Search for: wavelet-analysis
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    Mammograms enhancement using wavelet transform and piecewise linear and nonlinear coefficient mapping

    , Article Middle East Conference on Biomedical Engineering, MECBME ; Feb , 2014 , p. 107-110 Beheshti, S. M. A ; Noubari, H. A ; Fatemizadeh, E ; Rezaee, M ; Khalili, M ; Sharif University of Technology
    In this paper a multi-scale image enhancement strategy using wavelets as applied to digital mammograms is presented. For multiresolution wavelet analysis redundant dyadic discrete wavelet transform is utilized to allow translation invariance and low resolution enhancement capability. Two alternative nonlinear gain adjustments, piecewise linear and Gaussian form of gain adjustment are used for coefficient modification for the enhancement of subtle details such as microcalcification and low resolution edges of different lesion types. The results of comparing these methods of gain adjustment are presented. This comparing has done by defining new parameters for measuring quality of image based... 

    Non-event Related Tinnitus Assessment Using EEG Time-frequency Analyses

    , M.Sc. Thesis Sharif University of Technology Dabouei, Ali (Author) ; Jahed, Mehran (Supervisor) ; Mahmoudian, Saeed (Co-Advisor)
    Tinnitus is a perception of sound in the absence of an external source. Tinnitus is commonly associated with the hearing system and the sound perception areas in brain. Subjective tinnitus etiology has not been fully understood. Recent researches introduce some theories by means of finding structural differences in brain of tinnitus patients in comparison to normal people. In most previous researches on finding correlations of tinnitus in EEG signals, processes has been performed in time or frequency domain. According to these evidence and great performance of time-frequency analyses tools, especially wavelets in processing non-stationary signals, we utilized wavelet packets to decompose EEG... 

    EEG based Analysis and Classification of Children with Learning Disability Compared to Normal Children

    , M.Sc. Thesis Sharif University of Technology Mirmohammad Sadeghi, Delaram Alsadat (Author) ; Jahed, Mehran (Supervisor)
    Learning disability (LD) is a neurological condition that interferes with an individual’s ability to store, process, or produce information. There are different types of learning disabilities affecting reading, writing, speaking, spelling, etc. Based on a study conducted by National Center for Learning Disabilities, 2.4 million American public school students are diagnosed with learning disability. They attend school in order to learn and be successful while they do not know their learning process is different from their peers. LD diagnosis in children is especially important as such cases must be identified early enough in order to provide them with proper education.This project targets LD... 

    Parameter Reduction of Wavelet Transformation for Increasing the Accuracy of Integrated and Automatic History Matching

    , M.Sc. Thesis Sharif University of Technology Dehghani, Amin (Author) ; Pishvaie, Mahmoud Reza (Supervisor) ; Bozorgmehry, Ramein (Supervisor)
    One of problemsin an inverse problem like history matching is there is no unique solution. This means that maybe several diferent permeability maps can correctly reproduced the history of fieldt but there is no garanttee that these maps accurately predidct reservoir production behavior. One way to face and deal with this problem, except manipulation of solution and optimization algorithm (inverse modeling) is to use other data sources like seismic data, Variogram, pore volume, and fracture density or any other parameter obtained from geostatistical investigations.Multi-resolution wavelet analysis can be an appropriate tool to gather the necessary information to characterize the inverse model... 

    Object tracking in crowded video scenes based on the undecimated wavelet features and texture analysis

    , Article Eurasip Journal on Advances in Signal Processing ; Volume 2008 , 2008 ; 16876172 (ISSN) Khansari, M ; Rabiee, H. R ; Asadi, M ; Ghanbari, M ; Sharif University of Technology
    We propose a new algorithm for object tracking in crowded video scenes by exploiting the properties of undecimated wavelet packet transform (UWPT) and interframe texture analysis. The algorithm is initialized by the user through specifying a region around the object of interest at the reference frame. Then, coefficients of the UWPT of the region are used to construct a feature vector (FV) for every pixel in that region. Optimal search for the best match is then performed by using the generated FVs inside an adaptive search window. Adaptation of the search window is achieved by interframe texture analysis to find the direction and speed of the object motion. This temporal texture analysis... 

