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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> 8</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: denoise</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8</span> A Way of Converting Color Images to Gray Scale Ones for the Color-Blind: Applying to the part of the Tokyo Subway Map</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Katsuhiro%20Narikiyo">Katsuhiro Narikiyo</a>, <a href="https://publications.waset.org/abstracts/search?q=Shota%20Hashikawa"> Shota Hashikawa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a way of removing noises and reducing the number of colors contained in a JPEG image. Main purpose of this project is to convert color images to monochrome images for the color-blind. We treat the crispy color images like the Tokyo subway map. Each color in the image has an important information. But for the color blinds, similar colors cannot be distinguished. If we can convert those colors to different gray values, they can distinguish them. Therefore we try to convert color images to monochrome images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=color-blind" title="color-blind">color-blind</a>, <a href="https://publications.waset.org/abstracts/search?q=JPEG" title=" JPEG"> JPEG</a>, <a href="https://publications.waset.org/abstracts/search?q=monochrome%20image" title=" monochrome image"> monochrome image</a>, <a href="https://publications.waset.org/abstracts/search?q=denoise" title=" denoise"> denoise</a> </p> <a href="https://publications.waset.org/abstracts/2968/a-way-of-converting-color-images-to-gray-scale-ones-for-the-color-blind-applying-to-the-part-of-the-tokyo-subway-map" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2968.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">363</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">7</span> Bipolar Impulse Noise Removal and Edge Preservation in Color Images and Video Using Improved Kuwahara Filter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Reji%20Thankachan">Reji Thankachan</a>, <a href="https://publications.waset.org/abstracts/search?q=Varsha%20PS"> Varsha PS</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Both image capturing devices and human visual systems are nonlinear. Hence nonlinear filtering methods outperforms its linear counterpart in many applications. Linear methods are unable to remove impulsive noise in images by preserving its edges and fine details. In addition, linear algorithms are unable to remove signal dependent or multiplicative noise in images. This paper presents an approach to denoise and smoothen the Bipolar impulse noised images and videos using improved Kuwahara filter. It involves a 2 stage algorithm which includes a noise detection followed by filtering. Numerous simulation demonstrate that proposed method outperforms the existing method by eliminating the painting like flattening effect along the local feature direction while preserving edge with improvement in PSNR and MSE. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bipolar%20impulse%20noise" title="bipolar impulse noise">bipolar impulse noise</a>, <a href="https://publications.waset.org/abstracts/search?q=Kuwahara" title=" Kuwahara"> Kuwahara</a>, <a href="https://publications.waset.org/abstracts/search?q=PSNR%20MSE" title=" PSNR MSE"> PSNR MSE</a>, <a href="https://publications.waset.org/abstracts/search?q=PDF" title=" PDF"> PDF</a> </p> <a href="https://publications.waset.org/abstracts/19449/bipolar-impulse-noise-removal-and-edge-preservation-in-color-images-and-video-using-improved-kuwahara-filter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19449.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">501</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">6</span> Labview-Based System for Fiber Links Events Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bo%20Liu">Bo Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Qingshan%20Kong"> Qingshan Kong</a>, <a href="https://publications.waset.org/abstracts/search?q=Weiqing%20Huang"> Weiqing Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the rapid development of modern communication, diagnosing the fiber-optic quality and faults in real-time is widely focused. In this paper, a Labview-based system is proposed for fiber-optic faults detection. The wavelet threshold denoising method combined with Empirical Mode Decomposition (EMD) is applied to denoise the optical time domain reflectometer (OTDR) signal. Then the method based on Gabor representation is used to detect events. Experimental measurements show that signal to noise ratio (SNR) of the OTDR signal is improved by 1.34dB on average, compared with using the wavelet threshold denosing method. The proposed system has a high score in event detection capability and accuracy. The maximum detectable fiber length of the proposed Labview-based system can be 65km. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=empirical%20mode%20decomposition" title="empirical mode decomposition">empirical mode decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=events%20detection" title=" events detection"> events detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Gabor%20transform" title=" Gabor transform"> Gabor transform</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20time%20domain%20reflectometer" title=" optical time domain reflectometer"> optical time domain reflectometer</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20threshold%20denoising" title=" wavelet threshold denoising"> wavelet threshold denoising</a> </p> <a href="https://publications.waset.org/abstracts/105512/labview-based-system-for-fiber-links-events-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105512.