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: Independent Component Analysis (ICA)</title> <meta name="description" content="Search results for: Independent Component Analysis (ICA)"> <meta name="keywords" content="Independent Component Analysis (ICA)"> <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="Independent Component Analysis (ICA)" 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="Independent Component Analysis (ICA)"> <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> 30549</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Independent Component Analysis (ICA)</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">30549</span> Incremental Learning of Independent Topic Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takahiro%20Nishigaki">Takahiro Nishigaki</a>, <a href="https://publications.waset.org/abstracts/search?q=Katsumi%20Nitta"> Katsumi Nitta</a>, <a href="https://publications.waset.org/abstracts/search?q=Takashi%20Onoda"> Takashi Onoda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a method of applying Independent Topic Analysis (ITA) to increasing the number of document data. The number of document data has been increasing since the spread of the Internet. ITA was presented as one method to analyze the document data. ITA is a method for extracting the independent topics from the document data by using the Independent Component Analysis (ICA). ICA is a technique in the signal processing; however, it is difficult to apply the ITA to increasing number of document data. Because ITA must use the all document data so temporal and spatial cost is very high. Therefore, we present Incremental ITA which extracts the independent topics from increasing number of document data. Incremental ITA is a method of updating the independent topics when the document data is added after extracted the independent topics from a just previous the data. In addition, Incremental ITA updates the independent topics when the document data is added. And we show the result applied Incremental ITA to benchmark datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title="text mining">text mining</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20extraction" title=" topic extraction"> topic extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=independent" title=" independent"> independent</a>, <a href="https://publications.waset.org/abstracts/search?q=incremental" title=" incremental"> incremental</a>, <a href="https://publications.waset.org/abstracts/search?q=independent%20component%20analysis" title=" independent component analysis"> independent component analysis</a> </p> <a href="https://publications.waset.org/abstracts/58971/incremental-learning-of-independent-topic-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58971.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">309</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">30548</span> Statistical Analysis of Natural Images after Applying ICA and ISA</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peyman%20Sheikholharam%20Mashhadi">Peyman Sheikholharam Mashhadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Difficulties in analyzing real world images in classical image processing and machine vision framework have motivated researchers towards considering the biology-based vision. It is a common belief that mammalian visual cortex has been adapted to the statistics of the real world images through the evolution process. There are two well-known successful models of mammalian visual cortical cells: Independent Component Analysis (ICA) and Independent Subspace Analysis (ISA). In this paper, we statistically analyze the dependencies which remain in the components after applying these models to the natural images. Also, we investigate the response of feature detectors to gratings with various parameters in order to find optimal parameters of the feature detectors. Finally, the selectiveness of feature detectors to phase, in both models is considered. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=statistics" title="statistics">statistics</a>, <a href="https://publications.waset.org/abstracts/search?q=independent%20component%20analysis" title=" independent component analysis"> independent component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=independent%20subspace%20analysis" title=" independent subspace analysis"> independent subspace analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=phase" title=" phase"> phase</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20images" title=" natural images"> natural images</a> </p> <a href="https://publications.waset.org/abstracts/34292/statistical-analysis-of-natural-images-after-applying-ica-and-isa" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34292.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">30547</span> Exploiting Fast Independent Component Analysis Based Algorithm for Equalization of Impaired Baseband Received Signal</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Umair">Muhammad Umair</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Qasim%20Gilani"> Syed Qasim Gilani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A technique using Independent Component Analysis (ICA) for blind receiver signal processing is investigated. The problem of the receiver signal processing is viewed as of signal equalization and implementation imperfections compensation. Based on this, a model similar to a general ICA problem is developed for the received signal. Then, the use of ICA technique for blind signal equalization in the time domain is presented. The equalization is regarded as a signal separation problem, since the desired signal is separated from interference terms. This problem is addressed in the paper by over-sampling of the received signal. By using ICA for equalization, besides channel equalization, other transmission imperfections such as Direct current (DC) bias offset, carrier phase and In phase Quadrature phase imbalance will also be corrected. Simulation results for a system using 16-Quadraure Amplitude Modulation(QAM) are presented to show the performance of the proposed scheme. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blind%20equalization" title="blind equalization">blind equalization</a>, <a href="https://publications.waset.org/abstracts/search?q=blind%20signal%20separation" title=" blind signal separation"> blind signal separation</a>, <a href="https://publications.waset.org/abstracts/search?q=equalization" title=" equalization"> equalization</a>, <a href="https://publications.waset.org/abstracts/search?q=independent%20component%20analysis" title=" independent component analysis"> independent component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=transmission%20impairments" title=" transmission impairments"> transmission impairments</a>, <a href="https://publications.waset.org/abstracts/search?q=QAM%20receiver" title=" QAM receiver"> QAM receiver</a> </p> <a href="https://publications.waset.org/abstracts/94433/exploiting-fast-independent-component-analysis-based-algorithm-for-equalization-of-impaired-baseband-received-signal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94433.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">214</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">30546</span> Use of Landsat OLI Images in the Mapping of Landslides: Case of the Taounate Province in Northern Morocco</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Benchelha">S. Benchelha</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Chennaoui"> H. Chennaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Hakdaoui"> M. Hakdaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Baidder"> L. Baidder</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Mansouri"> H. Mansouri</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Ejjaaouani"> H. Ejjaaouani</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Benchelha"> T. Benchelha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Northern Morocco is characterized by relatively young mountains experiencing a very important dynamic compared to other areas of Morocco. The dynamics associated with the formation of the Rif chain (Alpine tectonics), is accompanied by instabilities essentially related to tectonic movements. The realization of important infrastructures (Roads, Highways,...) represents a triggering factor and favoring landslides. This paper is part of the establishment of landslides susceptibility map and concerns the mapping of unstable areas in the province of Taounate. The landslide was identified using the components of the false color (FCC) of images Landsat OLI: i) the first independent component (IC1), ii) The main component (PC), iii) Normalized difference index (NDI). This mapping for landslides class is validated by in-situ surveys. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=landslides" title="landslides">landslides</a>, <a href="https://publications.waset.org/abstracts/search?q=False%20Color%20Composite%20%28FCC%29" title=" False Color Composite (FCC)"> False Color Composite (FCC)</a>, <a href="https://publications.waset.org/abstracts/search?q=Independent%20Component%20Analysis%20%28ICA%29" title=" Independent Component Analysis (ICA)"> Independent Component Analysis (ICA)</a>, <a href="https://publications.waset.org/abstracts/search?q=Principal%20Component%20Analysis%20%28PCA%29" title=" Principal Component Analysis (PCA)"> Principal Component Analysis (PCA)</a>, <a href="https://publications.waset.org/abstracts/search?q=Normalized%20Difference%20Index%20%28NDI%29" title=" Normalized Difference Index (NDI)"> Normalized Difference Index (NDI)</a>, <a href="https://publications.waset.org/abstracts/search?q=Normalized%20Difference%20Mid%20Red%20Index%20%28NDMIDR%29" title=" Normalized Difference Mid Red Index (NDMIDR)"> Normalized Difference Mid Red Index (NDMIDR)</a> </p> <a href="https://publications.waset.org/abstracts/73841/use-of-landsat-oli-images-in-the-mapping-of-landslides-case-of-the-taounate-province-in-northern-morocco" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73841.