CINXE.COM

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Search results for: explosion detection</title> <meta name="description" content="Search results for: explosion detection"> <meta name="keywords" content="explosion detection"> <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 ml-auto" type="button" data-toggle="collapse" data-target="#navbarMenu" aria-controls="navbarMenu" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="w-100"> <div class="d-none d-lg-flex flex-row-reverse"> <form method="get" action="https://waset.org/search" class="form-inline my-2 my-lg-0"> <input class="form-control mr-sm-2" type="search" placeholder="Search Conferences" value="explosion detection" name="q" aria-label="Search"> <button class="btn btn-light my-2 my-sm-0" type="submit"><i class="fas fa-search"></i></button> </form> </div> <div class="collapse navbar-collapse mt-1" id="navbarMenu"> <ul class="navbar-nav ml-auto align-items-center" id="mainNavMenu"> <li class="nav-item"> <a class="nav-link" href="https://waset.org/conferences" title="Conferences in 2024/2025/2026">Conferences</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/disciplines" title="Disciplines">Disciplines</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/committees" rel="nofollow">Committees</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" id="navbarDropdownPublications" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> Publications </a> <div class="dropdown-menu" aria-labelledby="navbarDropdownPublications"> <a class="dropdown-item" href="https://publications.waset.org/abstracts">Abstracts</a> <a class="dropdown-item" href="https://publications.waset.org">Periodicals</a> <a class="dropdown-item" href="https://publications.waset.org/archive">Archive</a> </div> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/page/support" title="Support">Support</a> </li> </ul> </div> </div> </nav> </div> </header> <main> <div class="container mt-4"> <div class="row"> <div class="col-md-9 mx-auto"> <form method="get" action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="explosion detection"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 3602</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: explosion detection</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3422</span> Comparative Analysis of Edge Detection Techniques for Extracting Characters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rana%20Gill">Rana Gill</a>, <a href="https://publications.waset.org/abstracts/search?q=Chandandeep%20Kaur"> Chandandeep Kaur </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Segmentation of images can be implemented using different fundamental algorithms like edge detection (discontinuity based segmentation), region growing (similarity based segmentation), iterative thresholding method. A comprehensive literature review relevant to the study gives description of different techniques for vehicle number plate detection and edge detection techniques widely used on different types of images. This research work is based on edge detection techniques and calculating threshold on the basis of five edge operators. Five operators used are Prewitt, Roberts, Sobel, LoG and Canny. Segmentation of characters present in different type of images like vehicle number plate, name plate of house and characters on different sign boards are selected as a case study in this work. The proposed methodology has seven stages. The proposed system has been implemented using MATLAB R2010a. Comparison of all the five operators has been done on the basis of their performance. From the results it is found that Canny operators produce best results among the used operators and performance of different edge operators in decreasing order is: Canny>Log>Sobel>Prewitt>Roberts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=segmentation" title="segmentation">segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20detection" title=" edge detection"> edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=text" title=" text"> text</a>, <a href="https://publications.waset.org/abstracts/search?q=extracting%20characters" title=" extracting characters"> extracting characters</a> </p> <a href="https://publications.waset.org/abstracts/9054/comparative-analysis-of-edge-detection-techniques-for-extracting-characters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9054.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">426</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">3421</span> A Dynamic Ensemble Learning Approach for Online Anomaly Detection in Alibaba Datacenters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wanyi%20Zhu">Wanyi Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xia%20Ming"> Xia Ming</a>, <a href="https://publications.waset.org/abstracts/search?q=Huafeng%20Wang"> Huafeng Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Junda%20Chen"> Junda Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Lu%20Liu"> Lu Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiangwei%20Jiang"> Jiangwei Jiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Guohua%20Liu"> Guohua Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Anomaly detection is a first and imperative step needed to respond to unexpected problems and to assure high performance and security in large data center management. This paper presents an online anomaly detection system through an innovative approach of ensemble machine learning and adaptive differentiation algorithms, and applies them to performance data collected from a continuous monitoring system for multi-tier web applications running in Alibaba data centers. We evaluate the effectiveness and efficiency of this algorithm with production traffic data and compare with the traditional anomaly detection approaches such as a static threshold and other deviation-based detection techniques. The experiment results show that our algorithm correctly identifies the unexpected performance variances of any running application, with an acceptable false positive rate. This proposed approach has already been deployed in real-time production environments to enhance the efficiency and stability in daily data center operations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alibaba%20data%20centers" title="Alibaba data centers">Alibaba data centers</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20computation" title=" big data computation"> big data computation</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20ensemble%20learning" title=" dynamic ensemble learning"> dynamic ensemble learning</a> </p> <a href="https://publications.waset.org/abstracts/86171/a-dynamic-ensemble-learning-approach-for-online-anomaly-detection-in-alibaba-datacenters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86171.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">200</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3420</span> Medical Image Watermark and Tamper Detection Using Constant Correlation Spread Spectrum Watermarking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peter%20U.%20Eze">Peter U. Eze</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Udaya"> P. Udaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Robin%20J.%20Evans"> Robin J. Evans</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data hiding can be achieved by Steganography or invisible digital watermarking. For digital watermarking, both accurate retrieval of the embedded watermark and the integrity of the cover image are important. Medical image security in Teleradiology is one of the applications where the embedded patient record needs to be extracted with accuracy as well as the medical image integrity verified. In this research paper, the Constant Correlation Spread Spectrum digital watermarking for medical image tamper detection and accurate embedded watermark retrieval is introduced. In the proposed method, a watermark bit from a patient record is spread in a medical image sub-block such that the correlation of all watermarked sub-blocks with a spreading code, W, would have a constant value, <em>p.</em> The constant correlation <em>p</em>, spreading code, W and the size of the sub-blocks constitute the secret key. Tamper detection is achieved by flagging any sub-block whose correlation value deviates by more than a small value, ℇ, from <em>p</em>. The major features of our new scheme include: (1) Improving watermark detection accuracy for high-pixel depth medical images by reducing the Bit Error Rate (BER) to Zero and (2) block-level tamper detection in a single computational process with simultaneous watermark detection, thereby increasing utility with the same computational cost. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Constant%20Correlation" title="Constant Correlation">Constant Correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=Medical%20Image" title=" Medical Image"> Medical Image</a>, <a href="https://publications.waset.org/abstracts/search?q=Spread%20Spectrum" title=" Spread Spectrum"> Spread Spectrum</a>, <a href="https://publications.waset.org/abstracts/search?q=Tamper%20Detection" title=" Tamper Detection"> Tamper Detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Watermarking" title=" Watermarking"> Watermarking</a> </p> <a href="https://publications.waset.org/abstracts/84629/medical-image-watermark-and-tamper-detection-using-constant-correlation-spread-spectrum-watermarking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84629.