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quantization (VQ)</title> <meta name="description" content="Search results for: vector quantization (VQ)"> <meta name="keywords" content="vector quantization (VQ)"> <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 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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="vector quantization (VQ)"> <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> 1143</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: vector quantization (VQ)</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1143</span> Vector Quantization Based on Vector Difference Scheme for Image Enhancement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Biji%20Jacob">Biji Jacob</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vector quantization algorithm which uses minimum distance calculation for codebook generation, a time consuming calculation performed on each pixel values leads to computation complexity. The codebook is updated by comparing the distance of each vector to their centroid vector and measure for their closeness. In this paper vector quantization is modified based on vector difference algorithm for image enhancement purpose. In the proposed scheme, vector differences between the vectors are considered as the new generation vectors or new codebook vectors. The codebook is updated by comparing the new generation vector with a threshold value having minimum error with the parent vector. The minimum error decides the fitness of each newly generated vector. Thus the codebook is generated in an adaptive manner and the fitness value is determined for the suppression of the degraded portion of the image and thereby leads to the enhancement of the image through the adaptive searching capability of the vector quantization through vector difference algorithm. Experimental results shows that the vector difference scheme efficiently modifies the vector quantization algorithm for enhancing the image with peak signal to noise ratio (PSNR), mean square error (MSE), Euclidean distance (E_dist) as the performance parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=codebook" title="codebook">codebook</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20enhancement" title=" image enhancement"> image enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20difference" title=" vector difference"> vector difference</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20quantization" title=" vector quantization"> vector quantization</a> </p> <a href="https://publications.waset.org/abstracts/39597/vector-quantization-based-on-vector-difference-scheme-for-image-enhancement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39597.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">267</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">1142</span> Voltage Problem Location Classification Using Performance of Least Squares Support Vector Machine LS-SVM and Learning Vector Quantization LVQ</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Khaled%20Abduesslam">M. Khaled Abduesslam</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Ali"> Mohammed Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Basher%20H.%20Alsdai"> Basher H. Alsdai</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Nizam%20Inayati"> Muhammad Nizam Inayati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the voltage problem location classification using performance of Least Squares Support Vector Machine (LS-SVM) and Learning Vector Quantization (LVQ) in electrical power system for proper voltage problem location implemented by IEEE 39 bus New-England. The data was collected from the time domain simulation by using Power System Analysis Toolbox (PSAT). Outputs from simulation data such as voltage, phase angle, real power and reactive power were taken as input to estimate voltage stability at particular buses based on Power Transfer Stability Index (PTSI).The simulation data was carried out on the IEEE 39 bus test system by considering load bus increased on the system. To verify of the proposed LS-SVM its performance was compared to Learning Vector Quantization (LVQ). The results showed that LS-SVM is faster and better as compared to LVQ. The results also demonstrated that the LS-SVM was estimated by 0% misclassification whereas LVQ had 7.69% misclassification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=IEEE%2039%20bus" title="IEEE 39 bus">IEEE 39 bus</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20squares%20support%20vector%20machine" title=" least squares support vector machine"> least squares support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20vector%20quantization" title=" learning vector quantization"> learning vector quantization</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20collapse" title=" voltage collapse"> voltage collapse</a> </p> <a href="https://publications.waset.org/abstracts/11211/voltage-problem-location-classification-using-performance-of-least-squares-support-vector-machine-ls-svm-and-learning-vector-quantization-lvq" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11211.