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">290</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">30545</span> A Stable Method for Determination of the Number of Independent Components</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuyan%20Yi">Yuyan Yi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jingyi%20Zheng"> Jingyi Zheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Nedret%20Billor"> Nedret Billor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Independent component analysis (ICA) is one of the most commonly used blind source separation (BSS) techniques for signal pre-processing, such as noise reduction and feature extraction. The main parameter in the ICA method is the number of independent components (IC). Although there have been several methods for the determination of the number of ICs, it has not been given sufficient attentionto this important parameter. In this study, wereview the mostused methods fordetermining the number of ICs and providetheir advantages and disadvantages. Further, wepropose an improved version of column-wise ICAByBlock method for the determination of the number of ICs.To assess the performance of the proposed method, we compare the column-wise ICAbyBlock with several existing methods through different ICA methods by using simulated and real signal data. Results show that the proposed column-wise ICAbyBlock is an effective and stable method for determining the optimal number of components in ICA. This method is simple, and results can be demonstrated intuitively with good visualizations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=independent%20component%20analysis" title="independent component analysis">independent component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20number" title=" optimal number"> optimal number</a>, <a href="https://publications.waset.org/abstracts/search?q=column-wise" title=" column-wise"> column-wise</a>, <a href="https://publications.waset.org/abstracts/search?q=correlation%20coefficient" title=" correlation coefficient"> correlation coefficient</a>, <a href="https://publications.waset.org/abstracts/search?q=cross-validation" title=" cross-validation"> cross-validation</a>, <a href="https://publications.waset.org/abstracts/search?q=ICAByblock" title=" ICAByblock"> ICAByblock</a> </p> <a href="https://publications.waset.org/abstracts/150582/a-stable-method-for-determination-of-the-number-of-independent-components" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150582.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">99</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">30544</span> Web Search Engine Based Naming Procedure for Independent Topic</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takahiro%20Nishigaki">Takahiro Nishigaki</a>, <a href="https://publications.waset.org/abstracts/search?q=Takashi%20Onoda"> Takashi Onoda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, the number of document data has been increasing since the spread of the Internet. Many methods have been studied for extracting topics from large document data. We proposed Independent Topic Analysis (ITA) to extract topics independent of each other from large document data such as newspaper data. ITA is a method for extracting the independent topics from the document data by using the Independent Component Analysis. The topic represented by ITA is represented by a set of words. However, the set of words is quite different from the topics the user imagines. For example, the top five words with high independence of a topic are as follows. Topic1 = {"scor", "game", "lead", "quarter", "rebound"}. This Topic 1 is considered to represent the topic of "SPORTS". This topic name "SPORTS" has to be attached by the user. ITA cannot name topics. Therefore, in this research, we propose a method to obtain topics easy for people to understand by using the web search engine, topics given by the set of words given by independent topic analysis. In particular, we search a set of topical words, and the title of the homepage of the search result is taken as the topic name. And we also use the proposed method for some data and verify its effectiveness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=independent%20topic%20analysis" title="independent topic analysis">independent topic analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20extraction" title=" topic extraction"> topic extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20naming" title=" topic naming"> topic naming</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20search%20engine" title=" web search engine"> web search engine</a> </p> <a href="https://publications.waset.org/abstracts/98583/web-search-engine-based-naming-procedure-for-independent-topic" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98583.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">119</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">30543</span> Assessment of an ICA-Based Method for Detecting the Effect of Attention in the Auditory Late Response</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Siavash%20Mirahmadizoghi">Siavash Mirahmadizoghi</a>, <a href="https://publications.waset.org/abstracts/search?q=Steven%20Bell"> Steven Bell</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Simpson"> David Simpson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work a new independent component analysis (ICA) based method for noise reduction in evoked potentials is evaluated on for auditory late responses (ALR) captured with a 63-channel electroencephalogram (EEG) from 10 normal-hearing subjects. The performance of the new method is compared with a single channel alternative in terms of signal to noise ratio (SNR), the number of channels with an SNR above an empirically derived statistical critical value and an estimate of the effect of attention on the major components in the ALR waveform. The results show that the multichannel signal processing method can significantly enhance the quality of the ALR signal and also detect the effect of the attention on the ALR better than the single channel alternative. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auditory%20late%20response%20%28ALR%29" title="auditory late response (ALR)">auditory late response (ALR)</a>, <a href="https://publications.waset.org/abstracts/search?q=attention" title=" attention"> attention</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG" title=" EEG"> EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=independent%20component%20analysis%20%28ICA%29" title=" independent component analysis (ICA)"> independent component analysis (ICA)</a>, <a href="https://publications.waset.org/abstracts/search?q=multichannel%20signal%20processing" title=" multichannel signal processing"> multichannel signal processing</a> </p> <a href="https://publications.waset.org/abstracts/11551/assessment-of-an-ica-based-method-for-detecting-the-effect-of-attention-in-the-auditory-late-response" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11551.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">505</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">30542</span> A Combination of Independent Component Analysis, Relative Wavelet Energy and Support Vector Machine for Mental State Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nguyen%20The%20Hoang%20Anh">Nguyen The Hoang Anh</a>, <a href="https://publications.waset.org/abstracts/search?q=Tran%20Huy%20Hoang"> Tran Huy Hoang</a>, <a href="https://publications.waset.org/abstracts/search?q=Vu%20Tat%20Thang"> Vu Tat Thang</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20T.%20Quyen%20Bui"> T. T. Quyen Bui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mental state classification is an important step for realizing a control system based on electroencephalography (EEG) signals which could benefit a lot of paralyzed people including the locked-in or Amyotrophic Lateral Sclerosis. Considering that EEG signals are nonstationary and often contaminated by various types of artifacts, classifying thoughts into correct mental states is not a trivial problem. In this work, our contribution is that we present and realize a novel model which integrates different techniques: Independent component analysis (ICA), relative wavelet energy, and support vector machine (SVM) for the same task. We applied our model to classify thoughts in two types of experiment whether with two or three mental states. The experimental results show that the presented model outperforms other models using Artificial Neural Network, K-Nearest Neighbors, etc. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG" title="EEG">EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=ICA" title=" ICA"> ICA</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet" title=" wavelet"> wavelet</a> </p> <a href="https://publications.waset.org/abstracts/46144/a-combination-of-independent-component-analysis-relative-wavelet-energy-and-support-vector-machine-for-mental-state-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46144.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">384</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">30541</span> Frontal Oscillatory Activity and Phase–Amplitude Coupling during Chan Meditation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arthur%20C.