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">194</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">3419</span> A Microfluidic Biosensor for Detection of EGFR 19 Deletion Mutation Targeting Non-Small Cell Lung Cancer on Rolling Circle Amplification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ji%20Su%20Kim">Ji Su Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Bo%20Ram%20Choi"> Bo Ram Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ju%20Yeon%20Cho"> Ju Yeon Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyukjin%20Lee"> Hyukjin Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epidermal growth factor receptor (EGFR) 19 deletion mutation gene is over-expressed in carcinoma patient. EGFR 19 deletion mutation is known as typical biomarker of non-small cell lung cancer (NSCLC), which one section in the coding exon 19 of EGFR is deleted. Therefore, there have been many attempts over the years to detect EGFR 19 deletion mutation for replacing conventional diagnostic method such as PCR and tissue biopsy. We developed a simple and facile detection platform based on Rolling Circle Amplification (RCA), which provides highly amplified products in isothermal amplification of the ligated DNA template. Limit of detection (~50 nM) and a faster detection time (~30 min) could be achieved by introducing RCA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EGFR19" title="EGFR19">EGFR19</a>, <a href="https://publications.waset.org/abstracts/search?q=cancer" title=" cancer"> cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnosis" title=" diagnosis"> diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=rolling%20circle%20amplification%20%28RCA%29" title=" rolling circle amplification (RCA)"> rolling circle amplification (RCA)</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrogel" title=" hydrogel"> hydrogel</a> </p> <a href="https://publications.waset.org/abstracts/72641/a-microfluidic-biosensor-for-detection-of-egfr-19-deletion-mutation-targeting-non-small-cell-lung-cancer-on-rolling-circle-amplification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72641.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">255</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">3418</span> Feedforward Neural Network with Backpropagation for Epilepsy Seizure Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Natalia%20%20Espinosa">Natalia Espinosa</a>, <a href="https://publications.waset.org/abstracts/search?q=Arthur%20Amorim"> Arthur Amorim</a>, <a href="https://publications.waset.org/abstracts/search?q=Rudolf%20%20Huebner"> Rudolf Huebner</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Artificial%20Neural%20Network%20%28ANN%29" title="Artificial Neural Network (ANN)">Artificial Neural Network (ANN)</a>, <a href="https://publications.waset.org/abstracts/search?q=Discrete%20Wavelet%20Transform%20%28DWT%29" title=" Discrete Wavelet Transform (DWT)"> Discrete Wavelet Transform (DWT)</a>, <a href="https://publications.waset.org/abstracts/search?q=Epilepsy%20Detection" title=" Epilepsy Detection "> Epilepsy Detection </a>, <a href="https://publications.waset.org/abstracts/search?q=Seizure." title=" Seizure."> Seizure.</a> </p> <a href="https://publications.waset.org/abstracts/122872/feedforward-neural-network-with-backpropagation-for-epilepsy-seizure-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122872.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">222</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3417</span> Biologically Inspired Small Infrared Target Detection Using Local Contrast Mechanisms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tian%20Xia">Tian Xia</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuan%20Yan%20Tang"> Yuan Yan Tang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to obtain higher small target detection accuracy, this paper presents an effective algorithm inspired by the local contrast mechanism. The proposed method can enhance target signal and suppress background clutter simultaneously. In the first stage, a enhanced image is obtained using the proposed Weighted Laplacian of Gaussian. In the second stage, an adaptive threshold is adopted to segment the target. Experimental results on two changeling image sequences show that the proposed method can detect the bright and dark targets simultaneously, and is not sensitive to sea-sky line of the infrared image. So it is fit for IR small infrared target detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=small%20target%20detection" title="small target detection">small target detection</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20contrast" title=" local contrast"> local contrast</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20vision%20system" title=" human vision system"> human vision system</a>, <a href="https://publications.waset.org/abstracts/search?q=Laplacian%20of%20Gaussian" title=" Laplacian of Gaussian"> Laplacian of Gaussian</a> </p> <a href="https://publications.waset.org/abstracts/19199/biologically-inspired-small-infrared-target-detection-using-local-contrast-mechanisms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19199.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">468</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">3416</span> Cognitive Methods for Detecting Deception During the Criminal Investigation Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Laid%20Fekih">Laid Fekih</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: It is difficult to detect lying, deception, and misrepresentation just by looking at verbal or non-verbal expression during the criminal investigation process, as there is a common belief that it is possible to tell whether a person is lying or telling the truth just by looking at the way they act or behave. The process of detecting lies and deception during the criminal investigation process needs more studies and research to overcome the difficulties facing the investigators. Method: The present study aimed to identify the effectiveness of cognitive methods and techniques in detecting deception during the criminal investigation. It adopted the quasi-experimental method and covered a sample of (20) defendants distributed randomly into two homogeneous groups, an experimental group of (10) defendants be subject to criminal investigation by applying cognitive techniques to detect deception and a second experimental group of (10) defendants be subject to the direct investigation method. The tool that used is a guided interview based on models of investigative questions according to the cognitive deception detection approach, which consists of three techniques of Vrij: imposing the cognitive burden, encouragement to provide more information, and ask unexpected questions, and the Direct Investigation Method. Results: Results revealed a significant difference between the two groups in term of lie detection accuracy in favour of defendants be subject to criminal investigation by applying cognitive techniques, the cognitive deception detection approach produced superior total accuracy rates both with human observers and through an analysis of objective criteria. The cognitive deception detection approach produced superior accuracy results in truth detection: 71%, deception detection: 70% compared to a direct investigation method truth detection: 52%; deception detection: 49%. Conclusion: The study recommended if practitioners use a cognitive deception detection technique, they will correctly classify more individuals than when they use a direct investigation method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=the%20cognitive%20lie%20detection%20approach" title="the cognitive lie detection approach">the cognitive lie detection approach</a>, <a href="https://publications.waset.org/abstracts/search?q=deception" title=" deception"> deception</a>, <a href="https://publications.waset.org/abstracts/search?q=criminal%20investigation" title=" criminal investigation"> criminal investigation</a>, <a href="https://publications.waset.org/abstracts/search?q=mental%20health" title=" mental health"> mental health</a> </p> <a href="https://publications.waset.org/abstracts/171148/cognitive-methods-for-detecting-deception-during-the-criminal-investigation-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171148.