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">131</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">5</span> System Identification in Presence of Outliers </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chao%20Yu">Chao Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Qing-Guo%20Wang"> Qing-Guo Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Dan%20Zhang"> Dan Zhang </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The outlier detection problem for dynamic systems is formulated as a matrix decomposition problem with low-rank, sparse matrices and further recast as a semidefinite programming (SDP) problem. A fast algorithm is presented to solve the resulting problem while keeping the solution matrix structure and it can greatly reduce the computational cost over the standard interior-point method. The computational burden is further reduced by proper construction of subsets of the raw data without violating low rank property of the involved matrix. The proposed method can make exact detection of outliers in case of no or little noise in output observations. In case of significant noise, a novel approach based on under-sampling with averaging is developed to denoise while retaining the saliency of outliers and so-filtered data enables successful outlier detection with the proposed method while the existing filtering methods fail. Use of recovered “clean” data from the proposed method can give much better parameter estimation compared with that based on the raw data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=outlier%20detection" title="outlier detection">outlier detection</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20identification" title=" system identification"> system identification</a>, <a href="https://publications.waset.org/abstracts/search?q=matrix%20decomposition" title=" matrix decomposition"> matrix decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=low-rank%20matrix" title=" low-rank matrix"> low-rank matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=sparsity" title=" sparsity"> sparsity</a>, <a href="https://publications.waset.org/abstracts/search?q=semidefinite%20programming" title=" semidefinite programming"> semidefinite programming</a>, <a href="https://publications.waset.org/abstracts/search?q=interior-point%20methods" title=" interior-point methods"> interior-point methods</a>, <a href="https://publications.waset.org/abstracts/search?q=denoising" title=" denoising"> denoising</a> </p> <a href="https://publications.waset.org/abstracts/13363/system-identification-in-presence-of-outliers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13363.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">312</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">4</span> Denoising Convolutional Neural Network Assisted Electrocardiogram Signal Watermarking for Secure Transmission in E-Healthcare Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jyoti%20Rani">Jyoti Rani</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashima%20Anand"> Ashima Anand</a>, <a href="https://publications.waset.org/abstracts/search?q=Shivendra%20Shivani"> Shivendra Shivani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, physiological signals obtained in telemedicine have been stored independently from patient information. In addition, people have increasingly turned to mobile devices for information on health-related topics. Major authentication and security issues may arise from this storing, degrading the reliability of diagnostics. This study introduces an approach to reversible watermarking, which ensures security by utilizing the electrocardiogram (ECG) signal as a carrier for embedding patient information. In the proposed work, Pan-Tompkins++ is employed to convert the 1D ECG signal into a 2D signal. The frequency subbands of a signal are extracted using RDWT(Redundant discrete wavelet transform), and then one of the subbands is subjected to MSVD (Multiresolution singular valued decomposition for masking. Finally, the encrypted watermark is embedded within the signal. The experimental results show that the watermarked signal obtained is indistinguishable from the original signals, ensuring the preservation of all diagnostic information. In addition, the DnCNN (Denoising convolutional neural network) concept is used to denoise the retrieved watermark for improved accuracy. The proposed ECG signal-based watermarking method is supported by experimental results and evaluations of its effectiveness. The results of the robustness tests demonstrate that the watermark is susceptible to the most prevalent watermarking attacks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ECG" title="ECG">ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=VMD" title=" VMD"> VMD</a>, <a href="https://publications.waset.org/abstracts/search?q=watermarking" title=" watermarking"> watermarking</a>, <a href="https://publications.waset.org/abstracts/search?q=PanTompkins%2B%2B" title=" PanTompkins++"> PanTompkins++</a>, <a href="https://publications.waset.org/abstracts/search?q=RDWT" title=" RDWT"> RDWT</a>, <a href="https://publications.waset.org/abstracts/search?q=DnCNN" title=" DnCNN"> DnCNN</a>, <a href="https://publications.waset.org/abstracts/search?q=MSVD" title=" MSVD"> MSVD</a>, <a href="https://publications.waset.org/abstracts/search?q=chaotic%20encryption" title=" chaotic encryption"> chaotic encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=attacks" title=" attacks"> attacks</a> </p> <a href="https://publications.waset.org/abstracts/174732/denoising-convolutional-neural-network-assisted-electrocardiogram-signal-watermarking-for-secure-transmission-in-e-healthcare-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174732.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">114</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">3</span> Enhancing Temporal Extrapolation of Wind Speed Using a Hybrid Technique: A Case Study in West Coast of Denmark</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Elshafei">B. Elshafei</a>, <a href="https://publications.waset.org/abstracts/search?q=X.%20Mao"> X. Mao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The demand for renewable energy is significantly increasing, major investments are being supplied to the wind power generation industry as a leading source of clean energy. The wind energy sector is entirely dependable and driven by the prediction of wind speed, which by the nature of wind is very stochastic and widely random. This s0tudy employs deep multi-fidelity Gaussian process regression, used to predict wind speeds for medium term time horizons. Data of the RUNE experiment in the west coast of Denmark were provided by the Technical University of Denmark, which represent the wind speed across the study area from the period between December 2015 and March 2016. The study aims to investigate the effect of pre-processing the data by denoising the signal using empirical wavelet transform (EWT) and engaging the vector components of wind speed to increase the number of input data layers for data fusion using deep multi-fidelity Gaussian process regression (GPR). The outcomes were compared using root mean square error (RMSE) and the results demonstrated a significant increase in the accuracy of predictions which demonstrated that using vector components of the wind speed as additional predictors exhibits more accurate predictions than strategies that ignore them, reflecting the importance of the inclusion of all sub data and pre-processing signals for wind speed forecasting models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20fusion" title="data fusion">data fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20regression" title=" Gaussian process regression"> Gaussian process regression</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20denoise" title=" signal denoise"> signal denoise</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal%20extrapolation" title=" temporal extrapolation"> temporal extrapolation</a> </p> <a href="https://publications.waset.org/abstracts/127870/enhancing-temporal-extrapolation-of-wind-speed-using-a-hybrid-technique-a-case-study-in-west-coast-of-denmark" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127870.