%20Tsai">Arthur C. Tsai</a>, <a href="https://publications.waset.org/abstracts/search?q=Chii-Shyang%20Kuo"> Chii-Shyang Kuo</a>, <a href="https://publications.waset.org/abstracts/search?q=Vincent%20S.%20C.%20Chien"> Vincent S. C. Chien</a>, <a href="https://publications.waset.org/abstracts/search?q=Michelle%20Liou"> Michelle Liou</a>, <a href="https://publications.waset.org/abstracts/search?q=Philip%20E.%20Cheng"> Philip E. Cheng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Meditation enhances mental abilities and it is an antidote to anxiety. However, very little is known about brain mechanisms and cortico-subcortical interactions underlying meditation-induced anxiety relief. In this study, the changes of phase-amplitude coupling (PAC) in which the amplitude of the beta frequency band were modulated in phase with delta rhythm were investigated after eight-week of meditation training. The study hypothesized that through a concentrate but relaxed mental training the delta-beta coupling in the frontal regions is attenuated. The delta-beta coupling analysis was applied to within and between maximally-independent component sources returned from the extended infomax independent components analysis (ICA) algorithm on the continuous EEG data during mediation. A unique meditative concentration task through relaxing body and mind was used with a constant level of moderate mental effort, so as to approach an ‘emptiness’ meditative state. A pre-test/post-test control group design was used in this study. To evaluate cross-frequency phase-amplitude coupling of component sources, the modulation index (MI) with statistics to calculate circular phase statistics were estimated. Our findings reveal that a significant delta-beta decoupling was observed in a set of frontal regions bilaterally. In addition, beta frequency band of prefrontal component were amplitude modulated in phase with the delta rhythm of medial frontal component. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=phase-amplitude%20coupling" title="phase-amplitude coupling">phase-amplitude coupling</a>, <a href="https://publications.waset.org/abstracts/search?q=ICA" title=" ICA"> ICA</a>, <a href="https://publications.waset.org/abstracts/search?q=meditation" title=" meditation"> meditation</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG" title=" EEG"> EEG</a> </p> <a href="https://publications.waset.org/abstracts/71520/frontal-oscillatory-activity-and-phase-amplitude-coupling-during-chan-meditation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71520.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">30540</span> Utilizing the Principal Component Analysis on Multispectral Aerial Imagery for Identification of Underlying Structures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marcos%20Bosques-Perez">Marcos Bosques-Perez</a>, <a href="https://publications.waset.org/abstracts/search?q=Walter%20Izquierdo"> Walter Izquierdo</a>, <a href="https://publications.waset.org/abstracts/search?q=Harold%20Martin"> Harold Martin</a>, <a href="https://publications.waset.org/abstracts/search?q=Liangdon%20Deng"> Liangdon Deng</a>, <a href="https://publications.waset.org/abstracts/search?q=Josue%20Rodriguez"> Josue Rodriguez</a>, <a href="https://publications.waset.org/abstracts/search?q=Thony%20Yan"> Thony Yan</a>, <a href="https://publications.waset.org/abstracts/search?q=Mercedes%20Cabrerizo"> Mercedes Cabrerizo</a>, <a href="https://publications.waset.org/abstracts/search?q=Armando%20Barreto"> Armando Barreto</a>, <a href="https://publications.waset.org/abstracts/search?q=Naphtali%20Rishe"> Naphtali Rishe</a>, <a href="https://publications.waset.org/abstracts/search?q=Malek%20Adjouadi"> Malek Adjouadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aerial imagery is a powerful tool when it comes to analyzing temporal changes in ecosystems and extracting valuable information from the observed scene. It allows us to identify and assess various elements such as objects, structures, textures, waterways, and shadows. To extract meaningful information, multispectral cameras capture data across different wavelength bands of the electromagnetic spectrum. In this study, the collected multispectral aerial images were subjected to principal component analysis (PCA) to identify independent and uncorrelated components or features that extend beyond the visible spectrum captured in standard RGB images. The results demonstrate that these principal components contain unique characteristics specific to certain wavebands, enabling effective object identification and image segmentation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=multispectral" title=" multispectral"> multispectral</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a> </p> <a href="https://publications.waset.org/abstracts/170875/utilizing-the-principal-component-analysis-on-multispectral-aerial-imagery-for-identification-of-underlying-structures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170875.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">176</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">30539</span> Study on Acoustic Source Detection Performance Improvement of Microphone Array Installed on Drones Using Blind Source Separation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Youngsun%20Moon">Youngsun Moon</a>, <a href="https://publications.waset.org/abstracts/search?q=Yeong-Ju%20Go"> Yeong-Ju Go</a>, <a href="https://publications.waset.org/abstracts/search?q=Jong-Soo%20Choi"> Jong-Soo Choi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most drones that currently have surveillance/reconnaissance missions are basically equipped with optical equipment, but we also need to use a microphone array to estimate the location of the acoustic source. This can provide additional information in the absence of optical equipment. The purpose of this study is to estimate Direction of Arrival (DOA) based on Time Difference of Arrival (TDOA) estimation of the acoustic source in the drone. The problem is that it is impossible to measure the clear target acoustic source because of the drone noise. To overcome this problem is to separate the drone noise and the target acoustic source using Blind Source Separation(BSS) based on Independent Component Analysis(ICA). ICA can be performed assuming that the drone noise and target acoustic source are independent and each signal has non-gaussianity. For maximized non-gaussianity each signal, we use Negentropy and Kurtosis based on probability theory. As a result, we can improve TDOA estimation and DOA estimation of the target source in the noisy environment. We simulated the performance of the DOA algorithm applying BSS algorithm, and demonstrated the simulation through experiment at the anechoic wind tunnel. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aeroacoustics" title="aeroacoustics">aeroacoustics</a>, <a href="https://publications.waset.org/abstracts/search?q=acoustic%20source%20detection" title=" acoustic source detection"> acoustic source detection</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20difference%20of%20arrival" title=" time difference of arrival"> time difference of arrival</a>, <a href="https://publications.waset.org/abstracts/search?q=direction%20of%20arrival" title=" direction of arrival"> direction of arrival</a>, <a href="https://publications.waset.org/abstracts/search?q=blind%20source%20separation" title=" blind source separation"> blind source separation</a>, <a href="https://publications.waset.org/abstracts/search?q=independent%20component%20analysis" title=" independent component analysis"> independent component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=drone" title=" drone"> drone</a> </p> <a href="https://publications.waset.org/abstracts/94236/study-on-acoustic-source-detection-performance-improvement-of-microphone-array-installed-on-drones-using-blind-source-separation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94236.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">162</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">30538</span> Evaluation of Real-Time Background Subtraction Technique for Moving Object Detection Using Fast-Independent Component Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Naoum%20Abderrahmane">Naoum Abderrahmane</a>, <a href="https://publications.waset.org/abstracts/search?q=Boumehed%20Meriem"> Boumehed Meriem</a>, <a href="https://publications.waset.org/abstracts/search?q=Alshaqaqi%20Belal"> Alshaqaqi Belal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background subtraction algorithm is a larger used technique for detecting moving objects in video surveillance to extract the foreground objects from a reference background image. There are many challenges to test a good background subtraction algorithm, like changes in illumination, dynamic background such as swinging leaves, rain, snow, and the changes in the background, for example, moving and stopping of vehicles. In this paper, we propose an efficient and accurate background subtraction method for moving object detection in video surveillance. The main idea is to use a developed fast-independent component analysis (ICA) algorithm to separate background, noise, and foreground masks from an image sequence in practical environments. The fast-ICA algorithm is adapted and adjusted with a matrix calculation and searching for an optimum non-quadratic function to be faster and more robust. Moreover, in order to estimate the de-mixing matrix and the denoising de-mixing matrix parameters, we propose to convert all images to YCrCb color space, where the luma component Y (brightness of the color) gives suitable results. The proposed technique has been verified on the publicly available datasets CD net 2012 and CD net 2014, and experimental results show that our algorithm can detect competently and accurately moving objects in challenging conditions compared to other methods in the literature in terms of quantitative and qualitative evaluations with real-time frame rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=background%20subtraction" title="background subtraction">background subtraction</a>, <a href="https://publications.waset.org/abstracts/search?q=moving%20object%20detection" title=" moving object detection"> moving object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=fast-ICA" title=" fast-ICA"> fast-ICA</a>, <a href="https://publications.waset.org/abstracts/search?q=de-mixing%20matrix" title=" de-mixing matrix"> de-mixing matrix</a> </p> <a href="https://publications.waset.org/abstracts/156716/evaluation-of-real-time-background-subtraction-technique-for-moving-object-detection-using-fast-independent-component-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156716.