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transform</title> <meta name="description" content="Search results for: contourlet transform"> <meta name="keywords" content="contourlet transform"> <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="contourlet transform"> <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> 1527</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: contourlet transform</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1527</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">1526</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">1525</span> Multiscale Edge Detection Based on Nonsubsampled Contourlet Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Enqing%20Chen">Enqing Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianbo%20Wang"> Jianbo Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is well known that the wavelet transform provides a very effective framework for multiscale edges analysis. However, wavelets are not very effective in representing images containing distributed discontinuities such as edges. In this paper, we propose a novel multiscale edge detection method in nonsubsampled contourlet transform (NSCT) domain, which is based on the dominant multiscale, multidirection edge expression and outstanding edge location of NSCT. Through real images experiments, simulation results demonstrate that the proposed method is better than other edge detection methods based on Canny operator, wavelet and contourlet. Additionally, the proposed method also works well for noisy images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=edge%20detection" title="edge detection">edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=NSCT" title=" NSCT"> NSCT</a>, <a href="https://publications.waset.org/abstracts/search?q=shift%20invariant" title=" shift invariant"> shift invariant</a>, <a href="https://publications.waset.org/abstracts/search?q=modulus%20maxima" title=" modulus maxima"> modulus maxima</a> </p> <a href="https://publications.waset.org/abstracts/9528/multiscale-edge-detection-based-on-nonsubsampled-contourlet-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9528.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">488</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1524</span> Contourlet Transform and Local Binary Pattern Based Feature Extraction for Bleeding Detection in Endoscopic Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mekha%20Mathew">Mekha Mathew</a>, <a href="https://publications.waset.org/abstracts/search?q=Varun%20P%20Gopi"> Varun P Gopi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wireless Capsule Endoscopy (WCE) has become a great device in Gastrointestinal (GI) tract diagnosis, which can examine the entire GI tract, especially the small intestine without invasiveness and sedation. Bleeding in the digestive tract is a symptom of a disease rather than a disease itself. Hence the detection of bleeding is important in diagnosing many diseases. In this paper we proposes a novel method for distinguishing bleeding regions from normal regions based on Contourlet transform and Local Binary Pattern (LBP). Experiments show that this method provides a high accuracy rate of 96.38% in CIE XYZ colour space for k-Nearest Neighbour (k-NN) classifier. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wireless%20Capsule%20Endoscopy" title="Wireless Capsule Endoscopy">Wireless Capsule Endoscopy</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20binary%20pattern" title=" local binary pattern"> local binary pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=k-NN%20classifier" title=" k-NN classifier"> k-NN classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=contourlet%20transform" title=" contourlet transform"> contourlet transform</a> </p> <a href="https://publications.waset.org/abstracts/17314/contourlet-transform-and-local-binary-pattern-based-feature-extraction-for-bleeding-detection-in-endoscopic-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17314.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">485</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">1523</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">1522</span> Feature Extraction Based on Contourlet Transform and Log Gabor Filter for Detection of Ulcers in Wireless Capsule Endoscopy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nimisha%20Elsa%20Koshy">Nimisha Elsa Koshy</a>, <a href="https://publications.waset.org/abstracts/search?q=Varun%20P.%20Gopi"> Varun P. Gopi</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20I.%20Thajudin%20Ahamed"> V. I. Thajudin Ahamed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The entire visualization of GastroIntestinal (GI) tract is not possible with conventional endoscopic exams. Wireless Capsule Endoscopy (WCE) is a low risk, painless, noninvasive procedure for diagnosing diseases such as bleeding, polyps, ulcers, and Crohns disease within the human digestive tract, especially the small intestine that was unreachable using the traditional endoscopic methods. However, analysis of massive images of WCE detection is tedious and time consuming to physicians. Hence, researchers have developed software methods to detect these diseases automatically. Thus, the effectiveness of WCE can be improved. In this paper, a novel textural feature extraction method is proposed based on Contourlet transform and Log Gabor filter to distinguish ulcer regions from normal regions. The results show that the proposed method performs well with a high accuracy rate of 94.16% using Support Vector Machine (SVM) classifier in HSV colour space. <p class="card-text"><strong>Keywords:</strong> <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=log%20gabor%20filter" title=" log gabor filter"> log gabor filter</a>, <a href="https://publications.waset.org/abstracts/search?q=ulcer" title=" ulcer"> ulcer</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20capsule%20endoscopy" title=" wireless capsule endoscopy"> wireless capsule endoscopy</a> </p> <a href="https://publications.waset.org/abstracts/17330/feature-extraction-based-on-contourlet-transform-and-log-gabor-filter-for-detection-of-ulcers-in-wireless-capsule-endoscopy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17330.