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wavelet</title> <meta name="description" content="Search results for: Continuous wavelet"> <meta name="keywords" content="Continuous wavelet"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Continuous wavelet"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 2475</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Continuous wavelet</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2475</span> Detection of Coupling Misalignment in a Rotor System Using Wavelet Transforms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prabhakar%20Sathujoda">Prabhakar Sathujoda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vibration analysis of a misaligned rotor coupling bearing system has been carried out while decelerating through its critical speed. The finite element method (FEM) is used to model the rotor system and simulate flexural vibrations. A flexible coupling with a frictionless joint is considered in the present work. The continuous wavelet transform is used to extract the misalignment features from the simulated time response. Subcritical speeds at one-half, one-third, and one-fourth the critical speed have appeared in the wavelet transformed vibration response of a misaligned rotor coupling bearing system. These features are also verified through a parametric study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Continuous%20Wavelet%20Transform" title="Continuous Wavelet Transform">Continuous Wavelet Transform</a>, <a href="https://publications.waset.org/abstracts/search?q=Flexible%20Coupling" title=" Flexible Coupling"> Flexible Coupling</a>, <a href="https://publications.waset.org/abstracts/search?q=Rotor%20System" title=" Rotor System"> Rotor System</a>, <a href="https://publications.waset.org/abstracts/search?q=Sub%20Critical%20Speed" title=" Sub Critical Speed"> Sub Critical Speed</a> </p> <a href="https://publications.waset.org/abstracts/123448/detection-of-coupling-misalignment-in-a-rotor-system-using-wavelet-transforms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123448.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">162</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2474</span> Enhancement of Pulsed Eddy Current Response Based on Power Spectral Density after Continuous Wavelet Transform Decomposition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Benyahia">A. Benyahia</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Zergoug"> M. Zergoug</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Amir"> M. Amir</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Fodil"> M. Fodil</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main objective of this work is to enhance the Pulsed Eddy Current (PEC) response from the aluminum structure using signal processing. Cracks and metal loss in different structures cause changes in PEC response measurements. In this paper, time-frequency analysis is used to represent PEC response, which generates a large quantity of data and reduce the noise due to measurement. Power Spectral Density (PSD) after Wavelet Decomposition (PSD-WD) is proposed for defect detection. The experimental results demonstrate that the cracks in the surface can be extracted satisfactorily by the proposed methods. The validity of the proposed method is discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DT" title="DT">DT</a>, <a href="https://publications.waset.org/abstracts/search?q=pulsed%20eddy%20current" title=" pulsed eddy current"> pulsed eddy current</a>, <a href="https://publications.waset.org/abstracts/search?q=continuous%20wavelet%20transform" title=" continuous wavelet transform"> continuous wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=Mexican%20hat%20wavelet%20mother" title=" Mexican hat wavelet mother"> Mexican hat wavelet mother</a>, <a href="https://publications.waset.org/abstracts/search?q=defect%20detection" title=" defect detection"> defect detection</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20spectral%20density." title=" power spectral density."> power spectral density.</a> </p> <a href="https://publications.waset.org/abstracts/88425/enhancement-of-pulsed-eddy-current-response-based-on-power-spectral-density-after-continuous-wavelet-transform-decomposition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88425.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">237</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2473</span> Analysis of Ionospheric Variations over Japan during 23rd Solar Cycle Using Wavelet Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20S.%20Seema">C. S. Seema</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20R.%20Prince"> P. R. Prince</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The characterization of spatio-temporal inhomogeneities occurring in the ionospheric F₂ layer is remarkable since these variations are direct consequences of electrodynamical coupling between magnetosphere and solar events. The temporal and spatial variations of the F₂ layer, which occur with a period of several days or even years, mainly owe to geomagnetic and meteorological activities. The hourly F₂ layer critical frequency (foF2) over 23rd solar cycle (1996-2008) of three ionosonde stations (Wakkanai, Kokunbunji, and Okinawa) in northern hemisphere, which falls within same longitudinal span, is analyzed using continuous wavelet techniques. Morlet wavelet is used to transform continuous time series data of foF2 to a two dimensional time-frequency space, quantifying the time evolution of the oscillatory modes. The presence of significant time patterns (periodicities) at a particular time period and the time location of each periodicity are detected from the two-dimensional representation of the wavelet power, in the plane of scale and period of the time series. The mean strength of each periodicity over the entire period of analysis is studied using global wavelet spectrum. The quasi biennial, annual, semiannual, 27 day, diurnal and 12 hour variations of foF2 are clearly evident in the wavelet power spectra in all the three stations. Critical frequency oscillations with multi-day periods (2-3 days and 9 days in the low latitude station, 6-7 days in all stations and 15 days in mid-high latitude station) are also superimposed over large time scaled variations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=continuous%20wavelet%20analysis" title="continuous wavelet analysis">continuous wavelet analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=critical%20frequency" title=" critical frequency"> critical frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=ionosphere" title=" ionosphere"> ionosphere</a>, <a href="https://publications.waset.org/abstracts/search?q=solar%20cycle" title=" solar cycle"> solar cycle</a> </p> <a href="https://publications.waset.org/abstracts/75814/analysis-of-ionospheric-variations-over-japan-during-23rd-solar-cycle-using-wavelet-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75814.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">220</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2472</span> Time-Frequency Modelling and Analysis of Faulty Rotor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20X.%20Tchomeni">B. X. Tchomeni</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20A.%20Alugongo"> A. A. Alugongo</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20B.%20Tengen"> T. B. Tengen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, de Laval rotor system has been characterized by a hinge model and its transient response numerically treated for a dynamic solution. The effect of the ensuing non-linear disturbances namely rub and breathing crack is numerically simulated. Subsequently, three analysis methods: Orbit Analysis, Fast Fourier Transform (FFT) and Wavelet Transform (WT) are employed to extract features of the vibration signal of the faulty system. An analysis of the system response orbits clearly indicates the perturbations due to the rotor-to-stator contact. The sensitivities of WT to the variation in system speed have been investigated by Continuous Wavelet Transform (CWT). The analysis reveals that features of crack, rubs and unbalance in vibration response can be useful for condition monitoring. WT reveals its ability to detect non-linear signal, and obtained results provide a useful tool method for detecting machinery faults. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Continuous%20wavelet" title="Continuous wavelet">Continuous wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=crack" title=" crack"> crack</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet" title=" discrete wavelet"> discrete wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20acceleration" title=" high acceleration"> high acceleration</a>, <a href="https://publications.waset.org/abstracts/search?q=low%20acceleration" title=" low acceleration"> low acceleration</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear" title=" nonlinear"> nonlinear</a>, <a href="https://publications.waset.org/abstracts/search?q=rotor-stator" title=" rotor-stator"> rotor-stator</a>, <a href="https://publications.waset.org/abstracts/search?q=rub" title=" rub"> rub</a> </p> <a href="https://publications.waset.org/abstracts/33449/time-frequency-modelling-and-analysis-of-faulty-rotor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33449.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">349</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2471</span> Theory and Practice of Wavelets in Signal Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jalal%20Karam">Jalal Karam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The methods of Fourier, Laplace, and Wavelet Transforms provide transfer functions and relationships between the input and the output signals in linear time invariant systems. This paper shows the equivalence among these three methods and in each case presenting an application of the appropriate (Fourier, Laplace or Wavelet) to the convolution theorem. In addition, it is shown that the same holds for a direct integration method. The Biorthogonal wavelets Bior3.5 and Bior3.9 are examined and the zeros distribution of their polynomials associated ﬁlters are located. This paper also presents the signiﬁcance of utilizing wavelets as effective tools in processing speech signals for common multimedia applications in general, and for recognition and compression in particular. Theoretically and practically, wavelets have proved to be effective and competitive. The practical use of the Continuous Wavelet Transform (CWT) in processing and analysis of speech is then presented along with explanations of how the human ear can be thought of as a natural wavelet transformer of speech. This generates a variety of approaches for applying the (CWT) to many paradigms analysing speech, sound and music. For perception, the ﬂexibility of implementation of this transform allows the construction of numerous scales and we include two of them. Results for speech recognition and speech compression are then included. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=continuous%20wavelet%20transform" title="continuous wavelet transform">continuous wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=biorthogonal%20wavelets" title=" biorthogonal wavelets"> biorthogonal wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20perception" title=" speech perception"> speech perception</a>, <a href="https://publications.waset.org/abstracts/search?q=recognition%20and%20compression" title=" recognition and compression"> recognition and compression</a> </p> <a href="https://publications.waset.org/abstracts/5822/theory-and-practice-of-wavelets-in-signal-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5822.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">416</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2470</span> 3D Object Model Reconstruction Based on Polywogs Wavelet Network Parametrization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Othmani">Mohamed Othmani</a>, <a href="https://publications.waset.org/abstracts/search?q=Yassine%20Khlifi"> Yassine Khlifi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a technique for compact three dimensional (3D) object model reconstruction using wavelet networks. It consists to transform an input surface vertices into signals,and uses wavelet network parameters for signal approximations. To prove this, we use a wavelet network architecture founded on several mother wavelet families. POLYnomials WindOwed with Gaussians (POLYWOG) wavelet families are used to maximize the probability to select the best wavelets which ensure the good generalization of the network. To achieve a better reconstruction, the network is trained several iterations to optimize the wavelet network parameters until the error criterion is small enough. Experimental results will shown that our proposed technique can effectively reconstruct an irregular 3D object models when using the optimized wavelet network parameters. We will prove that an accurateness reconstruction depends on the best choice of the mother wavelets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3d%20object" title="3d object">3d object</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=parametrization" title=" parametrization"> parametrization</a>, <a href="https://publications.waset.org/abstracts/search?q=polywog%20wavelets" title=" polywog wavelets"> polywog wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=reconstruction" title=" reconstruction"> reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20networks" title=" wavelet networks"> wavelet networks</a> </p> <a href="https://publications.waset.org/abstracts/49814/3d-object-model-reconstruction-based-on-polywogs-wavelet-network-parametrization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49814.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">284</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2469</span> Optimal Mother Wavelet Function for Shoulder Muscles of Upper Limb Amputees</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amanpreet%20Kaur">Amanpreet Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wavelet transform (WT) is a powerful statistical tool used in applied mathematics for signal and image processing. The different mother, wavelet basis function, has been compared to select the optimal wavelet function that represents the electromyogram signal characteristics of upper limb amputees. Four different EMG electrode has placed on different location of shoulder muscles. Twenty one wavelet functions from different wavelet families were investigated. These functions included Daubechies (db1-db10), Symlets (sym1-sym5), Coiflets (coif1-coif5) and Discrete Meyer. Using mean square error value, the significance of the mother wavelet functions has been determined for teres, pectorals, and infraspinatus around shoulder muscles. The results show that the best mother wavelet is the db3 from the Daubechies family for efficient classification of the signal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daubechies" title="Daubechies">Daubechies</a>, <a href="https://publications.waset.org/abstracts/search?q=upper%20limb%20amputation" title=" upper limb amputation"> upper limb amputation</a>, <a href="https://publications.waset.org/abstracts/search?q=shoulder%20muscles" title=" shoulder muscles"> shoulder muscles</a>, <a href="https://publications.waset.org/abstracts/search?q=Symlets" title=" Symlets"> Symlets</a>, <a href="https://publications.waset.org/abstracts/search?q=Coiflets" title=" Coiflets"> Coiflets</a> </p> <a href="https://publications.waset.org/abstracts/103654/optimal-mother-wavelet-function-for-shoulder-muscles-of-upper-limb-amputees" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103654.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">235</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2468</span> Bayesian Inference for High Dimensional Dynamic Spatio-Temporal Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sofia%20M.