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: cepstral coefficients</title> <meta name="description" content="Search results for: cepstral coefficients"> <meta name="keywords" content="cepstral coefficients"> <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="cepstral coefficients" 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="cepstral coefficients"> <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> 908</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: cepstral coefficients</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">908</span> Evaluation of Features Extraction Algorithms for a Real-Time Isolated Word Recognition System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomyslav%20Sledevi%C4%8D">Tomyslav Sledevič</a>, <a href="https://publications.waset.org/abstracts/search?q=Art%C5%ABras%20Serackis"> Artūras Serackis</a>, <a href="https://publications.waset.org/abstracts/search?q=Gintautas%20Tamulevi%C4%8Dius"> Gintautas Tamulevičius</a>, <a href="https://publications.waset.org/abstracts/search?q=Dalius%20Navakauskas"> Dalius Navakauskas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comparative evaluation of features extraction algorithm for a real-time isolated word recognition system based on FPGA. The Mel-frequency cepstral, linear frequency cepstral, linear predictive and their cepstral coefficients were implemented in hardware/software design. The proposed system was investigated in the speaker-dependent mode for 100 different Lithuanian words. The robustness of features extraction algorithms was tested recognizing the speech records at different signals to noise rates. The experiments on clean records show highest accuracy for Mel-frequency cepstral and linear frequency cepstral coefficients. For records with 15 dB signal to noise rate the linear predictive cepstral coefficients give best result. The hard and soft part of the system is clocked on 50 MHz and 100 MHz accordingly. For the classification purpose, the pipelined dynamic time warping core was implemented. The proposed word recognition system satisfies the real-time requirements and is suitable for applications in embedded systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=isolated%20word%20recognition" title="isolated word recognition">isolated word recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=features%20extraction" title=" features extraction"> features extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=MFCC" title=" MFCC"> MFCC</a>, <a href="https://publications.waset.org/abstracts/search?q=LFCC" title=" LFCC"> LFCC</a>, <a href="https://publications.waset.org/abstracts/search?q=LPCC" title=" LPCC"> LPCC</a>, <a href="https://publications.waset.org/abstracts/search?q=LPC" title=" LPC"> LPC</a>, <a href="https://publications.waset.org/abstracts/search?q=FPGA" title=" FPGA"> FPGA</a>, <a href="https://publications.waset.org/abstracts/search?q=DTW" title=" DTW"> DTW</a> </p> <a href="https://publications.waset.org/abstracts/2136/evaluation-of-features-extraction-algorithms-for-a-real-time-isolated-word-recognition-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2136.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">496</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">907</span> The Combination of the Mel Frequency Cepstral Coefficients (MFCC), Perceptual Linear Prediction (PLP), JITTER and SHIMMER Coefficients for the Improvement of Automatic Recognition System for Dysarthric Speech</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Brahim-Fares%20Zaidi">Brahim-Fares Zaidi</a>, <a href="https://publications.waset.org/abstracts/search?q=Malika%20Boudraa"> Malika Boudraa</a>, <a href="https://publications.waset.org/abstracts/search?q=Sid-Ahmed%20Selouani"> Sid-Ahmed Selouani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Our work aims to improve our Automatic Recognition System for Dysarthria Speech (ARSDS) based on the Hidden Models of Markov (HMM) and the Hidden Markov Model Toolkit (HTK) to help people who are sick. With pronunciation problems, we applied two techniques of speech parameterization based on Mel Frequency Cepstral Coefficients (MFCC's) and Perceptual Linear Prediction (PLP's) and concatenated them with JITTER and SHIMMER coefficients in order to increase the recognition rate of a dysarthria speech. For our tests, we used the NEMOURS database that represents speakers with dysarthria and normal speakers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hidden%20Markov%20model%20toolkit%20%28HTK%29" title="hidden Markov model toolkit (HTK)">hidden Markov model toolkit (HTK)</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20models%20of%20Markov%20%28HMM%29" title=" hidden models of Markov (HMM)"> hidden models of Markov (HMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=Mel-frequency%20cepstral%20coefficients%20%28MFCC%29" title=" Mel-frequency cepstral coefficients (MFCC)"> Mel-frequency cepstral coefficients (MFCC)</a>, <a href="https://publications.waset.org/abstracts/search?q=perceptual%20linear%20prediction%20%28PLP%E2%80%99s%29" title=" perceptual linear prediction (PLP’s)"> perceptual linear prediction (PLP’s)</a> </p> <a href="https://publications.waset.org/abstracts/143303/the-combination-of-the-mel-frequency-cepstral-coefficients-mfcc-perceptual-linear-prediction-plp-jitter-and-shimmer-coefficients-for-the-improvement-of-automatic-recognition-system-for-dysarthric-speech" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143303.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">161</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">906</span> Robust Features for Impulsive Noisy Speech Recognition Using Relative Spectral Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hajer%20Rahali">Hajer Rahali</a>, <a href="https://publications.waset.org/abstracts/search?q=Zied%20Hajaiej"> Zied Hajaiej</a>, <a href="https://publications.waset.org/abstracts/search?q=Noureddine%20Ellouze"> Noureddine Ellouze</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The goal of speech parameterization is to extract the relevant information about what is being spoken from the audio signal. In speech recognition systems Mel-Frequency Cepstral Coefficients (MFCC) and Relative Spectral Mel-Frequency Cepstral Coefficients (RASTA-MFCC) are the two main techniques used. It will be shown in this paper that it presents some modifications to the original MFCC method. In our work the effectiveness of proposed changes to MFCC called Modified Function Cepstral Coefficients (MODFCC) were tested and compared against the original MFCC and RASTA-MFCC features. The prosodic features such as jitter and shimmer are added to baseline spectral features. The above-mentioned techniques were tested with impulsive signals under various noisy conditions within AURORA databases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auditory%20filter" title="auditory filter">auditory filter</a>, <a href="https://publications.waset.org/abstracts/search?q=impulsive%20noise" title=" impulsive noise"> impulsive noise</a>, <a href="https://publications.waset.org/abstracts/search?q=MFCC" title=" MFCC"> MFCC</a>, <a href="https://publications.waset.org/abstracts/search?q=prosodic%20features" title=" prosodic features"> prosodic features</a>, <a href="https://publications.waset.org/abstracts/search?q=RASTA%20filter" title=" RASTA filter"> RASTA filter</a> </p> <a href="https://publications.waset.org/abstracts/8911/robust-features-for-impulsive-noisy-speech-recognition-using-relative-spectral-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8911.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">425</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">905</span> The Combination of the Mel Frequency Cepstral Coefficients, Perceptual Linear Prediction, Jitter and Shimmer Coefficients for the Improvement of Automatic Recognition System for Dysarthric Speech</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Brahim%20Fares%20Zaidi">Brahim Fares Zaidi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Our work aims to improve our Automatic Recognition System for Dysarthria Speech based on the Hidden Models of Markov and the Hidden Markov Model Toolkit to help people who are sick. With pronunciation problems, we applied two techniques of speech parameterization based on Mel Frequency Cepstral Coefficients and Perceptual Linear Prediction and concatenated them with JITTER and SHIMMER coefficients in order to increase the recognition rate of a dysarthria speech. For our tests, we used the NEMOURS database that represents speakers with dysarthria and normal speakers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARSDS" title="ARSDS">ARSDS</a>, <a href="https://publications.waset.org/abstracts/search?q=HTK" title=" HTK"> HTK</a>, <a href="https://publications.waset.org/abstracts/search?q=HMM" title=" HMM"> HMM</a>, <a href="https://publications.waset.org/abstracts/search?q=MFCC" title=" MFCC"> MFCC</a>, <a href="https://publications.waset.org/abstracts/search?q=PLP" title=" PLP"> PLP</a> </p> <a href="https://publications.waset.org/abstracts/158636/the-combination-of-the-mel-frequency-cepstral-coefficients-perceptual-linear-prediction-jitter-and-shimmer-coefficients-for-the-improvement-of-automatic-recognition-system-for-dysarthric-speech" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158636.