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">540</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">1521</span> Sampling Two-Channel Nonseparable Wavelets and Its Applications in Multispectral Image Fusion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bin%20Liu">Bin Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Weijie%20Liu"> Weijie Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Bin%20Sun"> Bin Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Yihui%20Luo"> Yihui Luo </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to solve the problem of lower spatial resolution and block effect in the fusion method based on separable wavelet transform in the resulting fusion image, a new sampling mode based on multi-resolution analysis of two-channel non separable wavelet transform, whose dilation matrix is [1,1;1,-1], is presented and a multispectral image fusion method based on this kind of sampling mode is proposed. Filter banks related to this kind of wavelet are constructed, and multiresolution decomposition of the intensity of the MS and panchromatic image are performed in the sampled mode using the constructed filter bank. The low- and high-frequency coefficients are fused by different fusion rules. The experiment results show that this method has good visual effect. The fusion performance has been noted to outperform the IHS fusion method, as well as, the fusion methods based on DWT, IHS-DWT, IHS-Contourlet transform, and IHS-Curvelet transform in preserving both spectral quality and high spatial resolution information. Furthermore, when compared with the fusion method based on nonsubsampled two-channel non separable wavelet, the proposed method has been observed to have higher spatial resolution and good global spectral information. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20fusion" title="image fusion">image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=two-channel%20sampled%20nonseparable%20wavelets" title=" two-channel sampled nonseparable wavelets"> two-channel sampled nonseparable wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=multispectral%20image" title=" multispectral image"> multispectral image</a>, <a href="https://publications.waset.org/abstracts/search?q=panchromatic%20image" title=" panchromatic image"> panchromatic image</a> </p> <a href="https://publications.waset.org/abstracts/15357/sampling-two-channel-nonseparable-wavelets-and-its-applications-in-multispectral-image-fusion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15357.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">440</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1520</span> CT Medical Images Denoising Based on New Wavelet Thresholding Compared with Curvelet and Contourlet</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amir%20Moslemi">Amir Moslemi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amir%20movafeghi"> Amir movafeghi</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahab%20Moradi"> Shahab Moradi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the most important challenging factors in medical images is nominated as noise.Image denoising refers to the improvement of a digital medical image that has been infected by Additive White Gaussian Noise (AWGN). The digital medical image or video can be affected by different types of noises. They are impulse noise, Poisson noise and AWGN. Computed tomography (CT) images are subjected to low quality due to the noise. The quality of CT images is dependent on the absorbed dose to patients directly in such a way that increase in absorbed radiation, consequently absorbed dose to patients (ADP), enhances the CT images quality. In this manner, noise reduction techniques on the purpose of images quality enhancement exposing no excess radiation to patients is one the challenging problems for CT images processing. In this work, noise reduction in CT images was performed using two different directional 2 dimensional (2D) transformations; i.e., Curvelet and Contourlet and Discrete wavelet transform(DWT) thresholding methods of BayesShrink and AdaptShrink, compared to each other and we proposed a new threshold in wavelet domain for not only noise reduction but also edge retaining, consequently the proposed method retains the modified coefficients significantly that result in good visual quality. Data evaluations were accomplished by using two criterions; namely, peak signal to noise ratio (PSNR) and Structure similarity (Ssim). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computed%20tomography%20%28CT%29" title="computed tomography (CT)">computed tomography (CT)</a>, <a href="https://publications.waset.org/abstracts/search?q=noise%20reduction" title=" noise reduction"> noise reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=curve-let" title=" curve-let"> curve-let</a>, <a href="https://publications.waset.org/abstracts/search?q=contour-let" title=" contour-let"> contour-let</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20to%20noise%20peak-peak%20ratio%20%28PSNR%29" title=" signal to noise peak-peak ratio (PSNR)"> signal to noise peak-peak ratio (PSNR)</a>, <a href="https://publications.waset.org/abstracts/search?q=structure%20similarity%20%28Ssim%29" title=" structure similarity (Ssim)"> structure similarity (Ssim)</a>, <a href="https://publications.waset.org/abstracts/search?q=absorbed%20dose%20to%20patient%20%28ADP%29" title=" absorbed dose to patient (ADP)"> absorbed dose to patient (ADP)</a> </p> <a href="https://publications.waset.org/abstracts/37368/ct-medical-images-denoising-based-on-new-wavelet-thresholding-compared-with-curvelet-and-contourlet" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37368.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">440</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1519</span> Donoho-Stark鈥檚 and Hardy鈥檚 Uncertainty Principles for the Short-Time Quaternion Offset Linear Canonical Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Younus%20Bhat">Mohammad Younus Bhat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The quaternion offset linear canonical transform (QOLCT), which isa time-shifted and frequency-modulated version of the quaternion linear canonical transform (QLCT), provides a more general framework of most existing signal processing tools. For the generalized QOLCT, the classical Heisenberg鈥檚 and Lieb鈥檚 uncertainty principles have been studied recently. In this paper, we first define the short-time quaternion offset linear canonical transform (ST-QOLCT) and drive its relationship with the quaternion Fourier transform (QFT). The crux of the paper lies in the generalization of several well-known uncertainty principles for the ST-QOLCT, including Donoho-Stark鈥檚 uncertainty principle, Hardy鈥檚 uncertainty principle, Beurling鈥檚 uncertainty principle, and the logarithmic uncertainty principle. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Quaternion%20Fourier%20transform" title="Quaternion Fourier transform">Quaternion Fourier transform</a>, <a href="https://publications.waset.org/abstracts/search?q=Quaternion%20offset%20linear%20canonical%20transform" title=" Quaternion offset linear canonical transform"> Quaternion offset linear canonical transform</a>, <a href="https://publications.waset.org/abstracts/search?q=short-time%20quaternion%20offset%20linear%20canonical%20transform" title=" short-time quaternion offset linear canonical transform"> short-time quaternion offset linear canonical transform</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20principle" title=" uncertainty principle"> uncertainty principle</a> </p> <a href="https://publications.waset.org/abstracts/142375/donoho-starks-and-hardys-uncertainty-principles-for-the-short-time-quaternion-offset-linear-canonical-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142375.