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wavelet</title> <meta name="description" content="Search results for: discrete wavelet"> <meta name="keywords" content="discrete 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="discrete 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> 839</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: discrete wavelet</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">839</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">B眉lent Kantar</a>, <a href="https://publications.waset.org/abstracts/search?q=Numan%20%C3%9Cnald%C4%B1"> Numan 脺nald谋</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">838</span> A Robust Hybrid Blind Digital Image Watermarking System Using Discrete Wavelet Transform and Contourlet Transform </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nidal%20F.%20Shilbayeh">Nidal F. Shilbayeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Belal%20AbuHaija"> Belal AbuHaija</a>, <a href="https://publications.waset.org/abstracts/search?q=Zainab%20N.%20Al-Qudsy"> Zainab N. Al-Qudsy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a hybrid blind digital watermarking system using Discrete Wavelet Transform (DWT) and Contourlet Transform (CT) has been implemented and tested. The implemented combined digital watermarking system has been tested against five common types of image attacks. The performance evaluation shows improved results in terms of imperceptibility, robustness, and high tolerance against these attacks; accordingly, the system is very effective and applicable. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform%20%28DWT%29" title="discrete wavelet transform (DWT)">discrete wavelet transform (DWT)</a>, <a href="https://publications.waset.org/abstracts/search?q=contourlet%20transform%20%28CT%29" title=" contourlet transform (CT)"> contourlet transform (CT)</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20image%20watermarking" title=" digital image watermarking"> digital image watermarking</a>, <a href="https://publications.waset.org/abstracts/search?q=copyright%20protection" title=" copyright protection"> copyright protection</a>, <a href="https://publications.waset.org/abstracts/search?q=geometric%20attack" title=" geometric attack"> geometric attack</a> </p> <a href="https://publications.waset.org/abstracts/69379/a-robust-hybrid-blind-digital-image-watermarking-system-using-discrete-wavelet-transform-and-contourlet-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69379.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">394</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">837</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">836</span> Fault Diagnosis in Induction Motors Using the Discrete Wavelet Transform </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Yahia">Khaled Yahia </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the problem of stator faults diagnosis in induction motors. Using the discrete wavelet transform (DWT) for the current Park鈥檚 vector modulus (CPVM) analysis, the inter-turn short-circuit faults diagnosis can be achieved. This method is based on the decomposition of the CPVM signal, where wavelet approximation and detail coefficients of this signal have been extracted. The energy evaluation of a known bandwidth detail permits to define a fault severity factor (FSF). This method has been tested through the simulation of an induction motor using a mathematical model based on the winding-function approach. Simulation, as well as experimental, results show the effectiveness of the used method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=induction%20motors%20%28IMs%29" title="induction motors (IMs)">induction motors (IMs)</a>, <a href="https://publications.waset.org/abstracts/search?q=inter-turn%20short-circuits%20diagnosis" title=" inter-turn short-circuits diagnosis"> inter-turn short-circuits diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform%20%28DWT%29" title=" discrete wavelet transform (DWT)"> discrete wavelet transform (DWT)</a>, <a href="https://publications.waset.org/abstracts/search?q=current%20park%E2%80%99s%20vector%20modulus%20%28CPVM%29" title=" current park鈥檚 vector modulus (CPVM) "> current park鈥檚 vector modulus (CPVM) </a> </p> <a href="https://publications.waset.org/abstracts/31450/fault-diagnosis-in-induction-motors-using-the-discrete-wavelet-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31450.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">569</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">835</span> Fault Diagnosis in Induction Motors Using Discrete Wavelet Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Yahia">K. Yahia</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Titaouine"> A. Titaouine</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ghoggal"> A. Ghoggal</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20E.%20Zouzou"> S. E. Zouzou</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Benchabane"> F. Benchabane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the problem of stator faults diagnosis in induction motors. Using the discrete wavelet transform (DWT) for the current Park鈥檚 vector modulus (CPVM) analysis, the inter-turn short-circuit faults diagnosis can be achieved. This method is based on the decomposition of the CPVM signal, where wavelet approximation and detail coefficients of this signal have been extracted. The energy evaluation of a known bandwidth detail permits to define a fault severity factor (FSF). This method has been tested through the simulation of an induction motor using a mathematical model based on the winding-function approach. Simulation, as well as experimental, results show the effectiveness of the used method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Induction%20Motors%20%28IMs%29" title="Induction Motors (IMs)">Induction Motors (IMs)</a>, <a href="https://publications.waset.org/abstracts/search?q=inter-turn%20short-circuits%20diagnosis" title=" inter-turn short-circuits diagnosis"> inter-turn short-circuits diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=Discrete%20Wavelet%20Transform%20%28DWT%29" title=" Discrete Wavelet Transform (DWT)"> Discrete Wavelet Transform (DWT)</a>, <a href="https://publications.waset.org/abstracts/search?q=Current%20Park%E2%80%99s%20Vector%20Modulus%20%28CPVM%29" title=" Current Park鈥檚 Vector Modulus (CPVM)"> Current Park鈥檚 Vector Modulus (CPVM)</a> </p> <a href="https://publications.waset.org/abstracts/22046/fault-diagnosis-in-induction-motors-using-discrete-wavelet-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22046.