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">108</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">904</span> The Capacity of Mel Frequency Cepstral Coefficients for Speech Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fawaz%20S.%20Al-Anzi">Fawaz S. Al-Anzi</a>, <a href="https://publications.waset.org/abstracts/search?q=Dia%20AbuZeina"> Dia AbuZeina</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Speech recognition is of an important contribution in promoting new technologies in human computer interaction. Today, there is a growing need to employ speech technology in daily life and business activities. However, speech recognition is a challenging task that requires different stages before obtaining the desired output. Among automatic speech recognition (ASR) components is the feature extraction process, which parameterizes the speech signal to produce the corresponding feature vectors. Feature extraction process aims at approximating the linguistic content that is conveyed by the input speech signal. In speech processing field, there are several methods to extract speech features, however, Mel Frequency Cepstral Coefficients (MFCC) is the popular technique. It has been long observed that the MFCC is dominantly used in the well-known recognizers such as the Carnegie Mellon University (CMU) Sphinx and the Markov Model Toolkit (HTK). Hence, this paper focuses on the MFCC method as the standard choice to identify the different speech segments in order to obtain the language phonemes for further training and decoding steps. Due to MFCC good performance, the previous studies show that the MFCC dominates the Arabic ASR research. In this paper, we demonstrate MFCC as well as the intermediate steps that are performed to get these coefficients using the HTK toolkit. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=speech%20recognition" title="speech recognition">speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=acoustic%20features" title=" acoustic features"> acoustic features</a>, <a href="https://publications.waset.org/abstracts/search?q=mel%20frequency" title=" mel frequency"> mel frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=cepstral%20coefficients" title=" cepstral coefficients"> cepstral coefficients</a> </p> <a href="https://publications.waset.org/abstracts/78382/the-capacity-of-mel-frequency-cepstral-coefficients-for-speech-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78382.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">259</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">903</span> The Optimum Mel-Frequency Cepstral Coefficients (MFCCs) Contribution to Iranian Traditional Music Genre Classification by Instrumental Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Abbasi%20Layegh">M. Abbasi Layegh</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Haghipour"> S. Haghipour</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Athari"> K. Athari</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Khosravi"> R. Khosravi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Tafkikialamdari"> M. Tafkikialamdari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An approach to find the optimum mel-frequency cepstral coefficients (MFCCs) for the Radif of Mirzâ Ábdollâh, which is the principal emblem and the heart of Persian music, performed by most famous Iranian masters on two Iranian stringed instruments ‘Tar’ and ‘Setar’ is proposed. While investigating the variance of MFCC for each record in themusic database of 1500 gushe of the repertoire belonging to 12 modal systems (dastgâh and âvâz), we have applied the Fuzzy C-Mean clustering algorithm on each of the 12 coefficient and different combinations of those coefficients. We have applied the same experiment while increasing the number of coefficients but the clustering accuracy remained the same. Therefore, we can conclude that the first 7 MFCCs (V-7MFCC) are enough for classification of The Radif of Mirzâ Ábdollâh. Classical machine learning algorithms such as MLP neural networks, K-Nearest Neighbors (KNN), Gaussian Mixture Model (GMM), Hidden Markov Model (HMM) and Support Vector Machine (SVM) have been employed. Finally, it can be realized that SVM shows a better performance in this study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=radif%20of%20Mirz%C3%A2%20%C3%81bdoll%C3%A2h" title="radif of Mirzâ Ábdollâh">radif of Mirzâ Ábdollâh</a>, <a href="https://publications.waset.org/abstracts/search?q=Gushe" title=" Gushe"> Gushe</a>, <a href="https://publications.waset.org/abstracts/search?q=mel%20frequency%20cepstral%20coefficients" title=" mel frequency cepstral coefficients"> mel frequency cepstral coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20c-mean%20clustering%20algorithm" title=" fuzzy c-mean clustering algorithm"> fuzzy c-mean clustering algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=k-nearest%20neighbors%20%28KNN%29" title=" k-nearest neighbors (KNN)"> k-nearest neighbors (KNN)</a>, <a href="https://publications.waset.org/abstracts/search?q=gaussian%20mixture%20model%20%28GMM%29" title=" gaussian mixture model (GMM)"> gaussian mixture model (GMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20markov%20model%20%28HMM%29" title=" hidden markov model (HMM)"> hidden markov model (HMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine%20%28SVM%29" title=" support vector machine (SVM)"> support vector machine (SVM)</a> </p> <a href="https://publications.waset.org/abstracts/37296/the-optimum-mel-frequency-cepstral-coefficients-mfccs-contribution-to-iranian-traditional-music-genre-classification-by-instrumental-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37296.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">446</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">902</span> Speech Emotion Recognition: A DNN and LSTM Comparison in Single and Multiple Feature Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thiago%20Spilborghs%20Bueno%20Meyer">Thiago Spilborghs Bueno Meyer</a>, <a href="https://publications.waset.org/abstracts/search?q=Plinio%20Thomaz%20Aquino%20Junior"> Plinio Thomaz Aquino Junior</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Through speech, which privileges the functional and interactive nature of the text, it is possible to ascertain the spatiotemporal circumstances, the conditions of production and reception of the discourse, the explicit purposes such as informing, explaining, convincing, etc. These conditions allow bringing the interaction between humans closer to the human-robot interaction, making it natural and sensitive to information. However, it is not enough to understand what is said; it is necessary to recognize emotions for the desired interaction. The validity of the use of neural networks for feature selection and emotion recognition was verified. For this purpose, it is proposed the use of neural networks and comparison of models, such as recurrent neural networks and deep neural networks, in order to carry out the classification of emotions through speech signals to verify the quality of recognition. It is expected to enable the implementation of robots in a domestic environment, such as the HERA robot from the RoboFEI@Home team, which focuses on autonomous service robots for the domestic environment. Tests were performed using only the Mel-Frequency Cepstral Coefficients, as well as tests with several characteristics of Delta-MFCC, spectral contrast, and the Mel spectrogram. To carry out the training, validation and testing of the neural networks, the eNTERFACE’05 database was used, which has 42 speakers from 14 different nationalities speaking the English language. The data from the chosen database are videos that, for use in neural networks, were converted into audios. It was found as a result, a classification of 51,969% of correct answers when using the deep neural network, when the use of the recurrent neural network was verified, with the classification with accuracy equal to 44.09%. The results are more accurate when only the Mel-Frequency Cepstral Coefficients are used for the classification, using the classifier with the deep neural network, and in only one case, it is possible to observe a greater accuracy by the recurrent neural network, which occurs in the use of various features and setting 73 for batch size and 100 training epochs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=emotion%20recognition" title="emotion recognition">emotion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=speech" title=" speech"> speech</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=human-robot%20interaction" title=" human-robot interaction"> human-robot interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a> </p> <a href="https://publications.waset.org/abstracts/145908/speech-emotion-recognition-a-dnn-and-lstm-comparison-in-single-and-multiple-feature-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145908.