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wavelets</title> <meta name="description" content="Search results for: morlet wavelets"> <meta name="keywords" content="morlet wavelets"> <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="morlet wavelets"> <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> 50</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: morlet wavelets</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">50</span> Using Wavelet Uncertainty Relations in Quantum Mechanics: From Trajectories Foam to Newtonian Determinism</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Paulo%20Castro">Paulo Castro</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20R.%20Croca"> J. R. Croca</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Gatta"> M. Gatta</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Moreira"> R. Moreira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Owing to the development of quantum mechanics, we will contextualize the foundations of the theory on the Fourier analysis framework, thus stating the unavoidable philosophical conclusions drawn by Niels Bohr. We will then introduce an alternative way of describing the undulatory aspects of quantum entities by using gaussian Morlet wavelets. The description has its roots in de Broglie's realistic program for quantum physics. It so happens that using wavelets it is possible to formulate a more general set of uncertainty relations. A set from which it is possible to theoretically describe both ends of the behavioral spectrum in reality: the indeterministic quantum trajectorial foam and the perfectly drawn Newtonian trajectories. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=philosophy%20of%20quantum%20mechanics" title="philosophy of quantum mechanics">philosophy of quantum mechanics</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20realism" title=" quantum realism"> quantum realism</a>, <a href="https://publications.waset.org/abstracts/search?q=morlet%20wavelets" title=" morlet wavelets"> morlet wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20relations" title=" uncertainty relations"> uncertainty relations</a>, <a href="https://publications.waset.org/abstracts/search?q=determinism" title=" determinism"> determinism</a> </p> <a href="https://publications.waset.org/abstracts/144113/using-wavelet-uncertainty-relations-in-quantum-mechanics-from-trajectories-foam-to-newtonian-determinism" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144113.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">171</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">49</span> Chebyshev Wavelets and Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emanuel%20Guariglia">Emanuel Guariglia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we deal with Chebyshev wavelets. We analyze their properties computing their Fourier transform. Moreover, we discuss the differential properties of Chebyshev wavelets due the connection coefficients. The differential properties of Chebyshev wavelets, expressed by the connection coefficients (also called refinable integrals), are given by finite series in terms of the Kronecker delta. Moreover, we treat the p-order derivative of Chebyshev wavelets and compute its Fourier transform. Finally, we expand the mother wavelet in Taylor series with an application both in fractional calculus and fractal geometry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chebyshev%20wavelets" title="Chebyshev wavelets">Chebyshev wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=Fourier%20transform" title=" Fourier transform"> Fourier transform</a>, <a href="https://publications.waset.org/abstracts/search?q=connection%20coefficients" title=" connection coefficients"> connection coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=Taylor%20series" title=" Taylor series"> Taylor series</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20fractional%20derivative" title=" local fractional derivative"> local fractional derivative</a>, <a href="https://publications.waset.org/abstracts/search?q=Cantor%20set" title=" Cantor set"> Cantor set</a> </p> <a href="https://publications.waset.org/abstracts/157194/chebyshev-wavelets-and-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157194.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">122</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">48</span> The Relationship between Spanish Economic Variables: Evidence from the Wavelet Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Concepcion%20Gonzalez-Concepcion">Concepcion Gonzalez-Concepcion</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Candelaria%20Gil-Fari%C3%B1a"> Maria Candelaria Gil-Fariña</a>, <a href="https://publications.waset.org/abstracts/search?q=Celina%20Pestano-Gabino"> Celina Pestano-Gabino</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We analyze six relevant economic and financial variables for the period 2000M1-2015M3 in the context of the Spanish economy: a financial index (IBEX35), a commodity (Crude Oil Price in euros), a foreign exchange index (EUR/USD), a bond (Spanish 10-Year Bond), the Spanish National Debt and the Consumer Price Index. The goal of this paper is to analyze the main relations between them by computing the Wavelet Power Spectrum and the Cross Wavelet Coherency associated with Morlet wavelets. By using a special toolbox in MATLAB, we focus our interest on the period variable. We decompose the time-frequency effects and improve the interpretation of the results by non-expert users in the theory of wavelets. The empirical evidence shows certain instability periods and reveals various changes and breaks in the causality relationships for sample data. These variables were individually analyzed with Daubechies Wavelets to visualize high-frequency variance, seasonality, and trend. The results are included in Proceeding 20th International Academic Conference, 2015, International Institute of Social and Economic Sciences (IISES), Madrid. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=economic%20and%20financial%20variables" title="economic and financial variables">economic and financial variables</a>, <a href="https://publications.waset.org/abstracts/search?q=Spain" title=" Spain"> Spain</a>, <a href="https://publications.waset.org/abstracts/search?q=time-frequency%20domain" title=" time-frequency domain"> time-frequency domain</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20coherency" title=" wavelet coherency"> wavelet coherency</a> </p> <a href="https://publications.waset.org/abstracts/56377/the-relationship-between-spanish-economic-variables-evidence-from-the-wavelet-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56377.