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">553</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">834</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">833</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">832</span> Stator Short-Circuits Fault Diagnosis in Induction Motors Using Extended Park鈥檚 Vector Approach through the Discrete Wavelet Transform </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Yahia">K. Yahia</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ghoggal"> A. Ghoggal</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Titaouine"> A. Titaouine</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20E.%20Zouzou"> S. E. Zouzou</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Benchabane"> F. Benchabane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the problem of stator faults diagnosis in induction motors. Using the discrete wavelet transform (DWT) for the current Park鈥檚 vector modulus (CPVM) analysis, the inter-turn short-circuit faults diagnosis can be achieved. This method is based on the decomposition of the CPVM signal, where wavelet approximation and detail coefficients of this signal have been extracted. The energy evaluation of a known bandwidth detail permits to define a fault severity factor (FSF). This method has been tested through the simulation of an induction motor using a mathematical model based on the winding-function approach. Simulation, as well as experimental, results show the effectiveness of the used method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Induction%20Motors%20%28IMs%29" title="Induction Motors (IMs)">Induction Motors (IMs)</a>, <a href="https://publications.waset.org/abstracts/search?q=Inter-turn%20Short-Circuits%20Diagnosis" title=" Inter-turn Short-Circuits Diagnosis"> Inter-turn Short-Circuits Diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=Discrete%20Wavelet%20Transform%20%28DWT%29" title=" Discrete Wavelet Transform (DWT)"> Discrete Wavelet Transform (DWT)</a>, <a href="https://publications.waset.org/abstracts/search?q=Current%20Park%E2%80%99s%20Vector%20Modulus%20%28CPVM%29" title=" Current Park鈥檚 Vector Modulus (CPVM)"> Current Park鈥檚 Vector Modulus (CPVM)</a> </p> <a href="https://publications.waset.org/abstracts/22006/stator-short-circuits-fault-diagnosis-in-induction-motors-using-extended-parks-vector-approach-through-the-discrete-wavelet-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22006.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">563</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">831</span> Morphology Operation and Discrete Wavelet Transform for Blood Vessels Segmentation in Retina Fundus</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rita%20Magdalena">Rita Magdalena</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20K.%20Caecar%20Pratiwi"> N. K. Caecar Pratiwi</a>, <a href="https://publications.waset.org/abstracts/search?q=Yunendah%20Nur%20Fuadah"> Yunendah Nur Fuadah</a>, <a href="https://publications.waset.org/abstracts/search?q=Sofia%20Saidah"> Sofia Saidah</a>, <a href="https://publications.waset.org/abstracts/search?q=Bima%20Sakti"> Bima Sakti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vessel segmentation of retinal fundus is important for biomedical sciences in diagnosing ailments related to the eye. Segmentation can simplify medical experts in diagnosing retinal fundus image state. Therefore, in this study, we designed a software using MATLAB which enables the segmentation of the retinal blood vessels on retinal fundus images. There are two main steps in the process of segmentation. The first step is image preprocessing that aims to improve the quality of the image to be optimum segmented. The second step is the image segmentation in order to perform the extraction process to retrieve the retina鈥檚 blood vessel from the eye fundus image. The image segmentation methods that will be analyzed in this study are Morphology Operation, Discrete Wavelet Transform and combination of both. The amount of data that used in this project is 40 for the retinal image and 40 for manually segmentation image. After doing some testing scenarios, the average accuracy for Morphology Operation method is 88.46 % while for Discrete Wavelet Transform is 89.28 %. By combining the two methods mentioned in later, the average accuracy was increased to 89.53 %. The result of this study is an image processing system that can segment the blood vessels in retinal fundus with high accuracy and low computation time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform" title="discrete wavelet transform">discrete wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=fundus%20retina" title=" fundus retina"> fundus retina</a>, <a href="https://publications.waset.org/abstracts/search?q=morphology%20operation" title=" morphology operation"> morphology operation</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=vessel" title=" vessel"> vessel</a> </p> <a href="https://publications.waset.org/abstracts/105620/morphology-operation-and-discrete-wavelet-transform-for-blood-vessels-segmentation-in-retina-fundus" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105620.