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wavelet transform (DWT-2D)</title> <meta name="description" content="Search results for: 2D discrete wavelet transform (DWT-2D)"> <meta name="keywords" content="2D discrete wavelet transform (DWT-2D)"> <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 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method="get" 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="2D discrete wavelet transform (DWT-2D)"> <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> 2185</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: 2D discrete wavelet transform (DWT-2D)</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2185</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">2184</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">2183</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">443</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">2182</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">2181</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">2180</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">2179</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">2178</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">2177</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">2176</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">2175</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">2174</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">2173</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">2172</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">2171</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">2170</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">2169</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">2168</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">347</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">2167</span> Applying Wavelet Transform to Ferroresonance Detection and Protection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chun-Wei%20Huang">Chun-Wei Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jyh-Cherng%20Gu"> Jyh-Cherng Gu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ming-Ta%20Yang"> Ming-Ta Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Non-synchronous breakage or line failure in power systems with light or no loads can lead to core saturation in transformers or potential transformers. This can cause component and capacitance matching resulting in the formation of resonant circuits, which trigger ferroresonance. This study employed a wavelet transform for the detection of ferroresonance. Simulation results demonstrate the efficacy of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ferroresonance" title="ferroresonance">ferroresonance</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title=" wavelet transform"> wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20electronic%20device" title=" intelligent electronic device"> intelligent electronic device</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a> </p> <a href="https://publications.waset.org/abstracts/12919/applying-wavelet-transform-to-ferroresonance-detection-and-protection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12919.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">496</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2166</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">222</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">2165</span> Fault Detection of Pipeline in Water Distribution Network System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shin%20Je%20Lee">Shin Je Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Go%20Bong%20Choi"> Go Bong Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeong%20Cheol%20Seo"> Jeong Cheol Seo</a>, <a href="https://publications.waset.org/abstracts/search?q=Jong%20Min%20Lee"> Jong Min Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Gibaek%20Lee"> Gibaek Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Water pipe network is installed underground and once equipped; it is difficult to recognize the state of pipes when the leak or burst happens. Accordingly, post management is often delayed after the fault occurs. Therefore, the systematic fault management system of water pipe network is required to prevent the accident and minimize the loss. In this work, we develop online fault detection system of water pipe network using data of pipes such as flow rate or pressure. The transient model describing water flow in pipelines is presented and simulated using Matlab. The fault situations such as the leak or burst can be also simulated and flow rate or pressure data when the fault happens are collected. Faults are detected using statistical methods of fast Fourier transform and discrete wavelet transform, and they are compared to find which method shows the better fault detection performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fault%20detection" title="fault detection">fault detection</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20pipeline%20model" title=" water pipeline model"> water pipeline model</a>, <a href="https://publications.waset.org/abstracts/search?q=fast%20Fourier%20transform" title=" fast Fourier transform"> fast Fourier transform</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform" title=" discrete wavelet transform"> discrete wavelet transform</a> </p> <a href="https://publications.waset.org/abstracts/5007/fault-detection-of-pipeline-in-water-distribution-network-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5007.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">2164</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">2163</span> Application of the Bionic Wavelet Transform and Psycho-Acoustic Model for Speech Compression </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chafik%20Barnoussi">Chafik Barnoussi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mourad%20Talbi"> Mourad Talbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Adnane%20Cherif"> Adnane Cherif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we propose a new speech compression system based on the application of the Bionic Wavelet Transform (BWT) combined with the psychoacoustic model. This compression system is a modified version of the compression system using a MDCT (Modified Discrete Cosine Transform) filter banks of 32 filters each and the psychoacoustic model. This modification consists in replacing the banks of the MDCT filter banks by the bionic wavelet coefficients which are obtained from the application of the BWT to the speech signal to be compressed. These two methods are evaluated and compared with each other by computing bits before and bits after compression. They are tested on different speech signals and the obtained simulation results show that the proposed technique outperforms the second technique and this in term of compressed file size. In term of SNR, PSNR and NRMSE, the outputs speech signals of the proposed compression system are with acceptable quality. In term of PESQ and speech signal intelligibility, the proposed speech compression technique permits to obtain reconstructed speech signals with good quality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=speech%20compression" title="speech compression">speech compression</a>, <a href="https://publications.waset.org/abstracts/search?q=bionic%20wavelet%20transform" title=" bionic wavelet transform"> bionic wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=filterbanks" title=" filterbanks"> filterbanks</a>, <a href="https://publications.waset.org/abstracts/search?q=psychoacoustic%20model" title=" psychoacoustic model"> psychoacoustic model</a> </p> <a href="https://publications.waset.org/abstracts/1921/application-of-the-bionic-wavelet-transform-and-psycho-acoustic-model-for-speech-compression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1921.