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">66</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">3415</span> Advancing in Cricket Analytics: Novel Approaches for Pitch and Ball Detection Employing OpenCV and YOLOV8</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pratham%20Madnur">Pratham Madnur</a>, <a href="https://publications.waset.org/abstracts/search?q=Prathamkumar%20Shetty"> Prathamkumar Shetty</a>, <a href="https://publications.waset.org/abstracts/search?q=Sneha%20Varur"> Sneha Varur</a>, <a href="https://publications.waset.org/abstracts/search?q=Gouri%20Parashetti"> Gouri Parashetti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to overcome conventional obstacles, this research paper investigates novel approaches for cricket pitch and ball detection that make use of cutting-edge technologies. The research integrates OpenCV for pitch inspection and modifies the YOLOv8 model for cricket ball detection in order to overcome the shortcomings of manual pitch assessment and traditional ball detection techniques. To ensure flexibility in a range of pitch environments, the pitch detection method leverages OpenCV’s color space transformation, contour extraction, and accurate color range defining features. Regarding ball detection, the YOLOv8 model emphasizes the preservation of minor object details to improve accuracy and is specifically trained to the unique properties of cricket balls. The methods are more reliable because of the careful preparation of the datasets, which include novel ball and pitch information. These cutting-edge methods not only improve cricket analytics but also set the stage for flexible methods in more general sports technology applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=OpenCV" title="OpenCV">OpenCV</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLOv8" title=" YOLOv8"> YOLOv8</a>, <a href="https://publications.waset.org/abstracts/search?q=cricket" title=" cricket"> cricket</a>, <a href="https://publications.waset.org/abstracts/search?q=custom%20dataset" title=" custom dataset"> custom dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=sports" title=" sports"> sports</a> </p> <a href="https://publications.waset.org/abstracts/182020/advancing-in-cricket-analytics-novel-approaches-for-pitch-and-ball-detection-employing-opencv-and-yolov8" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182020.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">80</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">3414</span> Development of Cost-effective Sensitive Methods for Pathogen Detection in Community Wastewater for Disease Surveillance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jesmin%20Akter">Jesmin Akter</a>, <a href="https://publications.waset.org/abstracts/search?q=Chang%20Hyuk%20Ahn"> Chang Hyuk Ahn</a>, <a href="https://publications.waset.org/abstracts/search?q=Ilho%20Kim"> Ilho Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jaiyeop%20Lee"> Jaiyeop Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Global pandemic coronavirus disease (COVID-19) caused by Severe acute respiratory syndrome SARS-CoV-2, to control the spread of the COVID-19 pandemic, wastewater surveillance has been used to monitor SARS-CoV2 prevalence in the community. The challenging part is establishing wastewater surveillance; there is a need for a well-equipped laboratory for wastewater sample analysis. According to many previous studies, reverse transcription-polymerase chain reaction (RT-PCR) based molecular tests are the most widely used and popular detection method worldwide. However, the RT-qPCR based approaches for the detection or quantification of SARS-CoV-2 genetic fragments ribonucleic acid (RNA) from wastewater require a specialized laboratory, skilled personnel, expensive instruments, and a workflow that typically requires 6 to 8 hours to provide results for just minimum samples. Rapid and reliable alternative detection methods are needed to enable less-well-qualified practitioners to set up and provide sensitive detection of SARS-CoV-2 within wastewater at less-specialized regional laboratories. Therefore, scientists and researchers are conducting experiments for rapid detection methods of COVID-19; in some cases, the structural and molecular characteristics of SARS-CoV-2 are unknown, and various strategies for the correct diagnosis of COVID-19 have been proposed by research laboratories, which are presented in the present study. The ongoing research and development of these highly sensitive and rapid technologies, namely RT-LAMP, ELISA, Biosensors, GeneXpert, allows a wide range of potential options not only for SARS-CoV-2 detection but also for other viruses as well. The effort of this study is to discuss the above effective and regional rapid detection and quantification methods in community wastewater as an essential step in advancing scientific goals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rapid%20detection" title="rapid detection">rapid detection</a>, <a href="https://publications.waset.org/abstracts/search?q=SARS-CoV-2" title=" SARS-CoV-2"> SARS-CoV-2</a>, <a href="https://publications.waset.org/abstracts/search?q=sensitive%20detection" title=" sensitive detection"> sensitive detection</a>, <a href="https://publications.waset.org/abstracts/search?q=wastewater%20surveillance" title=" wastewater surveillance"> wastewater surveillance</a> </p> <a href="https://publications.waset.org/abstracts/167037/development-of-cost-effective-sensitive-methods-for-pathogen-detection-in-community-wastewater-for-disease-surveillance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167037.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">85</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">3413</span> Design and Fabrication of Optical Nanobiosensors for Detection of MicroRNAs Involved in Neurodegenerative Diseases</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20Rahaie">Mahdi Rahaie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> MicroRNAs are a novel class of small RNAs which regulate gene expression by translational repression or degradation of messenger RNAs. To produce sensitive, simple and cost-effective assays for microRNAs, detection is in urgent demand due to important role of these biomolecules in progression of human disease such as Alzheimer’s, Multiple sclerosis, and some other neurodegenerative diseases. Herein, we report several novel, sensitive and specific microRNA nanobiosensors which were designed based on colorimetric and fluorescence detection of nanoparticles and hybridization chain reaction amplification as an enzyme-free amplification. These new strategies eliminate the need for enzymatic reactions, chemical changes, separation processes and sophisticated equipment whereas less limit of detection with most specify are acceptable. The important features of these methods are high sensitivity and specificity to differentiate between perfectly matched, mismatched and non-complementary target microRNAs and also decent response in the real sample analysis with blood plasma. These nanobiosensors can clinically be used not only for the early detection of neuro diseases but also for every sickness related to miRNAs by direct detection of the plasma microRNAs in real clinical samples, without a need for sample preparation, RNA extraction and/or amplification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybridization%20chain%20reaction" title="hybridization chain reaction">hybridization chain reaction</a>, <a href="https://publications.waset.org/abstracts/search?q=microRNA" title=" microRNA"> microRNA</a>, <a href="https://publications.waset.org/abstracts/search?q=nanobiosensor" title=" nanobiosensor"> nanobiosensor</a>, <a href="https://publications.waset.org/abstracts/search?q=neurodegenerative%20diseases" title=" neurodegenerative diseases"> neurodegenerative diseases</a> </p> <a href="https://publications.waset.org/abstracts/96000/design-and-fabrication-of-optical-nanobiosensors-for-detection-of-micrornas-involved-in-neurodegenerative-diseases" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/96000.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">151</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">3412</span> Detection of Keypoint in Press-Fit Curve Based on Convolutional Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shoujia%20Fang">Shoujia Fang</a>, <a href="https://publications.waset.org/abstracts/search?q=Guoqing%20Ding"> Guoqing Ding</a>, <a href="https://publications.waset.org/abstracts/search?q=Xin%20Chen"> Xin Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The quality of press-fit assembly is closely related to reliability and safety of product. The paper proposed a keypoint detection method based on convolutional neural network to improve the accuracy of keypoint detection in press-fit curve. It would provide an auxiliary basis for judging quality of press-fit assembly. The press-fit curve is a curve of press-fit force and displacement. Both force data and distance data are time-series data. Therefore, one-dimensional convolutional neural network is used to process the press-fit curve. After the obtained press-fit data is filtered, the multi-layer one-dimensional convolutional neural network is used to perform the automatic learning of press-fit curve features, and then sent to the multi-layer perceptron to finally output keypoint of the curve. We used the data of press-fit assembly equipment in the actual production process to train CNN model, and we used different data from the same equipment to evaluate the performance of detection. Compared with the existing research result, the performance of detection was significantly improved. This method can provide a reliable basis for the judgment of press-fit quality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=keypoint%20detection" title="keypoint detection">keypoint detection</a>, <a href="https://publications.waset.org/abstracts/search?q=curve%20feature" title=" curve feature"> curve feature</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=press-fit%20assembly" title=" press-fit assembly"> press-fit assembly</a> </p> <a href="https://publications.waset.org/abstracts/98263/detection-of-keypoint-in-press-fit-curve-based-on-convolutional-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98263.