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">211</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">1518</span> The Optical OFDM Equalization Based on the Fractional Fourier Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Cherifi">A. Cherifi</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20S.%20Bouazza"> B. S. Bouazza</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20O.%20Dahman"> A. O. Dahman</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Yagoubi"> B. Yagoubi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Transmission over Optical channels will introduce inter-symbol interference (ISI) as well as inter-channel (or inter-carrier) interference (ICI). To decrease the effects of ICI, this paper proposes equalizer for the Optical OFDM system based on the fractional Fourier transform (FrFFT). In this FrFT-OFDM system, traditional Fourier transform is replaced by fractional Fourier transform to modulate and demodulate the data symbols. The equalizer proposed consists of sampling the received signal in the different time per time symbol. Theoretical analysis and numerical simulation are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=OFDM" title="OFDM">OFDM</a>, <a href="https://publications.waset.org/abstracts/search?q=fractional%20fourier%20transform" title=" fractional fourier transform"> fractional fourier transform</a>, <a href="https://publications.waset.org/abstracts/search?q=internet%20and%20information%20technology" title=" internet and information technology"> internet and information technology</a> </p> <a href="https://publications.waset.org/abstracts/27211/the-optical-ofdm-equalization-based-on-the-fractional-fourier-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27211.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">406</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">1517</span> Equalization Algorithm for the Optical OFDM System Based on the Fractional Fourier Transform </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Cherifi">A. Cherifi</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Bouazza"> B. Bouazza</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20O.%20Dahmane"> A. O. Dahmane</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Yagoubi"> B. Yagoubi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Transmission over Optical channels will introduce inter-symbol interference (ISI) as well as inter-channel (or inter-carrier) interference (ICI). To decrease the effects of ICI, this paper proposes equalizer for the Optical OFDM system based on the fractional Fourier transform (FrFFT). In this FrFT-OFDM system, traditional Fourier transform is replaced by fractional Fourier transform to modulate and demodulate the data symbols. The equalizer proposed consists of sampling the received signal in the different time per time symbol. Theoretical analysis and numerical simulation are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=OFDM" title="OFDM">OFDM</a>, <a href="https://publications.waset.org/abstracts/search?q=%28FrFT%29%20fractional%20fourier%20transform" title=" (FrFT) fractional fourier transform"> (FrFT) fractional fourier transform</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20OFDM" title=" optical OFDM"> optical OFDM</a>, <a href="https://publications.waset.org/abstracts/search?q=equalization%20algorithm" title=" equalization algorithm"> equalization algorithm</a> </p> <a href="https://publications.waset.org/abstracts/23848/equalization-algorithm-for-the-optical-ofdm-system-based-on-the-fractional-fourier-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23848.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">430</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">1516</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">1515</span> Hit-Or-Miss Transform as a Tool for Similar Shape Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Osama%20Mohamed%20Elrajubi">Osama Mohamed Elrajubi</a>, <a href="https://publications.waset.org/abstracts/search?q=Idris%20El-Feghi"> Idris El-Feghi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Abu%20Baker%20Saghayer"> Mohamed Abu Baker Saghayer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes an identification of specific shapes within binary images using the morphological Hit-or-Miss Transform (HMT). Hit-or-Miss transform is a general binary morphological operation that can be used in searching of particular patterns of foreground and background pixels in an image. It is actually a basic operation of binary morphology since almost all other binary morphological operators are derived from it. The input of this method is a binary image and a structuring element (a template which will be searched in a binary image) while the output is another binary image. In this paper a modification of Hit-or-Miss transform has been proposed. The accuracy of algorithm is adjusted according to the similarity of the template and the sought template. The implementation of this method has been done by C language. The algorithm has been tested on several images and the results have shown that this new method can be used for similar shape detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hit-or-miss%20operator%20transform" title="hit-or-miss operator transform">hit-or-miss operator transform</a>, <a href="https://publications.waset.org/abstracts/search?q=HMT" title=" HMT"> HMT</a>, <a href="https://publications.waset.org/abstracts/search?q=binary%20morphological%20operation" title=" binary morphological operation"> binary morphological operation</a>, <a href="https://publications.waset.org/abstracts/search?q=shape%20detection" title=" shape detection"> shape detection</a>, <a href="https://publications.waset.org/abstracts/search?q=binary%20images%20processing" title=" binary images processing"> binary images processing</a> </p> <a href="https://publications.waset.org/abstracts/11881/hit-or-miss-transform-as-a-tool-for-similar-shape-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11881.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">331</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">1514</span> Content Based Face Sketch Images Retrieval in WHT, DCT, and DWT Transform Domain</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=W.%20S.%20Besbas">W. S. Besbas</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20Artemi"> M. A. Artemi</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20M.