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">441</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1141</span> Data Hiding by Vector Quantization in Color Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yung%20Gi%20Wu">Yung Gi Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the growing of computer and network, digital data can be spread to anywhere in the world quickly. In addition, digital data can also be copied or tampered easily so that the security issue becomes an important topic in the protection of digital data. Digital watermark is a method to protect the ownership of digital data. Embedding the watermark will influence the quality certainly. In this paper, Vector Quantization (VQ) is used to embed the watermark into the image to fulfill the goal of data hiding. This kind of watermarking is invisible which means that the users will not conscious the existing of embedded watermark even though the embedded image has tiny difference compared to the original image. Meanwhile, VQ needs a lot of computation burden so that we adopt a fast VQ encoding scheme by partial distortion searching (PDS) and mean approximation scheme to speed up the data hiding process. The watermarks we hide to the image could be gray, bi-level and color images. Texts are also can be regarded as watermark to embed. In order to test the robustness of the system, we adopt Photoshop to fulfill sharpen, cropping and altering to check if the extracted watermark is still recognizable. Experimental results demonstrate that the proposed system can resist the above three kinds of tampering in general cases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20hiding" title="data hiding">data hiding</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20quantization" title=" vector quantization"> vector quantization</a>, <a href="https://publications.waset.org/abstracts/search?q=watermark" title=" watermark"> watermark</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20image" title=" color image"> color image</a> </p> <a href="https://publications.waset.org/abstracts/28889/data-hiding-by-vector-quantization-in-color-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28889.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">364</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">1140</span> Attitude Stabilization of Satellites Using Random Dither Quantization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kazuma%20Okada">Kazuma Okada</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto"> Tomoaki Hashimoto</a>, <a href="https://publications.waset.org/abstracts/search?q=Hirokazu%20Tahara"> Hirokazu Tahara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, the effectiveness of random dither quantization method for linear feedback control systems has been shown in several papers. However, the random dither quantization method has not yet been applied to nonlinear feedback control systems. The objective of this paper is to verify the effectiveness of random dither quantization method for nonlinear feedback control systems. For this purpose, we consider the attitude stabilization problem of satellites using discrete-level actuators. Namely, this paper provides a control method based on the random dither quantization method for stabilizing the attitude of satellites using discrete-level actuators. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=quantized%20control" title="quantized control">quantized control</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20systems" title=" nonlinear systems"> nonlinear systems</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20dither%20quantization" title=" random dither quantization"> random dither quantization</a> </p> <a href="https://publications.waset.org/abstracts/76853/attitude-stabilization-of-satellites-using-random-dither-quantization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76853.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">242</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">1139</span> An Improved Face Recognition Algorithm Using Histogram-Based Features in Spatial and Frequency Domains</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qiu%20Chen">Qiu Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Koji%20Kotani"> Koji Kotani</a>, <a href="https://publications.waset.org/abstracts/search?q=Feifei%20Lee"> Feifei Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Tadahiro%20Ohmi"> Tadahiro Ohmi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose an improved face recognition algorithm using histogram-based features in spatial and frequency domains. For adding spatial information of the face to improve recognition performance, a region-division (RD) method is utilized. The facial area is firstly divided into several regions, then feature vectors of each facial part are generated by Binary Vector Quantization (BVQ) histogram using DCT coefficients in low frequency domains, as well as Local Binary Pattern (LBP) histogram in spatial domain. Recognition results with different regions are first obtained separately and then fused by weighted averaging. Publicly available ORL database is used for the evaluation of our proposed algorithm, which is consisted of 40 subjects with 10 images per subject containing variations in lighting, posing, and expressions. It is demonstrated that face recognition using RD method can achieve much higher recognition rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binary%20vector%20quantization%20%28BVQ%29" title="binary vector quantization (BVQ)">binary vector quantization (BVQ)</a>, <a href="https://publications.waset.org/abstracts/search?q=DCT%20coefficients" title="DCT coefficients">DCT coefficients</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=local%20binary%20patterns%20%28LBP%29" title=" local binary patterns (LBP)"> local binary patterns (LBP)</a> </p> <a href="https://publications.waset.org/abstracts/44892/an-improved-face-recognition-algorithm-using-histogram-based-features-in-spatial-and-frequency-domains" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44892.