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">195</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">830</span> Speech Intelligibility Improvement Using Variable Level Decomposition DWT</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samba%20Raju">Samba Raju</a>, <a href="https://publications.waset.org/abstracts/search?q=Chiluveru"> Chiluveru</a>, <a href="https://publications.waset.org/abstracts/search?q=Manoj%20Tripathy"> Manoj Tripathy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Intelligibility is an essential characteristic of a speech signal, which is used to help in the understanding of information in speech signal. Background noise in the environment can deteriorate the intelligibility of a recorded speech. In this paper, we presented a simple variance subtracted - variable level discrete wavelet transform, which improve the intelligibility of speech. The proposed algorithm does not require an explicit estimation of noise, i.e., prior knowledge of the noise; hence, it is easy to implement, and it reduces the computational burden. The proposed algorithm decides a separate decomposition level for each frame based on signal dominant and dominant noise criteria. The performance of the proposed algorithm is evaluated with speech intelligibility measure (STOI), and results obtained are compared with Universal Discrete Wavelet Transform (DWT) thresholding and Minimum Mean Square Error (MMSE) methods. The experimental results revealed that the proposed scheme outperformed competing methods <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform" title="discrete wavelet transform">discrete wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20intelligibility" title=" speech intelligibility"> speech intelligibility</a>, <a href="https://publications.waset.org/abstracts/search?q=STOI" title=" STOI"> STOI</a>, <a href="https://publications.waset.org/abstracts/search?q=standard%20deviation" title=" standard deviation"> standard deviation</a> </p> <a href="https://publications.waset.org/abstracts/115620/speech-intelligibility-improvement-using-variable-level-decomposition-dwt" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115620.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">148</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">829</span> Blind Watermarking Using Discrete Wavelet Transform Algorithm with Patchwork</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Toni%20Maristela%20C.%20Estabillo">Toni Maristela C. Estabillo</a>, <a href="https://publications.waset.org/abstracts/search?q=Michaela%20V.%20Matienzo"> Michaela V. Matienzo</a>, <a href="https://publications.waset.org/abstracts/search?q=Mikaela%20L.%20Sabangan"> Mikaela L. Sabangan</a>, <a href="https://publications.waset.org/abstracts/search?q=Rosette%20M.%20Tienzo"> Rosette M. Tienzo</a>, <a href="https://publications.waset.org/abstracts/search?q=Justine%20L.%20Bahinting"> Justine L. Bahinting</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study is about blind watermarking on images with different categories and properties using two algorithms namely, Discrete Wavelet Transform and Patchwork Algorithm. A program is created to perform watermark embedding, extraction and evaluation. The evaluation is based on three watermarking criteria namely: image quality degradation, perceptual transparency and security. Image quality is measured by comparing the original properties with the processed one. Perceptual transparency is measured by a visual inspection on a survey. Security is measured by implementing geometrical and non-geometrical attacks through a pass or fail testing. Values used to measure the following criteria are mostly based on Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR). The results are based on statistical methods used to interpret and collect data such as averaging, z Test and survey. The study concluded that the combined DWT and Patchwork algorithms were less efficient and less capable of watermarking than DWT algorithm only. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blind%20watermarking" title="blind watermarking">blind watermarking</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform%20algorithm" title=" discrete wavelet transform algorithm"> discrete wavelet transform algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=patchwork%20algorithm" title=" patchwork algorithm"> patchwork algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20watermark" title=" digital watermark"> digital watermark</a> </p> <a href="https://publications.waset.org/abstracts/49404/blind-watermarking-using-discrete-wavelet-transform-algorithm-with-patchwork" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49404.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">268</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">828</span> Effects of Tool State on the Output Parameters of Front Milling Using Discrete Wavelet Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bruno%20S.%20Soria">Bruno S. Soria</a>, <a href="https://publications.waset.org/abstracts/search?q=Mauricio%20R.%20Policena"> Mauricio R. Policena</a>, <a href="https://publications.waset.org/abstracts/search?q=Andre%20J.%20Souza"> Andre J. Souza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The state of the cutting tool is an important factor to consider during machining to achieve a good surface quality. The vibration generated during material cutting can also directly affect the surface quality and life of the cutting tool. In this work, the effect of mechanical broken failure (MBF) on carbide insert tools during face milling of AISI 304 stainless steel was evaluated using three levels of feed rate and two spindle speeds for each tool condition: three carbide inserts have perfect geometry, and three other carbide inserts have MBF. The axial and radial depths remained constant. The cutting forces were determined through a sensory system that consists of a piezoelectric dynamometer and data acquisition system. Discrete Wavelet Transform was used to separate the static part of the signals of force and vibration. The roughness of the machined surface was analyzed for each machining condition. The MBF of the tool increased the intensity and force of vibration and worsened the roughness factors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=face%20milling" title="face milling">face milling</a>, <a href="https://publications.waset.org/abstracts/search?q=stainless%20steel" title=" stainless steel"> stainless steel</a>, <a href="https://publications.waset.org/abstracts/search?q=tool%20condition%20monitoring" title=" tool condition monitoring"> tool condition monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20discrete%20transform" title=" wavelet discrete transform"> wavelet discrete transform</a> </p> <a href="https://publications.waset.org/abstracts/109363/effects-of-tool-state-on-the-output-parameters-of-front-milling-using-discrete-wavelet-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/109363.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">146</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">827</span> Statistical Wavelet Features, PCA, and SVM-Based Approach for EEG Signals Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20K.