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recognition</title> <meta name="description" content="Search results for: chord recognition"> <meta name="keywords" content="chord recognition"> <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 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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="chord recognition"> <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> 1736</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: chord recognition</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1616</span> A Communication Signal Recognition Algorithm Based on Holder Coefficient Characteristics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hui%20Zhang">Hui Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ye%20Tian"> Ye Tian</a>, <a href="https://publications.waset.org/abstracts/search?q=Fang%20Ye"> Fang Ye</a>, <a href="https://publications.waset.org/abstracts/search?q=Ziming%20Guo"> Ziming Guo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=communication%20signal" title="communication signal">communication signal</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=Holder%20coefficient" title=" Holder coefficient"> Holder coefficient</a>, <a href="https://publications.waset.org/abstracts/search?q=improved%20cloud%20model" title=" improved cloud model"> improved cloud model</a> </p> <a href="https://publications.waset.org/abstracts/101463/a-communication-signal-recognition-algorithm-based-on-holder-coefficient-characteristics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101463.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">156</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">1615</span> Emotion Recognition with Occlusions Based on Facial Expression Reconstruction and Weber Local Descriptor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jadisha%20Cornejo">Jadisha Cornejo</a>, <a href="https://publications.waset.org/abstracts/search?q=Helio%20Pedrini"> Helio Pedrini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recognition of emotions based on facial expressions has received increasing attention from the scientific community over the last years. Several fields of applications can benefit from facial emotion recognition, such as behavior prediction, interpersonal relations, human-computer interactions, recommendation systems. In this work, we develop and analyze an emotion recognition framework based on facial expressions robust to occlusions through the Weber Local Descriptor (WLD). Initially, the occluded facial expressions are reconstructed following an extension approach of Robust Principal Component Analysis (RPCA). Then, WLD features are extracted from the facial expression representation, as well as Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). The feature vector space is reduced using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Finally, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classifiers are used to recognize the expressions. Experimental results on three public datasets demonstrated that the WLD representation achieved competitive accuracy rates for occluded and non-occluded facial expressions compared to other approaches available in the literature. <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=facial%20expression" title=" facial expression"> facial expression</a>, <a href="https://publications.waset.org/abstracts/search?q=occlusion" title=" occlusion"> occlusion</a>, <a href="https://publications.waset.org/abstracts/search?q=fiducial%20landmarks" title=" fiducial landmarks"> fiducial landmarks</a> </p> <a href="https://publications.waset.org/abstracts/90510/emotion-recognition-with-occlusions-based-on-facial-expression-reconstruction-and-weber-local-descriptor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90510.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">182</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">1614</span> Proposed Solutions Based on Affective Computing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diego%20Adrian%20Cardenas%20Jorge">Diego Adrian Cardenas Jorge</a>, <a href="https://publications.waset.org/abstracts/search?q=Gerardo%20Mirando%20Guisado"> Gerardo Mirando Guisado</a>, <a href="https://publications.waset.org/abstracts/search?q=Alfredo%20Barrientos%20Padilla"> Alfredo Barrientos Padilla</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A system based on Affective Computing can detect and interpret human information like voice, facial expressions and body movement to detect emotions and execute a corresponding response. This data is important due to the fact that a person can communicate more effectively with emotions than can be possible with words. This information can be processed through technological components like Facial Recognition, Gait Recognition or Gesture Recognition. As of now, solutions proposed using this technology only consider one component at a given moment. This research investigation proposes two solutions based on Affective Computing taking into account more than one component for emotion detection. The proposals reflect the levels of dependency between hardware devices and software, as well as the interaction process between the system and the user which implies the development of scenarios where both proposals will be put to the test in a live environment. Both solutions are to be developed in code by software engineers to prove the feasibility. To validate the impact on society and business interest, interviews with stakeholders are conducted with an investment mind set where each solution is labeled on a scale of 1 through 5, being one a minimum possible investment and 5 the maximum. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=affective%20computing" title="affective computing">affective computing</a>, <a href="https://publications.waset.org/abstracts/search?q=emotions" title=" emotions"> emotions</a>, <a href="https://publications.waset.org/abstracts/search?q=emotion%20detection" title=" emotion detection"> emotion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title=" face recognition"> face recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=gait%20recognition" title=" gait recognition"> gait recognition</a> </p> <a href="https://publications.waset.org/abstracts/43577/proposed-solutions-based-on-affective-computing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43577.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">1613</span> Local Spectrum Feature Extraction for Face Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Imran%20Ahmad">Muhammad Imran Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Ruzelita%20Ngadiran"> Ruzelita Ngadiran</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Nazrin%20Md%20Isa"> Mohd Nazrin Md Isa</a>, <a href="https://publications.waset.org/abstracts/search?q=Nor%20Ashidi%20Mat%20Isa"> Nor Ashidi Mat Isa</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20ZaizuIlyas"> Mohd ZaizuIlyas</a>, <a href="https://publications.waset.org/abstracts/search?q=Raja%20Abdullah%20Raja%20Ahmad"> Raja Abdullah Raja Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Said%20Amirul%20Anwar%20Ab%20Hamid"> Said Amirul Anwar Ab Hamid</a>, <a href="https://publications.waset.org/abstracts/search?q=Muzammil%20Jusoh"> Muzammil Jusoh </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents two technique, local feature extraction using image spectrum and low frequency spectrum modelling using GMM to capture the underlying statistical information to improve the performance of face recognition system. Local spectrum features are extracted using overlap sub block window that are mapping on the face image. For each of this block, spatial domain is transformed to frequency domain using DFT. A low frequency coefficient is preserved by discarding high frequency coefficients by applying rectangular mask on the spectrum of the facial image. Low frequency information is non Gaussian in the feature space and by using combination of several Gaussian function that has different statistical properties, the best feature representation can be model using probability density function. The recognition process is performed using maximum likelihood value computed using pre-calculate GMM components. The method is tested using FERET data sets and is able to achieved 92% recognition rates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=local%20features%20modelling" title="local features modelling">local features modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20recognition%20system" title=" face recognition system"> face recognition system</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20mixture%20models" title=" Gaussian mixture models"> Gaussian mixture models</a>, <a href="https://publications.waset.org/abstracts/search?q=Feret" title=" Feret"> Feret</a> </p> <a href="https://publications.waset.org/abstracts/17388/local-spectrum-feature-extraction-for-face-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17388.