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">170</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">901</span> Biosignal Recognition for Personal Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hadri%20Hussain">Hadri Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=M.Nasir%20Ibrahim"> M.Nasir Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Chee-Ming%20Ting"> Chee-Ming Ting</a>, <a href="https://publications.waset.org/abstracts/search?q=Mariani%20Idroas"> Mariani Idroas</a>, <a href="https://publications.waset.org/abstracts/search?q=Fuad%20Numan"> Fuad Numan</a>, <a href="https://publications.waset.org/abstracts/search?q=Alias%20Mohd%20Noor"> Alias Mohd Noor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A biometric security system has become an important application in client identification and verification system. A conventional biometric system is normally based on unimodal biometric that depends on either behavioural or physiological information for authentication purposes. The behavioural biometric depends on human body biometric signal (such as speech) and biosignal biometric (such as electrocardiogram (ECG) and phonocardiogram or heart sound (HS)). The speech signal is commonly used in a recognition system in biometric, while the ECG and the HS have been used to identify a person’s diseases uniquely related to its cluster. However, the conventional biometric system is liable to spoof attack that will affect the performance of the system. Therefore, a multimodal biometric security system is developed, which is based on biometric signal of ECG, HS, and speech. The biosignal data involved in the biometric system is initially segmented, with each segment Mel Frequency Cepstral Coefficients (MFCC) method is exploited for extracting the feature. The Hidden Markov Model (HMM) is used to model the client and to classify the unknown input with respect to the modal. The recognition system involved training and testing session that is known as client identification (CID). In this project, twenty clients are tested with the developed system. The best overall performance at 44 kHz was 93.92% for ECG and the worst overall performance was ECG at 88.47%. The results were compared to the best overall performance at 44 kHz for (20clients) to increment of clients, which was 90.00% for HS and the worst overall performance falls at ECG at 79.91%. It can be concluded that the difference multimodal biometric has a substantial effect on performance of the biometric system and with the increment of data, even with higher frequency sampling, the performance still decreased slightly as predicted. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title="electrocardiogram">electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=phonocardiogram" title=" phonocardiogram"> phonocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20markov%20model" title=" hidden markov model"> hidden markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=mel%20frequency%20cepstral%20coeffiecients" title=" mel frequency cepstral coeffiecients"> mel frequency cepstral coeffiecients</a>, <a href="https://publications.waset.org/abstracts/search?q=client%20identification" title=" client identification"> client identification</a> </p> <a href="https://publications.waset.org/abstracts/48382/biosignal-recognition-for-personal-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48382.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">280</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">900</span> Terrain Classification for Ground Robots Based on Acoustic Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bernd%20Kiefer">Bernd Kiefer</a>, <a href="https://publications.waset.org/abstracts/search?q=Abraham%20Gebru%20Tesfay"> Abraham Gebru Tesfay</a>, <a href="https://publications.waset.org/abstracts/search?q=Dietrich%20Klakow"> Dietrich Klakow</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The motivation of our work is to detect different terrain types traversed by a robot based on acoustic data from the robot-terrain interaction. Different acoustic features and classifiers were investigated, such as Mel-frequency cepstral coefficient and Gamma-tone frequency cepstral coefficient for the feature extraction, and Gaussian mixture model and Feed forward neural network for the classification. We analyze the system’s performance by comparing our proposed techniques with some other features surveyed from distinct related works. We achieve precision and recall values between 87% and 100% per class, and an average accuracy at 95.2%. We also study the effect of varying audio chunk size in the application phase of the models and find only a mild impact on performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acoustic%20features" title="acoustic features">acoustic features</a>, <a href="https://publications.waset.org/abstracts/search?q=autonomous%20robots" title=" autonomous robots"> autonomous robots</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=terrain%20classification" title=" terrain classification"> terrain classification</a> </p> <a href="https://publications.waset.org/abstracts/71127/terrain-classification-for-ground-robots-based-on-acoustic-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71127.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">369</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">899</span> Estimation of Pressure Loss Coefficients in Combining Flows Using Artificial Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shahzad%20Yousaf">Shahzad Yousaf</a>, <a href="https://publications.waset.org/abstracts/search?q=Imran%20Shafi"> Imran Shafi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a new method for calculation of pressure loss coefficients by use of the artificial neural network (ANN) in tee junctions. Geometry and flow parameters are feed into ANN as the inputs for purpose of training the network. Efficacy of the network is demonstrated by comparison of the experimental and ANN based calculated data of pressure loss coefficients for combining flows in a tee junction. Reynolds numbers ranging from 200 to 14000 and discharge ratios varying from minimum to maximum flow for calculation of pressure loss coefficients have been used. Pressure loss coefficients calculated using ANN are compared to the models from literature used in junction flows. The results achieved after the application of ANN agrees reasonably to the experimental values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title="artificial neural networks">artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=combining%20flow" title=" combining flow"> combining flow</a>, <a href="https://publications.waset.org/abstracts/search?q=pressure%20loss%20coefficients" title=" pressure loss coefficients"> pressure loss coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=solar%20collector%20tee%20junctions" title=" solar collector tee junctions"> solar collector tee junctions</a> </p> <a href="https://publications.waset.org/abstracts/70094/estimation-of-pressure-loss-coefficients-in-combining-flows-using-artificial-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70094.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">390</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">898</span> Selection of Rayleigh Damping Coefficients for Seismic Response Analysis of Soil Layers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Huai-Feng%20Wang">Huai-Feng Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Meng-Lin%20Lou"> Meng-Lin Lou</a>, <a href="https://publications.waset.org/abstracts/search?q=Ru-Lin%20Zhang"> Ru-Lin Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One good analysis method in seismic response analysis is direct time integration, which widely adopts Rayleigh damping. An approach is presented for selection of Rayleigh damping coefficients to be used in seismic analyses to produce a response that is consistent with Modal damping response. In the presented approach, the expression of the error of peak response, acquired through complete quadratic combination method, and Rayleigh damping coefficients was set up and then the coefficients were produced by minimizing the error. Two finite element modes of soil layers, excited by 28 seismic waves, were used to demonstrate the feasibility and validity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rayleigh%20damping" title="Rayleigh damping">Rayleigh damping</a>, <a href="https://publications.waset.org/abstracts/search?q=modal%20damping" title=" modal damping"> modal damping</a>, <a href="https://publications.waset.org/abstracts/search?q=damping%20coefficients" title=" damping coefficients"> damping coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=seismic%20response%20analysis" title=" seismic response analysis"> seismic response analysis</a> </p> <a href="https://publications.waset.org/abstracts/57421/selection-of-rayleigh-damping-coefficients-for-seismic-response-analysis-of-soil-layers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57421.