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">96</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">30537</span> Determining Inventory Replenishment Policy for Major Component in Assembly-to-Order of Cooling System Manufacturing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tippawan%20Nasawan">Tippawan Nasawan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objective of this study is to find the replenishment policy in Assembly-to-Order manufacturing (ATO) which some of the major components have lead-time longer than customer lead-time. The variety of products, independent component demand, and long component lead-time are the difficulty that has resulted in the overstock problem. In addition, the ordering cost is trivial when compared to the cost of material of the major component. A conceptual design of the Decision Supporting System (DSS) has introduced to assist the replenishment policy. Component replenishment by using the variable which calls Available to Promise (ATP) for making the decision is one of the keys. The Poisson distribution is adopted to realize demand patterns in order to calculate Safety Stock (SS) at the specified Customer Service Level (CSL). When distribution cannot identify, nonparametric will be applied instead. The test result after comparing the ending inventory between the new policy and the old policy, the overstock has significantly reduced by 46.9 percent or about 469,891.51 US-Dollars for the cost of the major component (material cost only). Besides, the number of the major component inventory is also reduced by about 41 percent which helps to mitigate the chance of damage and keeping stock. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Assembly-to-Order" title="Assembly-to-Order">Assembly-to-Order</a>, <a href="https://publications.waset.org/abstracts/search?q=Decision%20Supporting%20System" title=" Decision Supporting System"> Decision Supporting System</a>, <a href="https://publications.waset.org/abstracts/search?q=Component%20replenishment" title=" Component replenishment "> Component replenishment </a>, <a href="https://publications.waset.org/abstracts/search?q=Poisson%20distribution" title=" Poisson distribution "> Poisson distribution </a> </p> <a href="https://publications.waset.org/abstracts/120812/determining-inventory-replenishment-policy-for-major-component-in-assembly-to-order-of-cooling-system-manufacturing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/120812.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">127</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">30536</span> A Single-Channel BSS-Based Method for Structural Health Monitoring of Civil Infrastructure under Environmental Variations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yanjie%20Zhu">Yanjie Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Andr%C3%A9%20Jesus"> André Jesus</a>, <a href="https://publications.waset.org/abstracts/search?q=Irwanda%20Laory"> Irwanda Laory</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Structural Health Monitoring (SHM), involving data acquisition, data interpretation and decision-making system aim to continuously monitor the structural performance of civil infrastructures under various in-service circumstances. The main value and purpose of SHM is identifying damages through data interpretation system. Research on SHM has been expanded in the last decades and a large volume of data is recorded every day owing to the dramatic development in sensor techniques and certain progress in signal processing techniques. However, efficient and reliable data interpretation for damage detection under environmental variations is still a big challenge. Structural damages might be masked because variations in measured data can be the result of environmental variations. This research reports a novel method based on single-channel Blind Signal Separation (BSS), which extracts environmental effects from measured data directly without any prior knowledge of the structure loading and environmental conditions. Despite the successful application in audio processing and bio-medical research fields, BSS has never been used to detect damage under varying environmental conditions. This proposed method optimizes and combines Ensemble Empirical Mode Decomposition (EEMD), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) together to separate structural responses due to different loading conditions respectively from a single channel input signal. The ICA is applying on dimension-reduced output of EEMD. Numerical simulation of a truss bridge, inspired from New Joban Line Arakawa Railway Bridge, is used to validate this method. All results demonstrate that the single-channel BSS-based method can recover temperature effects from mixed structural response recorded by a single sensor with a convincing accuracy. This will be the foundation of further research on direct damage detection under varying environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=damage%20detection" title="damage detection">damage detection</a>, <a href="https://publications.waset.org/abstracts/search?q=ensemble%20empirical%20mode%20decomposition%20%28EEMD%29" title=" ensemble empirical mode decomposition (EEMD)"> ensemble empirical mode decomposition (EEMD)</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20variations" title=" environmental variations"> environmental variations</a>, <a href="https://publications.waset.org/abstracts/search?q=independent%20component%20analysis%20%28ICA%29" title=" independent component analysis (ICA)"> independent component analysis (ICA)</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis%20%28PCA%29" title=" principal component analysis (PCA)"> principal component analysis (PCA)</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20health%20monitoring%20%28SHM%29" title=" structural health monitoring (SHM)"> structural health monitoring (SHM)</a> </p> <a href="https://publications.waset.org/abstracts/38249/a-single-channel-bss-based-method-for-structural-health-monitoring-of-civil-infrastructure-under-environmental-variations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38249.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">304</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">30535</span> The Impact of Political Connections on the Funtion of Independent Directors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chih-Lin%20Chang">Chih-Lin Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Tzu-Ching%20Weng"> Tzu-Ching Weng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this study is to explore the relationship between corporate political ties and independent directors' functions. With reference to the literature variables such as the characteristics of the relevant board of directors in the past, a single comprehensive function indicator is established as a substitute variable for the function of independent directors, and the impact of political connection on the independent board of directors is further discussed. This research takes Taiwan listed enterprises from 2014 to 2020 as the main research object and conducts empirical research through descriptive statistics, correlation and regression analysis. The empirical results show that companies with political connections will have a positive impact on the number of independent directors; political connections also have a significant positive relationship with the functional part of independent directors, which means that because companies have political connections, they have a positive impact on the seats or functions of independent directors. will pay more attention and increase their oversight functions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=political" title="political">political</a>, <a href="https://publications.waset.org/abstracts/search?q=connection" title=" connection"> connection</a>, <a href="https://publications.waset.org/abstracts/search?q=independent" title=" independent"> independent</a>, <a href="https://publications.waset.org/abstracts/search?q=director" title=" director"> director</a>, <a href="https://publications.waset.org/abstracts/search?q=function" title=" function"> function</a> </p> <a href="https://publications.waset.org/abstracts/156664/the-impact-of-political-connections-on-the-funtion-of-independent-directors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156664.