%20Salman"> R. M. Salman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Content based face sketch retrieval can be used to find images of criminals from their sketches for 'Crime Prevention'. This paper investigates the problem of CBIR of face sketch images in transform domain. Face sketch images that are similar to the query image are retrieved from the face sketch database. Features of the face sketch image are extracted in the spectrum domain of a selected transforms. These transforms are Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Walsh Hadamard Transform (WHT). For the performance analyses of features selection methods three face images databases are used. These are 'Sheffield face database', 'Olivetti Research Laboratory (ORL) face database', and 'Indian face database'. The City block distance measure is used to evaluate the performance of the retrieval process. The investigation concludes that, the retrieval rate is database dependent. But in general, the DCT is the best. On the other hand, the WHT is the best with respect to the speed of retrieving images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Content%20Based%20Image%20Retrieval%20%28CBIR%29" title="Content Based Image Retrieval (CBIR)">Content Based Image Retrieval (CBIR)</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20sketch%20image%20retrieval" title=" face sketch image retrieval"> face sketch image retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=features%20selection%20for%20CBIR" title=" features selection for CBIR"> features selection for CBIR</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20retrieval%20in%20transform%20domain" title=" image retrieval in transform domain"> image retrieval in transform domain</a> </p> <a href="https://publications.waset.org/abstracts/8251/content-based-face-sketch-images-retrieval-in-wht-dct-and-dwt-transform-domain" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8251.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">493</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">1513</span> Reduced Differential Transform Methods for Solving the Fractional Diffusion Equations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yildiray%20Keskin">Yildiray Keskin</a>, <a href="https://publications.waset.org/abstracts/search?q=Omer%20Acan"> Omer Acan</a>, <a href="https://publications.waset.org/abstracts/search?q=Murat%20Akkus"> Murat Akkus</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the solution of fractional diffusion equations is presented by means of the reduced differential transform method. Fractional partial differential equations have special importance in engineering and sciences. Application of reduced differential transform method to this problem shows the rapid convergence of the sequence constructed by this method to the exact solution. The numerical results show that the approach is easy to implement and accurate when applied to fractional diffusion equations. The method introduces a promising tool for solving many fractional partial differential equations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractional%20diffusion%20equations" title="fractional diffusion equations">fractional diffusion equations</a>, <a href="https://publications.waset.org/abstracts/search?q=Caputo%20fractional%20derivative" title=" Caputo fractional derivative"> Caputo fractional derivative</a>, <a href="https://publications.waset.org/abstracts/search?q=reduced%20differential%20transform%20method" title=" reduced differential transform method"> reduced differential transform method</a>, <a href="https://publications.waset.org/abstracts/search?q=partial" title=" partial"> partial</a> </p> <a href="https://publications.waset.org/abstracts/17526/reduced-differential-transform-methods-for-solving-the-fractional-diffusion-equations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17526.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">525</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">1512</span> An Image Enhancement Method Based on Curvelet Transform for CBCT-Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shahriar%20Farzam">Shahriar Farzam</a>, <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Rastgarpour"> Maryam Rastgarpour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image denoising plays extremely important role in digital image processing. Enhancement of clinical image research based on Curvelet has been developed rapidly in recent years. In this paper, we present a method for image contrast enhancement for cone beam CT (CBCT) images based on fast discrete curvelet transforms (FDCT) that work through Unequally Spaced Fast Fourier Transform (USFFT). These transforms return a table of Curvelet transform coefficients indexed by a scale parameter, an orientation and a spatial location. Accordingly, the coefficients obtained from FDCT-USFFT can be modified in order to enhance contrast in an image. Our proposed method first uses a two-dimensional mathematical transform, namely the FDCT through unequal-space fast Fourier transform on input image and then applies thresholding on coefficients of Curvelet to enhance the CBCT images. Consequently, applying unequal-space fast Fourier Transform leads to an accurate reconstruction of the image with high resolution. The experimental results indicate the performance of the proposed method is superior to the existing ones in terms of Peak Signal to Noise Ratio (PSNR) and Effective Measure of Enhancement (EME). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=curvelet%20transform" title="curvelet transform">curvelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=CBCT" title=" CBCT"> CBCT</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20enhancement" title=" image enhancement"> image enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20denoising" title=" image denoising"> image denoising</a> </p> <a href="https://publications.waset.org/abstracts/69244/an-image-enhancement-method-based-on-curvelet-transform-for-cbct-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69244.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">300</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">1511</span> Application of the Discrete Rationalized Haar Transform to Distributed Parameter System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Joon-Hoon%20Park">Joon-Hoon Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper the rationalized Haar transform is applied for distributed parameter system identification and estimation. A distributed parameter system is a dynamical and mathematical model described by a partial differential equation. And system identification concerns the problem of determining mathematical models from observed data. The Haar function has some disadvantages of calculation because it contains irrational numbers, for these reasons the rationalized Haar function that has only rational numbers. The algorithm adopted in this paper is based on the transform and operational matrix of the rationalized Haar function. This approach provides more convenient and efficient computational results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20parameter%20system" title="distributed parameter system">distributed parameter system</a>, <a href="https://publications.waset.org/abstracts/search?q=rationalized%20Haar%20transform" title=" rationalized Haar transform"> rationalized Haar transform</a>, <a href="https://publications.waset.org/abstracts/search?q=operational%20matrix" title=" operational matrix"> operational matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20identification" title=" system identification "> system identification </a> </p> <a href="https://publications.waset.org/abstracts/24246/application-of-the-discrete-rationalized-haar-transform-to-distributed-parameter-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24246.