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">438</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">897</span> Road Vehicle Recognition Using Magnetic Sensing Feature Extraction and Classification </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiao%20Chen">Xiao Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaoying%20Kong"> Xiaoying Kong</a>, <a href="https://publications.waset.org/abstracts/search?q=Min%20Xu"> Min Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a road vehicle detection approach for the intelligent transportation system. This approach mainly uses low-cost magnetic sensor and associated data collection system to collect magnetic signals. This system can measure the magnetic field changing, and it also can detect and count vehicles. We extend Mel Frequency Cepstral Coefficients to analyze vehicle magnetic signals. Vehicle type features are extracted using representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 2-dimensional map algorithm using Vector Quantization to classify vehicle magnetic features to four typical types of vehicles in Australian suburbs: sedan, VAN, truck, and bus. Experiments results show that our approach achieves a high level of accuracy for vehicle detection and classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vehicle%20classification" title="vehicle classification">vehicle classification</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=road%20traffic%20model" title=" road traffic model"> road traffic model</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20sensing" title=" magnetic sensing"> magnetic sensing</a> </p> <a href="https://publications.waset.org/abstracts/86644/road-vehicle-recognition-using-magnetic-sensing-feature-extraction-and-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86644.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">320</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">896</span> Robustness of the Deep Chroma Extractor and Locally-Normalized Quarter Tone Filters in Automatic Chord Estimation under Reverberant Conditions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Luis%20Alvarado">Luis Alvarado</a>, <a href="https://publications.waset.org/abstracts/search?q=Victor%20Poblete"> Victor Poblete</a>, <a href="https://publications.waset.org/abstracts/search?q=Isaac%20Gonzalez"> Isaac Gonzalez</a>, <a href="https://publications.waset.org/abstracts/search?q=Yetzabeth%20Gonzalez"> Yetzabeth Gonzalez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In MIREX 2016 (http://www.music-ir.org/mirex), the deep neural network (DNN)-Deep Chroma Extractor, proposed by Korzeniowski and Wiedmer, reached the highest score in an audio chord recognition task. In the present paper, this tool is assessed under acoustic reverberant environments and distinct source-microphone distances. The evaluation dataset comprises The Beatles and Queen datasets. These datasets are sequentially re-recorded with a single microphone in a real reverberant chamber at four reverberation times (0 -anechoic-, 1, 2, and 3 s, approximately), as well as four source-microphone distances (32, 64, 128, and 256 cm). It is expected that the performance of the trained DNN will dramatically decrease under these acoustic conditions with signals degraded by room reverberation and distance to the source. Recently, the effect of the bio-inspired Locally-Normalized Cepstral Coefficients (LNCC), has been assessed in a text independent speaker verification task using speech signals degraded by additive noise at different signal-to-noise ratios with variations of recording distance, and it has also been assessed under reverberant conditions with variations of recording distance. LNCC showed a performance so high as the state-of-the-art Mel Frequency Cepstral Coefficient filters. Based on these results, this paper proposes a variation of locally-normalized triangular filters called Locally-Normalized Quarter Tone (LNQT) filters. By using the LNQT spectrogram, robustness improvements of the trained Deep Chroma Extractor are expected, compared with classical triangular filters, and thus compensating the music signal degradation improving the accuracy of the chord recognition system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chord%20recognition" title="chord recognition">chord recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20networks" title=" deep neural networks"> deep neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=music%20information%20retrieval" title=" music information retrieval"> music information retrieval</a> </p> <a href="https://publications.waset.org/abstracts/92608/robustness-of-the-deep-chroma-extractor-and-locally-normalized-quarter-tone-filters-in-automatic-chord-estimation-under-reverberant-conditions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92608.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">232</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">895</span> Comparative Methods for Speech Enhancement and the Effects on Text-Independent Speaker Identification Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Ajgou">R. Ajgou</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Sbaa"> S. Sbaa</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Ghendir"> S. Ghendir</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Chemsa"> A. Chemsa</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Taleb-Ahmed"> A. Taleb-Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The speech enhancement algorithm is to improve speech quality. In this paper, we review some speech enhancement methods and we evaluated their performance based on Perceptual Evaluation of Speech Quality scores (PESQ, ITU-T P.862). All method was evaluated in presence of different kind of noise using TIMIT database and NOIZEUS noisy speech corpus.. The noise was taken from the AURORA database and includes suburban train noise, babble, car, exhibition hall, restaurant, street, airport and train station noise. Simulation results showed improved performance of speech enhancement for Tracking of non-stationary noise approach in comparison with various methods in terms of PESQ measure. Moreover, we have evaluated the effects of the speech enhancement technique on Speaker Identification system based on autoregressive (AR) model and Mel-frequency Cepstral coefficients (MFCC). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=speech%20enhancement" title="speech enhancement">speech enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=pesq" title=" pesq"> pesq</a>, <a href="https://publications.waset.org/abstracts/search?q=speaker%20recognition" title=" speaker recognition"> speaker recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=MFCC" title=" MFCC"> MFCC</a> </p> <a href="https://publications.waset.org/abstracts/31102/comparative-methods-for-speech-enhancement-and-the-effects-on-text-independent-speaker-identification-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31102.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">424</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">894</span> Estimation of Synchronous Machine Synchronizing and Damping Torque Coefficients </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20M.%20EL-Naggar">Khaled M. EL-Naggar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Synchronizing and damping torque coefficients of a synchronous machine can give a quite clear picture for machine behavior during transients. These coefficients are used as a power system transient stability measurement. In this paper, a crow search optimization algorithm is presented and implemented to study the power system stability during transients. The algorithm makes use of the machine responses to perform the stability study in time domain. The problem is formulated as a dynamic estimation problem. An objective function that minimizes the error square in the estimated coefficients is designed. The method is tested using practical system with different study cases. Results are reported and a thorough discussion is presented. The study illustrates that the proposed method can estimate the stability coefficients for the critical stable cases where other methods may fail. The tests proved that the proposed tool is an accurate and reliable tool for estimating the machine coefficients for assessment of power system stability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=estimation" title=" estimation"> estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=synchronous" title=" synchronous"> synchronous</a>, <a href="https://publications.waset.org/abstracts/search?q=machine" title=" machine"> machine</a>, <a href="https://publications.waset.org/abstracts/search?q=crow%20search" title=" crow search"> crow search</a> </p> <a href="https://publications.waset.org/abstracts/110946/estimation-of-synchronous-machine-synchronizing-and-damping-torque-coefficients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110946.