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">240</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">47</span> Periodicity Analysis of Long-Term Waterquality Data Series of the Hungarian Section of the River Tisza Using Morlet Wavelet Spectrum Estimation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P%C3%A9ter%20Tanos">Péter Tanos</a>, <a href="https://publications.waset.org/abstracts/search?q=J%C3%B3zsef%20Kov%C3%A1cs"> József Kovács</a>, <a href="https://publications.waset.org/abstracts/search?q=Ang%C3%A9la%20Anda"> Angéla Anda</a>, <a href="https://publications.waset.org/abstracts/search?q=G%C3%A1bor%20V%C3%A1rb%C3%ADr%C3%B3"> Gábor Várbíró</a>, <a href="https://publications.waset.org/abstracts/search?q=S%C3%A1ndor%20Moln%C3%A1r"> Sándor Molnár</a>, <a href="https://publications.waset.org/abstracts/search?q=Istv%C3%A1n%20G%C3%A1bor%20Hatvani"> István Gábor Hatvani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The River Tisza is the second largest river in Central Europe. In this study, Morlet wavelet spectrum (periodicity) analysis was used with chemical, biological and physical water quality data for the Hungarian section of the River Tisza. In the research 15, water quality parameters measured at 14 sampling sites in the River Tisza and 4 sampling sites in the main artificial changes were assessed for the time period 1993 - 2005. Results show that annual periodicity was not always to be found in the water quality parameters, at least at certain sampling sites. Periodicity was found to vary over space and time, but in general, an increase was observed in the company of higher trophic states of the river heading downstream. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=annual%20periodicity%20water%20quality" title="annual periodicity water quality">annual periodicity water quality</a>, <a href="https://publications.waset.org/abstracts/search?q=spatiotemporal%20variability%20of%20periodic%20behavior" title=" spatiotemporal variability of periodic behavior"> spatiotemporal variability of periodic behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=Morlet%20wavelet%20spectrum%20analysis" title=" Morlet wavelet spectrum analysis"> Morlet wavelet spectrum analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=River%20Tisza" title=" River Tisza"> River Tisza</a> </p> <a href="https://publications.waset.org/abstracts/60822/periodicity-analysis-of-long-term-waterquality-data-series-of-the-hungarian-section-of-the-river-tisza-using-morlet-wavelet-spectrum-estimation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60822.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">344</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">46</span> Nonstationary Increments and Casualty in the Aluminum Market</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andrew%20Clark">Andrew Clark</a> </p> <p class="card-text"><strong>Abstract:</strong></p> McCauley, Bassler, and Gunaratne show that integration I(d) processes as used in economics and finance do not necessarily produce stationary increments, which are required to determine causality in both the short term and the long term. This paper follows their lead and shows I(d) aluminum cash and futures log prices at daily and weekly intervals do not have stationary increments, which means prior causality studies using I(d) processes need to be re-examined. Wavelets based on undifferenced cash and futures log prices do have stationary increments and are used along with transfer entropy (versus cointegration) to measure causality. Wavelets exhibit causality at most daily time scales out to 1 year, and weekly time scales out to 1 year and more. To determine stationarity, localized stationary wavelets are used. LSWs have the benefit, versus other means of testing for stationarity, of using multiple hypothesis tests to determine stationarity. As informational flows exist between cash and futures at daily and weekly intervals, the aluminum market is efficient. Therefore, hedges used by producers and consumers of aluminum need not have a big concern in terms of the underestimation of hedge ratios. Questions about arbitrage given efficiency are addressed in the paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=transfer%20entropy" title="transfer entropy">transfer entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=nonstationary%20increments" title=" nonstationary increments"> nonstationary increments</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelets" title=" wavelets"> wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=localized%20stationary%20wavelets" title=" localized stationary wavelets"> localized stationary wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=localized%20stationary%20wavelets" title=" localized stationary wavelets"> localized stationary wavelets</a> </p> <a href="https://publications.waset.org/abstracts/133915/nonstationary-increments-and-casualty-in-the-aluminum-market" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133915.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">202</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">45</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">44</span> Pod and Wavelets Application for Aerodynamic Design Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bonchan%20Koo">Bonchan Koo</a>, <a href="https://publications.waset.org/abstracts/search?q=Junhee%20Han"> Junhee Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Dohyung%20Lee"> Dohyung Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The research attempts to evaluate the accuracy and efficiency of a design optimization procedure which combines wavelets-based solution algorithm and proper orthogonal decomposition (POD) database management technique. Aerodynamic design procedure calls for high fidelity computational fluid dynamic (CFD) simulations and the consideration of large number of flow conditions and design constraints. Even with significant computing power advancement, current level of integrated design process requires substantial computing time and resources. POD reduces the degree of freedom of full system through conducting singular value decomposition for various field simulations. For additional efficiency improvement of the procedure, adaptive wavelet technique is also being employed during POD training period. The proposed design procedure was applied to the optimization of wing aerodynamic performance. Throughout the research, it was confirmed that the POD/wavelets design procedure could significantly reduce the total design turnaround time and is also able to capture all detailed complex flow features as in full order analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=POD%20%28Proper%20Orthogonal%20Decomposition%29" title="POD (Proper Orthogonal Decomposition)">POD (Proper Orthogonal Decomposition)</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelets" title=" wavelets"> wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=CFD" title=" CFD"> CFD</a>, <a href="https://publications.waset.org/abstracts/search?q=design%20optimization" title=" design optimization"> design optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=ROM%20%28Reduced%20Order%20Model%29" title=" ROM (Reduced Order Model)"> ROM (Reduced Order Model)</a> </p> <a href="https://publications.waset.org/abstracts/21466/pod-and-wavelets-application-for-aerodynamic-design-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21466.