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">97</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">30534</span> Software Quality Assurance in Component Based Software Development – a Survey Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abeer%20Toheed%20Quadri">Abeer Toheed Quadri</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Abubakar"> Maria Abubakar</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehreen%20Sirshar"> Mehreen Sirshar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Component Based Software Development (CBSD) is a new trend in software development. Selection of quality components is not enough to ensure software quality in Component Based Software System (CBSS). A software product is considered to be a quality product if it satisfies its customer’s needs and has minimum defects. Authors’ survey different research papers and analyzes various techniques which ensure software quality in component based software development. This paper includes an investigation about how to improve the quality of a component based software system without effecting quality attributes. The reported information is identified from literature survey. The developments of component based systems are rising as they reduce the development time, effort and cost by means of reuse. After analysis, it has been explored that in order to achieve the quality in a CBSS we need to have the components that are certified through software measure because the predictability of software quality attributes of system depend on the quality attributes of the constituent components, integration process and the framework used. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CBSD%20%28component%20based%20software%20development%29" title="CBSD (component based software development)">CBSD (component based software development)</a>, <a href="https://publications.waset.org/abstracts/search?q=CBSS%20%28component%20based%20software%20system%29" title=" CBSS (component based software system)"> CBSS (component based software system)</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20components" title=" quality components"> quality components</a>, <a href="https://publications.waset.org/abstracts/search?q=SQA%20%28software%20quality%20assurance%29" title=" SQA (software quality assurance)"> SQA (software quality assurance)</a> </p> <a href="https://publications.waset.org/abstracts/26167/software-quality-assurance-in-component-based-software-development-a-survey-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26167.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">413</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">30533</span> Towards Real-Time Classification of Finger Movement Direction Using Encephalography Independent Components</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Mounir%20Tellache">Mohamed Mounir Tellache</a>, <a href="https://publications.waset.org/abstracts/search?q=Hiroyuki%20Kambara"> Hiroyuki Kambara</a>, <a href="https://publications.waset.org/abstracts/search?q=Yasuharu%20Koike"> Yasuharu Koike</a>, <a href="https://publications.waset.org/abstracts/search?q=Makoto%20Miyakoshi"> Makoto Miyakoshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Natsue%20Yoshimura"> Natsue Yoshimura</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study explores the practicality of using electroencephalographic (EEG) independent components to predict eight-direction finger movements in pseudo-real-time. Six healthy participants with individual-head MRI images performed finger movements in eight directions with two different arm configurations. The analysis was performed in two stages. The first stage consisted of using independent component analysis (ICA) to separate the signals representing brain activity from non-brain activity signals and to obtain the unmixing matrix. The resulting independent components (ICs) were checked, and those reflecting brain-activity were selected. Finally, the time series of the selected ICs were used to predict eight finger-movement directions using Sparse Logistic Regression (SLR). The second stage consisted of using the previously obtained unmixing matrix, the selected ICs, and the model obtained by applying SLR to classify a different EEG dataset. This method was applied to two different settings, namely the single-participant level and the group-level. For the single-participant level, the EEG dataset used in the first stage and the EEG dataset used in the second stage originated from the same participant. For the group-level, the EEG datasets used in the first stage were constructed by temporally concatenating each combination without repetition of the EEG datasets of five participants out of six, whereas the EEG dataset used in the second stage originated from the remaining participants. The average test classification results across datasets (mean ± S.D.) were 38.62 ± 8.36% for the single-participant, which was significantly higher than the chance level (12.50 ± 0.01%), and 27.26 ± 4.39% for the group-level which was also significantly higher than the chance level (12.49% ± 0.01%). The classification accuracy within [–45°, 45°] of the true direction is 70.03 ± 8.14% for single-participant and 62.63 ± 6.07% for group-level which may be promising for some real-life applications. Clustering and contribution analyses further revealed the brain regions involved in finger movement and the temporal aspect of their contribution to the classification. These results showed the possibility of using the ICA-based method in combination with other methods to build a real-time system to control prostheses. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain-computer%20interface" title="brain-computer interface">brain-computer interface</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalography" title=" electroencephalography"> electroencephalography</a>, <a href="https://publications.waset.org/abstracts/search?q=finger%20motion%20decoding" title=" finger motion decoding"> finger motion decoding</a>, <a href="https://publications.waset.org/abstracts/search?q=independent%20component%20analysis" title=" independent component analysis"> independent component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=pseudo%20real-time%20motion%20decoding" title=" pseudo real-time motion decoding"> pseudo real-time motion decoding</a> </p> <a href="https://publications.waset.org/abstracts/131557/towards-real-time-classification-of-finger-movement-direction-using-encephalography-independent-components" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131557.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">138</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">30532</span> Studying Frame-Resistant Steel Structures under Near Field Ground Motion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20A.%20Hashemi">S. A. Hashemi</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Khoshraftar"> A. Khoshraftar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the influence of the vertical seismic component on the non-linear dynamics analysis of three different structures. The subject structures were analyzed and designed according to recent codes. This paper considers three types of buildings: 5-, 10-, and 15-story buildings. The non-linear dynamics analysis of the structures with assuming elastic-perfectly-plastic behavior was performed using Ram Perform-3D software; the horizontal component was taken into consideration with and without the incorporation of the corresponding vertical component. Dynamic responses obtained for the horizontal component acting alone were compared with those obtained from the simultaneous application of both seismic components. The results show that the effect of the vertical component of the ground motion may increase the axial load significantly in the interior columns and consequently, the stories. The plastic mechanisms would be changed. The P-Delta effect is expected to increase. The punching base plate shear of the columns should be considered. Moreover, the vertical component increases the input energy when the structures exhibit inelastic behavior and are taller. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=inelastic%20behavior" title="inelastic behavior">inelastic behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=non-linear%20dynamic%20analysis" title=" non-linear dynamic analysis"> non-linear dynamic analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=steel%20structure" title=" steel structure"> steel structure</a>, <a href="https://publications.waset.org/abstracts/search?q=vertical%20component" title=" vertical component"> vertical component</a> </p> <a href="https://publications.waset.org/abstracts/30902/studying-frame-resistant-steel-structures-under-near-field-ground-motion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30902.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">317</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">30531</span> Prediction of Slaughter Body Weight in Rabbits: Multivariate Approach through Path Coefficient and Principal Component Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20A.%20Bindu">K. A. Bindu</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20V.%20Raja"> T. V. Raja</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20M.