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">509</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">1510</span> Meteosat Second Generation Image Compression Based on the Radon Transform and Linear Predictive Coding: Comparison and Performance </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cherifi%20Mehdi">Cherifi Mehdi</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> Image compression is used to reduce the number of bits required to represent an image. The Meteosat Second Generation satellite (MSG) allows the acquisition of 12 image files every 15 minutes. Which results a large databases sizes. The transform selected in the images compression should contribute to reduce the data representing the images. The Radon transform retrieves the Radon points that represent the sum of the pixels in a given angle for each direction. Linear predictive coding (LPC) with filtering provides a good decorrelation of Radon points using a Predictor constitute by the Symmetric Nearest Neighbor filter (SNN) coefficients, which result losses during decompression. Finally, Run Length Coding (RLC) gives us a high and fixed compression ratio regardless of the input image. In this paper, a novel image compression method based on the Radon transform and linear predictive coding (LPC) for MSG images is proposed. MSG image compression based on the Radon transform and the LPC provides a good compromise between compression and quality of reconstruction. A comparison of our method with other whose two based on DCT and one on DWT bi-orthogonal filtering is evaluated to show the power of the Radon transform in its resistibility against the quantization noise and to evaluate the performance of our method. Evaluation criteria like PSNR and the compression ratio allows showing the efficiency of our method of compression. <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=radon%20transform" title=" radon transform"> radon transform</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20predictive%20coding%20%28LPC%29" title=" linear predictive coding (LPC)"> linear predictive coding (LPC)</a>, <a href="https://publications.waset.org/abstracts/search?q=run%20lengthcoding%20%28RLC%29" title=" run lengthcoding (RLC)"> run lengthcoding (RLC)</a>, <a href="https://publications.waset.org/abstracts/search?q=meteosat%20second%20generation%20%28MSG%29" title=" meteosat second generation (MSG)"> meteosat second generation (MSG)</a> </p> <a href="https://publications.waset.org/abstracts/16434/meteosat-second-generation-image-compression-based-on-the-radon-transform-and-linear-predictive-coding-comparison-and-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16434.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">421</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1509</span> A Two-Dimensional Problem Micropolar Thermoelastic Medium under the Effect of Laser Irradiation and Distributed Sources</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Devinder%20Singh">Devinder Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajneesh%20Kumar"> Rajneesh Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Arvind%20Kumar"> Arvind Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present investigation deals with the deformation of micropolar generalized thermoelastic solid subjected to thermo-mechanical loading due to a thermal laser pulse. Laplace transform and Fourier transform techniques are used to solve the problem. Thermo-mechanical laser interactions are taken as distributed sources to describe the application of the approach. The closed form expressions of normal stress, tangential stress, coupled stress and temperature are obtained in the domain. Numerical inversion technique of Laplace transform and Fourier transform has been implied to obtain the resulting quantities in the physical domain after developing a computer program. The normal stress, tangential stress, coupled stress and temperature are depicted graphically to show the effect of relaxation times. Some particular cases of interest are deduced from the present investigation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pulse%20laser" title="pulse laser">pulse laser</a>, <a href="https://publications.waset.org/abstracts/search?q=integral%20transform" title=" integral transform"> integral transform</a>, <a href="https://publications.waset.org/abstracts/search?q=thermoelastic" title=" thermoelastic"> thermoelastic</a>, <a href="https://publications.waset.org/abstracts/search?q=boundary%20value%20problem" title=" boundary value problem"> boundary value problem</a> </p> <a href="https://publications.waset.org/abstracts/33535/a-two-dimensional-problem-micropolar-thermoelastic-medium-under-the-effect-of-laser-irradiation-and-distributed-sources" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33535.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">615</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">1508</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">1507</span> Differential Transform Method: Some Important Examples</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Jamil%20Amir">M. Jamil Amir</a>, <a href="https://publications.waset.org/abstracts/search?q=Rabia%20Iqbal"> Rabia Iqbal</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Yaseen"> M. Yaseen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we solve some differential equations analytically by using differential transform method. For this purpose, we consider four models of Laplace equation with two Dirichlet and two Neumann boundary conditions and K(2,2) equation and obtain the corresponding exact solutions. The obtained results show the simplicity of the method and massive reduction in calculations when one compares it with other iterative methods, available in literature. It is worth mentioning that here only a few number of iterations are required to reach the closed form solutions as series expansions of some known functions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=differential%20transform%20method" title="differential transform method">differential transform method</a>, <a href="https://publications.waset.org/abstracts/search?q=laplace%20equation" title=" laplace equation"> laplace equation</a>, <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20boundary%20conditions" title=" Dirichlet boundary conditions"> Dirichlet boundary conditions</a>, <a href="https://publications.waset.org/abstracts/search?q=Neumann%20boundary%20conditions" title=" Neumann boundary conditions"> Neumann boundary conditions</a> </p> <a href="https://publications.waset.org/abstracts/18605/differential-transform-method-some-important-examples" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18605.