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">349</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">1138</span> Fast Adjustable Threshold for Uniform Neural Network Quantization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Goncharenko">Alexander Goncharenko</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrey%20Denisov"> Andrey Denisov</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergey%20Alyamkin"> Sergey Alyamkin</a>, <a href="https://publications.waset.org/abstracts/search?q=Evgeny%20Terentev"> Evgeny Terentev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The neural network quantization is highly desired procedure to perform before running neural networks on mobile devices. Quantization without fine-tuning leads to accuracy drop of the model, whereas commonly used training with quantization is done on the full set of the labeled data and therefore is both time- and resource-consuming. Real life applications require simplification and acceleration of quantization procedure that will maintain accuracy of full-precision neural network, especially for modern mobile neural network architectures like Mobilenet-v1, MobileNet-v2 and MNAS. Here we present a method to significantly optimize training with quantization procedure by introducing the trained scale factors for discretization thresholds that are separate for each filter. Using the proposed technique, we quantize the modern mobile architectures of neural networks with the set of train data of only &sim; 10% of the total ImageNet 2012 sample. Such reduction of train dataset size and small number of trainable parameters allow to fine-tune the network for several hours while maintaining the high accuracy of quantized model (accuracy drop was less than 0.5%). Ready-for-use models and code are available in the GitHub repository. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distillation" title="distillation">distillation</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=quantization" title=" quantization"> quantization</a> </p> <a href="https://publications.waset.org/abstracts/107507/fast-adjustable-threshold-for-uniform-neural-network-quantization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107507.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">325</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">1137</span> Numerical Simulations on Feasibility of Stochastic Model Predictive Control for Linear Discrete-Time Systems with Random Dither Quantization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Taiki%20Baba">Taiki Baba</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto"> Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The random dither quantization method enables us to achieve much better performance than the simple uniform quantization method for the design of quantized control systems. Motivated by this fact, the stochastic model predictive control method in which a performance index is minimized subject to probabilistic constraints imposed on the state variables of systems has been proposed for linear feedback control systems with random dither quantization. In other words, a method for solving optimal control problems subject to probabilistic state constraints for linear discrete-time control systems with random dither quantization has been already established. To our best knowledge, however, the feasibility of such a kind of optimal control problems has not yet been studied. Our objective in this paper is to investigate the feasibility of stochastic model predictive control problems for linear discrete-time control systems with random dither quantization. To this end, we provide the results of numerical simulations that verify the feasibility of stochastic model predictive control problems for linear discrete-time control systems with random dither quantization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20systems" title=" stochastic systems"> stochastic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20constraints" title=" probabilistic constraints"> probabilistic constraints</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20dither%20quantization" title=" random dither quantization"> random dither quantization</a> </p> <a href="https://publications.waset.org/abstracts/78538/numerical-simulations-on-feasibility-of-stochastic-model-predictive-control-for-linear-discrete-time-systems-with-random-dither-quantization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78538.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">282</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">1136</span> Energy States of Some Diatomic Molecules: Exact Quantization Rule Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Babatunde%20J.%20Falaye">Babatunde J. Falaye</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, we obtain the approximate analytical solutions of the radial Schrödinger equation for the Deng-Fan diatomic molecular potential by using exact quantization rule approach. The wave functions have been expressed by hypergeometric functions via the functional analysis approach. An extension to rotational-vibrational energy eigenvalues of some diatomic molecules are also presented. It is shown that the calculated energy levels are in good agreement with the ones obtained previously E_nl-D (shifted Deng-Fan). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Schr%C3%B6dinger%20equation" title="Schrödinger equation">Schrödinger equation</a>, <a href="https://publications.waset.org/abstracts/search?q=exact%20quantization%20rule" title=" exact quantization rule"> exact quantization rule</a>, <a href="https://publications.waset.org/abstracts/search?q=functional%20analysis" title=" functional analysis"> functional analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Deng-Fan%20potential" title=" Deng-Fan potential"> Deng-Fan potential</a> </p> <a href="https://publications.waset.org/abstracts/17622/energy-states-of-some-diatomic-molecules-exact-quantization-rule-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17622.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">1135</span> Stochastic Model Predictive Control for Linear Discrete-Time Systems with Random Dither Quantization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto">Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, feedback control systems using random dither quantizers have been proposed for linear discrete-time systems. However, the constraints imposed on state and control variables have not yet been taken into account for the design of feedback control systems with random dither quantization. Model predictive control is a kind of optimal feedback control in which control performance over a finite future is optimized with a performance index that has a moving initial and terminal time. An important advantage of model predictive control is its ability to handle constraints imposed on state and control variables. Based on the model predictive control approach, the objective of this paper is to present a control method that satisfies probabilistic state constraints for linear discrete-time feedback control systems with random dither quantization. In other words, this paper provides a method for solving the optimal control problems subject to probabilistic state constraints for linear discrete-time feedback control systems with random dither quantization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title="optimal control">optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20systems" title=" stochastic systems"> stochastic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20dither" title=" random dither"> random dither</a>, <a href="https://publications.waset.org/abstracts/search?q=quantization" title=" quantization"> quantization</a> </p> <a href="https://publications.waset.org/abstracts/63970/stochastic-model-predictive-control-for-linear-discrete-time-systems-with-random-dither-quantization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63970.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">445</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">1134</span> Stabilization of Rotational Motion of Spacecrafts Using Quantized Two Torque Inputs Based on Random Dither</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yusuke%20Kuramitsu">Yusuke Kuramitsu</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto"> Tomoaki Hashimoto</a>, <a href="https://publications.waset.org/abstracts/search?q=Hirokazu%20Tahara"> Hirokazu Tahara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The control problem of underactuated spacecrafts has attracted a considerable amount of interest. The control method for a spacecraft equipped with less than three control torques is useful when one of the three control torques had failed. On the other hand, the quantized control of systems is one of the important research topics in recent years. The random dither quantization method that transforms a given continuous signal to a discrete signal by adding artificial random noise to the continuous signal before quantization has also attracted a considerable amount of interest. The objective of this study is to develop the control method based on random dither quantization method for stabilizing the rotational motion of a rigid spacecraft with two control inputs. In this paper, the effectiveness of random dither quantization control method for the stabilization of rotational motion of spacecrafts with two torque inputs is verified by numerical simulations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spacecraft%20control" title="spacecraft control">spacecraft control</a>, <a href="https://publications.waset.org/abstracts/search?q=quantized%20control" title=" quantized control"> quantized control</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20control" title=" nonlinear control"> nonlinear control</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20dither%20method" title=" random dither method"> random dither method</a> </p> <a href="https://publications.waset.org/abstracts/99540/stabilization-of-rotational-motion-of-spacecrafts-using-quantized-two-torque-inputs-based-on-random-dither" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99540.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">180</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1133</span> Quantization of Damped Systems Based on the Doubling of Degrees of Freedom</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20I.