%20Chaurasiya">R. K. Chaurasiya</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20D.%20Londhe"> N. D. Londhe</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Ghosh"> S. Ghosh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the support-vectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform" title="discrete wavelet transform">discrete wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title=" electroencephalogram"> electroencephalogram</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/18113/statistical-wavelet-features-pca-and-svm-based-approach-for-eeg-signals-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18113.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">638</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">826</span> Implementation of Invisible Digital Watermarking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=V.%20Monisha">V. Monisha</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Sindhuja"> D. Sindhuja</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Sowmiya"> M. Sowmiya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over the decade, the applications about multimedia have been developed rapidly. The advancement in the communication field at the faster pace, it is necessary to protect the data during transmission. Thus, security of multimedia contents becomes a vital issue, and it is a need for protecting the digital content against malfunctions. Digital watermarking becomes the solution for the copyright protection and authentication of data in the network. In multimedia applications, embedded watermarks should be robust, and imperceptible. For improving robustness, the discrete wavelet transform is used. Both encoding and extraction algorithm can be done using MATLAB R2012a. In this Discrete wavelet transform (DWT) domain of digital image, watermarking algorithm is used, and hardware implementation can be done on Xilinx based FPGA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20watermarking" title="digital watermarking">digital 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=robustness" title=" robustness"> robustness</a>, <a href="https://publications.waset.org/abstracts/search?q=FPGA" title=" FPGA"> FPGA</a> </p> <a href="https://publications.waset.org/abstracts/47802/implementation-of-invisible-digital-watermarking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47802.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">413</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">825</span> Stator Short-Circuits Fault Diagnosis in Induction Motors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Yahia">K. Yahia</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Sahraoui"> M. Sahraoui</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Guettaf"> A. Guettaf </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the problem of stator faults diagnosis in induction motors. Using the discrete wavelet transform (DWT) for the current Park鈥檚 vector modulus (CPVM) analysis, the inter-turn short-circuit faults diagnosis can be achieved. This method is based on the decomposition of the CPVM signal, where wavelet approximation and detail coefficients of this signal have been extracted. The energy evaluation of a known bandwidth detail permits to define a fault severity factor (FSF). This method has been tested through the simulation of an induction motor using a mathematical model based on the winding-function approach. Simulation, as well as experimental results, show the effectiveness of the used method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=induction%20motors%20%28IMs%29" title="induction motors (IMs)">induction motors (IMs)</a>, <a href="https://publications.waset.org/abstracts/search?q=inter-turn%20short-circuits%20diagnosis" title=" inter-turn short-circuits diagnosis"> inter-turn short-circuits diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform%20%28DWT%29" title=" discrete wavelet transform (DWT)"> discrete wavelet transform (DWT)</a>, <a href="https://publications.waset.org/abstracts/search?q=Current%20Park%E2%80%99s%20Vector%20Modulus%20%28CPVM%29" title=" Current Park鈥檚 Vector Modulus (CPVM)"> Current Park鈥檚 Vector Modulus (CPVM)</a> </p> <a href="https://publications.waset.org/abstracts/82115/stator-short-circuits-fault-diagnosis-in-induction-motors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82115.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">457</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">824</span> Video Compression Using Contourlet Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Delara%20Kazempour">Delara Kazempour</a>, <a href="https://publications.waset.org/abstracts/search?q=Mashallah%20Abasi%20Dezfuli"> Mashallah Abasi Dezfuli</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20Javidan"> Reza Javidan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Video compression used for channels with limited bandwidth and storage devices has limited storage capabilities. One of the most popular approaches in video compression is the usage of different transforms. Discrete cosine transform is one of the video compression methods that have some problems such as blocking, noising and high distortion inappropriate effect in compression ratio. wavelet transform is another approach is better than cosine transforms in balancing of compression and quality but the recognizing of curve curvature is so limit. Because of the importance of the compression and problems of the cosine and wavelet transforms, the contourlet transform is most popular in video compression. In the new proposed method, we used contourlet transform in video image compression. Contourlet transform can save details of the image better than the previous transforms because this transform is multi-scale and oriented. This transform can recognize discontinuity such as edges. In this approach we lost data less than previous approaches. Contourlet transform finds discrete space structure. This transform is useful for represented of two dimension smooth images. This transform, produces compressed images with high compression ratio along with texture and edge preservation. Finally, the results show that the majority of the images, the parameters of the mean square error and maximum signal-to-noise ratio of the new method based contourlet transform compared to wavelet transform are improved but in most of the images, the parameters of the mean square error and maximum signal-to-noise ratio in the cosine transform is better than the method based on contourlet transform. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20compression" title="video compression">video compression</a>, <a href="https://publications.waset.org/abstracts/search?q=contourlet%20transform" title=" contourlet transform"> contourlet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20cosine%20transform" title=" discrete cosine transform"> discrete cosine transform</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/6930/video-compression-using-contourlet-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6930.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">444</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">823</span> Image Compression Based on Regression SVM and Biorthogonal Wavelets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zikiou%20Nadia">Zikiou Nadia</a>, <a href="https://publications.waset.org/abstracts/search?q=Lahdir%20Mourad"> Lahdir Mourad</a>, <a href="https://publications.waset.org/abstracts/search?q=Ameur%20Soltane"> Ameur Soltane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose an effective method for image compression based on SVM Regression (SVR), with three different kernels, and biorthogonal 2D Discrete Wavelet Transform. SVM regression could learn dependency from training data and compressed using fewer training points (support vectors) to represent the original data and eliminate the redundancy. Biorthogonal wavelet has been used to transform the image and the coefficients acquired are then trained with different kernels SVM (Gaussian, Polynomial, and Linear). Run-length and Arithmetic coders are used to encode the support vectors and its corresponding weights, obtained from the SVM regression. The peak signal noise ratio (PSNR) and their compression ratios of several test images, compressed with our algorithm, with different kernels are presented. Compared with other kernels, Gaussian kernel achieves better image quality. Experimental results show that the compression performance of our method gains much improvement. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20compression" title="image compression">image compression</a>, <a href="https://publications.waset.org/abstracts/search?q=2D%20discrete%20wavelet%20transform%20%28DWT-2D%29" title=" 2D discrete wavelet transform (DWT-2D)"> 2D discrete wavelet transform (DWT-2D)</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20regression%20%28SVR%29" title=" support vector regression (SVR)"> support vector regression (SVR)</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM%20Kernels" title=" SVM Kernels"> SVM Kernels</a>, <a href="https://publications.waset.org/abstracts/search?q=run-length" title=" run-length"> run-length</a>, <a href="https://publications.waset.org/abstracts/search?q=arithmetic%20coding" title=" arithmetic coding"> arithmetic coding</a> </p> <a href="https://publications.waset.org/abstracts/17954/image-compression-based-on-regression-svm-and-biorthogonal-wavelets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17954.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">822</span> Fuzzy Wavelet Model to Forecast the Exchange Rate of IDR/USD</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tri%20Wijayanti%20Septiarini">Tri Wijayanti Septiarini</a>, <a href="https://publications.waset.org/abstracts/search?q=Agus%20Maman%20Abadi"> Agus Maman Abadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Rifki%20Taufik"> Muhammad Rifki Taufik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The exchange rate of IDR/USD can be the indicator to analysis Indonesian economy. The exchange rate as a important factor because it has big effect in Indonesian economy overall. So, it needs the analysis data of exchange rate. There is decomposition data of exchange rate of IDR/USD to be frequency and time. It can help the government to monitor the Indonesian economy. This method is very effective to identify the case, have high accurate result and have simple structure. In this paper, data of exchange rate that used is weekly data from December 17, 2010 until November 11, 2014. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=the%20exchange%20rate" title="the exchange rate">the exchange rate</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20mamdani" title=" fuzzy mamdani"> fuzzy mamdani</a>, <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=fuzzy%20wavelet" title=" fuzzy wavelet "> fuzzy wavelet </a> </p> <a href="https://publications.waset.org/abstracts/21207/fuzzy-wavelet-model-to-forecast-the-exchange-rate-of-idrusd" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21207.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">571</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">821</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> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">820</span> High-Capacity Image Steganography using Wavelet-based Fusion on Deep Convolutional Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amal%20Khalifa">Amal Khalifa</a>, <a href="https://publications.waset.org/abstracts/search?q=Nicolas%20Vana%20Santos"> Nicolas Vana Santos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Steganography has been known for centuries as an efficient approach for covert communication. Due to its popularity and ease of access, image steganography has attracted researchers to find secure techniques for hiding information within an innocent looking cover image. In this research, we propose a novel deep-learning approach to digital image steganography. The proposed method, DeepWaveletFusion, uses convolutional neural networks (CNN) to hide a secret image into a cover image of the same size. Two CNNs are trained back-to-back to merge the Discrete Wavelet Transform (DWT) of both colored images and eventually be able to blindly extract the hidden image. Based on two different image similarity metrics, a weighted gain function is used to guide the learning process and maximize the quality of the retrieved secret image and yet maintaining acceptable imperceptibility. Experimental results verified the high recoverability of DeepWaveletFusion which outperformed similar deep-learning-based methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=steganography" title=" steganography"> steganography</a>, <a href="https://publications.waset.org/abstracts/search?q=image" title=" image"> image</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform" title=" discrete wavelet transform"> discrete wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=fusion" title=" fusion"> fusion</a> </p> <a href="https://publications.waset.org/abstracts/170293/high-capacity-image-steganography-using-wavelet-based-fusion-on-deep-convolutional-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170293.