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">138</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">2</span> Performance Evaluation and Comparison between the Empirical Mode Decomposition, Wavelet Analysis, and Singular Spectrum Analysis Applied to the Time Series Analysis in Atmospheric Science</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Olivier%20Delage">Olivier Delage</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Bencherif"> Hassan Bencherif</a>, <a href="https://publications.waset.org/abstracts/search?q=Alain%20Bourdier"> Alain Bourdier</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Signal decomposition approaches represent an important step in time series analysis, providing useful knowledge and insight into the data and underlying dynamics characteristics while also facilitating tasks such as noise removal and feature extraction. As most of observational time series are nonlinear and nonstationary, resulting of several physical processes interaction at different time scales, experimental time series have fluctuations at all time scales and requires the development of specific signal decomposition techniques. Most commonly used techniques are data driven, enabling to obtain well-behaved signal components without making any prior-assumptions on input data. Among the most popular time series decomposition techniques, most cited in the literature, are the empirical mode decomposition and its variants, the empirical wavelet transform and singular spectrum analysis. With increasing popularity and utility of these methods in wide ranging applications, it is imperative to gain a good understanding and insight into the operation of these algorithms. In this work, we describe all of the techniques mentioned above as well as their ability to denoise signals, to capture trends, to identify components corresponding to the physical processes involved in the evolution of the observed system and deduce the dimensionality of the underlying dynamics. Results obtained with all of these methods on experimental total ozone columns and rainfall time series will be discussed and compared <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=denoising" title="denoising">denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20mode%20decomposition" title=" empirical mode decomposition"> empirical mode decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=singular%20spectrum%20analysis" title=" singular spectrum analysis"> singular spectrum analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a>, <a href="https://publications.waset.org/abstracts/search?q=underlying%20dynamics" title=" underlying dynamics"> underlying dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20analysis" title=" wavelet analysis"> wavelet analysis</a> </p> <a href="https://publications.waset.org/abstracts/165886/performance-evaluation-and-comparison-between-the-empirical-mode-decomposition-wavelet-analysis-and-singular-spectrum-analysis-applied-to-the-time-series-analysis-in-atmospheric-science" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165886.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">126</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">1</span> An Improved Total Variation Regularization Method for Denoising Magnetocardiography</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yanping%20Liao">Yanping Liao</a>, <a href="https://publications.waset.org/abstracts/search?q=Congcong%20He"> Congcong He</a>, <a href="https://publications.waset.org/abstracts/search?q=Ruigang%20Zhao"> Ruigang Zhao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The application of magnetocardiography signals to detect cardiac electrical function is a new technology developed in recent years. The magnetocardiography signal is detected with Superconducting Quantum Interference Devices (SQUID) and has considerable advantages over electrocardiography (ECG). It is difficult to extract Magnetocardiography (MCG) signal which is buried in the noise, which is a critical issue to be resolved in cardiac monitoring system and MCG applications. In order to remove the severe background noise, the Total Variation (TV) regularization method is proposed to denoise MCG signal. The approach transforms the denoising problem into a minimization optimization problem and the Majorization-minimization algorithm is applied to iteratively solve the minimization problem. However, traditional TV regularization method tends to cause step effect and lacks constraint adaptability. In this paper, an improved TV regularization method for denoising MCG signal is proposed to improve the denoising precision. The improvement of this method is mainly divided into three parts. First, high-order TV is applied to reduce the step effect, and the corresponding second derivative matrix is used to substitute the first order. Then, the positions of the non-zero elements in the second order derivative matrix are determined based on the peak positions that are detected by the detection window. Finally, adaptive constraint parameters are defined to eliminate noises and preserve signal peak characteristics. Theoretical analysis and experimental results show that this algorithm can effectively improve the output signal-to-noise ratio and has superior performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=constraint%20parameters" title="constraint parameters">constraint parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=derivative%20matrix" title=" derivative matrix"> derivative matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetocardiography" title=" magnetocardiography"> magnetocardiography</a>, <a href="https://publications.waset.org/abstracts/search?q=regular%20term" title=" regular term"> regular term</a>, <a href="https://publications.waset.org/abstracts/search?q=total%20variation" title=" total variation"> total variation</a> </p> <a href="https://publications.waset.org/abstracts/108358/an-improved-total-variation-regularization-method-for-denoising-magnetocardiography" 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