%20Karadimitriou">Sofia M. Karadimitriou</a>, <a href="https://publications.waset.org/abstracts/search?q=Kostas%20Triantafyllopoulos"> Kostas Triantafyllopoulos</a>, <a href="https://publications.waset.org/abstracts/search?q=Timothy%20Heaton"> Timothy Heaton</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Reduced dimension Dynamic Spatio-Temporal Models (DSTMs) jointly describe the spatial and temporal evolution of a function observed subject to noise. A basic state space model is adopted for the discrete temporal variation, while a continuous autoregressive structure describes the continuous spatial evolution. Application of such a DSTM relies upon the pre-selection of a suitable reduced set of basic functions and this can present a challenge in practice. In this talk, we propose an online estimation method for high dimensional spatio-temporal data based upon DSTM and we attempt to resolve this issue by allowing the basis to adapt to the observed data. Specifically, we present a wavelet decomposition in order to obtain a parsimonious approximation of the spatial continuous process. This parsimony can be achieved by placing a Laplace prior distribution on the wavelet coefficients. The aim of using the Laplace prior, is to filter wavelet coefficients with low contribution, and thus achieve the dimension reduction with significant computation savings. We then propose a Hierarchical Bayesian State Space model, for the estimation of which we offer an appropriate particle filter algorithm. The proposed methodology is illustrated using real environmental data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multidimensional%20Laplace%20prior" title="multidimensional Laplace prior">multidimensional Laplace prior</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20filtering" title=" particle filtering"> particle filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=spatio-temporal%20modelling" title=" spatio-temporal modelling"> spatio-temporal modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelets" title=" wavelets"> wavelets</a> </p> <a href="https://publications.waset.org/abstracts/43799/bayesian-inference-for-high-dimensional-dynamic-spatio-temporal-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43799.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">427</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2467</span> Does Pakistan Stock Exchange Offer Diversification Benefits to Regional and International Investors: A Time-Frequency (Wavelets) Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Syed%20Jawad%20Hussain%20Shahzad">Syed Jawad Hussain Shahzad</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Zakaria"> Muhammad Zakaria</a>, <a href="https://publications.waset.org/abstracts/search?q=Mobeen%20Ur%20Rehman"> Mobeen Ur Rehman</a>, <a href="https://publications.waset.org/abstracts/search?q=Saniya%20Khaild"> Saniya Khaild</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study examines the co-movement between the Pakistan, Indian, S&P 500 and Nikkei 225 stock markets using weekly data from 1998 to 2013. The time-frequency relationship between the selected stock markets is conducted by using measures of continuous wavelet power spectrum, cross-wavelet transform and cross (squared) wavelet coherency. The empirical evidence suggests strong dependence between Pakistan and Indian stock markets. The co-movement of Pakistani index with U.S and Japanese, the developed markets, varies over time and frequency where the long-run relationship is dominant. The results of cross wavelet and wavelet coherence analysis indicate moderate covariance and correlation between stock indexes and the markets are in phase (i.e. cyclical in nature) over varying durations. Pakistan stock market was lagging during the entire period in relation to Indian stock market, corresponding to the 8~32 and then 64~256 weeks scale. Similar findings are evident for S&P 500 and Nikkei 225 indexes, however, the relationship occurs during the later period of study. All three wavelet indicators suggest strong evidence of higher co-movement during 2008-09 global financial crises. The empirical analysis reveals a strong evidence that the portfolio diversification benefits vary across frequencies and time. This analysis is unique and have several practical implications for regional and international investors while assigning the optimal weightage of different assets in portfolio formulation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=co-movement" title="co-movement">co-movement</a>, <a href="https://publications.waset.org/abstracts/search?q=Pakistan%20stock%20exchange" title=" Pakistan stock exchange"> Pakistan stock exchange</a>, <a href="https://publications.waset.org/abstracts/search?q=S%26P%20500" title=" S&P 500"> S&P 500</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikkei%20225" title=" Nikkei 225"> Nikkei 225</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20analysis" title=" wavelet analysis"> wavelet analysis</a> </p> <a href="https://publications.waset.org/abstracts/30445/does-pakistan-stock-exchange-offer-diversification-benefits-to-regional-and-international-investors-a-time-frequency-wavelets-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30445.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">358</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2466</span> Multi-Focus Image Fusion Using SFM and Wavelet Packet</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Somkait%20Udomhunsakul">Somkait Udomhunsakul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a multi-focus image fusion method using Spatial Frequency Measurements (SFM) and Wavelet Packet was proposed. The proposed fusion approach, firstly, the two fused images were transformed and decomposed into sixteen subbands using Wavelet packet. Next, each subband was partitioned into sub-blocks and each block was identified the clearer regions by using the Spatial Frequency Measurement (SFM). Finally, the recovered fused image was reconstructed by performing the Inverse Wavelet Transform. From the experimental results, it was found that the proposed method outperformed the traditional SFM based methods in terms of objective and subjective assessments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-focus%20image%20fusion" title="multi-focus image fusion">multi-focus image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20packet" title=" wavelet packet"> wavelet packet</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20frequency%20measurement" title=" spatial frequency measurement"> spatial frequency measurement</a> </p> <a href="https://publications.waset.org/abstracts/4886/multi-focus-image-fusion-using-sfm-and-wavelet-packet" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4886.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">474</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2465</span> Review: Wavelet New Tool for Path Loss Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Danladi%20Ali">Danladi Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdullahi%20Mukaila"> Abdullahi Mukaila</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, GSM signal strength (power) was monitored in an indoor environment. Samples of the GSM signal strength was measured on mobile equipment (ME). One-dimensional multilevel wavelet is used to predict the fading phenomenon of the GSM signal measured and neural network clustering to determine the average power received in the study area. The wavelet prediction revealed that the GSM signal is attenuated due to the fast fading phenomenon which fades about 7 times faster than the radio wavelength while the neural network clustering determined that -75dBm appeared more frequently followed by -85dBm. The work revealed that significant part of the signal measured is dominated by weak signal and the signal followed more of Rayleigh than Gaussian distribution. This confirmed the wavelet prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decomposition" title="decomposition">decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=propagation" title=" propagation"> propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=model" title=" model"> model</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet" title=" wavelet"> wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20strength%20and%20spectral%20efficiency" title=" signal strength and spectral efficiency"> signal strength and spectral efficiency</a> </p> <a href="https://publications.waset.org/abstracts/38599/review-wavelet-new-tool-for-path-loss-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38599.