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">467</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">43</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">42</span> A Numerical Solution Based on Operational Matrix of Differentiation of Shifted Second Kind Chebyshev Wavelets for a Stefan Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rajeev">Rajeev</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20K.%20Raigar"> N. K. Raigar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, one dimensional phase change problem (a Stefan problem) is considered and a numerical solution of this problem is discussed. First, we use similarity transformation to convert the governing equations into ordinary differential equations with its boundary conditions. The solutions of ordinary differential equation with the associated boundary conditions and interface condition (Stefan condition) are obtained by using a numerical approach based on operational matrix of differentiation of shifted second kind Chebyshev wavelets. The obtained results are compared with existing exact solution which is sufficiently accurate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=operational%20matrix%20of%20differentiation" title="operational matrix of differentiation">operational matrix of differentiation</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20transformation" title=" similarity transformation"> similarity transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=shifted%20second%20kind%20chebyshev%20wavelets" title=" shifted second kind chebyshev wavelets"> shifted second kind chebyshev wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=stefan%20problem" title=" stefan problem"> stefan problem</a> </p> <a href="https://publications.waset.org/abstracts/30738/a-numerical-solution-based-on-operational-matrix-of-differentiation-of-shifted-second-kind-chebyshev-wavelets-for-a-stefan-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30738.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">402</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">41</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">40</span> Automatic Detection of Defects in Ornamental Limestone Using Wavelets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maria%20C.%20Proen%C3%A7a">Maria C. Proença</a>, <a href="https://publications.waset.org/abstracts/search?q=Marco%20Aniceto"> Marco Aniceto</a>, <a href="https://publications.waset.org/abstracts/search?q=Pedro%20N.%20Santos"> Pedro N. Santos</a>, <a href="https://publications.waset.org/abstracts/search?q=Jos%C3%A9%20C.%20Freitas"> José C. Freitas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A methodology based on wavelets is proposed for the automatic location and delimitation of defects in limestone plates. Natural defects include dark colored spots, crystal zones trapped in the stone, areas of abnormal contrast colors, cracks or fracture lines, and fossil patterns. Although some of these may or may not be considered as defects according to the intended use of the plate, the goal is to pair each stone with a map of defects that can be overlaid on a computer display. These layers of defects constitute a database that will allow the preliminary selection of matching tiles of a particular variety, with specific dimensions, for a requirement of N square meters, to be done on a desktop computer rather than by a two-hour search in the storage park, with human operators manipulating stone plates as large as 3 m x 2 m, weighing about one ton. Accident risks and work times are reduced, with a consequent increase in productivity. The base for the algorithm is wavelet decomposition executed in two instances of the original image, to detect both hypotheses – dark and clear defects. The existence and/or size of these defects are the gauge to classify the quality grade of the stone products. The tuning of parameters that are possible in the framework of the wavelets corresponds to different levels of accuracy in the drawing of the contours and selection of the defects size, which allows for the use of the map of defects to cut a selected stone into tiles with minimum waste, according the dimension of defects allowed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20detection" title="automatic detection">automatic detection</a>, <a href="https://publications.waset.org/abstracts/search?q=defects" title=" defects"> defects</a>, <a href="https://publications.waset.org/abstracts/search?q=fracture%20lines" title=" fracture lines"> fracture lines</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelets" title=" wavelets"> wavelets</a> </p> <a href="https://publications.waset.org/abstracts/39096/automatic-detection-of-defects-in-ornamental-limestone-using-wavelets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39096.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">247</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">39</span> Damage Detection in Beams Using Wavelet Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Goutham%20Kumar%20Dogiparti">Goutham Kumar Dogiparti</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20R.%20Seshu"> D. R. Seshu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the present study, wavelet analysis was used for locating damage in simply supported and cantilever beams. Study was carried out varying different levels and locations of damage. In numerical method, ANSYS software was used for modal analysis of damaged and undamaged beams. The mode shapes obtained from numerical analysis is processed using MATLAB wavelet toolbox to locate damage. Effect of several parameters such as (damage level, location) on the natural frequencies and mode shapes were also studied. The results indicated the potential of wavelets in identifying the damage location. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=damage" title="damage">damage</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a>, <a href="https://publications.waset.org/abstracts/search?q=beams" title=" beams"> beams</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelets" title=" wavelets"> wavelets</a> </p> <a href="https://publications.waset.org/abstracts/42920/damage-detection-in-beams-using-wavelet-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42920.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">365</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">38</span> Development of a Model Based on Wavelets and Matrices for the Treatment of Weakly Singular Partial Integro-Differential Equations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Somveer%20Singh">Somveer Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Vineet%20Kumar%20Singh"> Vineet Kumar Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present a new model based on viscoelasticity for the Non-Newtonian fluids.We use a matrix formulated algorithm to approximate solutions of a class of partial integro-differential equations with the given initial and boundary conditions. Some numerical results are presented to simplify application of operational matrix formulation and reduce the computational cost. Convergence analysis, error estimation and numerical stability of the method are also investigated. Finally, some test examples are given to demonstrate accuracy and efficiency of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Legendre%20Wavelets" title="Legendre Wavelets">Legendre Wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=operational%20matrices" title=" operational matrices"> operational matrices</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20integro-differential%20equation" title=" partial integro-differential equation"> partial integro-differential equation</a>, <a href="https://publications.waset.org/abstracts/search?q=viscoelasticity" title=" viscoelasticity"> viscoelasticity</a> </p> <a href="https://publications.waset.org/abstracts/58007/development-of-a-model-based-on-wavelets-and-matrices-for-the-treatment-of-weakly-singular-partial-integro-differential-equations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58007.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">336</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">37</span> Application of Wavelet Based Approximation for the Solution of Partial Integro-Differential Equation Arising from Viscoelasticity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Somveer%20Singh">Somveer Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Vineet%20Kumar%20Singh"> Vineet Kumar Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work contributes a numerical method based on Legendre wavelet approximation for the treatment of partial integro-differential equation (PIDE). Operational matrices of Legendre wavelets reduce the solution of PIDE into the system of algebraic equations. Some useful results concerning the computational order of convergence and error estimates associated to the suggested scheme are presented. Illustrative examples are provided to show the effectiveness and accuracy of proposed numerical method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=legendre%20wavelets" title="legendre wavelets">legendre wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=operational%20matrices" title=" operational matrices"> operational matrices</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20integro-differential%20equation" title=" partial integro-differential equation"> partial integro-differential equation</a>, <a href="https://publications.waset.org/abstracts/search?q=viscoelasticity" title=" viscoelasticity"> viscoelasticity</a> </p> <a href="https://publications.waset.org/abstracts/57515/application-of-wavelet-based-approximation-for-the-solution-of-partial-integro-differential-equation-arising-from-viscoelasticity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57515.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">36</span> Speckle Noise Reduction Using Anisotropic Filter Based on Wavelets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kritika%20Bansal">Kritika Bansal</a>, <a href="https://publications.waset.org/abstracts/search?q=Akwinder%20Kaur"> Akwinder Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Shruti%20Gujral"> Shruti Gujral</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the approach of denoising is solved by using a new hybrid technique which associates the different denoising methods. Wavelet thresholding and anisotropic diffusion filter are the two different filters in our hybrid techniques. The Wavelet thresholding removes the noise by removing the high frequency components with lesser edge preservation, whereas an anisotropic diffusion filters is based on partial differential equation, (PDE) to remove the speckle noise. This PDE approach is used to preserve the edges and provides better smoothing. So our new method proposes a combination of these two filtering methods which performs better results in terms of peak signal to noise ratio (PSNR), coefficient of correlation (COC) and equivalent no of looks (ENL). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=denoising" title="denoising">denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=anisotropic%20diffusion%20filter" title=" anisotropic diffusion filter"> anisotropic diffusion filter</a>, <a href="https://publications.waset.org/abstracts/search?q=multiplicative%20noise" title=" multiplicative noise"> multiplicative noise</a>, <a href="https://publications.waset.org/abstracts/search?q=speckle" title=" speckle"> speckle</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelets" title=" wavelets"> wavelets</a> </p> <a href="https://publications.waset.org/abstracts/14626/speckle-noise-reduction-using-anisotropic-filter-based-on-wavelets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14626.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">512</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">35</span> Gear Fault Diagnosis Based on Optimal Morlet Wavelet Filter and Autocorrelation Enhancement </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20El%20Morsy">Mohamed El Morsy</a>, <a href="https://publications.waset.org/abstracts/search?q=Gabriela%20Achtenov%C3%A1"> Gabriela Achtenová</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Condition monitoring is used to increase machinery availability and machinery performance, whilst reducing consequential damage, increasing machine life, reducing spare parts inventories, and reducing breakdown maintenance. An efficient condition monitoring system provides early warning of faults by predicting them at an early stage. When a localized fault occurs in gears, the vibration signals always exhibit non-stationary behavior. The periodic impulsive feature of the vibration signal appears in the time domain and the corresponding gear mesh frequency (GMF) emerges in the frequency domain. However, one limitation of frequency-domain analysis is its inability to handle non-stationary waveform signals, which are very common when machinery faults occur. Particularly at the early stage of gear failure, the GMF contains very little energy and is often overwhelmed by noise and higher-level macro-structural vibrations. An effective signal processing method would be necessary to remove such corrupting noise and interference. In this paper, a new hybrid method based on optimal Morlet wavelet filter and autocorrelation enhancement is presented. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are selected or optimized based on maximum Kurtosis. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, an autocorrelation enhancement algorithm is applied to the filtered signal. The test stand is equipped with three dynamometers; the input dynamometer serves as the internal combustion engine, the output dynamometers induce a load on the output joint shaft flanges. The pitting defect is manufactured on the tooth side of a gear of the fifth speed on the secondary shaft. The gearbox used for experimental measurements is of the type most commonly used in modern small to mid-sized passenger cars with transversely mounted powertrain and front wheel drive: a five-speed gearbox with final drive gear and front wheel differential. The results obtained from practical experiments prove that the proposed method is very effective for gear fault diagnosis. <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=pitted%20gear" title=" pitted gear"> pitted gear</a>, <a href="https://publications.waset.org/abstracts/search?q=autocorrelation" title=" autocorrelation"> autocorrelation</a>, <a href="https://publications.waset.org/abstracts/search?q=gear%20fault%20diagnosis" title=" gear fault diagnosis"> gear fault diagnosis</a> </p> <a href="https://publications.waset.org/abstracts/13393/gear-fault-diagnosis-based-on-optimal-morlet-wavelet-filter-and-autocorrelation-enhancement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13393.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">388</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">34</span> Numerical Solution for Integro-Differential Equations by Using Quartic B-Spline Wavelet and Operational Matrices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khosrow%20Maleknejad">Khosrow Maleknejad</a>, <a href="https://publications.waset.org/abstracts/search?q=Yaser%20Rostami"> Yaser Rostami</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, semi-orthogonal B-spline scaling functions and wavelets and their dual functions are presented to approximate the solutions of integro-differential equations.The B-spline scaling functions and wavelets, their properties and the operational matrices of derivative for this function are presented to reduce the solution of integro-differential equations to the solution of algebraic equations. Here we compute B-spline scaling functions of degree 4 and their dual, then we will show that by using them we have better approximation results for the solution of integro-differential equations in comparison with less degrees of scaling functions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=%C4%B1ntegro-differential%20equations" title="ıntegro-differential equations">ıntegro-differential equations</a>, <a href="https://publications.waset.org/abstracts/search?q=quartic%20B-spline%20wavelet" title=" quartic B-spline wavelet"> quartic B-spline wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=operational%20matrices" title=" operational matrices"> operational matrices</a>, <a href="https://publications.waset.org/abstracts/search?q=dual%20functions" title=" dual functions"> dual functions</a> </p> <a href="https://publications.waset.org/abstracts/5002/numerical-solution-for-integro-differential-equations-by-using-quartic-b-spline-wavelet-and-operational-matrices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5002.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">456</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">33</span> Hybrid Thresholding Lifting Dual Tree Complex Wavelet Transform with Wiener Filter for Quality Assurance of Medical Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hilal%20Naimi">Hilal Naimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amelbahahouda%20Adamou-Mitiche"> Amelbahahouda Adamou-Mitiche</a>, <a href="https://publications.waset.org/abstracts/search?q=Lahcene%20Mitiche"> Lahcene Mitiche</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main problem in the area of medical imaging has been image denoising. The most defying for image denoising is to secure data carrying structures like surfaces and edges in order to achieve good visual quality. Different algorithms with different denoising performances have been proposed in previous decades. More recently, models focused on deep learning have shown a great promise to outperform all traditional approaches. However, these techniques are limited to the necessity of large sample size training and high computational costs. This research proposes a denoising approach basing on LDTCWT (Lifting Dual Tree Complex Wavelet Transform) using Hybrid Thresholding with Wiener filter to enhance the quality image. This research describes the LDTCWT as a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). To develop this approach, a hybrid thresholding function is modeled by integrating the Wiener filter into the thresholding function. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lifting%20wavelet%20transform" title="lifting wavelet transform">lifting wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20denoising" title=" image denoising"> image denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=dual%20tree%20complex%20wavelet%20transform" title=" dual tree complex wavelet transform"> dual tree complex wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20shrinkage" title=" wavelet shrinkage"> wavelet shrinkage</a>, <a href="https://publications.waset.org/abstracts/search?q=wiener%20filter" title=" wiener filter"> wiener filter</a> </p> <a href="https://publications.waset.org/abstracts/135374/hybrid-thresholding-lifting-dual-tree-complex-wavelet-transform-with-wiener-filter-for-quality-assurance-of-medical-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135374.