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">140</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">893</span> Electron-Ion Recombination of N^{2+} and O^{3+} Ions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shahin%20%20A.%20Abdel-Naby">Shahin A. Abdel-Naby</a>, <a href="https://publications.waset.org/abstracts/search?q=Asad%20T.%20Hassan"> Asad T. Hassan</a>, <a href="https://publications.waset.org/abstracts/search?q=Stuart%20Loch"> Stuart Loch</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Fogle"> Michael Fogle</a>, <a href="https://publications.waset.org/abstracts/search?q=Negil%20R.%20%20Badnell"> Negil R. Badnell</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20S.%20Pindzola"> Michael S. Pindzola</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accurate and reliable laboratory astrophysical data for electron-ion recombination are needed for plasma modeling. Dielectronic recombination (DR) rate coefficients are calculated for boron-like nitrogen and oxygen ions using state-of-the-art multi-configuration Breit-Pauli atomic structure AUTOSTRUCTURE collisional package within the generalized collisional-radiative framework. The calculations are performed in intermediate coupling scheme associated with n = 0 (2  2) and n = 1 (2  3) core-excitations. Good agreements are found between the theoretically convoluted rate coefficients and the experimental measurements performed at CRYRING heavy-ion storage ring for both ions. Fitting coefficients for the rate coefficients are produced for these ions in the temperature range q2(102-107) K, where q is the ion charge before recombination. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Atomic%20data" title="Atomic data">Atomic data</a>, <a href="https://publications.waset.org/abstracts/search?q=atomic%20processes" title=" atomic processes"> atomic processes</a>, <a href="https://publications.waset.org/abstracts/search?q=electron-ion%20collision" title=" electron-ion collision"> electron-ion collision</a>, <a href="https://publications.waset.org/abstracts/search?q=plasma" title=" plasma"> plasma</a> </p> <a href="https://publications.waset.org/abstracts/123894/electron-ion-recombination-of-n2-and-o3-ions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123894.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">167</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">892</span> Methyltrioctylammonium Chloride as a Separation Solvent for Binary Mixtures: Evaluation Based on Experimental Activity Coefficients</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Kabane">B. Kabane</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20G.%20Redhi"> G. G. Redhi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An ammonium based ionic liquid (methyltrioctylammonium chloride) [N<sub>8 8 8 1</sub>] [Cl] was investigated as an extraction potential solvent for volatile organic solvents (in this regard, solutes), which includes alkenes, alkanes, ketones, alkynes, aromatic hydrocarbons, tetrahydrofuran (THF), alcohols, thiophene, water and acetonitrile based on the experimental activity coefficients at infinite THF measurements were conducted by the use of gas-liquid chromatography at four different temperatures (313.15 to 343.15) K. Experimental data of activity coefficients obtained across the examined temperatures were used in order to calculate the physicochemical properties at infinite dilution such as partial molar excess enthalpy, Gibbs free energy and entropy term. Capacity and selectivity data for selected petrochemical extraction problems (heptane/thiophene, heptane/benzene, cyclohaxane/cyclohexene, hexane/toluene, hexane/hexene) were computed from activity coefficients data and compared to the literature values with other ionic liquids. Evaluation of activity coefficients at infinite dilution expands the knowledge and provides a good understanding related to the interactions between the ionic liquid and the investigated compounds. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=separation" title="separation">separation</a>, <a href="https://publications.waset.org/abstracts/search?q=activity%20coefficients" title=" activity coefficients"> activity coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=methyltrioctylammonium%20chloride" title=" methyltrioctylammonium chloride"> methyltrioctylammonium chloride</a>, <a href="https://publications.waset.org/abstracts/search?q=ionic%20liquid" title=" ionic liquid"> ionic liquid</a>, <a href="https://publications.waset.org/abstracts/search?q=capacity" title=" capacity "> capacity </a> </p> <a href="https://publications.waset.org/abstracts/112731/methyltrioctylammonium-chloride-as-a-separation-solvent-for-binary-mixtures-evaluation-based-on-experimental-activity-coefficients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112731.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">143</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">891</span> Primes as Sums and Differences of Two Binomial Coefficients and Two Powersums</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Benjamin%20Lee%20Warren">Benjamin Lee Warren</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many problems exist in additive number theory which is essential to determine the primes that are the sum of two elements from a given single-variable polynomial sequence, and most of them are unattackable in the present day. Here, we determine solutions for this problem to a few certain sequences (certain binomial coefficients and power sums) using only elementary algebra and some algebraic factoring methods (as well as Euclid’s Lemma and Faulhaber’s Formula). In particular, we show that there are finitely many primes as sums of two of these types of elements. Several cases are fully illustrated, and bounds are presented for the cases not fully illustrated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binomial%20coefficients" title="binomial coefficients">binomial coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20sums" title=" power sums"> power sums</a>, <a href="https://publications.waset.org/abstracts/search?q=primes" title=" primes"> primes</a>, <a href="https://publications.waset.org/abstracts/search?q=algebra" title=" algebra"> algebra</a> </p> <a href="https://publications.waset.org/abstracts/160042/primes-as-sums-and-differences-of-two-binomial-coefficients-and-two-powersums" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160042.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">104</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">890</span> Combined Odd Pair Autoregressive Coefficients for Epileptic EEG Signals Classification by Radial Basis Function Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Boukari%20Nassim">Boukari Nassim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes the use of odd pair autoregressive coefficients (Yule _Walker and Burg) for the feature extraction of electroencephalogram (EEG) signals. In the classification: the radial basis function neural network neural network (RBFNN) is employed. The RBFNN is described by his architecture and his characteristics: as the RBF is defined by the spread which is modified for improving the results of the classification. Five types of EEG signals are defined for this work: Set A, Set B for normal signals, Set C, Set D for interictal signals, set E for ictal signal (we can found that in Bonn university). In outputs, two classes are given (AC, AD, AE, BC, BD, BE, CE, DE), the best accuracy is calculated at 99% for the combined odd pair autoregressive coefficients. Our method is very effective for the diagnosis of epileptic EEG signals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title="epilepsy">epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG%20signals%20classification" title=" EEG signals classification"> EEG signals classification</a>, <a href="https://publications.waset.org/abstracts/search?q=combined%20odd%20pair%20autoregressive%20coefficients" title=" combined odd pair autoregressive coefficients"> combined odd pair autoregressive coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20neural%20network" title=" radial basis function neural network"> radial basis function neural network</a> </p> <a href="https://publications.waset.org/abstracts/47454/combined-odd-pair-autoregressive-coefficients-for-epileptic-eeg-signals-classification-by-radial-basis-function-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47454.