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">448</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2464</span> Using Morlet Wavelet Filter to Denoising Geoelectric ‘Disturbances’ Map of Moroccan Phosphate Deposit ‘Disturbances’</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saad%20Bakkali">Saad Bakkali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Morocco is a major producer of phosphate, with an annual output of 19 million tons and reserves in excess of 35 billion cubic meters. This represents more than 75% of world reserves. Resistivity surveys have been successfully used in the Oulad Abdoun phosphate basin. A Schlumberger resistivity survey over an area of 50 hectares was carried out. A new field procedure based on analytic signal response of resistivity data was tested to deal with the presence of phosphate deposit disturbances. A resistivity map was expected to allow the electrical resistivity signal to be imaged in 2D. 2D wavelet is standard tool in the interpretation of geophysical potential field data. Wavelet transform is particularly suitable in denoising, filtering and analyzing geophysical data singularities. Wavelet transform tools are applied to analysis of a moroccan phosphate deposit ‘disturbances’. Wavelet approach applied to modeling surface phosphate “disturbances” was found to be consistently useful. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=resistivity" title="resistivity">resistivity</a>, <a href="https://publications.waset.org/abstracts/search?q=Schlumberger" title=" Schlumberger"> Schlumberger</a>, <a href="https://publications.waset.org/abstracts/search?q=phosphate" title=" phosphate"> phosphate</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet" title=" wavelet"> wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=Morocco" title=" Morocco"> Morocco</a> </p> <a href="https://publications.waset.org/abstracts/36526/using-morlet-wavelet-filter-to-denoising-geoelectric-disturbances-map-of-moroccan-phosphate-deposit-disturbances" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36526.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">421</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2463</span> Preventive Maintenance of Rotating Machinery Based on Vibration Diagnosis of Rolling Bearing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20Bensana">T. Bensana</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Mekhilef"> S. Mekhilef</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The methodology of vibration based condition monitoring technology has been developing at a rapid stage in the recent years suiting to the maintenance of sophisticated and complicated machines. The ability of wavelet analysis to efficiently detect non-stationary, non-periodic, transient features of the vibration signal makes it a demanding tool for condition monitoring. This paper presents a methodology for fault diagnosis of rolling element bearings based on wavelet envelope power spectrum technique is analysed in both the time and frequency domains. In the time domain the auto-correlation of the wavelet de-noised signal is applied to evaluate the period of the fault pulses. However, in the frequency domain the wavelet envelope power spectrum has been used to identify the fault frequencies with the single sided complex Laplace wavelet as the mother wavelet function. Results show the superiority of the proposed method and its effectiveness in extracting fault features from the raw vibration signal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=preventive%20maintenance" title="preventive maintenance">preventive maintenance</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnostics" title=" fault diagnostics"> fault diagnostics</a>, <a href="https://publications.waset.org/abstracts/search?q=rolling%20element%20bearings" title=" rolling element bearings"> rolling element bearings</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20de-noising" title=" wavelet de-noising"> wavelet de-noising</a> </p> <a href="https://publications.waset.org/abstracts/18460/preventive-maintenance-of-rotating-machinery-based-on-vibration-diagnosis-of-rolling-bearing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18460.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">379</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2462</span> Application of EEG Wavelet Power to Prediction of Antidepressant Treatment Response</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dorota%20Witkowska">Dorota Witkowska</a>, <a href="https://publications.waset.org/abstracts/search?q=Pawe%C5%82%20Gosek"> Paweł Gosek</a>, <a href="https://publications.waset.org/abstracts/search?q=Lukasz%20Swiecicki"> Lukasz Swiecicki</a>, <a href="https://publications.waset.org/abstracts/search?q=Wojciech%20Jernajczyk"> Wojciech Jernajczyk</a>, <a href="https://publications.waset.org/abstracts/search?q=Bruce%20J.%20West"> Bruce J. West</a>, <a href="https://publications.waset.org/abstracts/search?q=Miroslaw%20Latka"> Miroslaw Latka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In clinical practice, the selection of an antidepressant often degrades to lengthy trial-and-error. In this work we employ a normalized wavelet power of alpha waves as a biomarker of antidepressant treatment response. This novel EEG metric takes into account both non-stationarity and intersubject variability of alpha waves. We recorded resting, 19-channel EEG (closed eyes) in 22 inpatients suffering from unipolar (UD, n=10) or bipolar (BD, n=12) depression. The EEG measurement was done at the end of the short washout period which followed previously unsuccessful pharmacotherapy. The normalized alpha wavelet power of 11 responders was markedly different than that of 11 nonresponders at several, mostly temporoparietal sites. Using the prediction of treatment response based on the normalized alpha wavelet power, we achieved 81.8% sensitivity and 81.8% specificity for channel T4. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=alpha%20waves" title="alpha waves">alpha waves</a>, <a href="https://publications.waset.org/abstracts/search?q=antidepressant" title=" antidepressant"> antidepressant</a>, <a href="https://publications.waset.org/abstracts/search?q=treatment%20outcome" title=" treatment outcome"> treatment outcome</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet" title=" wavelet"> wavelet</a> </p> <a href="https://publications.waset.org/abstracts/2686/application-of-eeg-wavelet-power-to-prediction-of-antidepressant-treatment-response" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2686.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">315</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2461</span> High Sensitivity Crack Detection and Locating with Optimized Spatial Wavelet Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Ghanbari%20Mardasi">A. Ghanbari Mardasi</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Wu"> N. Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Wu"> C. Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, a spatial wavelet-based crack localization technique for a thick beam is presented. Wavelet scale in spatial wavelet transformation is optimized to enhance crack detection sensitivity. A windowing function is also employed to erase the edge effect of the wavelet transformation, which enables the method to detect and localize cracks near the beam/measurement boundaries. Theoretical model and vibration analysis considering the crack effect are first proposed and performed in MATLAB based on the Timoshenko beam model. Gabor wavelet family is applied to the beam vibration mode shapes derived from the theoretical beam model to magnify the crack effect so as to locate the crack. Relative wavelet coefficient is obtained for sensitivity analysis by comparing the coefficient values at different positions of the beam with the lowest value in the intact area of the beam. Afterward, the optimal wavelet scale corresponding to the highest relative wavelet coefficient at the crack position is obtained for each vibration mode, through numerical simulations. The same procedure is performed for cracks with different sizes and positions in order to find the optimal scale range for the Gabor wavelet family. Finally, Hanning window is applied to different vibration mode shapes in order to overcome the edge effect problem of wavelet transformation and its effect on the localization of crack close to the measurement boundaries. Comparison of the wavelet coefficients distribution of windowed and initial mode shapes demonstrates that window function eases the identification of the cracks close to the boundaries. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=edge%20effect" title="edge effect">edge effect</a>, <a href="https://publications.waset.org/abstracts/search?q=scale%20optimization" title=" scale optimization"> scale optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=small%20crack%20locating" title=" small crack locating"> small crack locating</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20wavelet" title=" spatial wavelet"> spatial wavelet</a> </p> <a href="https://publications.waset.org/abstracts/68932/high-sensitivity-crack-detection-and-locating-with-optimized-spatial-wavelet-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68932.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">357</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2460</span> Applying Wavelet Transform to Ferroresonance Detection and Protection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chun-Wei%20Huang">Chun-Wei Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jyh-Cherng%20Gu"> Jyh-Cherng Gu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ming-Ta%20Yang"> Ming-Ta Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Non-synchronous breakage or line failure in power systems with light or no loads can lead to core saturation in transformers or potential transformers. This can cause component and capacitance matching resulting in the formation of resonant circuits, which trigger ferroresonance. This study employed a wavelet transform for the detection of ferroresonance. Simulation results demonstrate the efficacy of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ferroresonance" title="ferroresonance">ferroresonance</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title=" wavelet transform"> wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20electronic%20device" title=" intelligent electronic device"> intelligent electronic device</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a> </p> <a href="https://publications.waset.org/abstracts/12919/applying-wavelet-transform-to-ferroresonance-detection-and-protection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12919.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">496</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2459</span> Error Analysis of Wavelet-Based Image Steganograhy Scheme</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Geeta%20Kasana">Geeta Kasana</a>, <a href="https://publications.waset.org/abstracts/search?q=Kulbir%20Singh"> Kulbir Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Satvinder%20Singh"> Satvinder Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a steganographic scheme for digital images using Integer Wavelet Transform (IWT) is proposed. The cover image is decomposed into wavelet sub bands using IWT. Each of the subband is divided into blocks of equal size and secret data is embedded into the largest and smallest pixel values of each block of the subband. Visual quality of stego images is acceptable as PSNR between cover image and stego is above 40 dB, imperceptibility is maintained. Experimental results show better tradeoff between capacity and visual perceptivity compared to the existing algorithms. Maximum possible error analysis is evaluated for each of the wavelet subbands of an image. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DWT" title="DWT">DWT</a>, <a href="https://publications.waset.org/abstracts/search?q=IWT" title=" IWT"> IWT</a>, <a href="https://publications.waset.org/abstracts/search?q=MSE" title=" MSE"> MSE</a>, <a href="https://publications.waset.org/abstracts/search?q=PSNR" title=" PSNR"> PSNR</a> </p> <a href="https://publications.waset.org/abstracts/19367/error-analysis-of-wavelet-based-image-steganograhy-scheme" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19367.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">504</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2458</span> Numerical Solutions of Fredholm Integral Equations by B-Spline Wavelet Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ritu%20Rani">Ritu Rani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we apply minimalistically upheld linear semi-orthogonal B-spline wavelets, exceptionally developed for the limited interim to rough the obscure function present in the integral equations. Semi-orthogonal wavelets utilizing B-spline uniquely developed for the limited interim and these wavelets can be spoken to in a shut frame. This gives a minimized help. Semi-orthogonal wavelets frame the premise in the space L²(R). Utilizing this premise, an arbitrary function in L²(R) can be communicated as the wavelet arrangement. For the limited interim, the wavelet arrangement cannot be totally introduced by utilizing this premise. This is on the grounds that backings of some premise are truncated at the left or right end purposes of the interim. Subsequently, an uncommon premise must be brought into the wavelet development on the limited interim. These functions are alluded to as the limit scaling functions and limit wavelet functions. B-spline wavelet method has been connected to fathom linear and nonlinear integral equations and their systems. The above method diminishes the integral equations to systems of algebraic equations and afterward these systems can be illuminated by any standard numerical methods. Here, we have connected Newton's method with suitable starting speculation for solving these systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semi-orthogonal" title="semi-orthogonal">semi-orthogonal</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20arrangement" title=" wavelet arrangement"> wavelet arrangement</a>, <a href="https://publications.waset.org/abstracts/search?q=integral%20equations" title=" integral equations"> integral equations</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20development" title=" wavelet development "> wavelet development </a> </p> <a href="https://publications.waset.org/abstracts/125473/numerical-solutions-of-fredholm-integral-equations-by-b-spline-wavelet-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/125473.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">174</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2457</span> A Hybrid Watermarking Scheme Using Discrete and Discrete Stationary Wavelet Transformation For Color Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B%C3%BClent%20Kantar">Bülent Kantar</a>, <a href="https://publications.waset.org/abstracts/search?q=Numan%20%C3%9Cnald%C4%B1"> Numan Ünaldı</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a new method which includes robust and invisible digital watermarking on images that is colored. Colored images are used as watermark. Frequency region is used for digital watermarking. Discrete wavelet transform and discrete stationary wavelet transform are used for frequency region transformation. Low, medium and high frequency coefficients are obtained by applying the two-level discrete wavelet transform to the original image. Low frequency coefficients are obtained by applying one level discrete stationary wavelet transform separately to all frequency coefficient of the two-level discrete wavelet transformation of the original image. For every low frequency coefficient obtained from one level discrete stationary wavelet transformation, watermarks are added. Watermarks are added to all frequency coefficients of two-level discrete wavelet transform. Totally, four watermarks are added to original image. In order to get back the watermark, the original and watermarked images are applied with two-level discrete wavelet transform and one level discrete stationary wavelet transform. The watermark is obtained from difference of the discrete stationary wavelet transform of the low frequency coefficients. A total of four watermarks are obtained from all frequency of two-level discrete wavelet transform. Obtained watermark results are compared with real watermark results, and a similarity result is obtained. A watermark is obtained from the highest similarity values. Proposed methods of watermarking are tested against attacks of the geometric and image processing. The results show that proposed watermarking method is robust and invisible. All features of frequencies of two level discrete wavelet transform watermarking are combined to get back the watermark from the watermarked image. Watermarks have been added to the image by converting the binary image. These operations provide us with better results in getting back the watermark from watermarked image by attacking of the geometric and image processing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=watermarking" title="watermarking">watermarking</a>, <a href="https://publications.waset.org/abstracts/search?q=DWT" title=" DWT"> DWT</a>, <a href="https://publications.waset.org/abstracts/search?q=DSWT" title=" DSWT"> DSWT</a>, <a href="https://publications.waset.org/abstracts/search?q=copy%20right%20protection" title=" copy right protection"> copy right protection</a>, <a href="https://publications.waset.org/abstracts/search?q=RGB" title=" RGB "> RGB </a> </p> <a href="https://publications.waset.org/abstracts/16927/a-hybrid-watermarking-scheme-using-discrete-and-discrete-stationary-wavelet-transformation-for-color-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16927.