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">228</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">3411</span> A Character Detection Method for Ancient Yi Books Based on Connected Components and Regressive Character Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xu%20Han">Xu Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Shanxiong%20Chen"> Shanxiong Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Shiyu%20Zhu"> Shiyu Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaoyu%20Lin"> Xiaoyu Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Fujia%20Zhao"> Fujia Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Dingwang%20Wang"> Dingwang Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Character detection is an important issue for character recognition of ancient Yi books. The accuracy of detection directly affects the recognition effect of ancient Yi books. Considering the complex layout, the lack of standard typesetting and the mixed arrangement between images and texts, we propose a character detection method for ancient Yi books based on connected components and regressive character segmentation. First, the scanned images of ancient Yi books are preprocessed with nonlocal mean filtering, and then a modified local adaptive threshold binarization algorithm is used to obtain the binary images to segment the foreground and background for the images. Second, the non-text areas are removed by the method based on connected components. Finally, the single character in the ancient Yi books is segmented by our method. The experimental results show that the method can effectively separate the text areas and non-text areas for ancient Yi books and achieve higher accuracy and recall rate in the experiment of character detection, and effectively solve the problem of character detection and segmentation in character recognition of ancient books. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CCS%20concepts" title="CCS concepts">CCS concepts</a>, <a href="https://publications.waset.org/abstracts/search?q=computing%20methodologies" title=" computing methodologies"> computing methodologies</a>, <a href="https://publications.waset.org/abstracts/search?q=interest%20point" title=" interest point"> interest point</a>, <a href="https://publications.waset.org/abstracts/search?q=salient%20region%20detections" title=" salient region detections"> salient region detections</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a> </p> <a href="https://publications.waset.org/abstracts/115647/a-character-detection-method-for-ancient-yi-books-based-on-connected-components-and-regressive-character-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115647.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">132</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">3410</span> Motion-Based Detection and Tracking of Multiple Pedestrians</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Harras">A. Harras</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Tsuji"> A. Tsuji</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Terada"> K. Terada</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tracking of moving people has gained a matter of great importance due to rapid technological advancements in the field of computer vision. The objective of this study is to design a motion based detection and tracking multiple walking pedestrians randomly in different directions. In our proposed method, Gaussian mixture model (GMM) is used to determine moving persons in image sequences. It reacts to changes that take place in the scene like different illumination; moving objects start and stop often, etc. Background noise in the scene is eliminated through applying morphological operations and the motions of tracked people which is determined by using the Kalman filter. The Kalman filter is applied to predict the tracked location in each frame and to determine the likelihood of each detection. We used a benchmark data set for the evaluation based on a side wall stationary camera. The actual scenes from the data set are taken on a street including up to eight people in front of the camera in different two scenes, the duration is 53 and 35 seconds, respectively. In the case of walking pedestrians in close proximity, the proposed method has achieved the detection ratio of 87%, and the tracking ratio is 77 % successfully. When they are deferred from each other, the detection ratio is increased to 90% and the tracking ratio is also increased to 79%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20detection" title="automatic detection">automatic detection</a>, <a href="https://publications.waset.org/abstracts/search?q=tracking" title=" tracking"> tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=pedestrians" title=" pedestrians"> pedestrians</a>, <a href="https://publications.waset.org/abstracts/search?q=counting" title=" counting"> counting</a> </p> <a href="https://publications.waset.org/abstracts/82912/motion-based-detection-and-tracking-of-multiple-pedestrians" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82912.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">257</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">3409</span> Plastic Pipe Defect Detection Using Nonlinear Acoustic Modulation </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gigih%20Priyandoko">Gigih Priyandoko</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Fairusham%20Ghazali"> Mohd Fairusham Ghazali</a>, <a href="https://publications.waset.org/abstracts/search?q=Tan%20Siew%20Fun"> Tan Siew Fun </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses about the defect detection of plastic pipe by using nonlinear acoustic wave modulation method. It is a sensitive method for damage detection and it is based on the propagation of high frequency acoustic waves in plastic pipe with low frequency excitation. The plastic pipe is excited simultaneously with a slow amplitude modulated vibration pumping wave and a constant amplitude probing wave. The frequency of both the excitation signals coincides with the resonances of the plastic pipe. A PVP pipe is used as the specimen as it is commonly used for the conveyance of liquid in many fields. The results obtained are being observed and the difference between uncracked specimen and cracked specimen can be distinguished clearly. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=plastic%20pipe" title="plastic pipe">plastic pipe</a>, <a href="https://publications.waset.org/abstracts/search?q=defect%20detection" title=" defect detection"> defect detection</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20acoustic%20modulation" title=" nonlinear acoustic modulation"> nonlinear acoustic modulation</a>, <a href="https://publications.waset.org/abstracts/search?q=excitation" title=" excitation"> excitation</a> </p> <a href="https://publications.waset.org/abstracts/16837/plastic-pipe-defect-detection-using-nonlinear-acoustic-modulation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16837.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">451</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">3408</span> Aspects and Studies of Fractal Geometry in Automatic Breast Cancer Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mrinal%20Kanti%20Bhowmik">Mrinal Kanti Bhowmik</a>, <a href="https://publications.waset.org/abstracts/search?q=Kakali%20Das%20Jr."> Kakali Das Jr.