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">889</span> Forensic Speaker Verification in Noisy Environmental by Enhancing the Speech Signal Using ICA Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Kamil%20Hasan%20Al-Ali">Ahmed Kamil Hasan Al-Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Bouchra%20Senadji"> Bouchra Senadji</a>, <a href="https://publications.waset.org/abstracts/search?q=Ganesh%20Naik"> Ganesh Naik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose a system to real environmental noise and channel mismatch for forensic speaker verification systems. This method is based on suppressing various types of real environmental noise by using independent component analysis (ICA) algorithm. The enhanced speech signal is applied to mel frequency cepstral coefficients (MFCC) or MFCC feature warping to extract the essential characteristics of the speech signal. Channel effects are reduced using an intermediate vector (i-vector) and probabilistic linear discriminant analysis (PLDA) approach for classification. The proposed algorithm is evaluated by using an Australian forensic voice comparison database, combined with car, street and home noises from QUT-NOISE at a signal to noise ratio (SNR) ranging from -10 dB to 10 dB. Experimental results indicate that the MFCC feature warping-ICA achieves a reduction in equal error rate about (48.22%, 44.66%, and 50.07%) over using MFCC feature warping when the test speech signals are corrupted with random sessions of street, car, and home noises at -10 dB SNR. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=noisy%20forensic%20speaker%20verification" title="noisy forensic speaker verification">noisy forensic speaker verification</a>, <a href="https://publications.waset.org/abstracts/search?q=ICA%20algorithm" title=" ICA algorithm"> ICA algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=MFCC" title=" MFCC"> MFCC</a>, <a href="https://publications.waset.org/abstracts/search?q=MFCC%20feature%20warping" title=" MFCC feature warping"> MFCC feature warping</a> </p> <a href="https://publications.waset.org/abstracts/66332/forensic-speaker-verification-in-noisy-environmental-by-enhancing-the-speech-signal-using-ica-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66332.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">408</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">888</span> Aerodynamic Coefficients Prediction from Minimum Computation Combinations Using OpenVSP Software</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marine%20Segui">Marine Segui</a>, <a href="https://publications.waset.org/abstracts/search?q=Ruxandra%20Mihaela%20Botez"> Ruxandra Mihaela Botez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> OpenVSP is an aerodynamic solver developed by National Aeronautics and Space Administration (NASA) that allows building a reliable model of an aircraft. This software performs an aerodynamic simulation according to the angle of attack of the aircraft makes between the incoming airstream, and its speed. A reliable aerodynamic model of the Cessna Citation X was designed but it required a lot of computation time. As a consequence, a prediction method was established that allowed predicting lift and drag coefficients for all Mach numbers and for all angles of attack, exclusively for stall conditions, from a computation of three angles of attack and only one Mach number. Aerodynamic coefficients given by the prediction method for a Cessna Citation X model were finally compared with aerodynamics coefficients obtained using a complete OpenVSP study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aerodynamic" title="aerodynamic">aerodynamic</a>, <a href="https://publications.waset.org/abstracts/search?q=coefficient" title=" coefficient"> coefficient</a>, <a href="https://publications.waset.org/abstracts/search?q=cruise" title=" cruise"> cruise</a>, <a href="https://publications.waset.org/abstracts/search?q=improving" title=" improving"> improving</a>, <a href="https://publications.waset.org/abstracts/search?q=longitudinal" title=" longitudinal"> longitudinal</a>, <a href="https://publications.waset.org/abstracts/search?q=openVSP" title=" openVSP"> openVSP</a>, <a href="https://publications.waset.org/abstracts/search?q=solver" title=" solver"> solver</a>, <a href="https://publications.waset.org/abstracts/search?q=time" title=" time"> time</a> </p> <a href="https://publications.waset.org/abstracts/85268/aerodynamic-coefficients-prediction-from-minimum-computation-combinations-using-openvsp-software" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85268.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">235</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">887</span> Subclasses of Bi-Univalent Functions Associated with Hohlov Operator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rashidah%20Omar">Rashidah Omar</a>, <a href="https://publications.waset.org/abstracts/search?q=Suzeini%20Abdul%20Halim"> Suzeini Abdul Halim</a>, <a href="https://publications.waset.org/abstracts/search?q=Aini%20Janteng"> Aini Janteng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The coefficients estimate problem for Taylor-Maclaurin series is still an open problem especially for a function in the subclass of bi-univalent functions. A function <em>f </em>ϵ<em> A </em>is said to be bi-univalent in the open unit disk <em>D</em> if both <em>f </em>and <em>f<sup>-1</sup></em> are univalent in <em>D</em>. The symbol <em>A</em> denotes the class of all analytic functions <em>f</em> in <em>D</em> and it is normalized by the conditions <em>f</em>(0) = <em>f’</em>(0) – 1=0. The class of bi-univalent is denoted by  The subordination concept is used in determining second and third Taylor-Maclaurin coefficients. The upper bound for second and third coefficients is estimated for functions in the subclasses of bi-univalent functions which are subordinated to the function φ. An analytic function <em>f</em> is subordinate to an analytic function <em>g</em> if there is an analytic function <em>w</em> defined on <em>D</em> with <em>w</em>(0) = 0 and |<em>w</em>(z)| < 1 satisfying <em>f</em>(<em>z</em>) = <em>g</em>[<em>w</em>(<em>z</em>)]. In this paper, two subclasses of bi-univalent functions associated with Hohlov operator are introduced. The bound for second and third coefficients of functions in these subclasses is determined using subordination. The findings would generalize the previous related works of several earlier authors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analytic%20functions" title="analytic functions">analytic functions</a>, <a href="https://publications.waset.org/abstracts/search?q=bi-univalent%20functions" title=" bi-univalent functions"> bi-univalent functions</a>, <a href="https://publications.waset.org/abstracts/search?q=Hohlov%20operator" title=" Hohlov operator"> Hohlov operator</a>, <a href="https://publications.waset.org/abstracts/search?q=subordination" title=" subordination"> subordination</a> </p> <a href="https://publications.waset.org/abstracts/72671/subclasses-of-bi-univalent-functions-associated-with-hohlov-operator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72671.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">293</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">886</span> Heuristic Classification of Hydrophone Recordings</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daniel%20M.%20Wolff">Daniel M. Wolff</a>, <a href="https://publications.waset.org/abstracts/search?q=Patricia%20Gray"> Patricia Gray</a>, <a href="https://publications.waset.org/abstracts/search?q=Rafael%20de%20la%20Parra%20Venegas"> Rafael de la Parra Venegas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An unsupervised machine listening system is constructed and applied to a dataset of 17,195 30-second marine hydrophone recordings. The system is then heuristically supplemented with anecdotal listening, contextual recording information, and supervised learning techniques to reduce the number of false positives. Features for classification are assembled by extracting the following data from each of the audio files: the spectral centroid, root-mean-squared values for each frequency band of a 10-octave filter bank, and mel-frequency cepstral coefficients in 5-second frames. In this way both time- and frequency-domain information are contained in the features to be passed to a clustering algorithm. Classification is performed using the k-means algorithm and then a k-nearest neighbors search. Different values of k are experimented with, in addition to different combinations of the available feature sets. Hypothesized class labels are 'primarily anthrophony' and 'primarily biophony', where the best class result conforming to the former label has 104 members after heuristic pruning. This demonstrates how a large audio dataset has been made more tractable with machine learning techniques, forming the foundation of a framework designed to acoustically monitor and gauge biological and anthropogenic activity in a marine environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anthrophony" title="anthrophony">anthrophony</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrophone" title=" hydrophone"> hydrophone</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means" title=" k-means"> k-means</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/89273/heuristic-classification-of-hydrophone-recordings" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89273.