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">535</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2456</span> Wavelet Based Signal Processing for Fault Location in Airplane Cable </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Reza%20Rezaeipour%20Honarmandzad">Reza Rezaeipour Honarmandzad </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wavelet analysis is an exciting method for solving difficult problems in mathematics, physics, and engineering, with modern applications as diverse as wave propagation, data compression, signal processing, image processing, pattern recognition, etc. Wavelets allow complex information such as signals, images and patterns to be decomposed into elementary forms at different positions and scales and subsequently reconstructed with high precision. In this paper a wavelet-based signal processing algorithm for airplane cable fault location is proposed. An orthogonal discrete wavelet decomposition and reconstruction algorithm is used to eliminate the noise in the aircraft cable fault signal. The experiment result has shown that the character of emission pulse and reflect pulse used to test the aircraft cable fault point are reserved and the high-frequency noise are eliminated by means of the proposed algorithm in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wavelet%20analysis" title="wavelet analysis">wavelet analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=orthogonal%20discrete%20wavelet" title=" orthogonal discrete wavelet"> orthogonal discrete wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=noise" title=" noise"> noise</a>, <a href="https://publications.waset.org/abstracts/search?q=aircraft%20cable%20fault%20signal" title=" aircraft cable fault signal"> aircraft cable fault signal</a> </p> <a href="https://publications.waset.org/abstracts/29799/wavelet-based-signal-processing-for-fault-location-in-airplane-cable" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29799.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">524</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2455</span> Algorithms Utilizing Wavelet to Solve Various Partial Differential Equations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20P.%20Mredula">K. P. Mredula</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20C.%20Vakaskar"> D. C. Vakaskar </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The article traces developments and evolution of various algorithms developed for solving partial differential equations using the significant combination of wavelet with few already explored solution procedures. The approach depicts a study over a decade of traces and remarks on the modifications in implementing multi-resolution of wavelet, finite difference approach, finite element method and finite volume in dealing with a variety of partial differential equations in the areas like plasma physics, astrophysics, shallow water models, modified Burger equations used in optical fibers, biology, fluid dynamics, chemical kinetics etc. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-resolution" title="multi-resolution">multi-resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=Haar%20Wavelet" title=" Haar Wavelet"> Haar Wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20differential%20equation" title=" partial differential equation"> partial differential equation</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20methods" title=" numerical methods"> numerical methods</a> </p> <a href="https://publications.waset.org/abstracts/59280/algorithms-utilizing-wavelet-to-solve-various-partial-differential-equations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59280.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">299</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2454</span> Analysis of EEG Signals Using Wavelet Entropy and Approximate Entropy: A Case Study on Depression Patients</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Subha%20D.%20Puthankattil">Subha D. Puthankattil</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul%20K.%20Joseph"> Paul K. Joseph</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Analyzing brain signals of the patients suffering from the state of depression may lead to interesting observations in the signal parameters that is quite different from a normal control. The present study adopts two different methods: Time frequency domain and nonlinear method for the analysis of EEG signals acquired from depression patients and age and sex matched normal controls. The time frequency domain analysis is realized using wavelet entropy and approximate entropy is employed for the nonlinear method of analysis. The ability of the signal processing technique and the nonlinear method in differentiating the physiological aspects of the brain state are revealed using Wavelet entropy and Approximate entropy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG" title="EEG">EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=depression" title=" depression"> depression</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20entropy" title=" wavelet entropy"> wavelet entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=approximate%20entropy" title=" approximate entropy"> approximate entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=relative%20wavelet%20energy" title=" relative wavelet energy"> relative wavelet energy</a>, <a href="https://publications.waset.org/abstracts/search?q=multiresolution%20decomposition" title=" multiresolution decomposition"> multiresolution decomposition</a> </p> <a href="https://publications.waset.org/abstracts/11836/analysis-of-eeg-signals-using-wavelet-entropy-and-approximate-entropy-a-case-study-on-depression-patients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11836.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">332</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2453</span> Feature Extraction Technique for Prediction the Antigenic Variants of the Influenza Virus</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Majid%20Forghani">Majid Forghani</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Khachay"> Michael Khachay</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In genetics, the impact of neighboring amino acids on a target site is referred as the nearest-neighbor effect or simply neighbor effect. In this paper, a new method called wavelet particle decomposition representing the one-dimensional neighbor effect using wavelet packet decomposition is proposed. The main idea lies in known dependence of wavelet packet sub-bands on location and order of neighboring samples. The method decomposes the value of a signal sample into small values called particles that represent a part of the neighbor effect information. The results have shown that the information obtained from the particle decomposition can be used to create better model variables or features. As an example, the approach has been applied to improve the correlation of test and reference sequence distance with titer in the hemagglutination inhibition assay. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=antigenic%20variants" title="antigenic variants">antigenic variants</a>, <a href="https://publications.waset.org/abstracts/search?q=neighbor%20effect" title=" neighbor effect"> neighbor effect</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20packet" title=" wavelet packet"> wavelet packet</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20particle%20decomposition" title=" wavelet particle decomposition"> wavelet particle decomposition</a> </p> <a href="https://publications.waset.org/abstracts/96149/feature-extraction-technique-for-prediction-the-antigenic-variants-of-the-influenza-virus" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/96149.