</a>, <a href="https://publications.waset.org/abstracts/search?q=Barin%20Kumar%20De"> Barin Kumar De</a>, <a href="https://publications.waset.org/abstracts/search?q=Debotosh%20Bhattacharjee"> Debotosh Bhattacharjee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Breast cancer is the most common cancer and a leading cause of death for women in the 35 to 55 age group. Early detection of breast cancer can decrease the mortality rate of breast cancer. Mammography is considered as a ‘Gold Standard’ for breast cancer detection and a very popular modality, presently used for breast cancer screening and detection. The screening of digital mammograms often leads to over diagnosis and a consequence to unnecessary traumatic & painful biopsies. For that reason recent studies involving the use of thermal imaging as a screening technique have generated a growing interest especially in cases where the mammography is limited, as in young patients who have dense breast tissue. Tumor is a significant sign of breast cancer in both mammography and thermography. The tumors are complex in structure and they also exhibit a different statistical and textural features compared to the breast background tissue. Fractal geometry is a geometry which is used to describe this type of complex structure as per their main characteristic, where traditional Euclidean geometry fails. Over the last few years, fractal geometrics have been applied mostly in many medical image (1D, 2D, or 3D) analysis applications. In breast cancer detection using digital mammogram images, also it plays a significant role. Fractal is also used in thermography for early detection of the masses using the thermal texture. This paper presents an overview of the recent aspects and initiatives of fractals in breast cancer detection in both mammography and thermography. The scope of fractal geometry in automatic breast cancer detection using digital mammogram and thermogram images are analysed, which forms a foundation for further study on application of fractal geometry in medical imaging for improving the efficiency of automatic detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractal" title="fractal">fractal</a>, <a href="https://publications.waset.org/abstracts/search?q=tumor" title=" tumor"> tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=thermography" title=" thermography"> thermography</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a> </p> <a href="https://publications.waset.org/abstracts/22188/aspects-and-studies-of-fractal-geometry-in-automatic-breast-cancer-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22188.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">388</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">3407</span> Erosion of Culture through Democratization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mladen%20Milicevic">Mladen Milicevic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper explores how the explosion of computer technologies has allowed for the democratization of many aspects of human activities, which were in the past only available through the institutionalized channels of production and distribution. We will going to use as an example the music recording industries, just to illustrate this process, but the analogies to other activities and aspects of human life can easily be extrapolated from it. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aura" title="aura">aura</a>, <a href="https://publications.waset.org/abstracts/search?q=democratization" title=" democratization"> democratization</a>, <a href="https://publications.waset.org/abstracts/search?q=music%20industry" title=" music industry"> music industry</a>, <a href="https://publications.waset.org/abstracts/search?q=music%20sharing" title=" music sharing"> music sharing</a>, <a href="https://publications.waset.org/abstracts/search?q=paradigm-shift" title=" paradigm-shift"> paradigm-shift</a> </p> <a href="https://publications.waset.org/abstracts/29176/erosion-of-culture-through-democratization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29176.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">235</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3406</span> Short-Path Near-Infrared Laser Detection of Environmental Gases by Wavelength-Modulation Spectroscopy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Isao%20Tomita">Isao Tomita</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The detection of environmental gases, 12CO_2, 13CO_2, and CH_4, using near-infrared semiconductor lasers with a short laser path length is studied by means of wavelength-modulation spectroscopy. The developed system is compact and has high sensitivity enough to detect the absorption peaks of isotopic 13CO_2 of a 3-% CO_2 gas at 2 um with a path length of 2.4 m, where its peak size is two orders of magnitude smaller than that of the ordinary 12CO_2 peaks. In addition, the detection of 12CO_2 peaks of a 385-ppm (0.0385-%) CO_2 gas in the air is made at 2 um with a path length of 1.4 m. Furthermore, in pursuing the detection of an ancient environmental CH_4 gas confined to a bubble in ice at the polar regions, measurements of the absorption spectrum for a trace gas of CH_4 in a small area are attempted. For a 100-% CH_4 gas trapped in a 1 mm^3 glass container, the absorption peaks of CH_4 are obtained at 1.65 um with a path length of 3 mm, and also the gas pressure is extrapolated from the measured data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=environmental%20gases" title="environmental gases">environmental gases</a>, <a href="https://publications.waset.org/abstracts/search?q=Near-Infrared%20Laser%20Detection" title=" Near-Infrared Laser Detection"> Near-Infrared Laser Detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Wavelength-Modulation%20Spectroscopy" title=" Wavelength-Modulation Spectroscopy"> Wavelength-Modulation Spectroscopy</a>, <a href="https://publications.waset.org/abstracts/search?q=gas%20pressure" title=" gas pressure"> gas pressure</a> </p> <a href="https://publications.waset.org/abstracts/15017/short-path-near-infrared-laser-detection-of-environmental-gases-by-wavelength-modulation-spectroscopy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15017.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">423</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">3405</span> Investigating the Viability of Ultra-Low Parameter Count Networks for Real-Time Football Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tim%20Farrelly">Tim Farrelly</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, AI-powered object detection systems have opened the doors for innovative new applications and products, especially those operating in the real world or ‘on edge’ – namely, in sport. This paper investigates the viability of an ultra-low parameter convolutional neural network specially designed for the detection of footballs on ‘on the edge’ devices. The main contribution of this paper is the exploration of integrating new design features (depth-wise separable convolutional blocks and squeezed and excitation modules) into an ultra-low parameter network and demonstrating subsequent improvements in performance. The results show that tracking the ball from Full HD images with negligibly high accu-racy is possible in real-time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20vision%20applications" title=" machine vision applications"> machine vision applications</a>, <a href="https://publications.waset.org/abstracts/search?q=sport" title=" sport"> sport</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20design" title=" network design"> network design</a> </p> <a href="https://publications.waset.org/abstracts/145298/investigating-the-viability-of-ultra-low-parameter-count-networks-for-real-time-football-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145298.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">144</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">3404</span> HRV Analysis Based Arrhythmic Beat Detection Using kNN Classifier</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Onder%20Yakut">Onder Yakut</a>, <a href="https://publications.waset.org/abstracts/search?q=Oguzhan%20Timus"> Oguzhan Timus</a>, <a href="https://publications.waset.org/abstracts/search?q=Emine%20Dogru%20Bolat"> Emine Dogru Bolat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Health diseases have a vital significance affecting human being's life and life quality. Sudden death events can be prevented owing to early diagnosis and treatment methods. Electrical signals, taken from the human being's body using non-invasive methods and showing the heart activity is called Electrocardiogram (ECG). The ECG signal is used for following daily activity of the heart by clinicians. Heart Rate Variability (HRV) is a physiological parameter giving the variation between the heart beats. ECG data taken from MITBIH Arrhythmia Database is used in the model employed in this study. The detection of arrhythmic heart beats is aimed utilizing the features extracted from the HRV time domain parameters. The developed model provides a satisfactory performance with ~89% accuracy, 91.7 % sensitivity and 85% specificity rates for the detection of arrhythmic beats. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arrhythmic%20beat%20detection" title="arrhythmic beat detection">arrhythmic beat detection</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG" title=" ECG"> ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=HRV" title=" HRV"> HRV</a>, <a href="https://publications.waset.org/abstracts/search?q=kNN%20classifier" title=" kNN classifier"> kNN classifier</a> </p> <a href="https://publications.waset.org/abstracts/41219/hrv-analysis-based-arrhythmic-beat-detection-using-knn-classifier" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41219.