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">668</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">1612</span> Unsupervised Reciter Recognition Using Gaussian Mixture Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Alwosheel">Ahmad Alwosheel</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Alqaraawi"> Ahmed Alqaraawi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work proposes an unsupervised text-independent probabilistic approach to recognize Quran reciter voice. It is an accurate approach that works on real time applications. This approach does not require a prior information about reciter models. It has two phases, where in the training phase the reciters' acoustical features are modeled using Gaussian Mixture Models, while in the testing phase, unlabeled reciter's acoustical features are examined among GMM models. Using this approach, a high accuracy results are achieved with efficient computation time process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Quran" title="Quran">Quran</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=reciter%20recognition" title=" reciter recognition"> reciter recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20Mixture%20Model" title=" Gaussian Mixture Model"> Gaussian Mixture Model</a> </p> <a href="https://publications.waset.org/abstracts/46532/unsupervised-reciter-recognition-using-gaussian-mixture-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46532.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">380</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">1611</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">1610</span> A Fast, Reliable Technique for Face Recognition Based on Hidden Markov Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sameh%20Abaza">Sameh Abaza</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Ibrahim"> Mohamed Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Tarek%20Mahmoud"> Tarek Mahmoud</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the development in the digital image processing, its wide use in many applications such as medical, security, and others, the need for more accurate techniques that are reliable, fast and robust is vehemently demanded. In the field of security, in particular, speed is of the essence. In this paper, a pattern recognition technique that is based on the use of Hidden Markov Model (HMM), K-means and the Sobel operator method is developed. The proposed technique is proved to be fast with respect to some other techniques that are investigated for comparison. Moreover, it shows its capability of recognizing the normal face (center part) as well as face boundary. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=HMM" title="HMM">HMM</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=Sobel" title=" Sobel"> Sobel</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title=" face recognition"> face recognition</a> </p> <a href="https://publications.waset.org/abstracts/60973/a-fast-reliable-technique-for-face-recognition-based-on-hidden-markov-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60973.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">332</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">1609</span> Mood Recognition Using Indian Music</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vishwa%20Joshi">Vishwa Joshi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study of mood recognition in the field of music has gained a lot of momentum in the recent years with machine learning and data mining techniques and many audio features contributing considerably to analyze and identify the relation of mood plus music. In this paper we consider the same idea forward and come up with making an effort to build a system for automatic recognition of mood underlying the audio song’s clips by mining their audio features and have evaluated several data classification algorithms in order to learn, train and test the model describing the moods of these audio songs and developed an open source framework. Before classification, Preprocessing and Feature Extraction phase is necessary for removing noise and gathering features respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=music" title="music">music</a>, <a href="https://publications.waset.org/abstracts/search?q=mood" title=" mood"> mood</a>, <a href="https://publications.waset.org/abstracts/search?q=features" title=" features"> features</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/24275/mood-recognition-using-indian-music" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24275.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">500</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">1608</span> Iris Feature Extraction and Recognition Based on Two-Dimensional Gabor Wavelength Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bamidele%20Samson%20Alobalorun">Bamidele Samson Alobalorun</a>, <a href="https://publications.waset.org/abstracts/search?q=Ifedotun%20Roseline%20Idowu"> Ifedotun Roseline Idowu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Biometrics technologies apply the human body parts for their unique and reliable identification based on physiological traits. The iris recognition system is a biometric–based method for identification. The human iris has some discriminating characteristics which provide efficiency to the method. In order to achieve this efficiency, there is a need for feature extraction of the distinct features from the human iris in order to generate accurate authentication of persons. In this study, an approach for an iris recognition system using 2D Gabor for feature extraction is applied to iris templates. The 2D Gabor filter formulated the patterns that were used for training and equally sent to the hamming distance matching technique for recognition. A comparison of results is presented using two iris image subjects of different matching indices of 1,2,3,4,5 filter based on the CASIA iris image database. By comparing the two subject results, the actual computational time of the developed models, which is measured in terms of training and average testing time in processing the hamming distance classifier, is found with best recognition accuracy of 96.11% after capturing the iris localization or segmentation using the Daughman’s Integro-differential, the normalization is confined to the Daugman’s rubber sheet model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daugman%20rubber%20sheet" title="Daugman rubber sheet">Daugman rubber sheet</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=Hamming%20distance" title=" Hamming distance"> Hamming distance</a>, <a href="https://publications.waset.org/abstracts/search?q=iris%20recognition%20system" title=" iris recognition system"> iris recognition system</a>, <a href="https://publications.waset.org/abstracts/search?q=2D%20Gabor%20wavelet%20transform" title=" 2D Gabor wavelet transform"> 2D Gabor wavelet transform</a> </p> <a href="https://publications.waset.org/abstracts/170345/iris-feature-extraction-and-recognition-based-on-two-dimensional-gabor-wavelength-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170345.