%20Nawafleh">Khaled I. Nawafleh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, it provide the canonical approach for studying dissipated oscillators based on the doubling of degrees of freedom. Clearly, expressions for Lagrangians of the elementary modes of the system are given, which ends with the familiar classical equations of motion for the dissipative oscillator. The equation for one variable is the time reversed of the motion of the second variable. it discuss in detail the extended Bateman Lagrangian specifically for a dual extended damped oscillator time-dependent. A Hamilton-Jacobi analysis showing the equivalence with the Lagrangian approach is also obtained. For that purpose, the techniques of separation of variables were applied, and the quantization process was achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=doubling%20of%20degrees%20of%20freedom" title="doubling of degrees of freedom">doubling of degrees of freedom</a>, <a href="https://publications.waset.org/abstracts/search?q=dissipated%20harmonic%20oscillator" title=" dissipated harmonic oscillator"> dissipated harmonic oscillator</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamilton-Jacobi" title=" Hamilton-Jacobi"> Hamilton-Jacobi</a>, <a href="https://publications.waset.org/abstracts/search?q=time-dependent%20lagrangians" title=" time-dependent lagrangians"> time-dependent lagrangians</a>, <a href="https://publications.waset.org/abstracts/search?q=quantization" title=" quantization"> quantization</a> </p> <a href="https://publications.waset.org/abstracts/171405/quantization-of-damped-systems-based-on-the-doubling-of-degrees-of-freedom" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171405.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">68</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1132</span> Imprecise Vector: The Case of Subnormality</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dhruba%20Das">Dhruba Das</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, the author has put forward the actual mathematical explanation of subnormal imprecise vector. Every subnormal imprecise vector has to be defined with reference to a membership surface. The membership surface of normal imprecise vector has already defined based on Randomness-Impreciseness Consistency Principle. The Randomness- Impreciseness Consistency Principle leads to defining a normal law of impreciseness using two different laws of randomness. A normal imprecise vector is a special case of subnormal imprecise vector. Nothing however is available in the literature about the membership surface when a subnormal imprecise vector is defined. The author has shown here how to construct the membership surface of a subnormal imprecise vector. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=imprecise%20vector" title="imprecise vector">imprecise vector</a>, <a href="https://publications.waset.org/abstracts/search?q=membership%20surface" title=" membership surface"> membership surface</a>, <a href="https://publications.waset.org/abstracts/search?q=subnormal%20imprecise%20number" title=" subnormal imprecise number"> subnormal imprecise number</a>, <a href="https://publications.waset.org/abstracts/search?q=subnormal%20imprecise%20vector" title=" subnormal imprecise vector"> subnormal imprecise vector</a> </p> <a href="https://publications.waset.org/abstracts/44144/imprecise-vector-the-case-of-subnormality" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44144.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">1131</span> Efficient Human Motion Detection Feature Set by Using Local Phase Quantization Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arwa%20Alzughaibi">Arwa Alzughaibi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human Motion detection is a challenging task due to a number of factors including variable appearance, posture and a wide range of illumination conditions and background. So, the first need of such a model is a reliable feature set that can discriminate between a human and a non-human form with a fair amount of confidence even under difficult conditions. By having richer representations, the classification task becomes easier and improved results can be achieved. The Aim of this paper is to investigate the reliable and accurate human motion detection models that are able to detect the human motions accurately under varying illumination levels and backgrounds. Different sets of features are tried and tested including Histogram of Oriented Gradients (HOG), Deformable Parts Model (DPM), Local Decorrelated Channel Feature (LDCF) and Aggregate Channel Feature (ACF). However, we propose an efficient and reliable human motion detection approach by combining Histogram of oriented gradients (HOG) and local phase quantization (LPQ) as the feature set, and implementing search pruning algorithm based on optical flow to reduce the number of false positive. Experimental results show the effectiveness of combining local phase quantization descriptor and the histogram of gradient to perform perfectly well for a large range of illumination conditions and backgrounds than the state-of-the-art human detectors. Areaunder th ROC Curve (AUC) of the proposed method achieved 0.781 for UCF dataset and 0.826 for CDW dataset which indicates that it performs comparably better than HOG, DPM, LDCF and ACF methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20motion%20detection" title="human motion detection">human motion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=histograms%20of%20oriented%20gradient" title=" histograms of oriented gradient"> histograms of oriented gradient</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20phase%20quantization" title=" local phase quantization"> local phase quantization</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20phase%20quantization" title=" local phase quantization"> local phase quantization</a> </p> <a href="https://publications.waset.org/abstracts/48160/efficient-human-motion-detection-feature-set-by-using-local-phase-quantization-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48160.