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">352</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">3403</span> Design of an Improved Distributed Framework for Intrusion Detection System Based on Artificial Immune System and Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yulin%20Rao">Yulin Rao</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhixuan%20Li"> Zhixuan Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Burra%20Venkata%20Durga%20Kumar"> Burra Venkata Durga Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Intrusion detection refers to monitoring the actions of internal and external intruders on the system and detecting the behaviours that violate security policies in real-time. In intrusion detection, there has been much discussion about the application of neural network technology and artificial immune system (AIS). However, many solutions use static methods (signature-based and stateful protocol analysis) or centralized intrusion detection systems (CIDS), which are unsuitable for real-time intrusion detection systems that need to process large amounts of data and detect unknown intrusions. This article proposes a framework for a distributed intrusion detection system (DIDS) with multi-agents based on the concept of AIS and neural network technology to detect anomalies and intrusions. In this framework, multiple agents are assigned to each host and work together, improving the system's detection efficiency and robustness. The trainer agent in the central server of the framework uses the artificial neural network (ANN) rather than the negative selection algorithm of AIS to generate mature detectors. Mature detectors can distinguish between self-files and non-self-files after learning. Our analyzer agents use genetic algorithms to generate memory cell detectors. This kind of detector will effectively reduce false positive and false negative errors and act quickly on known intrusions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20immune%20system" title="artificial immune system">artificial immune system</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20artificial%20intelligence" title=" distributed artificial intelligence"> distributed artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent" title=" multi-agent"> multi-agent</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection%20system" title=" intrusion detection system"> intrusion detection system</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a> </p> <a href="https://publications.waset.org/abstracts/152818/design-of-an-improved-distributed-framework-for-intrusion-detection-system-based-on-artificial-immune-system-and-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152818.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">109</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">3402</span> Singular Perturbed Vector Field Method Applied to the Problem of Thermal Explosion of Polydisperse Fuel Spray</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ophir%20Nave">Ophir Nave</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In our research, we present the concept of singularly perturbed vector field (SPVF) method, and its application to thermal explosion of diesel spray combustion. Given a system of governing equations, which consist of hidden Multi-scale variables, the SPVF method transfer and decompose such system to fast and slow singularly perturbed subsystems (SPS). The SPVF method enables us to understand the complex system, and simplify the calculations. Later powerful analytical, numerical and asymptotic methods (e.g method of integral (invariant) manifold (MIM), the homotopy analysis method (HAM) etc.) can be applied to each subsystem. We compare the results obtained by the methods of integral invariant manifold and SPVF apply to spray droplets combustion model. The research deals with the development of an innovative method for extracting fast and slow variables in physical mathematical models. The method that we developed called singular perturbed vector field. This method based on a numerical algorithm applied to global quasi linearization applied to given physical model. The SPVF method applied successfully to combustion processes. Our results were compared to experimentally results. The SPVF is a general numerical and asymptotical method that reveals the hierarchy (multi-scale system) of a given system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=polydisperse%20spray" title="polydisperse spray">polydisperse spray</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20reduction" title=" model reduction"> model reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=asymptotic%20analysis" title=" asymptotic analysis"> asymptotic analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-scale%20systems" title=" multi-scale systems"> multi-scale systems</a> </p> <a href="https://publications.waset.org/abstracts/71377/singular-perturbed-vector-field-method-applied-to-the-problem-of-thermal-explosion-of-polydisperse-fuel-spray" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71377.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">219</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">3401</span> Effect of Composition Fuel on Safety of Combustion Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lourdes%20I.%20Meri%C3%B1o">Lourdes I. Meriño</a>, <a href="https://publications.waset.org/abstracts/search?q=Viatcheslav%20Kafarov"> Viatcheslav Kafarov</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20G%C3%B3mez"> Maria Gómez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fuel gas used in the burner receives as contributors other gases from different processes and this result in variability in the composition, which may cause an incomplete combustion. The burners are designed to operate in a certain curve, the calorific power dependent on the pressure and gas burners. When deviation of propane and C5+ is huge, there is a large release of energy, which causes it to work out the curves of the burners, because less pressure is required to force curve into operation. That increases the risk of explosion in an oven, besides of a higher environmental impact. There should be flame detection systems, and instrumentation equipment, such as local pressure gauges located at the entrance of the gas burners, to permit verification by the operator. Additionally, distributed control systems must be configured with different combustion instruments associated with respective alarms, as well as its operational windows, and windows control guidelines of integrity, leaving the design information of this equipment. Therefore, it is desirable to analyze when a plant is taken out of service and make good operational analysis to determine the impact of changes in fuel gas streams contributors, by varying the calorific power. Hence, poor combustion is one of the cause instability in the flame of the burner and having a great impact on process safety, the integrity of individuals and teams and environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=combustion%20process" title="combustion process">combustion process</a>, <a href="https://publications.waset.org/abstracts/search?q=fuel%20composition" title=" fuel composition"> fuel composition</a>, <a href="https://publications.waset.org/abstracts/search?q=safety" title=" safety"> safety</a>, <a href="https://publications.waset.org/abstracts/search?q=fuel%20gas" title=" fuel gas"> fuel gas</a> </p> <a href="https://publications.waset.org/abstracts/15092/effect-of-composition-fuel-on-safety-of-combustion-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15092.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">489</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3400</span> Intrusion Detection System Based on Peer to Peer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alireza%20Pour%20Ebrahimi">Alireza Pour Ebrahimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Vahid%20Abasi"> Vahid Abasi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently by the extension of internet usage, Research on the intrusion detection system takes a significant importance. Many of improvement systems prevent internal and external network attacks by providing security through firewalls and antivirus. In recently years, intrusion detection systems gradually turn from host-based systems and depend on O.S to the distributed systems which are running on multiple O.S. In this work, by considering the diversity of computer networks whit respect to structure, architecture, resource, services, users and also security goals requirement a fully distributed collaborative intrusion detection system based on peer to peer architecture is suggested. in this platform each partner device (matched device) considered as a peer-to-peer network. All transmitted information to network are visible only for device that use security scanning of a source. Experimental results show that the distributed architecture is significantly upgradeable in respect to centralized approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=network" title="network">network</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection%20system" title=" intrusion detection system"> intrusion detection system</a>, <a href="https://publications.waset.org/abstracts/search?q=peer%20to%20peer" title=" peer to peer"> peer to peer</a>, <a href="https://publications.waset.org/abstracts/search?q=internal%20and%20external%20network" title=" internal and external network "> internal and external network </a> </p> <a href="https://publications.waset.org/abstracts/25216/intrusion-detection-system-based-on-peer-to-peer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25216.