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">65</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">1607</span> Object Recognition System Operating from Different Type Vehicles Using Raspberry and OpenCV</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maria%20Pavlova">Maria Pavlova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In our days, it is possible to put the camera on different vehicles like quadcopter, train, airplane and etc. The camera also can be the input sensor in many different systems. That means the object recognition like non separate part of monitoring control can be key part of the most intelligent systems. The aim of this paper is to focus of the object recognition process during vehicles movement. During the vehicle’s movement the camera takes pictures from the environment without storage in Data Base. In case the camera detects a special object (for example human or animal), the system saves the picture and sends it to the work station in real time. This functionality will be very useful in emergency or security situations where is necessary to find a specific object. In another application, the camera can be mounted on crossroad where do not have many people and if one or more persons come on the road, the traffic lights became the green and they can cross the road. In this papers is presented the system has solved the aforementioned problems. It is presented architecture of the object recognition system includes the camera, Raspberry platform, GPS system, neural network, software and Data Base. The camera in the system takes the pictures. The object recognition is done in real time using the OpenCV library and Raspberry microcontroller. An additional feature of this library is the ability to display the GPS coordinates of the captured objects position. The results from this processes will be sent to remote station. So, in this case, we can know the location of the specific object. By neural network, we can learn the module to solve the problems using incoming data and to be part in bigger intelligent system. The present paper focuses on the design and integration of the image recognition like a part of smart systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=camera" title="camera">camera</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20recognition" title=" object recognition"> object recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=OpenCV" title=" OpenCV"> OpenCV</a>, <a href="https://publications.waset.org/abstracts/search?q=Raspberry" title=" Raspberry"> Raspberry</a> </p> <a href="https://publications.waset.org/abstracts/81695/object-recognition-system-operating-from-different-type-vehicles-using-raspberry-and-opencv" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81695.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">218</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">1606</span> The Study on How Social Cues in a Scene Modulate Basic Object Recognition Proces</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shih-Yu%20Lo">Shih-Yu Lo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Stereotypes exist in almost every society, affecting how people interact with each other. However, to our knowledge, the influence of stereotypes was rarely explored in the context of basic perceptual processes. This study aims to explore how the gender stereotype affects object recognition. Participants were presented with a series of scene pictures, followed by a target display with a man or a woman, holding a weapon or a non-weapon object. The task was to identify whether the object in the target display was a weapon or not. Although the gender of the object holder could not predict whether he or she held a weapon, and was irrelevant to the task goal, the participant nevertheless tended to identify the object as a weapon when the object holder was a man than a woman. The analysis based on the signal detection theory showed that the stereotype effect on object recognition mainly resulted from the participant’s bias to make a 'weapon' response when a man was in the scene instead of a woman in the scene. In addition, there was a trend that the participant’s sensitivity to differentiate a weapon from a non-threating object was higher when a woman was in the scene than a man was in the scene. The results of this study suggest that the irrelevant social cues implied in the visual scene can be very powerful that they can modulate the basic object recognition process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gender%20stereotype" title="gender stereotype">gender stereotype</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20recognition" title=" object recognition"> object recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20detection%20theory" title=" signal detection theory"> signal detection theory</a>, <a href="https://publications.waset.org/abstracts/search?q=weapon" title=" weapon"> weapon</a> </p> <a href="https://publications.waset.org/abstracts/92535/the-study-on-how-social-cues-in-a-scene-modulate-basic-object-recognition-proces" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92535.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">209</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">1605</span> The Effect of Artificial Intelligence on Civil Engineering Outputs and Designs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mina%20Youssef%20Makram%20Ibrahim">Mina Youssef Makram Ibrahim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Engineering identity contributes to the professional and academic sustainability of female engineers. Recognizability is an important factor that shapes an engineer's identity. People who are deprived of real recognition often fail to create a positive identity. This study draws on Hornet’s recognition theory to identify factors that influence female civil engineers' sense of recognition. Over the past decade, a survey was created and distributed to 330 graduate students in the Department of Civil, Civil and Environmental Engineering at Iowa State University. Survey items include demographics, perceptions of a civil engineer's identity, and factors that influence recognition of a civil engineer's identity, such as B. Opinions about society and family. Descriptive analysis of survey responses revealed that perceptions of civil engineering varied significantly. The definitions of civil engineering provided by participants included the terms structure, design and infrastructure. Almost half of the participants said the main reason for studying Civil Engineering was their interest in the subject, and the majority said they were proud to be a civil engineer. Many study participants reported that their parents viewed them as civil engineers. Institutional and operational treatment was also found to have a significant impact on the recognition of women civil engineers. Almost half of the participants reported feeling isolated or ignored at work because of their gender. This research highlights the importance of recognition in developing the identity of women engineers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=civil%20service" title="civil service">civil service</a>, <a href="https://publications.waset.org/abstracts/search?q=hiring" title=" hiring"> hiring</a>, <a href="https://publications.waset.org/abstracts/search?q=merit" title=" merit"> merit</a>, <a href="https://publications.waset.org/abstracts/search?q=policing%20civil%20engineering" title=" policing civil engineering"> policing civil engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=construction" title=" construction"> construction</a>, <a href="https://publications.waset.org/abstracts/search?q=surveying" title=" surveying"> surveying</a>, <a href="https://publications.waset.org/abstracts/search?q=mapping" title=" mapping"> mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=pile%20civil%20service" title=" pile civil service"> pile civil service</a>, <a href="https://publications.waset.org/abstracts/search?q=Kazakhstan" title=" Kazakhstan"> Kazakhstan</a>, <a href="https://publications.waset.org/abstracts/search?q=modernization" title=" modernization"> modernization</a>, <a href="https://publications.waset.org/abstracts/search?q=a%20national%20model%20of%20civil%20service" title=" a national model of civil service"> a national model of civil service</a>, <a href="https://publications.waset.org/abstracts/search?q=civil%20service%20reforms" title=" civil service reforms"> civil service reforms</a>, <a href="https://publications.waset.org/abstracts/search?q=bureaucracy%20civil%20engineering" title=" bureaucracy civil engineering"> bureaucracy civil engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=gender" title=" gender"> gender</a>, <a href="https://publications.waset.org/abstracts/search?q=identity" title=" identity"> identity</a>, <a href="https://publications.waset.org/abstracts/search?q=recognition" title=" recognition"> recognition</a> </p> <a href="https://publications.waset.org/abstracts/185125/the-effect-of-artificial-intelligence-on-civil-engineering-outputs-and-designs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185125.