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">384</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">2162</span> Image Compression on Region of Interest Based on SPIHT Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sudeepti%20Dayal">Sudeepti Dayal</a>, <a href="https://publications.waset.org/abstracts/search?q=Neelesh%20Gupta"> Neelesh Gupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image abbreviation is utilized for reducing the size of a file without demeaning the quality of the image to an objectionable level. The depletion in file size permits more images to be deposited in a given number of spaces. It also minimizes the time necessary for images to be transferred. Storage of medical images is a most researched area in the current scenario. To store a medical image, there are two parameters on which the image is divided, regions of interest and non-regions of interest. The best way to store an image is to compress it in such a way that no important information is lost. Compression can be done in two ways, namely lossy, and lossless compression. Under that, several compression algorithms are applied. In the paper, two algorithms are used which are, discrete cosine transform, applied to non-region of interest (lossy), and discrete wavelet transform, applied to regions of interest (lossless). The paper introduces SPIHT (set partitioning hierarchical tree) algorithm which is applied onto the wavelet transform to obtain good compression ratio from which an image can be stored efficiently. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Compression%20ratio" title="Compression ratio">Compression ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=DWT" title=" DWT"> DWT</a>, <a href="https://publications.waset.org/abstracts/search?q=SPIHT" title=" SPIHT"> SPIHT</a>, <a href="https://publications.waset.org/abstracts/search?q=DCT" title=" DCT"> DCT</a> </p> <a href="https://publications.waset.org/abstracts/43377/image-compression-on-region-of-interest-based-on-spiht-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43377.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">2161</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">2160</span> Hybrid Wavelet-Adaptive Neuro-Fuzzy Inference System Model for a Greenhouse Energy Demand Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Azzedine%20Hamza">Azzedine Hamza</a>, <a href="https://publications.waset.org/abstracts/search?q=Chouaib%20Chakour"> Chouaib Chakour</a>, <a href="https://publications.waset.org/abstracts/search?q=Messaoud%20Ramdani"> Messaoud Ramdani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Energy demand prediction plays a crucial role in achieving next-generation power systems for agricultural greenhouses. As a result, high prediction quality is required for efficient smart grid management and therefore low-cost energy consumption. The aim of this paper is to investigate the effectiveness of a hybrid data-driven model in day-ahead energy demand prediction. The proposed model consists of Discrete Wavelet Transform (DWT), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The DWT is employed to decompose the original signal in a set of subseries and then an ANFIS is used to generate the forecast for each subseries. The proposed hybrid method (DWT-ANFIS) was evaluated using a greenhouse energy demand data for a week and compared with ANFIS. The performances of the different models were evaluated by comparing the corresponding values of Mean Absolute Percentage Error (MAPE). It was demonstrated that discret wavelet transform can improve agricultural greenhouse energy demand modeling. <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=ANFIS" title=" ANFIS"> ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20consumption%20prediction" title=" energy consumption prediction"> energy consumption prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=greenhouse" title=" greenhouse"> greenhouse</a> </p> <a href="https://publications.waset.org/abstracts/163632/hybrid-wavelet-adaptive-neuro-fuzzy-inference-system-model-for-a-greenhouse-energy-demand-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163632.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">88</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">2159</span> Early Recognition and Grading of Cataract Using a Combined Log Gabor/Discrete Wavelet Transform with ANN and SVM</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hadeer%20R.%20M.%20Tawfik">Hadeer R. M. Tawfik</a>, <a href="https://publications.waset.org/abstracts/search?q=Rania%20A.%20K.%20Birry"> Rania A. K. Birry</a>, <a href="https://publications.waset.org/abstracts/search?q=Amani%20A.%20Saad"> Amani A. Saad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Eyes are considered to be the most sensitive and important organ for human being. Thus, any eye disorder will affect the patient in all aspects of life. Cataract is one of those eye disorders that lead to blindness if not treated correctly and quickly. This paper demonstrates a model for automatic detection, classification, and grading of cataracts based on image processing techniques and artificial intelligence. The proposed system is developed to ease the cataract diagnosis process for both ophthalmologists and patients. The wavelet transform combined with 2D Log Gabor Wavelet transform was used as feature extraction techniques for a dataset of 120 eye images followed by a classification process that classified the image set into three classes; normal, early, and advanced stage. A comparison between the two used classifiers, the support vector machine SVM and the artificial neural network ANN were done for the same dataset of 120 eye images. It was concluded that SVM gave better results than ANN. SVM success rate result was 96.8% accuracy where ANN success rate result was 92.3% accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cataract" title="cataract">cataract</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=grading" title=" grading"> grading</a>, <a href="https://publications.waset.org/abstracts/search?q=log-gabor" title=" log-gabor"> log-gabor</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines" title=" support vector machines"> support vector machines</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet" title=" wavelet"> wavelet</a> </p> <a href="https://publications.waset.org/abstracts/101464/early-recognition-and-grading-of-cataract-using-a-combined-log-gabordiscrete-wavelet-transform-with-ann-and-svm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101464.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">332</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2158</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">2157</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">2156</span> ANFIS Approach for Locating Faults in Underground Cables</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Magdy%20B.%20Eteiba">Magdy B. Eteiba</a>, <a href="https://publications.waset.org/abstracts/search?q=Wael%20Ismael%20Wahba"> Wael Ismael Wahba</a>, <a href="https://publications.waset.org/abstracts/search?q=Shimaa%20Barakat"> Shimaa Barakat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a fault identification, classification and fault location estimation method based on Discrete Wavelet Transform and Adaptive Network Fuzzy Inference System (ANFIS) for medium voltage cable in the distribution system. Different faults and locations are simulated by ATP/EMTP, and then certain selected features of the wavelet transformed signals are used as an input for a training process on the ANFIS. Then an accurate fault classifier and locator algorithm was designed, trained and tested using current samples only. The results obtained from ANFIS output were compared with the real output. From the results, it was found that the percentage error between ANFIS output and real output is less than three percent. Hence, it can be concluded that the proposed technique is able to offer high accuracy in both of the fault classification and fault location. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title="ANFIS">ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20location" title=" fault location"> fault location</a>, <a href="https://publications.waset.org/abstracts/search?q=underground%20cable" title=" underground cable"> underground cable</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/11080/anfis-approach-for-locating-faults-in-underground-cables" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11080.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right 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