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">257</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1130</span> Intracellular Strategies for Gene Delivery into Mammalian Cells Using Bacteria as a Vector</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kumaran%20Narayanan">Kumaran Narayanan</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrew%20N.%20Osahor"> Andrew N. Osahor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> E. coli has been engineered by our group and by others as a vector to deliver DNA into cultured human and animal cells. However, so far conditions to improve gene delivery using this vector have not been investigated, resulting in a major gap in our understanding of the requirements for this vector to function optimally. Our group recently published novel data showing that simple addition of the DNA transfection reagent Lipofectamine increased the efficiency of the E. coli vector by almost 3-fold, providing the first strong evidence that further optimization of bactofection is possible. This presentation will discuss advances that demonstrate the effects of several intracellular strategies that improve the efficiency of this vector. Conditions that promote endosomal escape of internalized bacteria to evade lysosomal destruction after entry in the cell, a known obstacle limiting this vector, are elucidated. Further, treatments that increase bacterial lysis so that the vector can release its transgene into the mammalian environment for expression will be discussed. These experiments will provide valuable new insight to advance this E. coli system as an important class of vector technology for genetic correction of human disease models in cells and whole animals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DNA" title="DNA">DNA</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20coli" title=" E. coli"> E. coli</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20expression" title=" gene expression"> gene expression</a>, <a href="https://publications.waset.org/abstracts/search?q=vector" title=" vector"> vector</a> </p> <a href="https://publications.waset.org/abstracts/45408/intracellular-strategies-for-gene-delivery-into-mammalian-cells-using-bacteria-as-a-vector" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45408.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">358</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">1129</span> Speed up Vector Median Filtering by Quasi Euclidean Norm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vinai%20K.%20Singh">Vinai K. Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For reducing impulsive noise without degrading image contours, median filtering is a powerful tool. In multiband images as for example colour images or vector fields obtained by optic flow computation, a vector median filter can be used. Vector median filters are defined on the basis of a suitable distance, the best performing distance being the Euclidean. Euclidean distance is evaluated by using the Euclidean norms which is quite demanding from the point of view of computation given that a square root is required. In this paper an optimal piece-wise linear approximation of the Euclidean norm is presented which is applied to vector median filtering. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=euclidean%20norm" title="euclidean norm">euclidean norm</a>, <a href="https://publications.waset.org/abstracts/search?q=quasi%20euclidean%20norm" title=" quasi euclidean norm"> quasi euclidean norm</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20median%20filtering" title=" vector median filtering"> vector median filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=applied%20mathematics" title=" applied mathematics"> applied mathematics</a> </p> <a href="https://publications.waset.org/abstracts/21942/speed-up-vector-median-filtering-by-quasi-euclidean-norm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21942.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">474</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">1128</span> Unequal Error Protection of VQ Image Transmission System </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khelifi%20Mustapha">Khelifi Mustapha</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Moulay%20lakhdar"> A. Moulay lakhdar</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Elawady"> I. Elawady </a> </p> <p class="card-text"><strong>Abstract:</strong></p> We will study the unequal error protection for VQ image. We have used the Reed Solomon (RS) Codes as Channel coding because they offer better performance in terms of channel error correction over a binary output channel. One such channel (binary input and output) should be considered if it is the case of the application layer, because it includes all the features of the layers located below and on the what it is usually not feasible to make changes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vector%20quantization" title="vector quantization">vector quantization</a>, <a href="https://publications.waset.org/abstracts/search?q=channel%20error%20correction" title=" channel error correction"> channel error correction</a>, <a href="https://publications.waset.org/abstracts/search?q=Reed-Solomon%20channel%20coding" title=" Reed-Solomon channel coding"> Reed-Solomon channel coding</a>, <a href="https://publications.waset.org/abstracts/search?q=application" title=" application"> application</a> </p> <a href="https://publications.waset.org/abstracts/21372/unequal-error-protection-of-vq-image-transmission-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21372.