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">63</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">1604</span> Evaluate the Changes in Stress Level Using Facial Thermal Imaging</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amin%20Derakhshan">Amin Derakhshan</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Mikaili"> Mohammad Mikaili</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Ali%20Khalilzadeh"> Mohammad Ali Khalilzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Amin%20Mohammadian"> Amin Mohammadian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a stress recognition system from multi-modal bio-potential signals. For stress recognition, Support Vector Machines (SVM) and LDA are applied to design the stress classifiers and its characteristics are investigated. Using gathered data under psychological polygraph experiments, the classifiers are trained and tested. The pattern recognition method classiﬁes stressful from non-stressful subjects based on labels which come from polygraph data. The successful classiﬁcation rate is 96% for 12 subjects. It means that facial thermal imaging due to its non-contact advantage could be a remarkable alternative for psycho-physiological methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stress" title="stress">stress</a>, <a href="https://publications.waset.org/abstracts/search?q=thermal%20imaging" title=" thermal imaging"> thermal imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=face" title=" face"> face</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a>, <a href="https://publications.waset.org/abstracts/search?q=polygraph" title=" polygraph"> polygraph</a> </p> <a href="https://publications.waset.org/abstracts/8628/evaluate-the-changes-in-stress-level-using-facial-thermal-imaging" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8628.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">487</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">1603</span> Hybrid Approach for Face Recognition Combining Gabor Wavelet and Linear Discriminant Analysis </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A%3A%20Annis%20Fathima">A: Annis Fathima</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Vaidehi"> V. Vaidehi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Ajitha"> S. Ajitha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Face recognition system finds many applications in surveillance and human computer interaction systems. As the applications using face recognition systems are of much importance and demand more accuracy, more robustness in the face recognition system is expected with less computation time. In this paper, a hybrid approach for face recognition combining Gabor Wavelet and Linear Discriminant Analysis (HGWLDA) is proposed. The normalized input grayscale image is approximated and reduced in dimension to lower the processing overhead for Gabor filters. This image is convolved with bank of Gabor filters with varying scales and orientations. LDA, a subspace analysis techniques are used to reduce the intra-class space and maximize the inter-class space. The techniques used are 2-dimensional Linear Discriminant Analysis (2D-LDA), 2-dimensional bidirectional LDA ((2D)2LDA), Weighted 2-dimensional bidirectional Linear Discriminant Analysis (Wt (2D)2 LDA). LDA reduces the feature dimension by extracting the features with greater variance. k-Nearest Neighbour (k-NN) classifier is used to classify and recognize the test image by comparing its feature with each of the training set features. The HGWLDA approach is robust against illumination conditions as the Gabor features are illumination invariant. This approach also aims at a better recognition rate using less number of features for varying expressions. The performance of the proposed HGWLDA approaches is evaluated using AT&T database, MIT-India face database and faces94 database. It is found that the proposed HGWLDA approach provides better results than the existing Gabor approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title="face recognition">face recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Gabor%20wavelet" title=" Gabor wavelet"> Gabor wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=LDA" title=" LDA"> LDA</a>, <a href="https://publications.waset.org/abstracts/search?q=k-NN%20classifier" title=" k-NN classifier"> k-NN classifier</a> </p> <a href="https://publications.waset.org/abstracts/11196/hybrid-approach-for-face-recognition-combining-gabor-wavelet-and-linear-discriminant-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11196.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">467</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">1602</span> An End-to-end Piping and Instrumentation Diagram Information Recognition System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Taekyong%20Lee">Taekyong Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Joon-Young%20Kim"> Joon-Young Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jae-Min%20Cha"> Jae-Min Cha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Piping and instrumentation diagram (P&ID) is an essential design drawing describing the interconnection of process equipment and the instrumentation installed to control the process. P&IDs are modified and managed throughout a whole life cycle of a process plant. For the ease of data transfer, P&IDs are generally handed over from a design company to an engineering company as portable document format (PDF) which is hard to be modified. Therefore, engineering companies have to deploy a great deal of time and human resources only for manually converting P&ID images into a computer aided design (CAD) file format. To reduce the inefficiency of the P&ID conversion, various symbols and texts in P&ID images should be automatically recognized. However, recognizing information in P&ID images is not an easy task. A P&ID image usually contains hundreds of symbol and text objects. Most objects are pretty small compared to the size of a whole image and are densely packed together. Traditional recognition methods based on geometrical features are not capable enough to recognize every elements of a P&ID image. To overcome these difficulties, state-of-the-art deep learning models, RetinaNet and connectionist text proposal network (CTPN) were used to build a system for recognizing symbols and texts in a P&ID image. Using the RetinaNet and the CTPN model carefully modified and tuned for P&ID image dataset, the developed system recognizes texts, equipment symbols, piping symbols and instrumentation symbols from an input P&ID image and save the recognition results as the pre-defined extensible markup language format. In the test using a commercial P&ID image, the P&ID information recognition system correctly recognized 97% of the symbols and 81.4% of the texts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=object%20recognition%20system" title="object recognition system">object recognition system</a>, <a href="https://publications.waset.org/abstracts/search?q=P%26ID" title=" P&ID"> P&ID</a>, <a href="https://publications.waset.org/abstracts/search?q=symbol%20recognition" title=" symbol recognition"> symbol recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20recognition" title=" text recognition"> text recognition</a> </p> <a href="https://publications.waset.org/abstracts/121363/an-end-to-end-piping-and-instrumentation-diagram-information-recognition-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/121363.