%20Rojan"> P. M. Rojan</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Siby"> A. Siby</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The multivariate path coefficient approach was employed to study the effects of various production and reproduction traits on the slaughter body weight of rabbits. Information on 562 rabbits maintained at the university rabbit farm attached to the Centre for Advanced Studies in Animal Genetics, and Breeding, Kerala Veterinary and Animal Sciences University, Kerala State, India was utilized. The manifest variables used in the study were age and weight of dam, birth weight, litter size at birth and weaning, weight at first, second and third months. The linear multiple regression analysis was performed by keeping the slaughter weight as the dependent variable and the remaining as independent variables. The model explained 48.60 percentage of the total variation present in the market weight of the rabbits. Even though the model used was significant, the standardized beta coefficients for the independent variables viz., age and weight of the dam, birth weight and litter sizes at birth and weaning were less than one indicating their negligible influence on the slaughter weight. However, the standardized beta coefficient of the second-month body weight was maximum followed by the first-month weight indicating their major role on the market weight. All the other factors influence indirectly only through these two variables. Hence it was concluded that the slaughter body weight can be predicted using the first and second-month body weights. The principal components were also developed so as to achieve more accuracy in the prediction of market weight of rabbits. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=component%20analysis" title="component analysis">component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate" title=" multivariate"> multivariate</a>, <a href="https://publications.waset.org/abstracts/search?q=slaughter" title=" slaughter"> slaughter</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/104373/prediction-of-slaughter-body-weight-in-rabbits-multivariate-approach-through-path-coefficient-and-principal-component-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104373.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">165</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">30530</span> Influence of Security Attributes in Component-Based Software Development</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Somayeh%20Zeinali">Somayeh Zeinali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A component is generally defined as a piece of executable software with a published interface. Component-based software engineering (CBSE) has become recognized as a new sub-discipline of software engineering. In the component-based software development, components cannot be completely secure and thus easily become vulnerable. Some researchers have investigated this issue and proposed approaches to detect component intrusions or protect distributed components. Software security also refers to the process of creating software that is considered secure.The terms “dependability”, “trustworthiness”, and “survivability” are used interchangeably to describe the properties of software security. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=component-based%20software%20development" title="component-based software development">component-based software development</a>, <a href="https://publications.waset.org/abstracts/search?q=component-based%20software%20engineering" title=" component-based software engineering "> component-based software engineering </a>, <a href="https://publications.waset.org/abstracts/search?q=software%20security%20attributes" title="software security attributes">software security attributes</a>, <a href="https://publications.waset.org/abstracts/search?q=dependability" title=" dependability"> dependability</a>, <a href="https://publications.waset.org/abstracts/search?q=component" title=" component"> component</a> </p> <a href="https://publications.waset.org/abstracts/26037/influence-of-security-attributes-in-component-based-software-development" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26037.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">559</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">30529</span> Poster : Incident Signals Estimation Based on a Modified MCA Learning Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rashid%20Ahmed">Rashid Ahmed </a>, <a href="https://publications.waset.org/abstracts/search?q=John%20N.%20Avaritsiotis"> John N. Avaritsiotis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many signal subspace-based approaches have already been proposed for determining the fixed Direction of Arrival (DOA) of plane waves impinging on an array of sensors. Two procedures for DOA estimation based neural networks are presented. First, Principal Component Analysis (PCA) is employed to extract the maximum eigenvalue and eigenvector from signal subspace to estimate DOA. Second, minor component analysis (MCA) is a statistical method of extracting the eigenvector associated with the smallest eigenvalue of the covariance matrix. In this paper, we will modify a Minor Component Analysis (MCA(R)) learning algorithm to enhance the convergence, where a convergence is essential for MCA algorithm towards practical applications. The learning rate parameter is also presented, which ensures fast convergence of the algorithm, because it has direct effect on the convergence of the weight vector and the error level is affected by this value. MCA is performed to determine the estimated DOA. Preliminary results will be furnished to illustrate the convergences results achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Direction%20of%20Arrival" title="Direction of Arrival">Direction of Arrival</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=Principle%20Component%20Analysis" title=" Principle Component Analysis"> Principle Component Analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Minor%20Component%20Analysis" title=" Minor Component Analysis"> Minor Component Analysis</a> </p> <a href="https://publications.waset.org/abstracts/8515/poster-incident-signals-estimation-based-on-a-modified-mca-learning-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8515.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">30528</span> The Association between Health-Related Quality of Life and Physical Activity in Different Domains with Other Factors in Croatian Male Police Officers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Goran%20Spori%C5%A1">Goran Sporiš</a>, <a href="https://publications.waset.org/abstracts/search?q=Dinko%20Vuleta"> Dinko Vuleta</a>, <a href="https://publications.waset.org/abstracts/search?q=Stefan%20Lovro"> Stefan Lovro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of the present study was to determine the associations between health-related quality of life (HRQOL) and physical activity (PA) in different domains. In this cross-sectional study, participants were 169 Croatian police officers (mean age 35.14±8.95 yrs, mean height 180.93±7.53 cm, mean weight 88.39±14.05 kg, mean body-mass index 26.90±3.39 kg/m2). The dependent variables were two general domains extracted from the HRQOL questionnaire: (1) physical component scale (PCS) and (2) mental component scale (MCS). The independent variables were job-related, transport, domestic and leisure-time PA, along with other factors: age, body-mass index, smoking status, psychological distress, socioeconomic status and time spent in sedentary behaviour. The associations between dependent and independent variables were analyzed by using multiple regression analysis. Significance was set up at p < 0.05. PCS was positively associated with leisure-time PA (β 0.28, p < 0.001) and socioeconomic status (SES) (β 0.16, p=0.005), but inversely associated with job-related PA (β -0.15, p=0.012), domestic-time PA (β -0.14, p=0.014), age (β -0.12, p=0.050), psychological distress (β -0.43, p<0.001) and sedentary behaviour (β -0.15, p=0.009). MCS was positively associated with leisure-time PA (β 0.19, p=0.013) and SES (β 0.20, p=0.002), while inversely associated with age (β -0.23, p=0.001), psychological distress (β -0.27, p<0.001) and sedentary behaviour (β -0.22, p=0.001). Our results added new information about the associations between domain-specific PA and both physical and mental component scale in police officers. Future studies should deal with the same associations in other stressful occupations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=health" title="health">health</a>, <a href="https://publications.waset.org/abstracts/search?q=fitness" title=" fitness"> fitness</a>, <a href="https://publications.waset.org/abstracts/search?q=police%20force" title=" police force"> police force</a>, <a href="https://publications.waset.org/abstracts/search?q=relations" title=" relations"> relations</a> </p> <a href="https://publications.waset.org/abstracts/63152/the-association-between-health-related-quality-of-life-and-physical-activity-in-different-domains-with-other-factors-in-croatian-male-police-officers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63152.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">299</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">30527</span> Blood Volume Pulse Extraction for Non-Contact Photoplethysmography Measurement from Facial Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ki%20Moo%20Lim">Ki Moo Lim</a>, <a href="https://publications.waset.org/abstracts/search?q=Iman%20R.%20Tayibnapis"> Iman R. Tayibnapis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> According to WHO estimation, 38 out of 56 million (68%) global deaths in 2012, were due to noncommunicable diseases (NCDs). To avert NCD, one of the solutions is early detection of diseases. In order to do that, we developed 'U-Healthcare Mirror', which is able to measure vital sign such as heart rate (HR) and respiration rate without any physical contact and consciousness. To measure HR in the mirror, we utilized digital camera. The camera records red, green, and blue (RGB) discoloration from user's facial image sequences. We extracted blood volume pulse (BVP) from the RGB discoloration because the discoloration of the facial skin is accordance with BVP. We used blind source separation (BSS) to extract BVP from the RGB discoloration and adaptive filters for removing noises. We utilized singular value decomposition (SVD) method to implement the BSS and the adaptive filters. HR was estimated from the obtained BVP. We did experiment for HR measurement by using our method and previous method that used independent component analysis (ICA) method. We compared both of them with HR measurement from commercial oximeter. The experiment was conducted under various distance between 30~110 cm and light intensity between 5~2000 lux. For each condition, we did measurement 7 times. The estimated HR showed 2.25 bpm of mean error and 0.73 of pearson correlation coefficient. The accuracy has improved compared to previous work. The optimal distance between the mirror and user for HR measurement was 50 cm with medium light intensity, around 550 lux. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blood%20volume%20pulse" title="blood volume pulse">blood volume pulse</a>, <a href="https://publications.waset.org/abstracts/search?q=heart%20rate" title=" heart rate"> heart rate</a>, <a href="https://publications.waset.org/abstracts/search?q=photoplethysmography" title=" photoplethysmography"> photoplethysmography</a>, <a href="https://publications.waset.org/abstracts/search?q=independent%20component%20analysis" title=" independent component analysis"> independent component analysis</a> </p> <a href="https://publications.waset.org/abstracts/49612/blood-volume-pulse-extraction-for-non-contact-photoplethysmography-measurement-from-facial-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49612.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">329</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">30526</span> Comparison of Power Generation Status of Photovoltaic Systems under Different Weather Conditions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhaojun%20Wang">Zhaojun Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zongdi%20Sun"> Zongdi Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Qinqin%20Cui"> Qinqin Cui</a>, <a href="https://publications.waset.org/abstracts/search?q=Xingwan%20Ren"> Xingwan Ren</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Based on multivariate statistical analysis theory, this paper uses the principal component analysis method, Mahalanobis distance analysis method and fitting method to establish the photovoltaic health model to evaluate the health of photovoltaic panels. First of all, according to weather conditions, the photovoltaic panel variable data are classified into five categories: sunny, cloudy, rainy, foggy, overcast. The health of photovoltaic panels in these five types of weather is studied. Secondly, a scatterplot of the relationship between the amount of electricity produced by each kind of weather and other variables was plotted. It was found that the amount of electricity generated by photovoltaic panels has a significant nonlinear relationship with time. The fitting method was used to fit the relationship between the amount of weather generated and the time, and the nonlinear equation was obtained. Then, using the principal component analysis method to analyze the independent variables under five kinds of weather conditions, according to the Kaiser-Meyer-Olkin test, it was found that three types of weather such as overcast, foggy, and sunny meet the conditions for factor analysis, while cloudy and rainy weather do not satisfy the conditions for factor analysis. Therefore, through the principal component analysis method, the main components of overcast weather are temperature, AQI, and pm2.5. The main component of foggy weather is temperature, and the main components of sunny weather are temperature, AQI, and pm2.5. Cloudy and rainy weather require analysis of all of their variables, namely temperature, AQI, pm2.5, solar radiation intensity and time. Finally, taking the variable values in sunny weather as observed values, taking the main components of cloudy, foggy, overcast and rainy weather as sample data, the Mahalanobis distances between observed value and these sample values are obtained. A comparative analysis was carried out to compare the degree of deviation of the Mahalanobis distance to determine the health of the photovoltaic panels under different weather conditions. It was found that the weather conditions in which the Mahalanobis distance fluctuations ranged from small to large were: foggy, cloudy, overcast and rainy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fitting" title="fitting">fitting</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahalanobis%20distance" title=" Mahalanobis distance"> Mahalanobis distance</a>, <a href="https://publications.waset.org/abstracts/search?q=SPSS" title=" SPSS"> SPSS</a>, <a href="https://publications.waset.org/abstracts/search?q=MATLAB" title=" MATLAB"> MATLAB</a> </p> <a href="https://publications.waset.org/abstracts/97522/comparison-of-power-generation-status-of-photovoltaic-systems-under-different-weather-conditions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97522.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">30525</span> Effect of Fault Depth on Near-Fault Peak Ground Velocity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yanyan%20Yu">Yanyan Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Haiping%20Ding"> Haiping Ding</a>, <a href="https://publications.waset.org/abstracts/search?q=Pengjun%20Chen"> Pengjun Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Yiou%20Sun"> Yiou Sun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fault depth is an important parameter to be determined in ground motion simulation, and peak ground velocity (PGV) demonstrates good application prospect. Using numerical simulation method, the variations of distribution and peak value of near-fault PGV with different fault depth were studied in detail, and the reason of some phenomena were discussed. The simulation results show that the distribution characteristics of PGV of fault-parallel (FP) component and fault-normal (FN) component are distinctly different; the value of PGV FN component is much larger than that of FP component. With the increase of fault depth, the distribution region of the FN component strong PGV moves forward along the rupture direction, while the strong PGV zone of FP component becomes gradually far away from the fault trace along the direction perpendicular to the strike. However, no matter FN component or FP component, the strong PGV distribution area and its value are both quickly reduced with increased fault depth. The results above suggest that the fault depth have significant effect on both FN component and FP component of near-fault PGV. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fault%20depth" title="fault depth">fault depth</a>, <a href="https://publications.waset.org/abstracts/search?q=near-fault" title=" near-fault"> near-fault</a>, <a href="https://publications.waset.org/abstracts/search?q=PGV" title=" PGV"> PGV</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20simulation" title=" numerical simulation"> numerical simulation</a> </p> <a href="https://publications.waset.org/abstracts/73475/effect-of-fault-depth-on-near-fault-peak-ground-velocity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73475.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">346</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">30524</span> Phrasemes With The Component 'Water' In Polish And Russian - Comparative Aspects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aleksandra%20Majewska">Aleksandra Majewska</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The subject of this article is phrasemes with the component 'water' in Polish and Russian. The purpose of the study is to analyse the collocations from the point of view of lexis and semantics. The material for analysis was extracted from phraseological dictionaries of Polish and Russian. From the point of view of lexis, an analysis was made of the inflectional component 'water' in phrasal expressions in both languages. Then, the phrasemes were divided into their corresponding semantic groups. That division became the subject of another comparative analysis in a further step. Finally, the functioning of some phrasemes compounds in the contexts of modern Polish and Russian was shown. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lingustic" title="lingustic">lingustic</a>, <a href="https://publications.waset.org/abstracts/search?q=language" title=" language"> language</a>, <a href="https://publications.waset.org/abstracts/search?q=phraseme" title=" phraseme"> phraseme</a>, <a href="https://publications.waset.org/abstracts/search?q=polish%20and%20Russian" title=" polish and Russian"> polish and Russian</a> </p> <a href="https://publications.waset.org/abstracts/186852/phrasemes-with-the-component-water-in-polish-and-russian-comparative-aspects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186852.