    Investigation of partial discharge propagation and location in multiple-α and single- α transformer windings using optimized wavelet analysis

    , Article Iranian Journal of Science and Technology, Transaction B: Engineering ; Volume 30, Issue 6 , 2006 , Pages 655-666 ; 03601307 (ISSN) Salay Naderi, M ; Vakilian, M ; Blackburn, T. R ; Phung, T. B ; Salay Naderi, M ; Sharif University of Technology
    Partial discharges (PD) are recognized as the main cause of the inner insulation deterioration process in power transformers. Therefore, the optimum inner insulation design is one of the challenges a transformer designer is faced with. Transformer strength, especially during transient conditions, is a criterion for transformer insulation designers. This challenge has made designers initiate and employ other types of winding, for example, rather than ordinary layer and disc windings employ the multiple-α windings. Multiple-α windings have a more complicated structure and are comprised of various parts with different physical structures and electrical characteristics. Typical partial discharge... 

    Feature extraction from optimal time-frequency and time-scale transforms for the classification of the knee joint vibroarthrographic signals

    , Article 3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003, 14 December 2003 through 17 December 2003 ; 2003 , Pages 709-712 ; 0780382927 (ISBN); 9780780382923 (ISBN) Eskandari, H ; Shamsollahi, M. B ; Rahimi, A ; Behzad, M ; Afkari, P ; Zamani, E. A ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2003
    In this study knee joint vibroarthrographic (VAG) signals are recorded during active knee movements, which are essentially non-stationary. Because of this nature, common frequency methods are unable to represent the signals, accurately. Both time-frequency and time-scale transforms are used in this research which are good tools for studying non-stationary signals. By optimizing the utilized transforms, it was concluded that the wavelet packet, having the ability of multiresolutional analysis, is a more promising method to extract features from the VAG signals. The performance of different feature extraction techniques were compared by using three new recorded and extensive databases,... 

    Generalizability of trunk muscle EMG and spinal forces

    , Article IEEE Engineering in Medicine and Biology Magazine ; Volume 20, Issue 6 , 2001 , Pages 72-81 ; 07395175 (ISSN) Sparto, P. J ; Parnianpour, M ; Sharif University of Technology
    The generalizability of trunk muscle EMG and spinal loading estimates obtained from an EMG-assisted biomechanical model was assessed over three occasions and three repetitions. The greatest sources of variability consisted of the intersubject differences and the interaction between subject and occasion. The ID (reliability coefficient) was less for trunk muscle activity compared with estimates of anteroposterior shear force, compression force, and gain computed from the biomechanical model. In order to obtain an ID of 0.8, we recommend five testing occasions for submaximal EMG measurements and three testing occasions for biomechanical estimates. Reproducible estimates of maximal trunk... 

    Interictal Noise Cancellation Based on Combination of ICA-based and Wavelet-based Denoising Approaches

    , M.Sc. Thesis Sharif University of Technology Zakizadeh, Mohammad (Author) ; Shamsollahi, Mohammad Bagher (Supervisor)
    Interictal EEG signals are very critical in diagnosis of epilepsy. Analysis of interictal EEG signals is very challenging due to contamination by various undesired signals like background EEG, muscular activity, noise, etc. Thus denoising of interictal signals has been an active research field in recent years. Primary purpose of this thesis is to denoise interictal EEG signals by using different combinations of ICA-based and wavelet denoising approaches. Then a new direction is pursued by using Morphological Component Analysis (MCA) which is a method for solving source separation problems based on morphological diversity of sources. Afterward MCA is modified by considering more prior... 

    Reconstruction of Noise Sources in VVER-1000 Reactors by Using Wavelet Analysis

    , M.Sc. Thesis Sharif University of Technology Saberian, Sorush (Author) ; Vossoughi, Nasser (Supervisor)
    Noise phenomena, which is considered as a nuisance factor, always acts as a barrier to achieve optimal performance in all systems. In Nuclear power reactors, noise can occur due to mechanical problems such as fluctuation in control rods, failure in placing Fuel assembly in their locations properly or other factors such as creation of small bubbles in moderator. Regardless of its cause, noise leaves its impact on cross section area of material within the reactor core. Change in material cross section area shows its effect in neutron flux. Although noise is a small disturbance and may not have much impact on the reactor momentarily performance itself, in case of continuity, it can leave... 