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">547</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">3399</span> Rapid and Culture-Independent Detection of Staphylococcus Aureus by PCR Based Protocols</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=V.%20Verma">V. Verma</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Riyaz-ul-Hassan"> Syed Riyaz-ul-Hassan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Staphylococcus aureus is one of the most commonly found pathogenic bacteria and is hard to eliminate from the human environment. It is responsible for many nosocomial infections, besides being the main causative agent of food intoxication by virtue of its variety of enterotoxins. Routine detection of S. aureus in food is usually carried out by traditional methods based on morphological and biochemical characterization. These methods are time-consuming and tedious. In addition, misclassifications with automated susceptibility testing systems or commercially available latex agglutination kits have been reported by several workers. Consequently, there is a need for methods to specifically discriminate S. aureus from other staphylococci as quickly as possible. Data on protocols developed using molecular means like PCR technology will be presented for rapid and specific detection of this pathogen in food, clinical and environmental samples, especially milk. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=food%20Pathogens" title="food Pathogens">food Pathogens</a>, <a href="https://publications.waset.org/abstracts/search?q=PCR%20technology" title=" PCR technology"> PCR technology</a>, <a href="https://publications.waset.org/abstracts/search?q=rapid%20and%20specific%20detection" title=" rapid and specific detection"> rapid and specific detection</a>, <a href="https://publications.waset.org/abstracts/search?q=staphylococcus%20aureus" title=" staphylococcus aureus"> staphylococcus aureus</a> </p> <a href="https://publications.waset.org/abstracts/42644/rapid-and-culture-independent-detection-of-staphylococcus-aureus-by-pcr-based-protocols" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42644.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">513</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">3398</span> Fabrication of Immune-Affinity Monolithic Array for Detection of α-Fetoprotein and Carcinoembryonic Antigen</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li%20Li">Li Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Li-Ru%20Xia"> Li-Ru Xia</a>, <a href="https://publications.waset.org/abstracts/search?q=He-Ye%20Wang"> He-Ye Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiao-Dong%20Bi"> Xiao-Dong Bi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we presented a highly sensitive immune-affinity monolithic array for detection of α-fetoprotein (AFP) and carcinoembryonic antigen (CEA). Firstly, the epoxy functionalized monolith arrays were fabricated using UV initiated copolymerization method. Scanning electron microscopy (SEM) image showed that the poly(BABEA-<em>co</em>-GMA) monolith exhibited a well-controlled skeletal and well-distributed porous structure. Then, AFP and CEA immune-affinity monolithic arrays were prepared by immobilization of AFP and CEA antibodies on epoxy functionalized monolith arrays. With a non-competitive immune response format, the presented AFP and CEA immune-affinity arrays were demonstrated as an inexpensive, flexible, homogeneous and stable array for detection of AFP and CEA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chemiluminescent%20detection" title="chemiluminescent detection">chemiluminescent detection</a>, <a href="https://publications.waset.org/abstracts/search?q=immune-affinity" title=" immune-affinity"> immune-affinity</a>, <a href="https://publications.waset.org/abstracts/search?q=monolithic%20copolymer%20array" title=" monolithic copolymer array"> monolithic copolymer array</a>, <a href="https://publications.waset.org/abstracts/search?q=UV-initiated%20copolymerization" title=" UV-initiated copolymerization"> UV-initiated copolymerization</a> </p> <a href="https://publications.waset.org/abstracts/43820/fabrication-of-immune-affinity-monolithic-array-for-detection-of-a-fetoprotein-and-carcinoembryonic-antigen" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43820.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">339</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">3397</span> An Optimal Matching Design Method of Space-Based Optical Payload for Typical Aerial Target Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yin%20Zhang">Yin Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Kai%20Qiao"> Kai Qiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiyang%20Zhi"> Xiyang Zhi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinnan%20Gong"> Jinnan Gong</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianming%20Hu"> Jianming Hu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to effectively detect aerial targets over long distances, an optimal matching design method of space-based optical payload is proposed. Firstly, main factors affecting optical detectability of small targets under complex environment are analyzed based on the full link of a detection system, including band center, band width and spatial resolution. Then a performance characterization model representing the relationship between image signal-to-noise ratio (SCR) and the above influencing factors is established to describe a detection system. Finally, an optimal matching design example is demonstrated for a typical aerial target by simulating and analyzing its SCR under different scene clutter coupling with multi-scale characteristics, and the optimized detection band and spatial resolution are presented. The method can provide theoretical basis and scientific guidance for space-based detection system design, payload specification demonstration and information processing algorithm optimization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=space-based%20detection" title="space-based detection">space-based detection</a>, <a href="https://publications.waset.org/abstracts/search?q=aerial%20targets" title=" aerial targets"> aerial targets</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20system%20design" title=" optical system design"> optical system design</a>, <a href="https://publications.waset.org/abstracts/search?q=detectability%20characterization" title=" detectability characterization"> detectability characterization</a> </p> <a href="https://publications.waset.org/abstracts/107378/an-optimal-matching-design-method-of-space-based-optical-payload-for-typical-aerial-target-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107378.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">168</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">3396</span> Hand Gesture Detection via EmguCV Canny Pruning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20N.%20Mosola">N. N. Mosola</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20J.%20Molete"> S. J. Molete</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20S.%20Masoebe"> L. S. Masoebe</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Letsae"> M. Letsae</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hand gesture recognition is a technique used to locate, detect, and recognize a hand gesture. Detection and recognition are concepts of Artificial Intelligence (AI). AI concepts are applicable in Human Computer Interaction (HCI), Expert systems (ES), etc. Hand gesture recognition can be used in sign language interpretation. Sign language is a visual communication tool. This tool is used mostly by deaf societies and those with speech disorder. Communication barriers exist when societies with speech disorder interact with others. This research aims to build a hand recognition system for Lesotho’s Sesotho and English language interpretation. The system will help to bridge the communication problems encountered by the mentioned societies. The system has various processing modules. The modules consist of a hand detection engine, image processing engine, feature extraction, and sign recognition. Detection is a process of identifying an object. The proposed system uses Canny pruning Haar and Haarcascade detection algorithms. Canny pruning implements the Canny edge detection. This is an optimal image processing algorithm. It is used to detect edges of an object. The system employs a skin detection algorithm. The skin detection performs background subtraction, computes the convex hull, and the centroid to assist in the detection process. Recognition is a process of gesture classification. Template matching classifies each hand gesture in real-time. The system was tested using various experiments. The results obtained show that time, distance, and light are factors that affect the rate of detection and ultimately recognition. Detection rate is directly proportional to the distance of the hand from the camera. Different lighting conditions were considered. The more the light intensity, the faster the detection rate. Based on the results obtained from this research, the applied methodologies are efficient and provide a plausible solution towards a light-weight, inexpensive system which can be used for sign language interpretation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=canny%20pruning" title="canny pruning">canny pruning</a>, <a href="https://publications.waset.org/abstracts/search?q=hand%20recognition" title=" hand recognition"> hand recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=skin%20tracking" title=" skin tracking"> skin tracking</a> </p> <a href="https://publications.waset.org/abstracts/91296/hand-gesture-detection-via-emgucv-canny-pruning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91296.