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thresholding</title> <meta name="description" content="Search results for: thresholding"> <meta name="keywords" content="thresholding"> <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 navbar-toggler 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style="font-size:.9rem"><span class="badge badge-info">80</span> Empirical Mode Decomposition Based Denoising by Customized Thresholding</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wahiba%20Mohguen">Wahiba Mohguen</a>, <a href="https://publications.waset.org/abstracts/search?q=Ra%C3%AFs%20El%E2%80%99hadi%20Bekka"> Raïs El’hadi Bekka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a denoising method called EMD-Custom that was based on Empirical Mode Decomposition (EMD) and the modified Customized Thresholding Function (Custom) algorithms. EMD was applied to decompose adaptively a noisy signal into intrinsic mode functions (IMFs). Then, all the noisy IMFs got threshold by applying the presented thresholding function to suppress noise and to improve the signal to noise ratio (SNR). The method was tested on simulated data and real ECG signal, and the results were compared to the EMD-Based signal denoising methods using the soft and hard thresholding. The results showed the superior performance of the proposed EMD-Custom denoising over the traditional approach. The performances were evaluated in terms of SNR in dB, and Mean Square Error (MSE). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=customized%20thresholding" title="customized thresholding">customized thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG%20signal" title=" ECG signal"> ECG signal</a>, <a href="https://publications.waset.org/abstracts/search?q=EMD" title=" EMD"> EMD</a>, <a href="https://publications.waset.org/abstracts/search?q=hard%20thresholding" title=" hard thresholding"> hard thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=soft-thresholding" title=" soft-thresholding"> soft-thresholding</a> </p> <a href="https://publications.waset.org/abstracts/67421/empirical-mode-decomposition-based-denoising-by-customized-thresholding" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67421.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">302</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">79</span> Binarization and Recognition of Characters from Historical Degraded Documents</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bency%20Jacob">Bency Jacob</a>, <a href="https://publications.waset.org/abstracts/search?q=S.B.%20Waykar"> S.B. Waykar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Degradations in historical document images appear due to aging of the documents. It is very difficult to understand and retrieve text from badly degraded documents as there is variation between the document foreground and background. Thresholding of such document images either result in broken characters or detection of false texts. Numerous algorithms exist that can separate text and background efficiently in the textual regions of the document; but portions of background are mistaken as text in areas that hardly contain any text. This paper presents a way to overcome these problems by a robust binarization technique that recovers the text from a severely degraded document images and thereby increases the accuracy of optical character recognition systems. The proposed document recovery algorithm efficiently removes degradations from document images. Here we are using the ostus method ,local thresholding and global thresholding and after the binarization training and recognizing the characters in the degraded documents. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binarization" title="binarization">binarization</a>, <a href="https://publications.waset.org/abstracts/search?q=denoising" title=" denoising"> denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20thresholding" title=" global thresholding"> global thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20thresholding" title=" local thresholding"> local thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=thresholding" title=" thresholding"> thresholding</a> </p> <a href="https://publications.waset.org/abstracts/33322/binarization-and-recognition-of-characters-from-historical-degraded-documents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33322.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">344</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">78</span> Hybrid Robust Estimation via Median Filter and Wavelet Thresholding with Automatic Boundary Correction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alsaidi%20M.%20Altaher">Alsaidi M. Altaher</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Tahir%20Ismail"> Mohd Tahir Ismail</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wavelet thresholding has been a power tool in curve estimation and data analysis. In the presence of outliers this non parametric estimator can not suppress the outliers involved. This study proposes a new two-stage combined method based on the use of the median filter as primary step before applying wavelet thresholding. After suppressing the outliers in a signal through the median filter, the classical wavelet thresholding is then applied for removing the remaining noise. We use automatic boundary corrections; using a low order polynomial model or local polynomial model as a more realistic rule to correct the bias at the boundary region; instead of using the classical assumptions such periodic or symmetric. A simulation experiment has been conducted to evaluate the numerical performance of the proposed method. Results show strong evidences that the proposed method is extremely effective in terms of correcting the boundary bias and eliminating outlier’s sensitivity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=boundary%20correction" title="boundary correction">boundary correction</a>, <a href="https://publications.waset.org/abstracts/search?q=median%20filter" title=" median filter"> median filter</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20thresholding" title=" wavelet thresholding"> wavelet thresholding</a> </p> <a href="https://publications.waset.org/abstracts/16883/hybrid-robust-estimation-via-median-filter-and-wavelet-thresholding-with-automatic-boundary-correction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16883.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">428</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">77</span> Scintigraphic Image Coding of Region of Interest Based on SPIHT Algorithm Using Global Thresholding and Huffman Coding</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Seddiki">A. Seddiki</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Djebbouri"> M. Djebbouri</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Guerchi"> D. Guerchi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Medical imaging produces human body pictures in digital form. Since these imaging techniques produce prohibitive amounts of data, compression is necessary for storage and communication purposes. Many current compression schemes provide a very high compression rate but with considerable loss of quality. On the other hand, in some areas in medicine, it may be sufficient to maintain high image quality only in region of interest (ROI). This paper discusses a contribution to the lossless compression in the region of interest of Scintigraphic images based on SPIHT algorithm and global transform thresholding using Huffman coding. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=global%20thresholding%20transform" title="global thresholding transform">global thresholding transform</a>, <a href="https://publications.waset.org/abstracts/search?q=huffman%20coding" title=" huffman coding"> huffman coding</a>, <a href="https://publications.waset.org/abstracts/search?q=region%20of%20interest" title=" region of interest"> region of interest</a>, <a href="https://publications.waset.org/abstracts/search?q=SPIHT%20coding" title=" SPIHT coding"> SPIHT coding</a>, <a href="https://publications.waset.org/abstracts/search?q=scintigraphic%20images" title=" scintigraphic images"> scintigraphic images</a> </p> <a href="https://publications.waset.org/abstracts/17067/scintigraphic-image-coding-of-region-of-interest-based-on-spiht-algorithm-using-global-thresholding-and-huffman-coding" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17067.