    Multidimensional lévy white noise in weighted besov spaces

    , Article Stochastic Processes and their Applications ; Volume 127, Issue 5 , 2017 , Pages 1599-1621 ; 03044149 (ISSN) Fageot, J ; Fallah, A ; Unser, M ; Sharif University of Technology
    In this paper, we study the Besov regularity of a general d-dimensional Lévy white noise. More precisely, we describe new sample paths properties of a given noise in terms of weighted Besov spaces. In particular, we characterize the smoothness and integrability properties of the noise using the indices introduced by Blumenthal, Getoor, and Pruitt. Our techniques rely on wavelet methods and generalized moments estimates for Lévy noises. © 2016 Elsevier B.V  

    Multi-view face detection and recognition under variable lighting using fuzzy logic

    , Article 2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR, Hong Kong, 30 August 2008 through 31 August 2008 ; Volume 1 , September , 2008 , Pages 74-79 ; 9781424422395 (ISBN) Shoja Ghiass, R ; Sadati, N ; Sharif University of Technology
    This paper presents a novel approach for detection and recognition of multi-view faces whose size and location is unknown and the illumination conditions are varying. The illumination is a big problem in face detection and recognition. In this paper, a new pre-processing method is proposed in order to cancel the effect of various illumination conditions on a face. Then, a binary face is obtained that its pixels are 0 or 1. Next, a fuzzy face model is produced from the distribution of zeros in the binary face. The model is used in order to detect faces in images by using a fuzzy approach. Because of the independency of the detection method to the skin color of face, persons with every kind of... 

    A new framework based on recurrence quantification analysis for epileptic seizure detection

    , Article IEEE Journal of Biomedical and Health Informatics ; Volume 17, Issue 3 , 2013 , Pages 572-578 ; 21682194 (ISSN) Niknazar, M ; Mousavi, S. R ; Vosoughi Vahdat, B ; Sayyah, M ; Sharif University of Technology
    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... 

    Novel margin features for mammographic mass classification

    , Article Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012 ; Volume 2 , 2012 , Pages 139-144 ; 9780769549132 (ISBN) Bagheri Khaligh, A ; Zarghami, A ; Manzuri Shalmani, M. T ; Sharif University of Technology
    Computer-Aided Diagnosis (CAD) systems are widely used for detection of various kinds of abnormalities in mammography images. Masses are one type of these abnormalities which are mostly characterized by their margin and shape. For classification of masses proper features are needed to be extracted. However, the number of well-known features for describing margin is much fewer than geometrical, shape, and textural ones. In addition, most of the existing margin features are highly dependent on segmentation accuracy. In this work, new features for describing margin of masses are presented which can handle inaccuracies in segmentation. These features are obtained from a set of waveforms by... 

    An empirical centre assignment in RBF network for quantification of anaesthesia using wavelet-domain features

    , Article 2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09, Antalya, 29 April 2009 through 2 May 2009 ; 2009 , Pages 510-513 ; 9781424420735 (ISBN) Taslimi, P ; Rabiee, H. R ; Shakouri Ganjavi, H ; National Institutes of Health, NIH; National Institute of Neurological Disorders and Stroke, NINDS; National Science Foundation, NSF ; Sharif University of Technology
    The assessment of the hypnotic state of the brain is crucial to the process of an operation under general anaesthesia. A noninvasive method of quantifying depth of anaesthesia is through analysis of electroencephalogram (EEG). Among number of works done in the field, no single algorithm has been found exhibiting a precise measure in all of the hypnotic states. One can categorise algorithms as either a state-quantifier or a trend measure. State-quantifier algorithms can discriminate between different hypnotic states such as awake, light sedation, deep anaesthesia, etc. On the other hand, trend measure algorithms are employed to specify the short-term changes in hypnotic brain conditions,... 