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">153</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">1601</span> Understanding the Interactive Nature in Auditory Recognition of Phonological/Grammatical/Semantic Errors at the Sentence Level: An Investigation Based upon Japanese EFL Learners’ Self-Evaluation and Actual Language Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hirokatsu%20Kawashima">Hirokatsu Kawashima</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One important element of teaching/learning listening is intensive listening such as listening for precise sounds, words, grammatical, and semantic units. Several classroom-based investigations have been conducted to explore the usefulness of auditory recognition of phonological, grammatical and semantic errors in such a context. The current study reports the results of one such investigation, which targeted auditory recognition of phonological, grammatical, and semantic errors at the sentence level. 56 Japanese EFL learners participated in this investigation, in which their recognition performance of phonological, grammatical and semantic errors was measured on a 9-point scale by learners’ self-evaluation from the perspective of 1) two types of similar English sound (vowel and consonant minimal pair words), 2) two types of sentence word order (verb phrase-based and noun phrase-based word orders), and 3) two types of semantic consistency (verb-purpose and verb-place agreements), respectively, and their general listening proficiency was examined using standardized tests. A number of findings have been made about the interactive relationships between the three types of auditory error recognition and general listening proficiency. Analyses based on the OPLS (Orthogonal Projections to Latent Structure) regression model have disclosed, for example, that the three types of auditory error recognition are linked in a non-linear way: the highest explanatory power for general listening proficiency may be attained when quadratic interactions between auditory recognition of errors related to vowel minimal pair words and that of errors related to noun phrase-based word order are embraced (R2=.33, p=.01). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auditory%20error%20recognition" title="auditory error recognition">auditory error recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=intensive%20listening" title=" intensive listening"> intensive listening</a>, <a href="https://publications.waset.org/abstracts/search?q=interaction" title=" interaction"> interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=investigation" title=" investigation"> investigation</a> </p> <a href="https://publications.waset.org/abstracts/24209/understanding-the-interactive-nature-in-auditory-recognition-of-phonologicalgrammaticalsemantic-errors-at-the-sentence-level-an-investigation-based-upon-japanese-efl-learners-self-evaluation-and-actual-language-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24209.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">513</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1600</span> Wolof Voice Response Recognition System: A Deep Learning Model for Wolof Audio Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Krishna%20Mohan%20Bathula">Krishna Mohan Bathula</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatou%20Bintou%20Loucoubar"> Fatou Bintou Loucoubar</a>, <a href="https://publications.waset.org/abstracts/search?q=FNU%20Kaleemunnisa"> FNU Kaleemunnisa</a>, <a href="https://publications.waset.org/abstracts/search?q=Christelle%20Scharff"> Christelle Scharff</a>, <a href="https://publications.waset.org/abstracts/search?q=Mark%20Anthony%20De%20Castro"> Mark Anthony De Castro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20speech%20recognition" title="automatic speech recognition">automatic speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=interactive%20voice%20response" title=" interactive voice response"> interactive voice response</a>, <a href="https://publications.waset.org/abstracts/search?q=voice%20response%20recognition" title=" voice response recognition"> voice response recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=wolof%20word%20classification" title=" wolof word classification"> wolof word classification</a> </p> <a href="https://publications.waset.org/abstracts/150305/wolof-voice-response-recognition-system-a-deep-learning-model-for-wolof-audio-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150305.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">117</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">1599</span> Makhraj Recognition Using Convolutional Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zan%20Azma%20Nasruddin">Zan Azma Nasruddin</a>, <a href="https://publications.waset.org/abstracts/search?q=Irwan%20Mazlin"> Irwan Mazlin</a>, <a href="https://publications.waset.org/abstracts/search?q=Nor%20Aziah%20Daud"> Nor Aziah Daud</a>, <a href="https://publications.waset.org/abstracts/search?q=Fauziah%20Redzuan"> Fauziah Redzuan</a>, <a href="https://publications.waset.org/abstracts/search?q=Fariza%20Hanis%20Abdul%20Razak"> Fariza Hanis Abdul Razak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper focuses on a machine learning that learn the correct pronunciation of Makhraj Huroofs. Usually, people need to find an expert to pronounce the Huroof accurately. In this study, the researchers have developed a system that is able to learn the selected Huroofs which are ha, tsa, zho, and dza using the Convolutional Neural Network. The researchers present the chosen type of the CNN architecture to make the system that is able to learn the data (Huroofs) as quick as possible and produces high accuracy during the prediction. The researchers have experimented the system to measure the accuracy and the cross entropy in the training process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title="convolutional neural network">convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=Makhraj%20recognition" title=" Makhraj recognition"> Makhraj recognition</a>, <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=signal%20processing" title=" signal processing"> signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=tensorflow" title=" tensorflow"> tensorflow</a> </p> <a href="https://publications.waset.org/abstracts/85389/makhraj-recognition-using-convolutional-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85389.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">335</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">1598</span> The Artificial Intelligence Technologies Used in PhotoMath Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tala%20Toonsi">Tala Toonsi</a>, <a href="https://publications.waset.org/abstracts/search?q=Marah%20Alagha"> Marah Alagha</a>, <a href="https://publications.waset.org/abstracts/search?q=Lina%20Alnowaiser"> Lina Alnowaiser</a>, <a href="https://publications.waset.org/abstracts/search?q=Hala%20Rajab"> Hala Rajab</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This report is about the Photomath app, which is an AI application that uses image recognition technology, specifically optical character recognition (OCR) algorithms. The (OCR) algorithm translates the images into a mathematical equation, and the app automatically provides a step-by-step solution. The application supports decimals, basic arithmetic, fractions, linear equations, and multiple functions such as logarithms. Testing was conducted to examine the usage of this app, and results were collected by surveying ten participants. Later, the results were analyzed. This paper seeks to answer the question: To what level the artificial intelligence features are accurate and the speed of process in this app. It is hoped this study will inform about the efficiency of AI in Photomath to the users. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=photomath" title="photomath">photomath</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20recognition" title=" image recognition"> image recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=app" title=" app"> app</a>, <a href="https://publications.waset.org/abstracts/search?q=OCR" title=" OCR"> OCR</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20equations." title=" mathematical equations."> mathematical equations.</a> </p> <a href="https://publications.waset.org/abstracts/145072/the-artificial-intelligence-technologies-used-in-photomath-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145072.