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">367</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">76</span> A Passive Digital Video Authentication Technique Using Wavelet Based Optical Flow Variation Thresholding</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20S.%20Remya">R. S. Remya</a>, <a href="https://publications.waset.org/abstracts/search?q=U.%20S.%20Sethulekshmi"> U. S. Sethulekshmi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Detecting the authenticity of a video is an important issue in digital forensics as Video is used as a silent evidence in court such as in child pornography, movie piracy cases, insurance claims, cases involving scientific fraud, traffic monitoring etc. The biggest threat to video data is the availability of modern open video editing tools which enable easy editing of videos without leaving any trace of tampering. In this paper, we propose an efficient passive method for inter-frame video tampering detection, its type and location by estimating the optical flow of wavelet features of adjacent frames and thresholding the variation in the estimated feature. The performance of the algorithm is compared with the z-score thresholding and achieved an efficiency above 95% on all the tested databases. The proposed method works well for videos with dynamic (forensics) as well as static (surveillance) background. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform" title="discrete wavelet transform">discrete wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20flow" title=" optical flow"> optical flow</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20flow%20variation" title=" optical flow variation"> optical flow variation</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20tampering" title=" video tampering"> video tampering</a> </p> <a href="https://publications.waset.org/abstracts/45252/a-passive-digital-video-authentication-technique-using-wavelet-based-optical-flow-variation-thresholding" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45252.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">359</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">75</span> Hybrid Thresholding Lifting Dual Tree Complex Wavelet Transform with Wiener Filter for Quality Assurance of Medical Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hilal%20Naimi">Hilal Naimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amelbahahouda%20Adamou-Mitiche"> Amelbahahouda Adamou-Mitiche</a>, <a href="https://publications.waset.org/abstracts/search?q=Lahcene%20Mitiche"> Lahcene Mitiche</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main problem in the area of medical imaging has been image denoising. The most defying for image denoising is to secure data carrying structures like surfaces and edges in order to achieve good visual quality. Different algorithms with different denoising performances have been proposed in previous decades. More recently, models focused on deep learning have shown a great promise to outperform all traditional approaches. However, these techniques are limited to the necessity of large sample size training and high computational costs. This research proposes a denoising approach basing on LDTCWT (Lifting Dual Tree Complex Wavelet Transform) using Hybrid Thresholding with Wiener filter to enhance the quality image. This research describes the LDTCWT as a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). To develop this approach, a hybrid thresholding function is modeled by integrating the Wiener filter into the thresholding function. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lifting%20wavelet%20transform" title="lifting wavelet transform">lifting wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20denoising" title=" image denoising"> image denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=dual%20tree%20complex%20wavelet%20transform" title=" dual tree complex wavelet transform"> dual tree complex wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20shrinkage" title=" wavelet shrinkage"> wavelet shrinkage</a>, <a href="https://publications.waset.org/abstracts/search?q=wiener%20filter" title=" wiener filter"> wiener filter</a> </p> <a href="https://publications.waset.org/abstracts/135374/hybrid-thresholding-lifting-dual-tree-complex-wavelet-transform-with-wiener-filter-for-quality-assurance-of-medical-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135374.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">163</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">74</span> An Online Adaptive Thresholding Method to Classify Google Trends Data Anomalies for Investor Sentiment Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Duygu%20Dere">Duygu Dere</a>, <a href="https://publications.waset.org/abstracts/search?q=Mert%20Ergeneci"> Mert Ergeneci</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaan%20Gokcesu"> Kaan Gokcesu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Google Trends data has gained increasing popularity in the applications of behavioral finance, decision science and risk management. Because of Google’s wide range of use, the Trends statistics provide significant information about the investor sentiment and intention, which can be used as decisive factors for corporate and risk management fields. However, an anomaly, a significant increase or decrease, in a certain query cannot be detected by the state of the art applications of computation due to the random baseline noise of the Trends data, which is modelled as an Additive white Gaussian noise (AWGN). Since through time, the baseline noise power shows a gradual change an adaptive thresholding method is required to track and learn the baseline noise for a correct classification. To this end, we introduce an online method to classify meaningful deviations in Google Trends data. Through extensive experiments, we demonstrate that our method can successfully classify various anomalies for plenty of different data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20data%20processing" title="adaptive data processing">adaptive data processing</a>, <a href="https://publications.waset.org/abstracts/search?q=behavioral%20finance" title=" behavioral finance "> behavioral finance </a>, <a href="https://publications.waset.org/abstracts/search?q=convex%20optimization" title=" convex optimization"> convex optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20learning" title=" online learning"> online learning</a>, <a href="https://publications.waset.org/abstracts/search?q=soft%20minimum%20thresholding" title=" soft minimum thresholding"> soft minimum thresholding</a> </p> <a href="https://publications.waset.org/abstracts/92282/an-online-adaptive-thresholding-method-to-classify-google-trends-data-anomalies-for-investor-sentiment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92282.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">167</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">73</span> Segmentation Using Multi-Thresholded Sobel Images: Application to the Separation of Stuck Pollen Grains</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Endrick%20Barnacin">Endrick Barnacin</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean-Luc%20Henry"> Jean-Luc Henry</a>, <a href="https://publications.waset.org/abstracts/search?q=Jimmy%20Nagau"> Jimmy Nagau</a>, <a href="https://publications.waset.org/abstracts/search?q=Jack%20Molinie"> Jack Molinie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Being able to identify biological particles such as spores, viruses, or pollens is important for health care professionals, as it allows for appropriate therapeutic management of patients. Optical microscopy is a technology widely used for the analysis of these types of microorganisms, because, compared to other types of microscopy, it is not expensive. The analysis of an optical microscope slide is a tedious and time-consuming task when done manually. However, using machine learning and computer vision, this process can be automated. The first step of an automated microscope slide image analysis process is segmentation. During this step, the biological particles are localized and extracted. Very often, the use of an automatic thresholding method is sufficient to locate and extract the particles. However, in some cases, the particles are not extracted individually because they are stuck to other biological elements. In this paper, we propose a stuck particles separation method based on the use of the Sobel operator and thresholding. We illustrate it by applying it to the separation of 813 images of adjacent pollen grains. The method correctly separated 95.4% of these images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title="image segmentation">image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=stuck%20particles%20separation" title=" stuck particles separation"> stuck particles separation</a>, <a href="https://publications.waset.org/abstracts/search?q=Sobel%20operator" title=" Sobel operator"> Sobel operator</a>, <a href="https://publications.waset.org/abstracts/search?q=thresholding" title=" thresholding"> thresholding</a> </p> <a href="https://publications.waset.org/abstracts/148891/segmentation-using-multi-thresholded-sobel-images-application-to-the-separation-of-stuck-pollen-grains" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148891.