    Detection of streamflow trends and variability in karun river-Iran as parts of climate change and climate variability

    , Article Proceedings of World Environmental and Water Resources Congress 2009 - World Environmental and Water Resources Congress 2009: Great Rivers, 17 May 2009 through 21 May 2009, Kansas City, MO ; Volume 342 , 2009 , Pages 4782-4793 ; 9780784410363 (ISBN) Farrokhi, A. R ; Abrishamchi, A ; Sharif University of Technology
    This paper describes the application of statistical and spectral procedures that identifies trends and periodicity in streamflow time series. The results of Mann-Kendall and seasonal Kendall tests (non-parametric tests which are known as appropriate tools in detecting linear trends of hydrological time series) shows negative trends especially during low water months (August to November). This downward trend is more significant in October. But these methods can not interpret periodic behavior. Hence spectral procedures were applied on data series to investigate periodicities in streamflow data series. Fourier and Continuous Wavelet Transform (CWT) analyses produce evidence of interannual... 

    Reduction of doppler estimation error by multi-frequency transmission

    , Article IET International Radar Conference 2009, Guilin, 20 April 2009 through 22 April 2009 ; Issue 551 CP , 2009 ; 9781849190107 (ISBN) Sebt, M. A ; Sheikhi, A ; Nayebi, M. M ; Sharif University of Technology
    In this paper, after introduction of symmetric multi- frequency signal, it will be presented how to estimate Doppler frequency from estimated frequency tones of returned echo. After that, three methods of Pisarenko, Prony and MUSIC for frequency estimation will be introduced, and then these methods are applied to estimate tone frequencies of a multi-carrier signal. It will be shown that estimation of Doppler frequency by multi-frequency signal transmission leads to less rms error than single-frequency signal, in the condition of equal SNR. Infact it will be suggested that distribution of the power of signal in spectrum instead of adding up all the power in one point, leads to less Doppler... 

    High-impedance fault detection using multi-resolution signal decomposition and adaptive neural fuzzy inference system

    , Article IET Generation, Transmission and Distribution ; Volume 2, Issue 1 , 2008 , Pages 110-118 ; 17518687 (ISSN) Etemadi, A. H ; Sanaye Pasand, M ; Sharif University of Technology
    High-impedance faults (HIFs) on distribution systems create unique challenges to protection engineers. HIFs do not produce enough fault current to be detected by conventional overcurrent relays or fuses. A method for HIF detection based on the nonlinear behaviour of current waveforms is presented. Using this method, HIFs can be distinguished successfully from other similar waveforms such as nonlinear load currents, secondary current of saturated current transformers and inrush currents. A wavelet multi-resolution signal decomposition method is used for feature extraction. Extracted features are fed to an adaptive neural fuzzy inference system (ANFIS) for identification and classification.... 

    Detection of a band-limited signal using an orthonormal, fully-decimated filter-bank

    , Article Scientia Iranica ; Volume 14, Issue 6 , 2007 , Pages 555-565 ; 10263098 (ISSN) Derakhtian, M ; Tadaion, A. A ; Nayebi, M. M ; Aref, M. R ; Sharif University of Technology
    Sharif University of Technology  2007
    In this paper, two methods are proposed for the detection of a band-limited signal in unknown variance white Gaussian noise. The complex amplitude and the frequency of the signal and the noise variance are assumed as unknown parameters. Using wavelet concepts, an orthonormal, fully-decimated filter-bank is employed to decompose the signal into its subband components. It is shown that, in this process, the noise is also decomposed into orthonormal zero-mean components. In the output, if a band-limited target signal is present, the respective single subband component (or two components in marginal cases) containing the target signal presents a non-zero mean. The presence of a non-zero mean... 

    Study on elastic response of structures to near-fault ground motions through record decomposition

    , Article Soil Dynamics and Earthquake Engineering ; Volume 30, Issue 7 , Jan , 2010 , Pages 536-546 ; 02677261 (ISSN) Ghahari, S. F ; Jahankhah, H ; Ali Ghannad, M ; Sharif University of Technology
    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...