybrid method is used, and the only difference is in the input data set. The main steps of the proposed method are constituted of outlier detection, decomposition of time series using wavelet transform, effective feature selection and prediction of each time series decomposed using Multilayer Perceptron (MLP) neural network. The combination of automatic...