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">130</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">72</span> RGB Color Based Real Time Traffic Sign Detection and Feature Extraction System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kay%20Thinzar%20Phu">Kay Thinzar Phu</a>, <a href="https://publications.waset.org/abstracts/search?q=Lwin%20Lwin%20Oo"> Lwin Lwin Oo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In an intelligent transport system and advanced driver assistance system, the developing of real-time traffic sign detection and recognition (TSDR) system plays an important part in recent research field. There are many challenges for developing real-time TSDR system due to motion artifacts, variable lighting and weather conditions and situations of traffic signs. Researchers have already proposed various methods to minimize the challenges problem. The aim of the proposed research is to develop an efficient and effective TSDR in real time. This system proposes an adaptive thresholding method based on RGB color for traffic signs detection and new features for traffic signs recognition. In this system, the RGB color thresholding is used to detect the blue and yellow color traffic signs regions. The system performs the shape identify to decide whether the output candidate region is traffic sign or not. Lastly, new features such as termination points, bifurcation points, and 90’ angles are extracted from validated image. This system uses Myanmar Traffic Sign dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20thresholding%20based%20on%20RGB%20color" title="adaptive thresholding based on RGB color">adaptive thresholding based on RGB color</a>, <a href="https://publications.waset.org/abstracts/search?q=blue%20color%20detection" title=" blue color detection"> blue color detection</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=yellow%20color%20detection" title=" yellow color detection"> yellow color detection</a> </p> <a href="https://publications.waset.org/abstracts/77127/rgb-color-based-real-time-traffic-sign-detection-and-feature-extraction-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77127.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">313</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">71</span> Speckle Noise Reduction Using Anisotropic Filter Based on Wavelets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kritika%20Bansal">Kritika Bansal</a>, <a href="https://publications.waset.org/abstracts/search?q=Akwinder%20Kaur"> Akwinder Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Shruti%20Gujral"> Shruti Gujral</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the approach of denoising is solved by using a new hybrid technique which associates the different denoising methods. Wavelet thresholding and anisotropic diffusion filter are the two different filters in our hybrid techniques. The Wavelet thresholding removes the noise by removing the high frequency components with lesser edge preservation, whereas an anisotropic diffusion filters is based on partial differential equation, (PDE) to remove the speckle noise. This PDE approach is used to preserve the edges and provides better smoothing. So our new method proposes a combination of these two filtering methods which performs better results in terms of peak signal to noise ratio (PSNR), coefficient of correlation (COC) and equivalent no of looks (ENL). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=denoising" title="denoising">denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=anisotropic%20diffusion%20filter" title=" anisotropic diffusion filter"> anisotropic diffusion filter</a>, <a href="https://publications.waset.org/abstracts/search?q=multiplicative%20noise" title=" multiplicative noise"> multiplicative noise</a>, <a href="https://publications.waset.org/abstracts/search?q=speckle" title=" speckle"> speckle</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelets" title=" wavelets"> wavelets</a> </p> <a href="https://publications.waset.org/abstracts/14626/speckle-noise-reduction-using-anisotropic-filter-based-on-wavelets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14626.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">512</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">70</span> Music Note Detection and Dictionary Generation from Music Sheet Using Image Processing Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Ammar">Muhammad Ammar</a>, <a href="https://publications.waset.org/abstracts/search?q=Talha%20Ali"> Talha Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdul%20Basit"> Abdul Basit</a>, <a href="https://publications.waset.org/abstracts/search?q=Bakhtawar%20Rajput"> Bakhtawar Rajput</a>, <a href="https://publications.waset.org/abstracts/search?q=Zobia%20Sohail"> Zobia Sohail</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Music note detection is an area of study for the past few years and has its own influence in music file generation from sheet music. We proposed a method to detect music notes on sheet music using basic thresholding and blob detection. Subsequently, we created a notes dictionary using a semi-supervised learning approach. After notes detection, for each test image, the new symbols are added to the dictionary. This makes the notes detection semi-automatic. The experiments are done on images from a dataset and also on the captured images. The developed approach showed almost 100% accuracy on the dataset images, whereas varying results have been seen on captured images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=music%20note" title="music note">music note</a>, <a href="https://publications.waset.org/abstracts/search?q=sheet%20music" title=" sheet music"> sheet music</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20music%20recognition" title=" optical music recognition"> optical music recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=blob%20detection" title=" blob detection"> blob detection</a>, <a href="https://publications.waset.org/abstracts/search?q=thresholding" title=" thresholding"> thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=dictionary%20generation" title=" dictionary generation"> dictionary generation</a> </p> <a href="https://publications.waset.org/abstracts/133670/music-note-detection-and-dictionary-generation-from-music-sheet-using-image-processing-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133670.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">181</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">69</span> Reversible and Adaptive Watermarking for MRI Medical Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nisar%20Ahmed%20Memon">Nisar Ahmed Memon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new medical image watermarking scheme delivering high embedding capacity is presented in this paper. Integer Wavelet Transform (IWT), Companding technique and adaptive thresholding are used in this scheme. The proposed scheme implants, recovers the hidden information and restores the input image to its pristine state at the receiving end. Magnetic Resonance Imaging (MRI) images are used for experimental purposes. The scheme first segment the MRI medical image into non-overlapping blocks and then inserts watermark into wavelet coefficients having a high frequency of each block. The scheme uses block-based watermarking adopting iterative optimization of threshold for companding in order to avoid the histogram pre and post processing. Results show that proposed scheme performs better than other reversible medical image watermarking schemes available in literature for MRI medical images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20thresholding" title="adaptive thresholding">adaptive thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=companding%20technique" title=" companding technique"> companding technique</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20authentication" title=" data authentication"> data authentication</a>, <a href="https://publications.waset.org/abstracts/search?q=reversible%20watermarking" title=" reversible watermarking"> reversible watermarking</a> </p> <a href="https://publications.waset.org/abstracts/57365/reversible-and-adaptive-watermarking-for-mri-medical-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57365.