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">163</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">32</span> Human Action Recognition Using Wavelets of Derived Beta Distributions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Neziha%20Jaouedi">Neziha Jaouedi</a>, <a href="https://publications.waset.org/abstracts/search?q=Noureddine%20Boujnah"> Noureddine Boujnah</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Salim%20Bouhlel"> Mohamed Salim Bouhlel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the framework of human machine interaction systems enhancement, we focus throw this paper on human behavior analysis and action recognition. Human behavior is characterized by actions and reactions duality (movements, psychological modification, verbal and emotional expression). It’s worth noting that many information is hidden behind gesture, sudden motion points trajectories and speeds, many research works reconstructed an information retrieval issues. In our work we will focus on motion extraction, tracking and action recognition using wavelet network approaches. Our contribution uses an analysis of human subtraction by Gaussian Mixture Model (GMM) and body movement through trajectory models of motion constructed from kalman filter. These models allow to remove the noise using the extraction of the main motion features and constitute a stable base to identify the evolutions of human activity. Each modality is used to recognize a human action using wavelets of derived beta distributions approach. The proposed approach has been validated successfully on a subset of KTH and UCF sports database. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feautures%20extraction" title="feautures extraction">feautures extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20action%20classifier" title=" human action classifier"> human action classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20neural%20network" title=" wavelet neural network"> wavelet neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=beta%20wavelet" title=" beta wavelet"> beta wavelet</a> </p> <a href="https://publications.waset.org/abstracts/79396/human-action-recognition-using-wavelets-of-derived-beta-distributions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79396.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">411</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">31</span> Sampling Two-Channel Nonseparable Wavelets and Its Applications in Multispectral Image Fusion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bin%20Liu">Bin Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Weijie%20Liu"> Weijie Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Bin%20Sun"> Bin Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Yihui%20Luo"> Yihui Luo </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to solve the problem of lower spatial resolution and block effect in the fusion method based on separable wavelet transform in the resulting fusion image, a new sampling mode based on multi-resolution analysis of two-channel non separable wavelet transform, whose dilation matrix is [1,1;1,-1], is presented and a multispectral image fusion method based on this kind of sampling mode is proposed. Filter banks related to this kind of wavelet are constructed, and multiresolution decomposition of the intensity of the MS and panchromatic image are performed in the sampled mode using the constructed filter bank. The low- and high-frequency coefficients are fused by different fusion rules. The experiment results show that this method has good visual effect. The fusion performance has been noted to outperform the IHS fusion method, as well as, the fusion methods based on DWT, IHS-DWT, IHS-Contourlet transform, and IHS-Curvelet transform in preserving both spectral quality and high spatial resolution information. Furthermore, when compared with the fusion method based on nonsubsampled two-channel non separable wavelet, the proposed method has been observed to have higher spatial resolution and good global spectral information. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20fusion" title="image fusion">image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=two-channel%20sampled%20nonseparable%20wavelets" title=" two-channel sampled nonseparable wavelets"> two-channel sampled nonseparable wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=multispectral%20image" title=" multispectral image"> multispectral image</a>, <a href="https://publications.waset.org/abstracts/search?q=panchromatic%20image" title=" panchromatic image"> panchromatic image</a> </p> <a href="https://publications.waset.org/abstracts/15357/sampling-two-channel-nonseparable-wavelets-and-its-applications-in-multispectral-image-fusion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15357.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">440</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">30</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">419</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">29</span> A Survey on Types of Noises and De-Noising Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amandeep%20Kaur">Amandeep Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Digital Image processing is a fundamental tool to perform various operations on the digital images for pattern recognition, noise removal and feature extraction. In this paper noise removal technique has been described for various types of noises. This paper comprises discussion about various noises available in the image due to different environmental, accidental factors. In this paper, various de-noising approaches have been discussed that utilize different wavelets and filters for de-noising. By analyzing various papers on image de-noising we extract that wavelet based de-noise approaches are much effective as compared to others. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=de-noising%20techniques" title="de-noising techniques">de-noising techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=edges" title=" edges"> edges</a>, <a href="https://publications.waset.org/abstracts/search?q=image" title=" image"> image</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/54155/a-survey-on-types-of-noises-and-de-noising-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54155.