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">157</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2452</span> Effects of Various Wavelet Transforms in Dynamic Analysis of Structures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Sadegh%20Naseralavi">Seyed Sadegh Naseralavi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sadegh%20Balaghi"> Sadegh Balaghi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ehsan%20Khojastehfar"> Ehsan Khojastehfar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Time history dynamic analysis of structures is considered as an exact method while being computationally intensive. Filtration of earthquake strong ground motions applying wavelet transform is an approach towards reduction of computational efforts, particularly in optimization of structures against seismic effects. Wavelet transforms are categorized into continuum and discrete transforms. Since earthquake strong ground motion is a discrete function, the discrete wavelet transform is applied in the present paper. Wavelet transform reduces analysis time by filtration of non-effective frequencies of strong ground motion. Filtration process may be repeated several times while the approximation induces more errors. In this paper, strong ground motion of earthquake has been filtered once applying each wavelet. Strong ground motion of Northridge earthquake is filtered applying various wavelets and dynamic analysis of sampled shear and moment frames is implemented. The error, regarding application of each wavelet, is computed based on comparison of dynamic response of sampled structures with exact responses. Exact responses are computed by dynamic analysis of structures applying non-filtered strong ground motion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title="wavelet transform">wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20error" title=" computational error"> computational error</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20duration" title=" computational duration"> computational duration</a>, <a href="https://publications.waset.org/abstracts/search?q=strong%20ground%20motion%20data" title=" strong ground motion data"> strong ground motion data</a> </p> <a href="https://publications.waset.org/abstracts/51519/effects-of-various-wavelet-transforms-in-dynamic-analysis-of-structures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51519.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">378</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2451</span> The Use of Haar Wavelet Mother Signal Tool for Performance Analysis Response of Distillation Column (Application to Moroccan Case Study) </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahacine%20Amrani">Mahacine Amrani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims at reviewing some Moroccan industrial applications of wavelet especially in the dynamic identification of a process model using Haar wavelet mother response. Two recent Moroccan study cases are described using dynamic data originated by a distillation column and an industrial polyethylene process plant. The purpose of the wavelet scheme is to build on-line dynamic models. In both case studies, a comparison is carried out between the Haar wavelet mother response model and a linear difference equation model. Finally it concludes, on the base of the comparison of the process performances and the best responses, which may be useful to create an estimated on-line internal model control and its application towards model-predictive controllers (MPC). All calculations were implemented using AutoSignal Software. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=process%20performance" title="process performance">process performance</a>, <a href="https://publications.waset.org/abstracts/search?q=model" title=" model"> model</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelets" title=" wavelets"> wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=Haar" title=" Haar"> Haar</a>, <a href="https://publications.waset.org/abstracts/search?q=Moroccan" title=" Moroccan"> Moroccan</a> </p> <a href="https://publications.waset.org/abstracts/36970/the-use-of-haar-wavelet-mother-signal-tool-for-performance-analysis-response-of-distillation-column-application-to-moroccan-case-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36970.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">317</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2450</span> Detection of Voltage Sag and Voltage Swell in Power Quality Using Wavelet Transforms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nor%20Asrina%20Binti%20Ramlee">Nor Asrina Binti Ramlee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Voltage sag, voltage swell, high-frequency noise and voltage transients are kinds of disturbances in power quality. They are also known as power quality events. Equipment used in the industry nowadays has become more sensitive to these events with the increasing complexity of equipment. This leads to the importance of distributing clean power quality to the consumer. To provide better service, the best analysis on power quality is very vital. Thus, this paper presents the events detection focusing on voltage sag and swell. The method is developed by applying time domain signal analysis using wavelet transform approach in MATLAB. Four types of mother wavelet namely Haar, Dmey, Daubechies, and Symlet are used to detect the events. This project analyzed real interrupted signal obtained from 22 kV transmission line in Skudai, Johor Bahru, Malaysia. The signals will be decomposed through the wavelet mothers. The best mother is the one that is capable to detect the time location of the event accurately. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=power%20quality" title="power quality">power quality</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20sag" title=" voltage sag"> voltage sag</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20swell" title=" voltage swell"> voltage swell</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title=" wavelet transform"> wavelet transform</a> </p> <a href="https://publications.waset.org/abstracts/68643/detection-of-voltage-sag-and-voltage-swell-in-power-quality-using-wavelet-transforms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68643.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">372</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2449</span> Frequency Domain Decomposition, Stochastic Subspace Identification and Continuous Wavelet Transform for Operational Modal Analysis of Three Story Steel Frame</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ardalan%20Sabamehr">Ardalan Sabamehr</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashutosh%20Bagchi"> Ashutosh Bagchi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, Structural Health Monitoring (SHM) based on the vibration of structures has attracted the attention of researchers in different fields such as: civil, aeronautical and mechanical engineering. Operational Modal Analysis (OMA) have been developed to identify modal properties of infrastructure such as bridge, building and so on. Frequency Domain Decomposition (FDD), Stochastic Subspace Identification (SSI) and Continuous Wavelet Transform (CWT) are the three most common methods in output only modal identification. FDD, SSI, and CWT operate based on the frequency domain, time domain, and time-frequency plane respectively. So, FDD and SSI are not able to display time and frequency at the same time. By the way, FDD and SSI have some difficulties in a noisy environment and finding the closed modes. CWT technique which is currently developed works on time-frequency plane and a reasonable performance in such condition. The other advantage of wavelet transform rather than other current techniques is that it can be applied for the non-stationary signal as well. The aim of this paper is to compare three most common modal identification techniques to find modal properties (such as natural frequency, mode shape, and damping ratio) of three story steel frame which was built in Concordia University Lab by use of ambient