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">170</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">885</span> Analysis and Modeling of the Building’s Facades in Terms of Different Convection Coefficients</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Enes%20Yasa">Enes Yasa</a>, <a href="https://publications.waset.org/abstracts/search?q=Guven%20Fidan"> Guven Fidan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Building Simulation tools need to better evaluate convective heat exchanges between external air and wall surfaces. Previous analysis demonstrated the significant effects of convective heat transfer coefficient values on the room energy balance. Some authors have pointed out that large discrepancies observed between widely used building thermal models can be attributed to the different correlations used to calculate or impose the value of the convective heat transfer coefficients. Moreover, numerous researchers have made sensitivity calculations and proved that the choice of Convective Heat Transfer Coefficient values can lead to differences from 20% to 40% of energy demands. The thermal losses to the ambient from a building surface or a roof mounted solar collector represent an important portion of the overall energy balance and depend heavily on the wind induced convection. In an effort to help designers make better use of the available correlations in the literature for the external convection coefficients due to the wind, a critical discussion and a suitable tabulation is presented, on the basis of algebraic form of the coefficients and their dependence upon characteristic length and wind direction, in addition to wind speed. Many research works have been conducted since early eighties focused on the convection heat transfer problems inside buildings. In this context, a Computational Fluid Dynamics (CFD) program has been used to predict external convective heat transfer coefficients at external building surfaces. For the building facades model, effects of wind speed and temperature differences between the surfaces and the external air have been analyzed, showing different heat transfer conditions and coefficients. In order to provide further information on external convective heat transfer coefficients, a numerical work is presented in this paper, using a Computational Fluid Dynamics (CFD) commercial package (CFX) to predict convective heat transfer coefficients at external building surface. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CFD%20in%20buildings" title="CFD in buildings">CFD in buildings</a>, <a href="https://publications.waset.org/abstracts/search?q=external%20convective%20heat%20transfer%20coefficients" title=" external convective heat transfer coefficients"> external convective heat transfer coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=building%20facades" title=" building facades"> building facades</a>, <a href="https://publications.waset.org/abstracts/search?q=thermal%20modelling" title="thermal modelling">thermal modelling</a> </p> <a href="https://publications.waset.org/abstracts/25092/analysis-and-modeling-of-the-buildings-facades-in-terms-of-different-convection-coefficients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25092.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">421</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">884</span> Recombination Rate Coefficients for NIII and OIV Ions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shahin%20A.%20Abdel-Naby">Shahin A. Abdel-Naby</a>, <a href="https://publications.waset.org/abstracts/search?q=Asad%20T.%20Hassan"> Asad T. Hassan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electron-ion recombination data are needed for plasma modeling. The recombination processes include radiative recombination (RR), dielectronic recombination (DR), and trielectronic recombination (TR). When a free electron is captured by an ion with simultaneous excitation of its core, a doubly-exited intermediate state may be formed. The doubly excited state relaxes either by electron emission (autoionization) or by radiative decay (photon emission). DR process takes place when the relaxation occurs to a bound state by photon emission. Reliable laboratory astrophysics data (theory and experiment) for DR rate coefficients are needed to determine the charge state distribution in photoionized sources such as X-ray binaries and active galactic nuclei. DR rate coefficients for NIII and OIV ions are calculated using state-of-the-art multi-configuration Breit-Pauli atomic structure AUTOSTRUCTURE collisional package within the generalized collisional-radiative framework. Level-resolved calculations for RR and DR rate coefficients from the ground and metastable initial states are produced in an intermediate coupling scheme associated with Δn = 0 (2→2) and Δn = 1 (2 →3) core-excitations. DR cross sections for these ions are convoluted with the experimental electron-cooler temperatures to produce DR rate coefficients. Good agreements are found between these rate coefficients and the experimental measurements performed at the CRYRING heavy-ion storage ring for both ions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=atomic%20data" title="atomic data">atomic data</a>, <a href="https://publications.waset.org/abstracts/search?q=atomic%20process" title=" atomic process"> atomic process</a>, <a href="https://publications.waset.org/abstracts/search?q=electron-ion%20collision" title=" electron-ion collision"> electron-ion collision</a>, <a href="https://publications.waset.org/abstracts/search?q=plasmas" title=" plasmas"> plasmas</a> </p> <a href="https://publications.waset.org/abstracts/137671/recombination-rate-coefficients-for-niii-and-oiv-ions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137671.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">150</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">883</span> Chebyshev Wavelets and Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emanuel%20Guariglia">Emanuel Guariglia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we deal with Chebyshev wavelets. We analyze their properties computing their Fourier transform. Moreover, we discuss the differential properties of Chebyshev wavelets due the connection coefficients. The differential properties of Chebyshev wavelets, expressed by the connection coefficients (also called refinable integrals), are given by finite series in terms of the Kronecker delta. Moreover, we treat the p-order derivative of Chebyshev wavelets and compute its Fourier transform. Finally, we expand the mother wavelet in Taylor series with an application both in fractional calculus and fractal geometry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chebyshev%20wavelets" title="Chebyshev wavelets">Chebyshev wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=Fourier%20transform" title=" Fourier transform"> Fourier transform</a>, <a href="https://publications.waset.org/abstracts/search?q=connection%20coefficients" title=" connection coefficients"> connection coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=Taylor%20series" title=" Taylor series"> Taylor series</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20fractional%20derivative" title=" local fractional derivative"> local fractional derivative</a>, <a href="https://publications.waset.org/abstracts/search?q=Cantor%20set" title=" Cantor set"> Cantor set</a> </p> <a href="https://publications.waset.org/abstracts/157194/chebyshev-wavelets-and-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157194.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">123</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">882</span> Numerical Study for the Estimation of Hydrodynamic Current Drag Coefficients for the Colombian Navy Frigates Using Computational Fluid Dynamics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mauricio%20Gracia">Mauricio Gracia</a>, <a href="https://publications.waset.org/abstracts/search?q=Luis%20Leal"> Luis Leal</a>, <a href="https://publications.waset.org/abstracts/search?q=Bharat%20Verma"> Bharat Verma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Computational fluid dynamics (CFD) has become nowadays an important tool in the process of hydrodynamic design of modern ships. CFD is used to model any phenomena related to fluid flow in a control volume like a ship or any offshore structure in the sea. In the present study, the current force drag coefficients for a Colombian Navy Frigate in deep and shallow water are estimated through the application of CFD. The study shows the process of simulating the ship current drag coefficients using the CFD simulations method, which is conducted using STAR-CCM+ software package. The Almirante Padilla class Frigate ship scale model is investigated. The results show the ship current drag coefficient calculated considering a current speed of 1 knot with a 90° drift angle for the full-scale ship. Predicted results were compared against the current drag coefficients published in the Lloyds register OCIMF report. It is shown that the simulation results agree fairly well with the published results and that STAR-CCM+ code can predict current drag coefficients. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CFD" title="CFD">CFD</a>, <a href="https://publications.waset.org/abstracts/search?q=current%20draft%20coefficient" title=" current draft coefficient"> current draft coefficient</a>, <a href="https://publications.waset.org/abstracts/search?q=STAR-CCM%2B" title=" STAR-CCM+"> STAR-CCM+</a>, <a href="https://publications.waset.org/abstracts/search?q=OCIMF" title=" OCIMF"> OCIMF</a>, <a href="https://publications.waset.org/abstracts/search?q=Bollard%20pull" title=" Bollard pull"> Bollard pull</a> </p> <a href="https://publications.waset.org/abstracts/132520/numerical-study-for-the-estimation-of-hydrodynamic-current-drag-coefficients-for-the-colombian-navy-frigates-using-computational-fluid-dynamics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132520.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">174</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">881</span> Computation of Drag and Lift Coefficients on Submerged Vanes in Open Channels</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anshul%20Jain">Anshul Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Deepak%20Kumar"> P. Deepak Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20K.%20S.%20Dikshit"> P. K. S. Dikshit</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To stabilize the riverbanks in the curved reaches of alluvial channels due to erosion and to stop sediment transportation, many models and theories have been put forth. One among such methods is to install flat vanes on the channel bed in predetermined manner. In practical, a relatively small no of vanes can produce bend flows which are practically uniform across the channel. The objective of the present study is to measure the drag and lift on such submerged vanes in open channels. Experiments were performed and the data collected have been presented and analyzed. Using the data collected herein, predictors for the coefficients of drag and lift have been developed. Such predictors yield the value of these coefficients for the known fluid properties and flow characteristic of the channel. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drag" title="drag">drag</a>, <a href="https://publications.waset.org/abstracts/search?q=lift" title=" lift"> lift</a>, <a href="https://publications.waset.org/abstracts/search?q=vanes" title=" vanes"> vanes</a>, <a href="https://publications.waset.org/abstracts/search?q=open%20channel" title=" open channel"> open channel</a> </p> <a href="https://publications.waset.org/abstracts/47361/computation-of-drag-and-lift-coefficients-on-submerged-vanes-in-open-channels" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47361.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">347</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">880</span> A Generalization of Planar Pascal’s Triangle to Polynomial Expansion and Connection with Sierpinski Patterns</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wajdi%20Mohamed%20Ratemi">Wajdi Mohamed Ratemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The very well-known stacked sets of numbers referred to as Pascal’s triangle present the coefficients of the binomial expansion of the form (x+y)n. This paper presents an approach (the Staircase Horizontal Vertical, SHV-method) to the generalization of planar Pascal’s triangle for polynomial expansion of the form (x+y+z+w+r+⋯)n. The presented generalization of Pascal’s triangle is different from other generalizations of Pascal’s triangles given in the literature. The coefficients of the generalized Pascal’s triangles, presented in this work, are generated by inspection, using embedded Pascal’s triangles. The coefficients of I-variables expansion are generated by horizontally laying out the Pascal’s elements of (I-1) variables expansion, in a staircase manner, and multiplying them with the relevant columns of vertically laid out classical Pascal’s elements, hence avoiding factorial calculations for generating the coefficients of the polynomial expansion. Furthermore, the classical Pascal’s triangle has some pattern built into it regarding its odd and even numbers. Such pattern is known as the Sierpinski’s triangle. In this study, a presentation of Sierpinski-like patterns of the generalized Pascal’s triangles is given. Applications related to those coefficients of the binomial expansion (Pascal’s triangle), or polynomial expansion (generalized Pascal’s triangles) can be in areas of combinatorics, and probabilities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pascal%E2%80%99s%20triangle" title="pascal’s triangle">pascal’s triangle</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20pascal%E2%80%99s%20triangle" title=" generalized pascal’s triangle"> generalized pascal’s triangle</a>, <a href="https://publications.waset.org/abstracts/search?q=polynomial%20expansion" title=" polynomial expansion"> polynomial expansion</a>, <a href="https://publications.waset.org/abstracts/search?q=sierpinski%E2%80%99s%20triangle" title=" sierpinski’s triangle"> sierpinski’s triangle</a>, <a href="https://publications.waset.org/abstracts/search?q=combinatorics" title=" combinatorics"> combinatorics</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilities" title=" probabilities"> probabilities</a> </p> <a href="https://publications.waset.org/abstracts/37988/a-generalization-of-planar-pascals-triangle-to-polynomial-expansion-and-connection-with-sierpinski-patterns" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37988.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">367</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">879</span> Micromechanics Modeling of 3D Network Smart Orthotropic Structures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=E.%20M.%20Hassan">E. M. Hassan</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20L.%20Kalamkarov"> A. L. Kalamkarov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Two micromechanical models for 3D smart composite with embedded periodic or nearly periodic network of generally orthotropic reinforcements and actuators are developed and applied to cubic structures with unidirectional orientation of constituents. Analytical formulas for the effective piezothermoelastic coefficients are derived using the Asymptotic Homogenization Method (AHM). Finite Element Analysis (FEA) is subsequently developed and used to examine the aforementioned periodic 3D network reinforced smart structures. The deformation responses from the FE simulations are used to extract effective coefficients. The results from both techniques are compared. This work considers piezoelectric materials that respond linearly to changes in electric field, electric displacement, mechanical stress and strain and thermal effects. This combination of electric fields and thermo-mechanical response in smart composite structures is characterized by piezoelectric and thermal expansion coefficients. The problem is represented by unit-cell and the models are developed using the AHM and the FEA to determine the effective piezoelectric and thermal expansion coefficients. Each unit cell contains a number of orthotropic inclusions in the form of structural reinforcements and actuators. Using matrix representation of the coupled response of the unit cell, the effective piezoelectric and thermal expansion coefficients are calculated and compared with results of the asymptotic homogenization method. A very good agreement is shown between these two approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asymptotic%20homogenization%20method" title="asymptotic homogenization method">asymptotic homogenization method</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20analysis" title=" finite element analysis"> finite element analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=effective%20piezothermoelastic%20coefficients" title=" effective piezothermoelastic coefficients"> effective piezothermoelastic coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20smart%20network%20composite%20structures" title=" 3D smart network composite structures"> 3D smart network composite structures</a> </p> <a href="https://publications.waset.org/abstracts/18190/micromechanics-modeling-of-3d-network-smart-orthotropic-structures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18190.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">400</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=cepstral%20coefficients&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=cepstral%20coefficients&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=cepstral%20coefficients&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=cepstral%20coefficients&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=cepstral%20coefficients&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=cepstral%20coefficients&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=cepstral%20coefficients&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=cepstral%20coefficients&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=cepstral%20coefficients&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=cepstral%20coefficients&page=30">30</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=cepstral%20coefficients&page=31">31</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=cepstral%20coefficients&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>