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">296</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">68</span> Robust Image Design Based Steganographic System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sadiq%20J.%20Abou-Loukh">Sadiq J. Abou-Loukh</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanan%20M.%20Habbi"> Hanan M. Habbi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a steganography to hide the transmitted information without excite suspicious and also illustrates the level of secrecy that can be increased by using cryptography techniques. The proposed system has been implemented firstly by encrypted image file one time pad key and secondly encrypted message that hidden to perform encryption followed by image embedding. Then the new image file will be created from the original image by using four triangles operation, the new image is processed by one of two image processing techniques. The proposed two processing techniques are thresholding and differential predictive coding (DPC). Afterwards, encryption or decryption keys are generated by functional key generator. The generator key is used one time only. Encrypted text will be hidden in the places that are not used for image processing and key generation system has high embedding rate (0.1875 character/pixel) for true color image (24 bit depth). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=encryption" title="encryption">encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=thresholding" title=" thresholding"> thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%0D%0Apredictive%20coding" title=" differential predictive coding"> differential predictive coding</a>, <a href="https://publications.waset.org/abstracts/search?q=four%20triangles%20operation" title=" four triangles operation "> four triangles operation </a> </p> <a href="https://publications.waset.org/abstracts/16654/robust-image-design-based-steganographic-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16654.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">67</span> A Combined Feature Extraction and Thresholding Technique for Silence Removal in Percussive Sounds </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Kishore%20Kumar">B. Kishore Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Pogula%20Rakesh"> Pogula Rakesh</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Kishore%20Kumar"> T. Kishore Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The music analysis is a part of the audio content analysis used to analyze the music by using the different features of audio signal. In music analysis, the first step is to divide the music signal to different sections based on the feature profiles of the music signal. In this paper, we present a music segmentation technique that will effectively segmentize the signal and thresholding technique to remove silence from the percussive sounds produced by percussive instruments, which uses two features of music, namely signal energy and spectral centroid. The proposed method impose thresholds on both the features which will vary depends on the music signal. Depends on the threshold, silence part is removed and the segmentation is done. The effectiveness of the proposed method is analyzed using MATLAB. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=percussive%20sounds" title="percussive sounds">percussive sounds</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20centroid" title=" spectral centroid"> spectral centroid</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20energy" title=" spectral energy"> spectral energy</a>, <a href="https://publications.waset.org/abstracts/search?q=silence%20removal" title=" silence removal"> silence removal</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a> </p> <a href="https://publications.waset.org/abstracts/25510/a-combined-feature-extraction-and-thresholding-technique-for-silence-removal-in-percussive-sounds" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25510.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">593</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">66</span> Automatic Thresholding for Data Gap Detection for a Set of Sensors in Instrumented Buildings</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Houda%20Najeh">Houda Najeh</a>, <a href="https://publications.waset.org/abstracts/search?q=St%C3%A9phane%20Ploix"> Stéphane Ploix</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahendra%20Pratap%20Singh"> Mahendra Pratap Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Karim%20Chabir"> Karim Chabir</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Naceur%20Abdelkrim"> Mohamed Naceur Abdelkrim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Building systems are highly vulnerable to different kinds of faults and failures. In fact, various faults, failures and human behaviors could affect the building performance. This paper tackles the detection of unreliable sensors in buildings. Different literature surveys on diagnosis techniques for sensor grids in buildings have been published but all of them treat only bias and outliers. Occurences of data gaps have also not been given an adequate span of attention in the academia. The proposed methodology comprises the automatic thresholding for data gap detection for a set of heterogeneous sensors in instrumented buildings. Sensor measurements are considered to be regular time series. However, in reality, sensor values are not uniformly sampled. So, the issue to solve is from which delay each sensor become faulty? The use of time series is required for detection of abnormalities on the delays. The efficiency of the method is evaluated on measurements obtained from a real power plant: an office at Grenoble Institute of technology equipped by 30 sensors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=building%20system" title="building system">building system</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=diagnosis" title=" diagnosis"> diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=outliers" title=" outliers"> outliers</a>, <a href="https://publications.waset.org/abstracts/search?q=delay" title=" delay"> delay</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20gap" title=" data gap"> data gap</a> </p> <a href="https://publications.waset.org/abstracts/97880/automatic-thresholding-for-data-gap-detection-for-a-set-of-sensors-in-instrumented-buildings" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97880.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">245</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">65</span> Hyperspectral Image Classification Using Tree Search Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shreya%20Pare">Shreya Pare</a>, <a href="https://publications.waset.org/abstracts/search?q=Parvin%20Akhter"> Parvin Akhter</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Remotely sensing image classification becomes a very challenging task owing to the high dimensionality of hyperspectral images. The pixel-wise classification methods fail to take the spatial structure information of an image. Therefore, to improve the performance of classification, spatial information can be integrated into the classification process. In this paper, the multilevel thresholding algorithm based on a modified fuzzy entropy function is used to perform the segmentation of hyperspectral images. The fuzzy parameters of the MFE function have been optimized by using a new meta-heuristic algorithm based on the Tree-Search algorithm. The segmented image is classified by a large distribution machine (LDM) classifier. Experimental results are shown on a hyperspectral image dataset. The experimental outputs indicate that the proposed technique (MFE-TSA-LDM) achieves much higher classification accuracy for hyperspectral images when compared to state-of-art classification techniques. The proposed algorithm provides accurate segmentation and classification maps, thus becoming more suitable for image classification with large spatial structures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperspectral%20images" title=" hyperspectral images"> hyperspectral images</a>, <a href="https://publications.waset.org/abstracts/search?q=large%20distribution%20margin" title=" large distribution margin"> large distribution margin</a>, <a href="https://publications.waset.org/abstracts/search?q=modified%20fuzzy%20entropy%20function" title=" modified fuzzy entropy function"> modified fuzzy entropy function</a>, <a href="https://publications.waset.org/abstracts/search?q=multilevel%20thresholding" title=" multilevel thresholding"> multilevel thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=tree%20search%20algorithm" title=" tree search algorithm"> tree search algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperspectral%20image%20classification%20using%20tree%20search%20algorithm" title=" hyperspectral image classification using tree search algorithm"> hyperspectral image classification using tree search algorithm</a> </p> <a href="https://publications.waset.org/abstracts/143284/hyperspectral-image-classification-using-tree-search-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143284.