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">90</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">819</span> Feedforward Neural Network with Backpropagation for Epilepsy Seizure Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Natalia%20%20Espinosa">Natalia Espinosa</a>, <a href="https://publications.waset.org/abstracts/search?q=Arthur%20Amorim"> Arthur Amorim</a>, <a href="https://publications.waset.org/abstracts/search?q=Rudolf%20%20Huebner"> Rudolf Huebner</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Artificial%20Neural%20Network%20%28ANN%29" title="Artificial Neural Network (ANN)">Artificial Neural Network (ANN)</a>, <a href="https://publications.waset.org/abstracts/search?q=Discrete%20Wavelet%20Transform%20%28DWT%29" title=" Discrete Wavelet Transform (DWT)"> Discrete Wavelet Transform (DWT)</a>, <a href="https://publications.waset.org/abstracts/search?q=Epilepsy%20Detection" title=" Epilepsy Detection "> Epilepsy Detection </a>, <a href="https://publications.waset.org/abstracts/search?q=Seizure." title=" Seizure."> Seizure.</a> </p> <a href="https://publications.waset.org/abstracts/122872/feedforward-neural-network-with-backpropagation-for-epilepsy-seizure-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122872.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">223</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">818</span> Robust Medical Image Watermarking based on Contourlet and Extraction Using ICA </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Saju">S. Saju</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Thirugnanam"> G. Thirugnanam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a medical image watermarking algorithm based on contourlet is proposed. Medical image watermarking is a special subcategory of image watermarking in the sense that images have special requirements. Watermarked medical images should not differ perceptually from their original counterparts because clinical reading of images must not be affected. Watermarking techniques based on wavelet transform are reported in many literatures but robustness and security using contourlet are better when compared to wavelet transform. The main challenge in exploring geometry in images comes from the discrete nature of the data. In this paper, original image is decomposed to two level using contourlet and the watermark is embedded in the resultant sub-bands. Sub-band selection is based on the value of Peak Signal to Noise Ratio (PSNR) that is calculated between watermarked and original image. To extract the watermark, Kernel ICA is used and it has a novel characteristic is that it does not require the transformation process to extract the watermark. Simulation results show that proposed scheme is robust against attacks such as Salt and Pepper noise, Median filtering and rotation. The performance measures like PSNR and Similarity measure are evaluated and compared with Discrete Wavelet Transform (DWT) to prove the robustness of the scheme. Simulations are carried out using Matlab Software. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20watermarking" title="digital watermarking">digital watermarking</a>, <a href="https://publications.waset.org/abstracts/search?q=independent%20component%20analysis" title=" independent component analysis"> independent component analysis</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=contourlet" title=" contourlet"> contourlet</a> </p> <a href="https://publications.waset.org/abstracts/21817/robust-medical-image-watermarking-based-on-contourlet-and-extraction-using-ica" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21817.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">528</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">817</span> Effective Supply Chain Coordination with Hybrid Demand Forecasting Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gurmail%20Singh">Gurmail Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Effective supply chain is the main priority of every organization which is the outcome of strategic corporate investments with deliberate management action. Value-driven supply chain is defined through development, procurement and by configuring the appropriate resources, metrics and processes. However, responsiveness of the supply chain can be improved by proper coordination. So the Bullwhip effect (BWE) and Net stock amplification (NSAmp) values were anticipated and used for the control of inventory in organizations by both discrete wavelet transform-Artificial neural network (DWT-ANN) and Adaptive Network-based fuzzy inference system (ANFIS). This work presents a comparative methodology of forecasting for the customers demand which is non linear in nature for a multilevel supply chain structure using hybrid techniques such as Artificial intelligence techniques including Artificial neural networks (ANN) and Adaptive Network-based fuzzy inference system (ANFIS) and Discrete wavelet theory (DWT). The productiveness of these forecasting models are shown by computing the data from real world problems for Bullwhip effect and Net stock amplification. The results showed that these parameters were comparatively less in case of discrete wavelet transform-Artificial neural network (DWT-ANN) model and using Adaptive network-based fuzzy inference system (ANFIS). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bullwhip%20effect" title="bullwhip effect">bullwhip effect</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20techniques" title=" hybrid techniques"> hybrid techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=net%20stock%20amplification" title=" net stock amplification"> net stock amplification</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20flexibility" title=" supply chain flexibility"> supply chain flexibility</a> </p> <a href="https://publications.waset.org/abstracts/94150/effective-supply-chain-coordination-with-hybrid-demand-forecasting-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94150.