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">171</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">1597</span> A Human Activity Recognition System Based on Sensory Data Related to Object Usage </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Abdullah">M. Abdullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Al-Wadud"> Al-Wadud</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sensor-based activity recognition systems usually accounts which sensors have been activated to perform an activity. The system then combines the conditional probabilities of those sensors to represent different activities and takes the decision based on that. However, the information about the sensors which are not activated may also be of great help in deciding which activity has been performed. This paper proposes an approach where the sensory data related to both usage and non-usage of objects are utilized to make the classification of activities. Experimental results also show the promising performance of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Na%C3%AFve%20Bayesian" title="Naïve Bayesian">Naïve Bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=based%20classification" title=" based classification"> based classification</a>, <a href="https://publications.waset.org/abstracts/search?q=activity%20recognition" title=" activity recognition"> activity recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20data" title=" sensor data"> sensor data</a>, <a href="https://publications.waset.org/abstracts/search?q=object-usage%20model" title=" object-usage model"> object-usage model</a> </p> <a href="https://publications.waset.org/abstracts/4112/a-human-activity-recognition-system-based-on-sensory-data-related-to-object-usage" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4112.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">322</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1596</span> Features Vector Selection for the Recognition of the Fragmented Handwritten Numeric Chains </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salim%20Ouchtati">Salim Ouchtati</a>, <a href="https://publications.waset.org/abstracts/search?q=Aissa%20Belmeguenai"> Aissa Belmeguenai</a>, <a href="https://publications.waset.org/abstracts/search?q=Mouldi%20Bedda"> Mouldi Bedda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, we propose an offline system for the recognition of the fragmented handwritten numeric chains. Firstly, we realized a recognition system of the isolated handwritten digits, in this part; the study is based mainly on the evaluation of neural network performances, trained with the gradient backpropagation algorithm. The used parameters to form the input vector of the neural network are extracted from the binary images of the isolated handwritten digit by several methods: the distribution sequence, sondes application, the Barr features, and the centered moments of the different projections and profiles. Secondly, the study is extended for the reading of the fragmented handwritten numeric chains constituted of a variable number of digits. The vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) was presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits). <p class="card-text"><strong>Keywords:</strong> <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=handwritten%20numeric%20chains" title=" handwritten numeric chains"> handwritten numeric chains</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a> </p> <a href="https://publications.waset.org/abstracts/50905/features-vector-selection-for-the-recognition-of-the-fragmented-handwritten-numeric-chains" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50905.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">265</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">1595</span> Semantic Data Schema Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A%C3%AFcha%20Ben%20Salem">Aïcha Ben Salem</a>, <a href="https://publications.waset.org/abstracts/search?q=Faouzi%20Boufares"> Faouzi Boufares</a>, <a href="https://publications.waset.org/abstracts/search?q=Sebastiao%20Correia"> Sebastiao Correia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The subject covered in this paper aims at assisting the user in its quality approach. The goal is to better extract, mix, interpret and reuse data. It deals with the semantic schema recognition of a data source. This enables the extraction of data semantics from all the available information, inculding the data and the metadata. Firstly, it consists of categorizing the data by assigning it to a category and possibly a sub-category, and secondly, of establishing relations between columns and possibly discovering the semantics of the manipulated data source. These links detected between columns offer a better understanding of the source and the alternatives for correcting data. This approach allows automatic detection of a large number of syntactic and semantic anomalies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=schema%20recognition" title="schema recognition">schema recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20data%20profiling" title=" semantic data profiling"> semantic data profiling</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-categorisation" title=" meta-categorisation"> meta-categorisation</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20dependencies%20inter%20columns" title=" semantic dependencies inter columns"> semantic dependencies inter columns</a> </p> <a href="https://publications.waset.org/abstracts/34129/semantic-data-schema-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34129.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">418</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">1594</span> Speech Recognition Performance by Adults: A Proposal for a Battery for Marathi</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20B.%20Rathna%20Kumar">S. B. Rathna Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Pranjali%20A%20Ujwane"> Pranjali A Ujwane</a>, <a href="https://publications.waset.org/abstracts/search?q=Panchanan%20Mohanty"> Panchanan Mohanty</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present study aimed to develop a battery for assessing speech recognition performance by adults in Marathi. A total of four word lists were developed by considering word frequency, word familiarity, words in common use, and phonemic balance. Each word list consists of 25 words (15 monosyllabic words in CVC structure and 10 monosyllabic words in CVCV structure). Equivalence analysis and performance-intensity function testing was carried using the four word lists on a total of 150 native speakers of Marathi belonging to different regions of Maharashtra (Vidarbha, Marathwada, Khandesh and Northern Maharashtra, Pune, and Konkan). The subjects were further equally divided into five groups based on above mentioned regions. It was found that there was no significant difference (p > 0.05) in the speech recognition performance between groups for each word list and between word lists for each group. Hence, the four word lists developed were equally difficult for all the groups and can be used interchangeably. The performance-intensity (PI) function curve showed semi-linear function, and the groups’ mean slope of the linear portions of the curve indicated an average linear slope of 4.64%, 4.73%, 4.68%, and 4.85% increase in word recognition score per dB for list 1, list 2, list 3 and list 4 respectively. Although, there is no data available on speech recognition tests for adults in Marathi, most of the findings of the study are in line with the findings of research reports on other languages. The four word lists, thus developed, were found to have sufficient reliability and validity in assessing speech recognition performance by adults in Marathi. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=speech%20recognition%20performance" title="speech recognition performance">speech recognition performance</a>, <a href="https://publications.waset.org/abstracts/search?q=phonemic%20balance" title=" phonemic balance"> phonemic balance</a>, <a href="https://publications.waset.org/abstracts/search?q=equivalence%20analysis" title=" equivalence analysis"> equivalence analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=performance-intensity%20function%20testing" title=" performance-intensity function testing"> performance-intensity function testing</a>, <a href="https://publications.waset.org/abstracts/search?q=reliability" title=" reliability"> reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=validity" title=" validity"> validity</a> </p> <a href="https://publications.waset.org/abstracts/41329/speech-recognition-performance-by-adults-a-proposal-for-a-battery-for-marathi" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41329.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">357</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">1593</span> Face Recognition Using Body-Worn Camera: Dataset and Baseline Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Almadan">Ali Almadan</a>, <a href="https://publications.waset.org/abstracts/search?q=Anoop%20Krishnan"> Anoop Krishnan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ajita%20Rattani"> Ajita Rattani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Facial recognition is a widely adopted technology in surveillance, border control, healthcare, banking services, and lately, in mobile user authentication with Apple introducing “Face ID” moniker with iPhone X. A lot of research has been conducted in the area of face recognition on datasets captured by surveillance cameras, DSLR, and mobile devices. Recently, face recognition technology has also been deployed on body-worn cameras to keep officers safe, enabling situational awareness and providing evidence for trial. However, limited academic research has been conducted on this topic so far, without the availability of any publicly available datasets with a sufficient sample size. This paper aims to advance research in the area of face recognition using body-worn cameras. To this aim, the contribution of this work is two-fold: (1) collection of a dataset consisting of a total of 136,939 facial images of 102 subjects captured using body-worn cameras in in-door and daylight conditions and (2) evaluation of various deep-learning architectures for face identification on the collected dataset. Experimental results suggest a maximum True Positive Rate(TPR) of 99.86% at False Positive Rate(FPR) of 0.000 obtained by SphereFace based deep learning architecture in daylight condition. The collected dataset and the baseline algorithms will promote further research and development. A downloadable link of the dataset and the algorithms is available by contacting the authors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title="face recognition">face recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=body-worn%20cameras" title=" body-worn cameras"> body-worn cameras</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=person%20identification" title=" person identification"> person identification</a> </p> <a href="https://publications.waset.org/abstracts/127551/face-recognition-using-body-worn-camera-dataset-and-baseline-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127551.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">163</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1592</span> Pre-Analysis of Printed Circuit Boards Based on Multispectral Imaging for Vision Based Recognition of Electronics Waste</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Florian%20Kleber">Florian Kleber</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20Kampel"> Martin Kampel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The increasing demand of gallium, indium and rare-earth elements for the production of electronics, e.g. solid state-lighting, photovoltaics, integrated circuits, and liquid crystal displays, will exceed the world-wide supply according to current forecasts. Recycling systems to reclaim these materials are not yet in place, which challenges the sustainability of these technologies. This paper proposes a multispectral imaging system as a basis for a vision based recognition system for valuable components of electronics waste. Multispectral images intend to enhance the contrast of images of printed circuit boards (single components, as well as labels) for further analysis, such as optical character recognition and entire printed circuit board recognition. The results show that a higher contrast is achieved in the near infrared compared to ultraviolet and visible light. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electronics%20waste" title="electronics waste">electronics waste</a>, <a href="https://publications.waset.org/abstracts/search?q=multispectral%20imaging" title=" multispectral imaging"> multispectral imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=printed%20circuit%20boards" title=" printed circuit boards"> printed circuit boards</a>, <a href="https://publications.waset.org/abstracts/search?q=rare-earth%20elements" title=" rare-earth elements"> rare-earth elements</a> </p> <a href="https://publications.waset.org/abstracts/15815/pre-analysis-of-printed-circuit-boards-based-on-multispectral-imaging-for-vision-based-recognition-of-electronics-waste" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15815.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">415</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">1591</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">1590</span> Multimodal Data Fusion Techniques in Audiovisual Speech Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hadeer%20M.%20Sayed">Hadeer M. Sayed</a>, <a href="https://publications.waset.org/abstracts/search?q=Hesham%20E.%20El%20Deeb"> Hesham E. El Deeb</a>, <a href="https://publications.waset.org/abstracts/search?q=Shereen%20A.%20Taie"> Shereen A. Taie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the big data era, we are facing a diversity of datasets from different sources in different domains that describe a single life event. These datasets consist of multiple modalities, each of which has a different representation, distribution, scale, and density. Multimodal fusion is the concept of integrating information from multiple modalities in a joint representation with the goal of predicting an outcome through a classification task or regression task. In this paper, multimodal fusion techniques are classified into two main classes: model-agnostic techniques and model-based approaches. It provides a comprehensive study of recent research in each class and outlines the benefits and limitations of each of them. Furthermore, the audiovisual speech recognition task is expressed as a case study of multimodal data fusion approaches, and the open issues through the limitations of the current studies are presented. This paper can be considered a powerful guide for interested researchers in the field of multimodal data fusion and audiovisual speech recognition particularly. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multimodal%20data" title="multimodal data">multimodal data</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20fusion" title=" data fusion"> data fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=audio-visual%20speech%20recognition" title=" audio-visual speech recognition"> audio-visual speech recognition</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/157362/multimodal-data-fusion-techniques-in-audiovisual-speech-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157362.