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">365</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">1127</span> An Intelligent Text Independent Speaker Identification Using VQ-GMM Model Based Multiple Classifier System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ben%20Soltane%20Cheima">Ben Soltane Cheima</a>, <a href="https://publications.waset.org/abstracts/search?q=Ittansa%20Yonas%20Kelbesa"> Ittansa Yonas Kelbesa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Speaker Identification (SI) is the task of establishing identity of an individual based on his/her voice characteristics. The SI task is typically achieved by two-stage signal processing: training and testing. The training process calculates speaker specific feature parameters from the speech and generates speaker models accordingly. In the testing phase, speech samples from unknown speakers are compared with the models and classified. Even though performance of speaker identification systems has improved due to recent advances in speech processing techniques, there is still need of improvement. In this paper, a Closed-Set Tex-Independent Speaker Identification System (CISI) based on a Multiple Classifier System (MCS) is proposed, using Mel Frequency Cepstrum Coefficient (MFCC) as feature extraction and suitable combination of vector quantization (VQ) and Gaussian Mixture Model (GMM) together with Expectation Maximization algorithm (EM) for speaker modeling. The use of Voice Activity Detector (VAD) with a hybrid approach based on Short Time Energy (STE) and Statistical Modeling of Background Noise in the pre-processing step of the feature extraction yields a better and more robust automatic speaker identification system. Also investigation of Linde-Buzo-Gray (LBG) clustering algorithm for initialization of GMM, for estimating the underlying parameters, in the EM step improved the convergence rate and systems performance. It also uses relative index as confidence measures in case of contradiction in identification process by GMM and VQ as well. Simulation results carried out on voxforge.org speech database using MATLAB highlight the efficacy of the proposed method compared to earlier work. <p class="card-text"><strong>Keywords:</strong> <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=speaker%20modeling" title=" speaker modeling"> speaker modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20matching" title=" feature matching"> feature matching</a>, <a href="https://publications.waset.org/abstracts/search?q=Mel%20frequency%20cepstrum%20coefficient%20%28MFCC%29" title=" Mel frequency cepstrum coefficient (MFCC)"> Mel frequency cepstrum coefficient (MFCC)</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=vector%20quantization%20%28VQ%29" title=" vector quantization (VQ)"> vector quantization (VQ)</a>, <a href="https://publications.waset.org/abstracts/search?q=Linde-Buzo-Gray%20%28LBG%29" title=" Linde-Buzo-Gray (LBG)"> Linde-Buzo-Gray (LBG)</a>, <a href="https://publications.waset.org/abstracts/search?q=expectation%20maximization%20%28EM%29" title=" expectation maximization (EM)"> expectation maximization (EM)</a>, <a href="https://publications.waset.org/abstracts/search?q=pre-processing" title=" pre-processing"> pre-processing</a>, <a href="https://publications.waset.org/abstracts/search?q=voice%20activity%20detection%20%28VAD%29" title=" voice activity detection (VAD)"> voice activity detection (VAD)</a>, <a href="https://publications.waset.org/abstracts/search?q=short%20time%20energy%20%28STE%29" title=" short time energy (STE)"> short time energy (STE)</a>, <a href="https://publications.waset.org/abstracts/search?q=background%20noise%20statistical%20modeling" title=" background noise statistical modeling"> background noise statistical modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=closed-set%20tex-independent%20speaker%20identification%20system%20%28CISI%29" title=" closed-set tex-independent speaker identification system (CISI)"> closed-set tex-independent speaker identification system (CISI)</a> </p> <a href="https://publications.waset.org/abstracts/16253/an-intelligent-text-independent-speaker-identification-using-vq-gmm-model-based-multiple-classifier-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16253.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">309</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1126</span> Efficient Antenna Array Beamforming with Robustness against Random Steering Mismatch</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ju-Hong%20Lee">Ju-Hong Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Ching-Wei%20Liao"> Ching-Wei Liao</a>, <a href="https://publications.waset.org/abstracts/search?q=Kun-Che%20Lee"> Kun-Che Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the problem of using antenna sensors for adaptive beamforming in the presence of random steering mismatch. We present an efficient adaptive array beamformer with robustness to deal with the considered problem. The robustness of the proposed beamformer comes from the efficient designation of the steering vector. Using the received array data vector, we construct an appropriate correlation matrix associated with the received array data vector and a correlation matrix associated with signal