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">127</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">816</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">815</span> Spatiotemporal Variability in Rainfall Trends over Sinai Peninsula Using Nonparametric Methods and Discrete Wavelet Transforms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mosaad%20Khadr">Mosaad Khadr</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Knowledge of the temporal and spatial variability of rainfall trends has been of great concern for efficient water resource planning, management. In this study annual, seasonal and monthly rainfall trends over the Sinai Peninsula were analyzed by using absolute homogeneity tests, nonparametric Mann鈥揔endall (MK) test and Sen鈥檚 slope estimator methods. The homogeneity of rainfall time-series was examined using four absolute homogeneity tests namely, the Pettitt test, standard normal homogeneity test, Buishand range test, and von Neumann ratio test. Further, the sequential change in the trend of annual and seasonal rainfalls is conducted using sequential MK (SQMK) method. Then the trend analysis based on discrete wavelet transform technique (DWT) in conjunction with SQMK method is performed. The spatial patterns of the detected rainfall trends were investigated using a geostatistical and deterministic spatial interpolation technique. The results achieved from the Mann鈥揔endall test to the data series (using the 5% significance level) highlighted that rainfall was generally decreasing in January, February, March, November, December, wet season, and annual rainfall. A significant decreasing trend in the winter and annual rainfall with significant levels were inferred based on the Mann-Kendall rank statistics and linear trend. Further, the discrete wavelet transform (DWT) analysis reveal that in general, intra- and inter-annual events (up to 4 years) are more influential in affecting the observed trends. The nature of the trend captured by both methods is similar for all of the cases. On the basis of spatial trend analysis, significant rainfall decreases were also noted in the investigated stations. Overall, significant downward trends in winter and annual rainfall over the Sinai Peninsula was observed during the study period. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=trend%20analysis" title="trend analysis">trend analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=rainfall" title=" rainfall"> rainfall</a>, <a href="https://publications.waset.org/abstracts/search?q=Mann%E2%80%93Kendall%20test" title=" Mann鈥揔endall test"> Mann鈥揔endall test</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform" title=" discrete wavelet transform"> discrete wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=Sinai%20Peninsula" title=" Sinai Peninsula"> Sinai Peninsula</a> </p> <a href="https://publications.waset.org/abstracts/105793/spatiotemporal-variability-in-rainfall-trends-over-sinai-peninsula-using-nonparametric-methods-and-discrete-wavelet-transforms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105793.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">170</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">814</span> Robust and Transparent Spread Spectrum Audio Watermarking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Akbar%20Attari">Ali Akbar Attari</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Asghar%20Beheshti%20Shirazi"> Ali Asghar Beheshti Shirazi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a blind and robust audio watermarking scheme based on spread spectrum in Discrete Wavelet Transform (DWT) domain. Watermarks are embedded in the low-frequency coefficients, which is less audible. The key idea is dividing the audio signal into small frames, and magnitude of the 6<sup>th</sup> level of DWT approximation coefficients is modifying based upon the Direct Sequence Spread Spectrum (DSSS) technique. Also, the psychoacoustic model for enhancing in imperceptibility, as well as Savitsky-Golay filter for increasing accuracy in extraction, is used. The experimental results illustrate high robustness against most common attacks, i.e. Gaussian noise addition, Low pass filter, Resampling, Requantizing, MP3 compression, without significant perceptual distortion (ODG is higher than -1). The proposed scheme has about 83 bps data payload. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=audio%20watermarking" title="audio watermarking">audio watermarking</a>, <a href="https://publications.waset.org/abstracts/search?q=spread%20spectrum" title=" spread spectrum"> spread spectrum</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform" title=" discrete wavelet transform"> discrete wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=psychoacoustic" title=" psychoacoustic"> psychoacoustic</a>, <a href="https://publications.waset.org/abstracts/search?q=Savitsky-Golay%20filter" title=" Savitsky-Golay filter"> Savitsky-Golay filter</a> </p> <a href="https://publications.waset.org/abstracts/86040/robust-and-transparent-spread-spectrum-audio-watermarking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86040.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">200</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">813</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">812</span> Detection and Classification of Mammogram Images Using Principle Component Analysis and Lazy Classifiers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rajkumar%20Kolangarakandy">Rajkumar Kolangarakandy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Feature extraction and selection is the primary part of any mammogram classification algorithms. The choice of feature, attribute or measurements have an important influence in any classification system. Discrete Wavelet Transformation (DWT) coefficients are one of the prominent features for representing images in frequency domain. The features obtained after the decomposition of the mammogram images using wavelet transformations have higher dimension. Even though the features are higher in dimension, they were highly correlated and redundant in nature. The dimensionality reduction techniques play an important role in selecting