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">112</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">1589</span> Distant Speech Recognition Using Laser Doppler Vibrometer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yunbin%20Deng">Yunbin Deng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most existing applications of automatic speech recognition relies on cooperative subjects at a short distance to a microphone. Standoff speech recognition using microphone arrays can extend the subject to sensor distance somewhat, but it is still limited to only a few feet. As such, most deployed applications of standoff speech recognitions are limited to indoor use at short range. Moreover, these applications require air passway between the subject and the sensor to achieve reasonable signal to noise ratio. This study reports long range (50 feet) automatic speech recognition experiments using a Laser Doppler Vibrometer (LDV) sensor. This study shows that the LDV sensor modality can extend the speech acquisition standoff distance far beyond microphone arrays to hundreds of feet. In addition, LDV enables 'listening' through the windows for uncooperative subjects. This enables new capabilities in automatic audio and speech intelligence, surveillance, and reconnaissance (ISR) for law enforcement, homeland security and counter terrorism applications. The Polytec LDV model OFV-505 is used in this study. To investigate the impact of different vibrating materials, five parallel LDV speech corpora, each consisting of 630 speakers, are collected from the vibrations of a glass window, a metal plate, a plastic box, a wood slate, and a concrete wall. These are the common materials the application could encounter in a daily life. These data were compared with the microphone counterpart to manifest the impact of various materials on the spectrum of the LDV speech signal. State of the art deep neural network modeling approaches is used to conduct continuous speaker independent speech recognition on these LDV speech datasets. Preliminary phoneme recognition results using time-delay neural network, bi-directional long short term memory, and model fusion shows great promise of using LDV for long range speech recognition. To author’s best knowledge, this is the first time an LDV is reported for long distance speech recognition application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=covert%20speech%20acquisition" title="covert speech acquisition">covert speech acquisition</a>, <a href="https://publications.waset.org/abstracts/search?q=distant%20speech%20recognition" title=" distant speech recognition"> distant speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=DSR" title=" DSR"> DSR</a>, <a href="https://publications.waset.org/abstracts/search?q=laser%20Doppler%20vibrometer" title=" laser Doppler vibrometer"> laser Doppler vibrometer</a>, <a href="https://publications.waset.org/abstracts/search?q=LDV" title=" LDV"> LDV</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20intelligence%20surveillance%20and%20reconnaissance" title=" speech intelligence surveillance and reconnaissance"> speech intelligence surveillance and reconnaissance</a>, <a href="https://publications.waset.org/abstracts/search?q=ISR" title=" ISR"> ISR</a> </p> <a href="https://publications.waset.org/abstracts/99091/distant-speech-recognition-using-laser-doppler-vibrometer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99091.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">179</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">1588</span> Interactive Shadow Play Animation System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bo%20Wan">Bo Wan</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiu%20Wen"> Xiu Wen</a>, <a href="https://publications.waset.org/abstracts/search?q=Lingling%20An"> Lingling An</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaoling%20Ding"> Xiaoling Ding</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper describes a Chinese shadow play animation system based on Kinect. Users, without any professional training, can personally manipulate the shadow characters to finish a shadow play performance by their body actions and get a shadow play video through giving the record command to our system if they want. In our system, Kinect is responsible for capturing human movement and voice commands data. Gesture recognition module is used to control the change of the shadow play scenes. After packaging the data from Kinect and the recognition result from gesture recognition module, VRPN transmits them to the server-side. At last, the server-side uses the information to control the motion of shadow characters and video recording. This system not only achieves human-computer interaction, but also realizes the interaction between people. It brings an entertaining experience to users and easy to operate for all ages. Even more important is that the application background of Chinese shadow play embodies the protection of the art of shadow play animation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hadow%20play%20animation" title="hadow play animation">hadow play animation</a>, <a href="https://publications.waset.org/abstracts/search?q=Kinect" title=" Kinect"> Kinect</a>, <a href="https://publications.waset.org/abstracts/search?q=gesture%20recognition" title=" gesture recognition"> gesture recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=VRPN" title=" VRPN"> VRPN</a>, <a href="https://publications.waset.org/abstracts/search?q=HCI" title=" HCI"> HCI</a> </p> <a href="https://publications.waset.org/abstracts/19293/interactive-shadow-play-animation-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19293.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">402</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">1587</span> Effective Stacking of Deep Neural Models for Automated Object Recognition in Retail Stores</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ankit%20Sinha">Ankit Sinha</a>, <a href="https://publications.waset.org/abstracts/search?q=Soham%20Banerjee"> Soham Banerjee</a>, <a href="https://publications.waset.org/abstracts/search?q=Pratik%20Chattopadhyay"> Pratik Chattopadhyay</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Automated product recognition in retail stores is an important real-world application in the domain of Computer Vision and Pattern Recognition. In this paper, we consider the problem of automatically identifying the classes of the products placed on racks in retail stores from an image of the rack and information about the query/product images. We improve upon the existing approaches in terms of effectiveness and memory requirement by developing a two-stage object detection and recognition pipeline comprising of a Faster-RCNN-based object localizer that detects the object regions in the rack image and a ResNet-18-based image encoder that classifies the detected regions into the appropriate classes. Each of the models is fine-tuned using appropriate data sets for better prediction and data augmentation is performed on each query image to prepare an extensive gallery set for fine-tuning the ResNet-18-based product recognition model. This encoder is trained using a triplet loss function following the strategy of online-hard-negative-mining for improved prediction. The proposed models are lightweight and can be connected in an end-to-end manner during deployment to automatically identify each product object placed in a rack image. Extensive experiments using Grozi-32k and GP-180 data sets verify the effectiveness of the proposed model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=retail%20stores" title="retail stores">retail stores</a>, <a href="https://publications.waset.org/abstracts/search?q=faster-RCNN" title=" faster-RCNN"> faster-RCNN</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20localization" title=" object localization"> object localization</a>, <a href="https://publications.waset.org/abstracts/search?q=ResNet-18" title=" ResNet-18"> ResNet-18</a>, <a href="https://publications.waset.org/abstracts/search?q=triplet%20loss" title=" triplet loss"> triplet loss</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20augmentation" title=" data augmentation"> data augmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=product%20recognition" title=" product recognition"> product recognition</a> </p> <a href="https://publications.waset.org/abstracts/153836/effective-stacking-of-deep-neural-models-for-automated-object-recognition-in-retail-stores" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153836.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">157</span> </span> </div> </div> <ul class="pagination"> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=chord%20recognition&page=4" rel="prev">&lsaquo;</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=chord%20recognition&page=1">1</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=chord%20recognition&page=2">2</a></li> <li class="page-item"><a class="page-link" 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