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">537</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">1506</span> 2.5D Face Recognition Using Gabor Discrete Cosine Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Cheraghian">Ali Cheraghian</a>, <a href="https://publications.waset.org/abstracts/search?q=Farshid%20Hajati"> Farshid Hajati</a>, <a href="https://publications.waset.org/abstracts/search?q=Soheila%20Gheisari"> Soheila Gheisari</a>, <a href="https://publications.waset.org/abstracts/search?q=Yongsheng%20Gao"> Yongsheng Gao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a novel 2.5D face recognition method based on Gabor Discrete Cosine Transform (GDCT). In the proposed method, the Gabor filter is applied to extract feature vectors from the texture and the depth information. Then, Discrete Cosine Transform (DCT) is used for dimensionality and redundancy reduction to improve computational efficiency. The system is combined texture and depth information in the decision level, which presents higher performance compared to methods, which use texture and depth information, separately. The proposed algorithm is examined on publically available Bosphorus database including models with pose variation. The experimental results show that the proposed method has a higher performance compared to the benchmark. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gabor%20filter" title="Gabor filter">Gabor filter</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=2.5d%20face%20recognition" title=" 2.5d face recognition"> 2.5d face recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=pose" title=" pose"> pose</a> </p> <a href="https://publications.waset.org/abstracts/37341/25d-face-recognition-using-gabor-discrete-cosine-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37341.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">328</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">1505</span> Development of a Few-View Computed Tomographic Reconstruction Algorithm Using Multi-Directional Total Variation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chia%20Jui%20Hsieh">Chia Jui Hsieh</a>, <a href="https://publications.waset.org/abstracts/search?q=Jyh%20Cheng%20Chen"> Jyh Cheng Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Chih%20Wei%20Kuo"> Chih Wei Kuo</a>, <a href="https://publications.waset.org/abstracts/search?q=Ruei%20Teng%20Wang"> Ruei Teng Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Woei%20Chyn%20Chu"> Woei Chyn Chu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Compressed sensing (CS) based computed tomographic (CT) reconstruction algorithm utilizes total variation (TV) to transform CT image into sparse domain and minimizes L1-norm of sparse image for reconstruction. Different from the traditional CS based reconstruction which only calculates x-coordinate and y-coordinate TV to transform CT images into sparse domain, we propose a multi-directional TV to transform tomographic image into sparse domain for low-dose reconstruction. Our method considers all possible directions of TV calculations around a pixel, so the sparse transform for CS based reconstruction is more accurate. In 2D CT reconstruction, we use eight-directional TV to transform CT image into sparse domain. Furthermore, we also use 26-directional TV for 3D reconstruction. This multi-directional sparse transform method makes CS based reconstruction algorithm more powerful to reduce noise and increase image quality. To validate and evaluate the performance of this multi-directional sparse transform method, we use both Shepp-Logan phantom and a head phantom as the targets for reconstruction with the corresponding simulated sparse projection data (angular sampling interval is 5 deg and 6 deg, respectively). From the results, the multi-directional TV method can reconstruct images with relatively less artifacts compared with traditional CS based reconstruction algorithm which only calculates x-coordinate and y-coordinate TV. We also choose RMSE, PSNR, UQI to be the parameters for quantitative analysis. From the results of quantitative analysis, no matter which parameter is calculated, the multi-directional TV method, which we proposed, is better. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing%20%28CS%29" title="compressed sensing (CS)">compressed sensing (CS)</a>, <a href="https://publications.waset.org/abstracts/search?q=low-dose%20CT%20reconstruction" title=" low-dose CT reconstruction"> low-dose CT reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=total%20variation%20%28TV%29" title=" total variation (TV)"> total variation (TV)</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-directional%20gradient%20operator" title=" multi-directional gradient operator"> multi-directional gradient operator</a> </p> <a href="https://publications.waset.org/abstracts/77716/development-of-a-few-view-computed-tomographic-reconstruction-algorithm-using-multi-directional-total-variation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77716.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">256</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">1504</span> A Fast Version of the Generalized Multi-Directional Radon Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ines%20Elouedi">Ines Elouedi</a>, <a href="https://publications.waset.org/abstracts/search?q=Atef%20Hammouda"> Atef Hammouda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a new fast version of the generalized Multi-Directional Radon Transform method. The new method uses the inverse Fast Fourier Transform to lead to a faster Generalized Radon projections. We prove in this paper that the fast algorithm leads to almost the same results of the eldest one but with a considerable lower time computation cost. The projection end result of the fast method is a parameterized Radon space where a high valued pixel allows the detection of a curve from the original image. The proposed fast inversion algorithm leads to an exact reconstruction of the initial image from the Radon space. We show examples of the impact of this algorithm on the pattern recognition domain. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fast%20generalized%20multi-directional%20Radon%20transform" title="fast generalized multi-directional Radon transform">fast generalized multi-directional Radon transform</a>, <a href="https://publications.waset.org/abstracts/search?q=curve" title=" curve"> curve</a>, <a href="https://publications.waset.org/abstracts/search?q=exact%20reconstruction" title=" exact reconstruction"> exact reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a> </p> <a href="https://publications.waset.org/abstracts/69691/a-fast-version-of-the-generalized-multi-directional-radon-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69691.