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">185</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">3395</span> An Improved Two-dimensional Ordered Statistical Constant False Alarm Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Weihao%20Wang">Weihao Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhulin%20Zong"> Zhulin Zong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Two-dimensional ordered statistical constant false alarm detection is a widely used method for detecting weak target signals in radar signal processing applications. The method is based on analyzing the statistical characteristics of the noise and clutter present in the radar signal and then using this information to set an appropriate detection threshold. In this approach, the reference cell of the unit to be detected is divided into several reference subunits. These subunits are used to estimate the noise level and adjust the detection threshold, with the aim of minimizing the false alarm rate. By using an ordered statistical approach, the method is able to effectively suppress the influence of clutter and noise, resulting in a low false alarm rate. The detection process involves a number of steps, including filtering the input radar signal to remove any noise or clutter, estimating the noise level based on the statistical characteristics of the reference subunits, and finally, setting the detection threshold based on the estimated noise level. One of the main advantages of two-dimensional ordered statistical constant false alarm detection is its ability to detect weak target signals in the presence of strong clutter and noise. This is achieved by carefully analyzing the statistical properties of the signal and using an ordered statistical approach to estimate the noise level and adjust the detection threshold. In conclusion, two-dimensional ordered statistical constant false alarm detection is a powerful technique for detecting weak target signals in radar signal processing applications. By dividing the reference cell into several subunits and using an ordered statistical approach to estimate the noise level and adjust the detection threshold, this method is able to effectively suppress the influence of clutter and noise and maintain a low false alarm rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=two-dimensional" title="two-dimensional">two-dimensional</a>, <a href="https://publications.waset.org/abstracts/search?q=ordered%20statistical" title=" ordered statistical"> ordered statistical</a>, <a href="https://publications.waset.org/abstracts/search?q=constant%20false%20alarm" title=" constant false alarm"> constant false alarm</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a>, <a href="https://publications.waset.org/abstracts/search?q=weak%20target%20signals" title=" weak target signals"> weak target signals</a> </p> <a href="https://publications.waset.org/abstracts/163351/an-improved-two-dimensional-ordered-statistical-constant-false-alarm-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163351.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">78</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">3394</span> Tool for Fast Detection of Java Code Snippets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tom%C3%A1%C5%A1%20Bubl%C3%ADk">Tomáš Bublík</a>, <a href="https://publications.waset.org/abstracts/search?q=Miroslav%20Virius"> Miroslav Virius</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents general results on the Java source code snippet detection problem. We propose the tool which uses graph and sub graph isomorphism detection. A number of solutions for all of these tasks have been proposed in the literature. However, although that all these solutions are really fast, they compare just the constant static trees. Our solution offers to enter an input sample dynamically with the Scripthon language while preserving an acceptable speed. We used several optimizations to achieve very low number of comparisons during the matching algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AST" title="AST">AST</a>, <a href="https://publications.waset.org/abstracts/search?q=Java" title=" Java"> Java</a>, <a href="https://publications.waset.org/abstracts/search?q=tree%20matching" title=" tree matching"> tree matching</a>, <a href="https://publications.waset.org/abstracts/search?q=scripthon%20source%20code%20recognition" title=" scripthon source code recognition"> scripthon source code recognition</a> </p> <a href="https://publications.waset.org/abstracts/8474/tool-for-fast-detection-of-java-code-snippets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8474.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">425</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">3393</span> Image Recognition and Anomaly Detection Powered by GANs: A Systematic Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Agastya%20Pratap%20Singh">Agastya Pratap Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Generative Adversarial Networks (GANs) have emerged as powerful tools in the fields of image recognition and anomaly detection due to their ability to model complex data distributions and generate realistic images. This systematic review explores recent advancements and applications of GANs in both image recognition and anomaly detection tasks. We discuss various GAN architectures, such as DCGAN, CycleGAN, and StyleGAN, which have been tailored to improve accuracy, robustness, and efficiency in visual data analysis. In image recognition, GANs have been used to enhance data augmentation, improve classification models, and generate high-quality synthetic images. In anomaly detection, GANs have proven effective in identifying rare and subtle abnormalities across various domains, including medical imaging, cybersecurity, and industrial inspection. The review also highlights the challenges and limitations associated with GAN-based methods, such as instability during training and mode collapse, and suggests future research directions to overcome these issues. Through this review, we aim to provide researchers with a comprehensive understanding of the capabilities and potential of GANs in transforming image recognition and anomaly detection practices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20networks" title="generative adversarial networks">generative adversarial networks</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20recognition" title=" image recognition"> image recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a>, <a href="https://publications.waset.org/abstracts/search?q=DCGAN" title=" DCGAN"> DCGAN</a>, <a href="https://publications.waset.org/abstracts/search?q=CycleGAN" title=" CycleGAN"> CycleGAN</a>, <a href="https://publications.waset.org/abstracts/search?q=StyleGAN" title=" StyleGAN"> StyleGAN</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20augmentation" title=" data augmentation"> data augmentation</a> </p> <a href="https://publications.waset.org/abstracts/192413/image-recognition-and-anomaly-detection-powered-by-gans-a-systematic-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192413.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">20</span> </span> </div> </div> <ul class="pagination"> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=explosion%20detection&page=6" rel="prev">&lsaquo;</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=explosion%20detection&page=1">1</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=explosion%20detection&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=explosion%20detection&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=explosion%20detection&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=explosion%20detection&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=explosion%20detection&page=6">6</a></li> <li class="page-item active"><span class="page-link">7</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=explosion%20detection&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=explosion%20detection&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=explosion%20detection&page=10">10</a></li> <li class="page-item disabled"><span class="page-link">...</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=explosion%20detection&page=120">120</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=explosion%20detection&page=121">121</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=explosion%20detection&page=8" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>