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">336</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">28</span> Wavelets Contribution on Textual Data Analysis </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Habiba%20Ben%20Abdessalem">Habiba Ben Abdessalem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The emergence of giant set of textual data was the push that has encouraged researchers to invest in this field. The purpose of textual data analysis methods is to facilitate access to such type of data by providing various graphic visualizations. Applying these methods requires a corpus pretreatment step, whose standards are set according to the objective of the problem studied. This step determines the forms list contained in contingency table by keeping only those information carriers. This step may, however, lead to noisy contingency tables, so the use of wavelet denoising function. The validity of the proposed approach is tested on a text database that offers economic and political events in Tunisia for a well definite period. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=textual%20data" title="textual data">textual data</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet" title=" wavelet"> wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=denoising" title=" denoising"> denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=contingency%20table" title=" contingency table"> contingency table</a> </p> <a href="https://publications.waset.org/abstracts/61909/wavelets-contribution-on-textual-data-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61909.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">277</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">27</span> Wavelet Based Residual Method of Detecting GSM Signal Strength Fading</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=Onah%20Festus%20Iloabuchi"> Onah Festus Iloabuchi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, GSM signal strength was measured in order to detect the type of the signal fading phenomenon using one-dimensional multilevel wavelet residual method and neural network clustering to determine the average GSM signal strength received in the study area. The wavelet residual method predicted that the GSM signal experienced slow fading and attenuated with MSE of 3.875dB. The neural network clustering revealed that mostly -75dB, -85dB and -95dB were received. This means that the signal strength received in the study is a weak signal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=one-dimensional%20multilevel%20wavelets" title="one-dimensional multilevel wavelets">one-dimensional multilevel wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=path%20loss" title=" path loss"> path loss</a>, <a href="https://publications.waset.org/abstracts/search?q=GSM%20signal%20strength" title=" GSM signal strength"> GSM signal strength</a>, <a href="https://publications.waset.org/abstracts/search?q=propagation" title=" propagation"> propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20environment" title=" urban environment"> urban environment</a> </p> <a href="https://publications.waset.org/abstracts/14434/wavelet-based-residual-method-of-detecting-gsm-signal-strength-fading" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14434.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">338</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">26</span> Optimal Control of Volterra Integro-Differential Systems Based on Legendre Wavelets and Collocation Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khosrow%20Maleknejad">Khosrow Maleknejad</a>, <a href="https://publications.waset.org/abstracts/search?q=Asyieh%20Ebrahimzadeh"> Asyieh Ebrahimzadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the numerical solution of optimal control problem (OCP) for systems governed by Volterra integro-differential (VID) equation is considered. The method is developed by means of the Legendre wavelet approximation and collocation method. The properties of Legendre wavelet accompany with Gaussian integration method are utilized to reduce the problem to the solution of nonlinear programming one. Some numerical examples are given to confirm the accuracy and ease of implementation of the method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=collocation%20method" title="collocation method">collocation method</a>, <a href="https://publications.waset.org/abstracts/search?q=Legendre%20wavelet" title=" Legendre wavelet"> Legendre wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title=" optimal control"> optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=Volterra%20integro-differential%20equation" title=" Volterra integro-differential equation"> Volterra integro-differential equation</a> </p> <a href="https://publications.waset.org/abstracts/5005/optimal-control-of-volterra-integro-differential-systems-based-on-legendre-wavelets-and-collocation-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5005.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">388</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">25</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">24</span> Multiscale Edge Detection Based on Nonsubsampled Contourlet Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Enqing%20Chen">Enqing Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianbo%20Wang"> Jianbo Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is well known that the wavelet transform provides a very effective framework for multiscale edges analysis. However, wavelets are not very effective in representing images containing distributed discontinuities such as edges. In this paper, we propose a novel multiscale edge detection method in nonsubsampled contourlet transform (NSCT) domain, which is based on the dominant multiscale, multidirection edge expression and outstanding edge location of NSCT. Through real images experiments, simulation results demonstrate that the proposed method is better than other edge detection methods based on Canny operator, wavelet and contourlet. Additionally, the proposed method also works well for noisy images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=edge%20detection" title="edge detection">edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=NSCT" title=" NSCT"> NSCT</a>, <a href="https://publications.waset.org/abstracts/search?q=shift%20invariant" title=" shift invariant"> shift invariant</a>, <a href="https://publications.waset.org/abstracts/search?q=modulus%20maxima" title=" modulus maxima"> modulus maxima</a> </p> <a href="https://publications.waset.org/abstracts/9528/multiscale-edge-detection-based-on-nonsubsampled-contourlet-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9528.