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">278</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">1503</span> Construction of Graph Signal Modulations via Graph Fourier Transform and Its Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xianwei%20Zheng">Xianwei Zheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuan%20Yan%20Tang"> Yuan Yan Tang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Classical window Fourier transform has been widely used in signal processing, image processing, machine learning and pattern recognition. The related Gabor transform is powerful enough to capture the texture information of any given dataset. Recently, in the emerging field of graph signal processing, researchers devoting themselves to develop a graph signal processing theory to handle the so-called graph signals. Among the new developing theory, windowed graph Fourier transform has been constructed to establish a time-frequency analysis framework of graph signals. The windowed graph Fourier transform is defined by using the translation and modulation operators of graph signals, following the similar calculations in classical windowed Fourier transform. Specifically, the translation and modulation operators of graph signals are defined by using the Laplacian eigenvectors as follows. For a given graph signal, its translation is defined by a similar manner as its definition in classical signal processing. Specifically, the translation operator can be defined by using the Fourier atoms; the graph signal translation is defined similarly by using the Laplacian eigenvectors. The modulation of the graph can also be established by using the Laplacian eigenvectors. The windowed graph Fourier transform based on these two operators has been applied to obtain time-frequency representations of graph signals. Fundamentally, the modulation operator is defined similarly to the classical modulation by multiplying a graph signal with the entries in each Fourier atom. However, a single Laplacian eigenvector entry cannot play a similar role as the Fourier atom. This definition ignored the relationship between the translation and modulation operators. In this paper, a new definition of the modulation operator is proposed and thus another time-frequency framework for graph signal is constructed. Specifically, the relationship between the translation and modulation operations can be established by the Fourier transform. Specifically, for any signal, the Fourier transform of its translation is the modulation of its Fourier transform. Thus, the modulation of any signal can be defined as the inverse Fourier transform of the translation of its Fourier transform. Therefore, similarly, the graph modulation of any graph signal can be defined as the inverse graph Fourier transform of the translation of its graph Fourier. The novel definition of the graph modulation operator established a relationship of the translation and modulation operations. The new modulation operation and the original translation operation are applied to construct a new framework of graph signal time-frequency analysis. Furthermore, a windowed graph Fourier frame theory is developed. Necessary and sufficient conditions for constructing windowed graph Fourier frames, tight frames and dual frames are presented in this paper. The novel graph signal time-frequency analysis framework is applied to signals defined on well-known graphs, e.g. Minnesota road graph and random graphs. Experimental results show that the novel framework captures new features of graph signals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=graph%20signals" title="graph signals">graph signals</a>, <a href="https://publications.waset.org/abstracts/search?q=windowed%20graph%20Fourier%20transform" title=" windowed graph Fourier transform"> windowed graph Fourier transform</a>, <a href="https://publications.waset.org/abstracts/search?q=windowed%20graph%20Fourier%20frames" title=" windowed graph Fourier frames"> windowed graph Fourier frames</a>, <a href="https://publications.waset.org/abstracts/search?q=vertex%20frequency%20analysis" title=" vertex frequency analysis"> vertex frequency analysis</a> </p> <a href="https://publications.waset.org/abstracts/63133/construction-of-graph-signal-modulations-via-graph-fourier-transform-and-its-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63133.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">341</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">1502</span> Nonparametric Estimation of Risk-Neutral Densities via Empirical Esscher Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manoel%20Pereira">Manoel Pereira</a>, <a href="https://publications.waset.org/abstracts/search?q=Alvaro%20Veiga"> Alvaro Veiga</a>, <a href="https://publications.waset.org/abstracts/search?q=Camila%20Epprecht"> Camila Epprecht</a>, <a href="https://publications.waset.org/abstracts/search?q=Renato%20Costa"> Renato Costa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces an empirical version of the Esscher transform for risk-neutral option pricing. Traditional parametric methods require the formulation of an explicit risk-neutral model and are operational only for a few probability distributions for the returns of the underlying. In our proposal, we make only mild assumptions on the pricing kernel and there is no need for the formulation of the risk-neutral model for the returns. First, we simulate sample paths for the returns under the physical distribution. Then, based on the empirical Esscher transform, the sample is reweighted, giving rise to a risk-neutralized sample from which derivative prices can be obtained by a weighted sum of the options pay-offs in each path. We compare our proposal with some traditional parametric pricing methods in four experiments with artificial and real data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=esscher%20transform" title="esscher transform">esscher transform</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20autoregressive%20Conditional%20Heteroscedastic%20%28GARCH%29" title=" generalized autoregressive Conditional Heteroscedastic (GARCH)"> generalized autoregressive Conditional Heteroscedastic (GARCH)</a>, <a href="https://publications.waset.org/abstracts/search?q=nonparametric%20option%20pricing" title=" nonparametric option pricing"> nonparametric option pricing</a> </p> <a href="https://publications.waset.org/abstracts/20964/nonparametric-estimation-of-risk-neutral-densities-via-empirical-esscher-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20964.