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">177</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">64</span> Laser Data Based Automatic Generation of Lane-Level Road Map for Intelligent Vehicles</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zehai%20Yu">Zehai Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Hui%20Zhu"> Hui Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Linglong%20Lin"> Linglong Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Huawei%20Liang"> Huawei Liang</a>, <a href="https://publications.waset.org/abstracts/search?q=Biao%20Yu"> Biao Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Weixin%20Huang"> Weixin Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the development of intelligent vehicle systems, a high-precision road map is increasingly needed in many aspects. The automatic lane lines extraction and modeling are the most essential steps for the generation of a precise lane-level road map. In this paper, an automatic lane-level road map generation system is proposed. To extract the road markings on the ground, the multi-region Otsu thresholding method is applied, which calculates the intensity value of laser data that maximizes the variance between background and road markings. The extracted road marking points are then projected to the raster image and clustered using a two-stage clustering algorithm. Lane lines are subsequently recognized from these clusters by the shape features of their minimum bounding rectangle. To ensure the storage efficiency of the map, the lane lines are approximated to cubic polynomial curves using a Bayesian estimation approach. The proposed lane-level road map generation system has been tested on urban and expressway conditions in Hefei, China. The experimental results on the datasets show that our method can achieve excellent extraction and clustering effect, and the fitted lines can reach a high position accuracy with an error of less than 10 cm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=curve%20fitting" title="curve fitting">curve fitting</a>, <a href="https://publications.waset.org/abstracts/search?q=lane-level%20road%20map" title=" lane-level road map"> lane-level road map</a>, <a href="https://publications.waset.org/abstracts/search?q=line%20recognition" title=" line recognition"> line recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-thresholding" title=" multi-thresholding"> multi-thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=two-stage%20clustering" title=" two-stage clustering"> two-stage clustering</a> </p> <a href="https://publications.waset.org/abstracts/132360/laser-data-based-automatic-generation-of-lane-level-road-map-for-intelligent-vehicles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132360.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">128</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">63</span> Iterative Segmentation and Application of Hausdorff Dilation Distance in Defect Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Shankar%20Bharathi">S. Shankar Bharathi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Inspection of surface defects on metallic components has always been challenging due to its specular property. Occurrences of defects such as scratches, rust, pitting are very common in metallic surfaces during the manufacturing process. These defects if unchecked can hamper the performance and reduce the life time of such component. Many of the conventional image processing algorithms in detecting the surface defects generally involve segmentation techniques, based on thresholding, edge detection, watershed segmentation and textural segmentation. They later employ other suitable algorithms based on morphology, region growing, shape analysis, neural networks for classification purpose. In this paper the work has been focused only towards detecting scratches. Global and other thresholding techniques were used to extract the defects, but it proved to be inaccurate in extracting the defects alone. However, this paper does not focus on comparison of different segmentation techniques, but rather describes a novel approach towards segmentation combined with hausdorff dilation distance. The proposed algorithm is based on the distribution of the intensity levels, that is, whether a certain gray level is concentrated or evenly distributed. The algorithm is based on extraction of such concentrated pixels. Defective images showed higher level of concentration of some gray level, whereas in non-defective image, there seemed to be no concentration, but were evenly distributed. This formed the basis in detecting the defects in the proposed algorithm. Hausdorff dilation distance based on mathematical morphology was used to strengthen the segmentation of the defects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=metallic%20surface" title="metallic surface">metallic surface</a>, <a href="https://publications.waset.org/abstracts/search?q=scratches" title=" scratches"> scratches</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=hausdorff%20dilation%20distance" title=" hausdorff dilation distance"> hausdorff dilation distance</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20vision" title=" machine vision"> machine vision</a> </p> <a href="https://publications.waset.org/abstracts/34958/iterative-segmentation-and-application-of-hausdorff-dilation-distance-in-defect-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34958.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">428</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">62</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">441</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">61</span> Hybrid Precoder Design Based on Iterative Hard Thresholding Algorithm for Millimeter Wave Multiple-Input-Multiple-Output Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ameni%20Mejri">Ameni Mejri</a>, <a href="https://publications.waset.org/abstracts/search?q=Moufida%20Hajjaj"> Moufida Hajjaj</a>, <a href="https://publications.waset.org/abstracts/search?q=Salem%20Hasnaoui"> Salem Hasnaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Ridha%20Bouallegue"> Ridha Bouallegue</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The technology advances have most lately made the millimeter wave (mmWave) communication possible. Due to the huge amount of spectrum that is available in MmWave frequency bands, this promising candidate is considered as a key technology for the deployment of 5G cellular networks. In order to enhance system capacity and achieve spectral efficiency, very large antenna arrays are employed at mmWave systems by exploiting array gain. However, it has been shown that conventional beamforming strategies are not suitable for mmWave hardware implementation. Therefore, new features are required for mmWave cellular applications. Unlike traditional multiple-input-multiple-output (MIMO) systems for which only digital precoders are essential to accomplish precoding, MIMO technology seems to be different at mmWave because of digital precoding limitations. Moreover, precoding implements a greater number of radio frequency (RF) chains supporting more signal mixers and analog-to-digital converters. As RF chain cost and power consumption is increasing, we need to resort to another alternative. Although the hybrid precoding architecture has been