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">488</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">23</span> Neural Network Based Path Loss Prediction for Global System for Mobile Communication in an Urban Environment</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> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we measured GSM signal strength in the Dnepropetrovsk city in order to predict path loss in study area using nonlinear autoregressive neural network prediction and we also, used neural network clustering to determine average GSM signal strength receive at the study area. The nonlinear auto-regressive neural network predicted that the GSM signal is attenuated with the mean square error (MSE) of 2.6748dB, this attenuation value is used to modify the COST 231 Hata and the Okumura-Hata models. The neural network clustering revealed that -75dB to -95dB is received more frequently. This means that the signal strength received at the study is mostly weak signal <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=one-dimensional%20multilevel%20wavelets" title="one-dimensional multilevel wavelets">one-dimensional multilevel wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=path%20loss" title=" path loss"> path loss</a>, <a href="https://publications.waset.org/abstracts/search?q=GSM%20signal%20strength" title=" GSM signal strength"> GSM signal strength</a>, <a href="https://publications.waset.org/abstracts/search?q=propagation" title=" propagation"> propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20environment%20and%20model" title=" urban environment and model"> urban environment and model</a> </p> <a href="https://publications.waset.org/abstracts/14119/neural-network-based-path-loss-prediction-for-global-system-for-mobile-communication-in-an-urban-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14119.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">382</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">22</span> Enhancement of Primary User Detection in Cognitive Radio by Scattering Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Moawad">A. Moawad</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20C.%20Yao"> K. C. Yao</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Mansour"> A. Mansour</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Gautier"> R. Gautier</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The detecting of an occupied frequency band is a major issue in cognitive radio systems. The detection process becomes difficult if the signal occupying the band of interest has faded amplitude due to multipath effects. These effects make it hard for an occupying user to be detected. This work mitigates the missed-detection problem in the context of cognitive radio in frequency-selective fading channel by proposing blind channel estimation method that is based on scattering transform. By initially applying conventional energy detection, the missed-detection probability is evaluated, and if it is greater than or equal to 50%, channel estimation is applied on the received signal followed by channel equalization to reduce the channel effects. In the proposed channel estimator, we modify the Morlet wavelet by using its first derivative for better frequency resolution. A mathematical description of the modified function and its frequency resolution is formulated in this work. The improved frequency resolution is required to follow the spectral variation of the channel. The channel estimation error is evaluated in the mean-square sense for different channel settings, and energy detection is applied to the equalized received signal. The simulation results show improvement in reducing the missed-detection probability as compared to the detection based on principal component analysis. This improvement is achieved at the expense of increased estimator complexity, which depends on the number of wavelet filters as related to the channel taps. Also, the detection performance shows an improvement in detection probability for low signal-to-noise scenarios over principal component analysis- based energy detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=channel%20estimation" title="channel estimation">channel estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=cognitive%20radio" title=" cognitive radio"> cognitive radio</a>, <a href="https://publications.waset.org/abstracts/search?q=scattering%20transform" title=" scattering transform"> scattering transform</a>, <a href="https://publications.waset.org/abstracts/search?q=spectrum%20sensing" title=" spectrum sensing"> spectrum sensing</a> </p> <a href="https://publications.waset.org/abstracts/79688/enhancement-of-primary-user-detection-in-cognitive-radio-by-scattering-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79688.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">196</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">21</span> Multi-Scaled Non-Local Means Filter for Medical Images Denoising: Empirical Mode Decomposition vs. Wavelet Transform </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hana%20Rabbouch">Hana Rabbouch</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, there has been considerable growth of denoising techniques mainly devoted to medical imaging. This important evolution is not only due to the progress of computing techniques, but also to the emergence of multi-resolution analysis (MRA) on both mathematical and algorithmic bases. In this paper, a comparative study is conducted between the two best-known MRA-based decomposition techniques: the Empirical Mode Decomposition (EMD) and the Discrete Wavelet Transform (DWT). The comparison is carried out in a framework of multi-scale denoising, where a Non-Local Means (NLM) filter is performed scale-by-scale to a sample of benchmark medical images. The results prove the effectiveness of the multiscaled denoising, especially when the NLM filtering is coupled with the EMD. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=medical%20imaging" title="medical imaging">medical imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=non%20local%20means" title=" non local means"> non local means</a>, <a href="https://publications.waset.org/abstracts/search?q=denoising" title=" denoising"> denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=multiscaled%20analysis" title=" multiscaled analysis"> multiscaled analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20mode%20decomposition" title=" empirical mode decomposition"> empirical mode decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelets" title=" wavelets"> wavelets</a> </p> <a href="https://publications.waset.org/abstracts/115243/multi-scaled-non-local-means-filter-for-medical-images-denoising-empirical-mode-decomposition-vs-wavelet-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115243.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">141</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=morlet%20wavelets&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=morlet%20wavelets&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div 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