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">40</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">30523</span> Implementation of a Method of Crater Detection Using Principal Component Analysis in FPGA</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Izuru%20Nomura">Izuru Nomura</a>, <a href="https://publications.waset.org/abstracts/search?q=Tatsuya%20Takino"> Tatsuya Takino</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuji%20Kageyama"> Yuji Kageyama</a>, <a href="https://publications.waset.org/abstracts/search?q=Shin%20Nagata"> Shin Nagata</a>, <a href="https://publications.waset.org/abstracts/search?q=Hiroyuki%20Kamata"> Hiroyuki Kamata</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose a method of crater detection from the image of the lunar surface captured by the small space probe. We use the principal component analysis (PCA) to detect craters. Nevertheless, considering　severe environment of the space, it is impossible to use generic computer in practice. Accordingly, we have to implement the method in FPGA. This paper compares FPGA and generic computer by the processing time of a method of crater detection using principal component analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=crater" title="crater">crater</a>, <a href="https://publications.waset.org/abstracts/search?q=PCA" title=" PCA"> PCA</a>, <a href="https://publications.waset.org/abstracts/search?q=eigenvector" title=" eigenvector"> eigenvector</a>, <a href="https://publications.waset.org/abstracts/search?q=strength%20value" title=" strength value"> strength value</a>, <a href="https://publications.waset.org/abstracts/search?q=FPGA" title=" FPGA"> FPGA</a>, <a href="https://publications.waset.org/abstracts/search?q=processing%20time" title=" processing time "> processing time </a> </p> <a href="https://publications.waset.org/abstracts/19004/implementation-of-a-method-of-crater-detection-using-principal-component-analysis-in-fpga" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19004.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">554</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">30522</span> Image Multi-Feature Analysis by Principal Component Analysis for Visual Surface Roughness Measurement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wei%20Zhang">Wei Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20He"> Yan He</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20Wang"> Yan Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yufeng%20Li"> Yufeng Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Chuanpeng%20Hao"> Chuanpeng Hao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Surface roughness is an important index for evaluating surface quality, needs to be accurately measured to ensure the performance of the workpiece. The roughness measurement based on machine vision involves various image features, some of which are redundant. These redundant features affect the accuracy and speed of the visual approach. Previous research used correlation analysis methods to select the appropriate features. However, this feature analysis is independent and cannot fully utilize the information of data. Besides, blindly reducing features lose a lot of useful information, resulting in unreliable results. Therefore, the focus of this paper is on providing a redundant feature removal approach for visual roughness measurement. In this paper, the statistical methods and gray-level co-occurrence matrix(GLCM) are employed to extract the texture features of machined images effectively. Then, the principal component analysis(PCA) is used to fuse all extracted features into a new one, which reduces the feature dimension and maintains the integrity of the original information. Finally, the relationship between new features and roughness is established by the support vector machine(SVM). The experimental results show that the approach can effectively solve multi-feature information redundancy of machined surface images and provides a new idea for the visual evaluation of surface roughness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20analysis" title="feature analysis">feature analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20vision" title=" machine vision"> machine vision</a>, <a href="https://publications.waset.org/abstracts/search?q=PCA" title=" PCA"> PCA</a>, <a href="https://publications.waset.org/abstracts/search?q=surface%20roughness" title=" surface roughness"> surface roughness</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a> </p> <a href="https://publications.waset.org/abstracts/138525/image-multi-feature-analysis-by-principal-component-analysis-for-visual-surface-roughness-measurement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138525.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">212</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">30521</span> Kohonen Self-Organizing Maps as a New Method for Determination of Salt Composition of Multi-Component Solutions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sergey%20A.%20Burikov">Sergey A. Burikov</a>, <a href="https://publications.waset.org/abstracts/search?q=Tatiana%20A.%20Dolenko"> Tatiana A. Dolenko</a>, <a href="https://publications.waset.org/abstracts/search?q=Kirill%20A.%20Gushchin"> Kirill A. Gushchin</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergey%20A.%20Dolenko"> Sergey A. Dolenko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper presents the results of clusterization by Kohonen self-organizing maps (SOM) applied for analysis of array of Raman spectra of multi-component solutions of inorganic salts, for determination of types of salts present in the solution. It is demonstrated that use of SOM is a promising method for solution of clusterization and classification problems in spectroscopy of multi-component objects, as attributing a pattern to some cluster may be used for recognition of component composition of the object. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kohonen%20self-organizing%20maps" title="Kohonen self-organizing maps">Kohonen self-organizing maps</a>, <a href="https://publications.waset.org/abstracts/search?q=clusterization" title=" clusterization"> clusterization</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-component%20solutions" title=" multi-component solutions"> multi-component solutions</a>, <a href="https://publications.waset.org/abstracts/search?q=Raman%20spectroscopy" title=" Raman spectroscopy"> Raman spectroscopy</a> </p> <a href="https://publications.waset.org/abstracts/14544/kohonen-self-organizing-maps-as-a-new-method-for-determination-of-salt-composition-of-multi-component-solutions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14544.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">443</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">30520</span> Differentiation between Different Rangeland Sites Using Principal Component Analysis in Semi-Arid Areas of Sudan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nancy%20Ibrahim%20Abdalla">Nancy Ibrahim Abdalla</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelaziz%20Karamalla%20Gaiballa"> Abdelaziz Karamalla Gaiballa </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rangelands in semi-arid areas provide a good source for feeding huge numbers of animals and serving environmental, economic and social importance; therefore, these areas are considered economically very important for the pastoral sector in Sudan. This paper investigates the means of differentiating between different rangelands sites according to soil types using principal component analysis to assist in monitoring and assessment purposes. Three rangeland sites were identified in the study area as flat sandy sites, sand dune site, and hard clay site. Principal component analysis (PCA) was used to reduce the number of factors needed to distinguish between rangeland sites and produce a new set of data including the most useful spectral information to run satellite image processing. It was performed using selected types of data (two vegetation indices, topographic data and vegetation surface reflectance within the three bands of MODIS data). Analysis with PCA indicated that there is a relatively high correspondence between vegetation and soil of the total variance in the data set. The results showed that the use of the principal component analysis (PCA) with the selected variables showed a high difference, reflected in the variance and eigenvalues and it can be used for differentiation between different range sites. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title="principal component analysis">principal component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=PCA" title=" PCA"> PCA</a>, <a href="https://publications.waset.org/abstracts/search?q=rangeland%20sites" title=" rangeland sites"> rangeland sites</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-arid%20areas" title=" semi-arid areas"> semi-arid areas</a>, <a href="https://publications.waset.org/abstracts/search?q=soil%20types" title=" soil types"> soil types</a> </p> <a href="https://publications.waset.org/abstracts/99240/differentiation-between-different-rangeland-sites-using-principal-component-analysis-in-semi-arid-areas-of-sudan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99240.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">186</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Independent%20Component%20Analysis%20%28ICA%29&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Independent%20Component%20Analysis%20%28ICA%29&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Independent%20Component%20Analysis%20%28ICA%29&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Independent%20Component%20Analysis%20%28ICA%29&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Independent%20Component%20Analysis%20%28ICA%29&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Independent%20Component%20Analysis%20%28ICA%29&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Independent%20Component%20Analysis%20%28ICA%29&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Independent%20Component%20Analysis%20%28ICA%29&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Independent%20Component%20Analysis%20%28ICA%29&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=Independent%20Component%20Analysis%20%28ICA%29&page=1018">1018</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Independent%20Component%20Analysis%20%28ICA%29&page=1019">1019</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Independent%20Component%20Analysis%20%28ICA%29&page=2" 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>