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">489</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1501</span> Adaptive Multiple Transforms Hardware Architecture for Versatile Video Coding</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20Damak">T. Damak</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Houidi"> S. Houidi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20Ben%20Ayed"> M. A. Ben Ayed</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Masmoudi"> N. Masmoudi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Versatile Video Coding standard (VVC) is actually under development by the Joint Video Exploration Team (or JVET). An Adaptive Multiple Transforms (AMT) approach was announced. It is based on different transform modules that provided an efficient coding. However, the AMT solution raises several issues especially regarding the complexity of the selected set of transforms. This can be an important issue, particularly for a future industrial adoption. This paper proposed an efficient hardware implementation of the most used transform in AMT approach: the DCT II. The developed circuit is adapted to different block sizes and can reach a minimum frequency of 192 MHz allowing an optimized execution time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20multiple%20transforms" title="adaptive multiple transforms">adaptive multiple transforms</a>, <a href="https://publications.waset.org/abstracts/search?q=AMT" title=" AMT"> AMT</a>, <a href="https://publications.waset.org/abstracts/search?q=DCT%20II" title=" DCT II"> DCT II</a>, <a href="https://publications.waset.org/abstracts/search?q=hardware" title=" hardware"> hardware</a>, <a href="https://publications.waset.org/abstracts/search?q=transform" title=" transform"> transform</a>, <a href="https://publications.waset.org/abstracts/search?q=versatile%20video%20coding" title=" versatile video coding"> versatile video coding</a>, <a href="https://publications.waset.org/abstracts/search?q=VVC" title=" VVC"> VVC</a> </p> <a href="https://publications.waset.org/abstracts/108705/adaptive-multiple-transforms-hardware-architecture-for-versatile-video-coding" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108705.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">1500</span> Chebyshev Wavelets and Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emanuel%20Guariglia">Emanuel Guariglia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we deal with Chebyshev wavelets. We analyze their properties computing their Fourier transform. Moreover, we discuss the differential properties of Chebyshev wavelets due the connection coefficients. The differential properties of Chebyshev wavelets, expressed by the connection coefficients (also called refinable integrals), are given by finite series in terms of the Kronecker delta. Moreover, we treat the p-order derivative of Chebyshev wavelets and compute its Fourier transform. Finally, we expand the mother wavelet in Taylor series with an application both in fractional calculus and fractal geometry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chebyshev%20wavelets" title="Chebyshev wavelets">Chebyshev wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=Fourier%20transform" title=" Fourier transform"> Fourier transform</a>, <a href="https://publications.waset.org/abstracts/search?q=connection%20coefficients" title=" connection coefficients"> connection coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=Taylor%20series" title=" Taylor series"> Taylor series</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20fractional%20derivative" title=" local fractional derivative"> local fractional derivative</a>, <a href="https://publications.waset.org/abstracts/search?q=Cantor%20set" title=" Cantor set"> Cantor set</a> </p> <a href="https://publications.waset.org/abstracts/157194/chebyshev-wavelets-and-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157194.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">122</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1499</span> A Low-Area Fully-Reconfigurable Hardware Design of Fast Fourier Transform System for 3GPP-LTE Standard</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xin-Yu%20Shih">Xin-Yu Shih</a>, <a href="https://publications.waset.org/abstracts/search?q=Yue-Qu%20Liu"> Yue-Qu Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Hong-Ru%20Chou"> Hong-Ru Chou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a low-area and fully-reconfigurable Fast Fourier Transform (FFT) hardware design for 3GPP-LTE communication standard. It can fully support 32 different FFT sizes, up to 2048 FFT points. Besides, a special processing element is developed for making reconfigurable computing characteristics possible, while first-in first-out (FIFO) scheduling scheme design technique is proposed for hardware-friendly FIFO resource arranging. In a synthesis chip realization via TSMC 40 nm CMOS technology, the hardware circuit only occupies core area of 0.2325 mm<sup>2</sup> and dissipates 233.5 mW at maximal operating frequency of 250 MHz. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reconfigurable" title="reconfigurable">reconfigurable</a>, <a href="https://publications.waset.org/abstracts/search?q=fast%20Fourier%20transform%20%28FFT%29" title=" fast Fourier transform (FFT)"> fast Fourier transform (FFT)</a>, <a href="https://publications.waset.org/abstracts/search?q=single-path%20delay%20feedback%20%28SDF%29" title=" single-path delay feedback (SDF)"> single-path delay feedback (SDF)</a>, <a href="https://publications.waset.org/abstracts/search?q=3GPP-LTE" title=" 3GPP-LTE"> 3GPP-LTE</a> </p> <a href="https://publications.waset.org/abstracts/62069/a-low-area-fully-reconfigurable-hardware-design-of-fast-fourier-transform-system-for-3gpp-lte-standard" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62069.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">278</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">1498</span> Solving Momentum and Energy Equation by Using Differential Transform Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mustafa%20Ekici">Mustafa Ekici</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Natural convection is a basic process which is important in a wide variety of practical applications. In essence, a heated fluid expands and rises from buoyancy due to decreased density. Numerous papers have been written on natural or mixed convection in vertical ducts heated on the side. These equations have been proved to be valuable tools for the modelling of many phenomena such as fluid dynamics. Finding solutions to such equations or system of equations are in general not an easy task. We propose a method, which is called differential transform method, of solving a non-linear equations and compare the results with some of the other techniques. Illustrative examples shows that the results are in good agreement. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=differential%20transform%20method" title="differential transform method">differential transform method</a>, <a href="https://publications.waset.org/abstracts/search?q=momentum" title=" momentum"> momentum</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20equation" title=" energy equation"> energy equation</a>, <a href="https://publications.waset.org/abstracts/search?q=boundry%20value%20problem" title=" boundry value problem"> boundry value problem</a> </p> <a href="https://publications.waset.org/abstracts/18213/solving-momentum-and-energy-equation-by-using-differential-transform-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18213.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 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