regarded as the best solution based on a combination between a baseband precoder and an RF precoder, we still do not get the optimal design of hybrid precoders. According to the mapping strategies from RF chains to the different antenna elements, there are two main categories of hybrid precoding architecture. Given as a hybrid precoding sub-array architecture, the partially-connected structure reduces hardware complexity by using a less number of phase shifters, whereas it sacrifices some beamforming gain. In this paper, we treat the hybrid precoder design in mmWave MIMO systems as a problem of matrix factorization. Thus, we adopt the alternating minimization principle in order to solve the design problem. Further, we present our proposed algorithm for the partially-connected structure, which is based on the iterative hard thresholding method. Through simulation results, we show that our hybrid precoding algorithm provides significant performance gains over existing algorithms. We also show that the proposed approach reduces significantly the computational complexity. Furthermore, valuable design insights are provided when we use the proposed algorithm to make simulation comparisons between the hybrid precoding partially-connected structure and the fully-connected structure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=alternating%20minimization" title="alternating minimization">alternating minimization</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20precoding" title=" hybrid precoding"> hybrid precoding</a>, <a href="https://publications.waset.org/abstracts/search?q=iterative%20hard%20thresholding" title=" iterative hard thresholding"> iterative hard thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=low-complexity" title=" low-complexity"> low-complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=millimeter%20wave%20communication" title=" millimeter wave communication"> millimeter wave communication</a>, <a href="https://publications.waset.org/abstracts/search?q=partially-connected%20structure" title=" partially-connected structure"> partially-connected structure</a> </p> <a href="https://publications.waset.org/abstracts/66477/hybrid-precoder-design-based-on-iterative-hard-thresholding-algorithm-for-millimeter-wave-multiple-input-multiple-output-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66477.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">321</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">60</span> A Trends Analysis of Yatch Simulator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jae-Neung%20Lee">Jae-Neung Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Keun-Chang%20Kwak"> Keun-Chang Kwak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes an analysis of Yacht Simulator international trends and also explains about Yacht. Examples of yacht Simulator using Yacht Simulator include image processing for totaling the total number of vehicles, edge/target detection, detection and evasion algorithm, image processing using SIFT (scale invariant features transform) matching, and application of median filter and thresholding. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=yacht%20simulator" title="yacht simulator">yacht simulator</a>, <a href="https://publications.waset.org/abstracts/search?q=simulator" title=" simulator"> simulator</a>, <a href="https://publications.waset.org/abstracts/search?q=trends%20analysis" title=" trends analysis"> trends analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=SIFT" title=" SIFT"> SIFT</a> </p> <a href="https://publications.waset.org/abstracts/23888/a-trends-analysis-of-yatch-simulator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23888.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">432</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">59</span> Algorithm for Automatic Real-Time Electrooculographic Artifact Correction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Norman%20Sinnigen">Norman Sinnigen</a>, <a href="https://publications.waset.org/abstracts/search?q=Igor%20Izyurov"> Igor Izyurov</a>, <a href="https://publications.waset.org/abstracts/search?q=Marina%20Krylova"> Marina Krylova</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamidreza%20Jamalabadi"> Hamidreza Jamalabadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sarah%20Alizadeh"> Sarah Alizadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20Walter"> Martin Walter</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: EEG is a non-invasive brain activity recording technique with a high temporal resolution that allows the use of real-time applications, such as neurofeedback. However, EEG data are susceptible to electrooculographic (EOG) and electromyography (EMG) artifacts (i.e., jaw clenching, teeth squeezing and forehead movements). Due to their non-stationary nature, these artifacts greatly obscure the information and power spectrum of EEG signals. Many EEG artifact correction methods are too time-consuming when applied to low-density EEG and have been focusing on offline processing or handling one single type of EEG artifact. A software-only real-time method for correcting multiple types of EEG artifacts of high-density EEG remains a significant challenge. Methods: We demonstrate an improved approach for automatic real-time EEG artifact correction of EOG and EMG artifacts. The method was tested on three healthy subjects using 64 EEG channels (Brain Products GmbH) and a sampling rate of 1,000 Hz. Captured EEG signals were imported in MATLAB with the lab streaming layer interface allowing buffering of EEG data. EMG artifacts were detected by channel variance and adaptive thresholding and corrected by using channel interpolation. Real-time independent component analysis (ICA) was applied for correcting EOG artifacts. Results: Our results demonstrate that the algorithm effectively reduces EMG artifacts, such as jaw clenching, teeth squeezing and forehead movements, and EOG artifacts (horizontal and vertical eye movements) of high-density EEG while preserving brain neuronal activity information. The average computation time of EOG and EMG artifact correction for 80 s (80,000 data points) 64-channel data is 300 – 700 ms depending on the convergence of ICA and the type and intensity of the artifact. Conclusion: An automatic EEG artifact correction algorithm based on channel variance, adaptive thresholding, and ICA improves high-density EEG recordings contaminated with EOG and EMG artifacts in real-time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG" title="EEG">EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=muscle%20artifacts" title=" muscle artifacts"> muscle artifacts</a>, <a href="https://publications.waset.org/abstracts/search?q=ocular%20artifacts" title=" ocular artifacts"> ocular artifacts</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20artifact%20correction" title=" real-time artifact correction"> real-time artifact correction</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20ICA" title=" real-time ICA"> real-time ICA</a> </p> <a href="https://publications.waset.org/abstracts/102844/algorithm-for-automatic-real-time-electrooculographic-artifact-correction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102844.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">180</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">58</span> Realistic Modeling of the Preclinical Small Animal Using Commercial Software</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Su%20Chul%20Han">Su Chul Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Seungwoo%20Park"> Seungwoo Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As the increasing incidence of cancer, the technology and modality of radiotherapy have advanced and the importance of preclinical model is increasing in the cancer research. Furthermore, the small animal dosimetry is an essential part of the evaluation of the relationship between the absorbed dose in preclinical small animal and biological effect in preclinical study. In this study, we carried out realistic modeling of the preclinical small animal phantom possible to verify irradiated dose using commercial software. The small animal phantom was modeling from 4D Digital Mouse whole body phantom. To manipulate Moby phantom in commercial software (Mimics, Materialise, Leuven, Belgium), we converted Moby phantom to DICOM image file of CT by Matlab and two- dimensional of CT images were converted to the three-dimensional image and it is possible to segment and crop CT image in Sagittal, Coronal