the optimum number of features from the higher dimension data, which are highly correlated. PCA is a mathematical tool that reduces the dimensionality of the data while retaining most of the variation in the dataset. In this paper, a multilevel classification of mammogram images using reduced discrete wavelet transformation coefficients and lazy classifiers is proposed. The classification is accomplished in two different levels. In the first level, mammogram ROIs extracted from the dataset is classified as normal and abnormal types. In the second level, all the abnormal mammogram ROIs is classified into benign and malignant too. A further classification is also accomplished based on the variation in structure and intensity distribution of the images in the dataset. The Lazy classifiers called Kstar, IBL and LWL are used for classification. The classification results obtained with the reduced feature set is highly promising and the result is also compared with the performance obtained without dimension reduction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=PCA" title="PCA">PCA</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transformation" title=" wavelet transformation"> wavelet transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=lazy%20classifiers" title=" lazy classifiers"> lazy classifiers</a>, <a href="https://publications.waset.org/abstracts/search?q=Kstar" title=" Kstar"> Kstar</a>, <a href="https://publications.waset.org/abstracts/search?q=IBL" title=" IBL"> IBL</a>, <a href="https://publications.waset.org/abstracts/search?q=LWL" title=" LWL"> LWL</a> </p> <a href="https://publications.waset.org/abstracts/36911/detection-and-classification-of-mammogram-images-using-principle-component-analysis-and-lazy-classifiers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36911.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">335</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">811</span> Numerical Solution of Integral Equations by Using Discrete GHM Multiwavelet</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Archit%20Yajnik">Archit Yajnik</a>, <a href="https://publications.waset.org/abstracts/search?q=Rustam%20Ali"> Rustam Ali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, numerical method based on discrete GHM multiwavelets is presented for solving the Fredholm integral equations of second kind. There is hardly any article available in the literature in which the integral equations are numerically solved using discrete GHM multiwavelet. A number of examples are demonstrated to justify the applicability of the method. In GHM multiwavelets, the values of scaling and wavelet functions are calculated only at t = 0, 0.5 and 1. The numerical solution obtained by the present approach is compared with the traditional Quadrature method. It is observed that the present approach is more accurate and computationally efficient as compared to quadrature method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GHM%20multiwavelet" title="GHM multiwavelet">GHM multiwavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=fredholm%20integral%20equations" title=" fredholm integral equations"> fredholm integral equations</a>, <a href="https://publications.waset.org/abstracts/search?q=quadrature%20method" title=" quadrature method"> quadrature method</a>, <a href="https://publications.waset.org/abstracts/search?q=function%20approximation" title=" function approximation"> function approximation</a> </p> <a href="https://publications.waset.org/abstracts/36311/numerical-solution-of-integral-equations-by-using-discrete-ghm-multiwavelet" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36311.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">462</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">810</span> Wavelet-Based Classification of Myocardial Ischemia, Arrhythmia, Congestive Heart Failure and Sleep Apnea</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Santanu%20Chattopadhyay">Santanu Chattopadhyay</a>, <a href="https://publications.waset.org/abstracts/search?q=Gautam%20Sarkar"> Gautam Sarkar</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabinda%20Das"> Arabinda Das</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents wavelet based classification of various heart diseases. Electrocardiogram signals of different heart patients have been studied. Statistical natures of electrocardiogram signals for different heart diseases have been compared with the statistical nature of electrocardiograms for normal persons. Under this study four different heart diseases have been considered as follows: Myocardial Ischemia (MI), Congestive Heart Failure (CHF), Arrhythmia and Sleep Apnea. Statistical nature of electrocardiograms for each case has been considered in terms of kurtosis values of two types of wavelet coefficients: approximate and detail. Nine wavelet decomposition levels have been considered in each case. Kurtosis corresponding to both approximate and detail coefficients has been considered for decomposition level one to decomposition level nine. Based on significant difference, few decomposition levels have been chosen and then used for classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arrhythmia" title="arrhythmia">arrhythmia</a>, <a href="https://publications.waset.org/abstracts/search?q=congestive%20heart%20failure" title=" congestive heart failure"> congestive heart failure</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform" title=" discrete wavelet transform"> discrete wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title=" electrocardiogram"> electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=myocardial%20ischemia" title=" myocardial ischemia"> myocardial ischemia</a>, <a href="https://publications.waset.org/abstracts/search?q=sleep%20apnea" title=" sleep apnea"> sleep apnea</a> </p> <a href="https://publications.waset.org/abstracts/112333/wavelet-based-classification-of-myocardial-ischemia-arrhythmia-congestive-heart-failure-and-sleep-apnea" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112333.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">134</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=discrete%20wavelet&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet&page=3">3</a></li> <li class="page-item"><a class="page-link" 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