vibration. The frame has made of Galvanized steel with 60 cm length, 27 cm width and 133 cm height with no brace along the long span and short space. Three uniaxial wired accelerations (MicroStarin with 100mv/g accuracy) have been attached to the middle of each floor and gateway receives the data and send to the PC by use of Node Commander Software. The real-time monitoring has been performed for 20 seconds with 512 Hz sampling rate. The test is repeated for 5 times in each direction by hand shaking and impact hammer. CWT is able to detect instantaneous frequency by used of ridge detection method. In this paper, partial derivative ridge detection technique has been applied to the local maxima of time-frequency plane to detect the instantaneous frequency. The extracted result from all three methods have been compared, and it demonstrated that CWT has the better performance in term of its accuracy in noisy environment. The modal parameters such as natural frequency, damping ratio and mode shapes are identified from all three methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ambient%20vibration" title="ambient vibration">ambient vibration</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency%20domain%20decomposition" title=" frequency domain decomposition"> frequency domain decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20subspace%20identification" title=" stochastic subspace identification"> stochastic subspace identification</a>, <a href="https://publications.waset.org/abstracts/search?q=continuous%20wavelet%20transform" title=" continuous wavelet transform"> continuous wavelet transform</a> </p> <a href="https://publications.waset.org/abstracts/56951/frequency-domain-decomposition-stochastic-subspace-identification-and-continuous-wavelet-transform-for-operational-modal-analysis-of-three-story-steel-frame" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56951.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">296</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2448</span> Constructions of Linear and Robust Codes Based on Wavelet Decompositions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alla%20Levina">Alla Levina</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergey%20Taranov"> Sergey Taranov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The classical approach to the providing noise immunity and integrity of information that process in computing devices and communication channels is to use linear codes. Linear codes have fast and efficient algorithms of encoding and decoding information, but this codes concentrate their detect and correct abilities in certain error configurations. To protect against any configuration of errors at predetermined probability can robust codes. This is accomplished by the use of perfect nonlinear and almost perfect nonlinear functions to calculate the code redundancy. The paper presents the error-correcting coding scheme using biorthogonal wavelet transform. Wavelet transform applied in various fields of science. Some of the wavelet applications are cleaning of signal from noise, data compression, spectral analysis of the signal components. The article suggests methods for constructing linear codes based on wavelet decomposition. For developed constructions we build generator and check matrix that contain the scaling function coefficients of wavelet. Based on linear wavelet codes we develop robust codes that provide uniform protection against all errors. In article we propose two constructions of robust code. The first class of robust code is based on multiplicative inverse in finite field. In the second robust code construction the redundancy part is a cube of information part. Also, this paper investigates the characteristics of proposed robust and linear codes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=robust%20code" title="robust code">robust code</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20code" title=" linear code"> linear code</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20decomposition" title=" wavelet decomposition"> wavelet decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=scaling%20function" title=" scaling function"> scaling function</a>, <a href="https://publications.waset.org/abstracts/search?q=error%20masking%20probability" title=" error masking probability"> error masking probability</a> </p> <a href="https://publications.waset.org/abstracts/16512/constructions-of-linear-and-robust-codes-based-on-wavelet-decompositions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16512.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">489</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2447</span> Hybrid Robust Estimation via Median Filter and Wavelet Thresholding with Automatic Boundary Correction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alsaidi%20M.%20Altaher">Alsaidi M. Altaher</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Tahir%20Ismail"> Mohd Tahir Ismail</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wavelet thresholding has been a power tool in curve estimation and data analysis. In the presence of outliers this non parametric estimator can not suppress the outliers involved. This study proposes a new two-stage combined method based on the use of the median filter as primary step before applying wavelet thresholding. After suppressing the outliers in a signal through the median filter, the classical wavelet thresholding is then applied for removing the remaining noise. We use automatic boundary corrections; using a low order polynomial model or local polynomial model as a more realistic rule to correct the bias at the boundary region; instead of using the classical assumptions such periodic or symmetric. A simulation experiment has been conducted to evaluate the numerical performance of the proposed method. Results show strong evidences that the proposed method is extremely effective in terms of correcting the boundary bias and eliminating outlier’s sensitivity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=boundary%20correction" title="boundary correction">boundary correction</a>, <a href="https://publications.waset.org/abstracts/search?q=median%20filter" title=" median filter"> median filter</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20thresholding" title=" wavelet thresholding"> wavelet thresholding</a> </p> <a href="https://publications.waset.org/abstracts/16883/hybrid-robust-estimation-via-median-filter-and-wavelet-thresholding-with-automatic-boundary-correction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16883.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">428</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2446</span> Wavelet Based Advanced Encryption Standard Algorithm for Image Encryption</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ajish%20Sreedharan">Ajish Sreedharan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the fast evolution of digital data exchange, security information becomes much important in data storage and transmission. Due to the increasing use of images in industrial process, it is essential to protect the confidential image data from unauthorized access. As encryption process is applied to the whole image in AES ,it is difficult to improve the efficiency. In this paper, wavelet decomposition is used to concentrate the main information of image to the low frequency part. Then, AES encryption is applied to the low frequency part. The high frequency parts are XORed with the encrypted low frequency part and a wavelet reconstruction is applied. Theoretical analysis and experimental results show that the proposed algorithm has high efficiency, and satisfied security suits for image data transmission. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transforms" title="discrete wavelet transforms">discrete wavelet transforms</a>, <a href="https://publications.waset.org/abstracts/search?q=AES" title=" AES"> AES</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20SBox" title=" dynamic SBox"> dynamic SBox</a> </p> <a href="https://publications.waset.org/abstracts/16582/wavelet-based-advanced-encryption-standard-algorithm-for-image-encryption" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16582.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">432</span> </span> </div> 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