and axial view). The CT images of small animals were modeling following process. Based on the profile line value, the thresholding was carried out to make a mask that was connection of all the regions of the equal threshold range. Using thresholding method, we segmented into three part (bone, body (tissue). lung), to separate neighboring pixels between lung and body (tissue), we used region growing function of Mimics software. We acquired 3D object by 3D calculation in the segmented images. The generated 3D object was smoothing by remeshing operation and smoothing operation factor was 0.4, iteration value was 5. The edge mode was selected to perform triangle reduction. The parameters were that tolerance (0.1mm), edge angle (15 degrees) and the number of iteration (5). The image processing 3D object file was converted to an STL file to output with 3D printer. We modified 3D small animal file using 3- Matic research (Materialise, Leuven, Belgium) to make space for radiation dosimetry chips. We acquired 3D object of realistic small animal phantom. The width of small animal phantom was 2.631 cm, thickness was 2.361 cm, and length was 10.817. Mimics software supported efficiency about 3D object generation and usability of conversion to STL file for user. The development of small preclinical animal phantom would increase reliability of verification of absorbed dose in small animal for preclinical study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mimics" title="mimics">mimics</a>, <a href="https://publications.waset.org/abstracts/search?q=preclinical%20small%20animal" title=" preclinical small animal"> preclinical small animal</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20printer" title=" 3D printer"> 3D printer</a> </p> <a href="https://publications.waset.org/abstracts/47930/realistic-modeling-of-the-preclinical-small-animal-using-commercial-software" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47930.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">366</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">57</span> Electrocardiogram Signal Denoising Using a Hybrid Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Latif">R. Latif</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20Jenkal"> W. Jenkal</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Toumanari"> A. Toumanari</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Hatim"> A. Hatim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an efficient method of electrocardiogram signal denoising based on a hybrid approach. Two techniques are brought together to create an efficient denoising process. The first is an Adaptive Dual Threshold Filter (ADTF) and the second is the Discrete Wavelet Transform (DWT). The presented approach is based on three steps of denoising, the DWT decomposition, the ADTF step and the highest peaks correction step. This paper presents some application of the approach on some electrocardiogram signals of the MIT-BIH database. The results of these applications are promising compared to other recently published techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybrid%20technique" title="hybrid technique">hybrid technique</a>, <a href="https://publications.waset.org/abstracts/search?q=ADTF" title=" ADTF"> ADTF</a>, <a href="https://publications.waset.org/abstracts/search?q=DWT" title=" DWT"> DWT</a>, <a href="https://publications.waset.org/abstracts/search?q=thresholding" title=" thresholding"> thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG%20signal" title=" ECG signal"> ECG signal</a> </p> <a href="https://publications.waset.org/abstracts/65458/electrocardiogram-signal-denoising-using-a-hybrid-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65458.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">322</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">56</span> An Extraction of Cancer Region from MR Images Using Fuzzy Clustering Means and Morphological Operations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramandeep%20Kaur">Ramandeep Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurjit%20Singh%20Bhathal"> Gurjit Singh Bhathal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cancer diagnosis is very difficult task. Magnetic resonance imaging (MRI) scan is used to produce image of any part of the body and provides an efficient way for diagnosis of cancer or tumor. In existing method, fuzzy clustering mean (FCM) is used for the diagnosis of the tumor. In the proposed method FCM is used to diagnose the cancer of the foot. FCM finds the centroids of the clusters of the foot cancer obtained from MRI images. FCM thresholding result shows the extract region of the cancer. Morphological operations are applied to get extracted region of cancer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20imaging%20%28MRI%29" title="magnetic resonance imaging (MRI)">magnetic resonance imaging (MRI)</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20C%20mean%20clustering" title=" fuzzy C mean clustering"> fuzzy C mean clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=morphological%20operations" title=" morphological operations"> morphological operations</a> </p> <a href="https://publications.waset.org/abstracts/5937/an-extraction-of-cancer-region-from-mr-images-using-fuzzy-clustering-means-and-morphological-operations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5937.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">398</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">55</span> Building and Tree Detection Using Multiscale Matched Filtering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdullah%20H.%20%C3%96zcan">Abdullah H. Özcan</a>, <a href="https://publications.waset.org/abstracts/search?q=Dilara%20Hisar"> Dilara Hisar</a>, <a href="https://publications.waset.org/abstracts/search?q=Yetkin%20Sayar"> Yetkin Sayar</a>, <a href="https://publications.waset.org/abstracts/search?q=Cem%20%C3%9Cnsalan"> Cem Ünsalan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, an automated building and tree detection method is proposed using DSM data and true orthophoto image. A multiscale matched filtering is used on DSM data. Therefore, first watershed transform is applied. Then, Otsu’s thresholding method is used as an adaptive threshold to segment each watershed region. Detected objects are masked with NDVI to separate buildings and trees. The proposed method is able to detect buildings and trees without entering any elevation threshold. We tested our method on ISPRS semantic labeling dataset and obtained promising results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=building%20detection" title="building detection">building detection</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20maximum%20filtering" title=" local maximum filtering"> local maximum filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=matched%20filtering" title=" matched filtering"> matched filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=multiscale" title=" multiscale"> multiscale</a> </p> <a href="https://publications.waset.org/abstracts/59277/building-and-tree-detection-using-multiscale-matched-filtering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59277.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">320</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">54</span> Automatic Segmentation of Lung Pleura Based On Curvature Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sasidhar%20B.">Sasidhar B.</a>, <a href="https://publications.waset.org/abstracts/search?q=Bhaskar%20Rao%20N."> Bhaskar Rao N.</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramesh%20Babu%20D.%20R."> Ramesh Babu D. R.</a>, <a href="https://publications.waset.org/abstracts/search?q=Ravi%20Shankar%20M."> Ravi Shankar M.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Segmentation of lung pleura is a preprocessing step in Computer-Aided Diagnosis (CAD) which helps in reducing false positives in detection of lung cancer. The existing methods fail in extraction of lung regions with the nodules at the pleura of the lungs. In this paper, a new method is proposed which segments lung regions with nodules at the pleura of the lungs based on curvature analysis and morphological operators. The proposed algorithm is tested on 06 patient’s dataset which consists of 60 images of Lung Image Database Consortium (LIDC) and the results are found to be satisfactory with 98.3% average overlap measure (AΩ). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=curvature%20analysis" title="curvature analysis">curvature analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=morphological%20operators" title=" morphological operators"> morphological operators</a>, <a href="https://publications.waset.org/abstracts/search?q=thresholding" title=" thresholding"> thresholding</a> </p> <a href="https://publications.waset.org/abstracts/20846/automatic-segmentation-of-lung-pleura-based-on-curvature-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20846.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">596</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">53</span> Robust Noisy Speech Identification Using Frame Classifier Derived Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Punnoose%20A.%20K.">Punnoose A. K.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an approach for identifying noisy speech recording using a multi-layer perception (MLP) trained to predict phonemes from acoustic features. Characteristics of the MLP posteriors are explored for clean speech and noisy speech at the frame level. Appropriate density functions are used to fit the softmax probability of the clean and noisy speech. A function that takes into account the ratio of the softmax probability density of noisy speech to clean speech is formulated. These phoneme independent scoring is weighted using a phoneme-specific weightage to make the scoring more robust. Simple thresholding is used to identify the noisy speech recording from the clean speech recordings. The approach is benchmarked on standard databases, with a focus on precision. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=noisy%20speech%20identification" title="noisy speech identification">noisy speech identification</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20pre-processing" title=" speech pre-processing"> speech pre-processing</a>, <a href="https://publications.waset.org/abstracts/search?q=noise%20robustness" title=" noise robustness"> noise robustness</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20engineering" title=" feature engineering"> feature engineering</a> </p> <a href="https://publications.waset.org/abstracts/144694/robust-noisy-speech-identification-using-frame-classifier-derived-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144694.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">127</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">52</span> Identifying Protein-Coding and Non-Coding Regions in Transcriptomes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Angela%20U.%20Makolo">Angela U. Makolo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Protein-coding and Non-coding regions determine the biology of a sequenced transcriptome. Research advances have shown that Non-coding regions are important in disease progression and clinical diagnosis. Existing bioinformatics tools have been targeted towards Protein-coding regions alone. Therefore, there are challenges associated with gaining biological insights from transcriptome sequence data. These tools are also limited to computationally intensive sequence alignment, which is inadequate and less accurate to identify both Protein-coding and Non-coding regions. Alignment-free techniques can overcome the limitation of identifying both regions. Therefore, this study was designed to develop an efficient sequence alignment-free model for identifying both Protein-coding and Non-coding regions in sequenced transcriptomes. Feature grouping and randomization procedures were applied to the input transcriptomes (37,503 data points). Successive iterations were carried out to compute the gradient vector that converged the developed Protein-coding and Non-coding Region Identifier (PNRI) model to the approximate coefficient vector. The logistic regression algorithm was used with a sigmoid activation function. A parameter vector was estimated for every sample in 37,503 data points in a bid to reduce the generalization error and cost. Maximum Likelihood Estimation (MLE) was used for parameter estimation by taking the log-likelihood of six features and combining them into a summation function. Dynamic thresholding was used to classify the Protein-coding and Non-coding regions, and the Receiver Operating Characteristic (ROC) curve was determined. The generalization performance of PNRI was determined in terms of F1 score, accuracy, sensitivity, and specificity. The average generalization performance of PNRI was determined using a benchmark of multi-species organisms. The generalization error for identifying Protein-coding and Non-coding regions decreased from 0.514 to 0.508 and to 0.378, respectively, after three iterations. The cost (difference between the predicted and the actual outcome) also decreased from 1.446 to 0.842 and to 0.718, respectively, for the first, second and third iterations. The iterations terminated at the 390th epoch, having an error of 0.036 and a cost of 0.316. The computed elements of the parameter vector that maximized the objective function were 0.043, 0.519, 0.715, 0.878, 1.157, and 2.575. The PNRI gave an ROC of 0.97, indicating an improved predictive ability. The PNRI identified both Protein-coding and Non-coding regions with an F1 score of 0.970, accuracy (0.969), sensitivity (0.966), and specificity of 0.973. Using 13 non-human multi-species model organisms, the average generalization performance of the traditional method was 74.4%, while that of the developed model was 85.2%, thereby making the developed model better in the identification of Protein-coding and Non-coding regions in transcriptomes. The developed Protein-coding and Non-coding region identifier model efficiently identified the Protein-coding and Non-coding transcriptomic regions. It could be used in genome annotation and in the analysis of transcriptomes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sequence%20alignment-free%20model" title="sequence alignment-free model">sequence alignment-free model</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20thresholding%20classification" title=" dynamic thresholding classification"> dynamic thresholding classification</a>, <a href="https://publications.waset.org/abstracts/search?q=input%20randomization" title=" input randomization"> input randomization</a>, <a href="https://publications.waset.org/abstracts/search?q=genome%20annotation" title=" genome annotation"> genome annotation</a> </p> <a href="https://publications.waset.org/abstracts/183177/identifying-protein-coding-and-non-coding-regions-in-transcriptomes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183177.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">68</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">51</span> Residual Evaluation by Thresholding and Neuro-Fuzzy System: Application to Actuator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20Kourd">Y. Kourd</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Lefebvre"> D. Lefebvre</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Guersi"> N. Guersi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The monitoring of industrial processes is required to ensure operating conditions of industrial systems through automatic detection and isolation of faults. In this paper we propose a method of fault diagnosis based on neuro-fuzzy technique and the choice of a threshold. The validation of this method on a test bench "Actuator Electro DAMADICS Benchmark". In the first phase of the method, we construct a model represents the normal state of the system to fault detection. With residuals analysis generated and the choice of thresholds for signatures table. These signatures provide us with groups of non-detectable faults. In the second phase, we build faulty models to see the flaws in the system that are not located in the first phase. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=residuals%20analysis" title="residuals analysis">residuals analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=threshold" title=" threshold"> threshold</a>, <a href="https://publications.waset.org/abstracts/search?q=neuro-fuzzy%20system" title=" neuro-fuzzy system"> neuro-fuzzy system</a>, <a href="https://publications.waset.org/abstracts/search?q=residual%20evaluation" title=" residual evaluation"> residual evaluation</a> </p> <a href="https://publications.waset.org/abstracts/9714/residual-evaluation-by-thresholding-and-neuro-fuzzy-system-application-to-actuator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9714.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info 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