sources. Then, the eigenvector associated with the largest eigenvalue of the constructed signal correlation matrix is designated as an appropriate estimate of the steering vector. Finally, the adaptive weight vector required for adaptive beamforming is obtained by using the estimated steering vector and the constructed correlation matrix of the array data vector. Simulation results confirm the effectiveness of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20beamforming" title="adaptive beamforming">adaptive beamforming</a>, <a href="https://publications.waset.org/abstracts/search?q=antenna%20array" title=" antenna array"> antenna array</a>, <a href="https://publications.waset.org/abstracts/search?q=linearly%20constrained%20minimum%20variance" title=" linearly constrained minimum variance"> linearly constrained minimum variance</a>, <a href="https://publications.waset.org/abstracts/search?q=robustness" title=" robustness"> robustness</a>, <a href="https://publications.waset.org/abstracts/search?q=steering%20vector" title=" steering vector"> steering vector</a> </p> <a href="https://publications.waset.org/abstracts/84543/efficient-antenna-array-beamforming-with-robustness-against-random-steering-mismatch" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84543.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">199</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">1125</span> A Video Surveillance System Using an Ensemble of Simple Neural Network Classifiers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rodrigo%20S.%20Moreira">Rodrigo S. Moreira</a>, <a href="https://publications.waset.org/abstracts/search?q=Nelson%20F.%20F.%20Ebecken"> Nelson F. F. Ebecken</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a maritime vessel tracker composed of an ensemble of WiSARD weightless neural network classifiers. A failure detector analyzes vessel movement with a Kalman filter and corrects the tracking, if necessary, using FFT matching. The use of the WiSARD neural network to track objects is uncommon. The additional contributions of the present study include a performance comparison with four state-of-art trackers, an experimental study of the features that improve maritime vessel tracking, the first use of an ensemble of classifiers to track maritime vessels and a new quantization algorithm that compares the values of pixel pairs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ram%20memory" title="ram memory">ram memory</a>, <a href="https://publications.waset.org/abstracts/search?q=WiSARD%20weightless%20neural%20network" title=" WiSARD weightless neural network"> WiSARD weightless neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20tracking" title=" object tracking"> object tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=quantization" title=" quantization"> quantization</a> </p> <a href="https://publications.waset.org/abstracts/49928/a-video-surveillance-system-using-an-ensemble-of-simple-neural-network-classifiers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49928.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">310</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">1124</span> Parallel Vector Processing Using Multi Level Orbital DATA</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nagi%20Mekhiel">Nagi Mekhiel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many applications use vector operations by applying single instruction to multiple data that map to different locations in conventional memory. Transferring data from memory is limited by access latency and bandwidth affecting the performance gain of vector processing. We present a memory system that makes all of its content available to processors in time so that processors need not to access the memory, we force each location to be available to all processors at a specific time. The data move in different orbits to become available to other processors in higher orbits at different time. We use this memory to apply parallel vector operations to data streams at first orbit level. Data processed in the first level move to upper orbit one data element at a time, allowing a processor in that orbit to apply another vector operation to deal with serial code limitations inherited in all parallel applications and interleaved it with lower level vector operations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Memory%20Organization" title="Memory Organization">Memory Organization</a>, <a href="https://publications.waset.org/abstracts/search?q=Parallel%20Processors" title=" Parallel Processors"> Parallel Processors</a>, <a href="https://publications.waset.org/abstracts/search?q=Serial%0D%0ACode" title=" Serial Code"> Serial Code</a>, <a href="https://publications.waset.org/abstracts/search?q=Vector%20Processing" title=" Vector Processing"> Vector Processing</a> </p> <a href="https://publications.waset.org/abstracts/59115/parallel-vector-processing-using-multi-level-orbital-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59115.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">270</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">1123</span> Exploring Deep Neural Network Compression: An Overview</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghorab%20Sara">Ghorab Sara</a>, <a href="https://publications.waset.org/abstracts/search?q=Meziani%20Lila"> Meziani Lila</a>, <a href="https://publications.waset.org/abstracts/search?q=Rubin%20Harvey%20Stuart"> Rubin Harvey Stuart</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The rapid growth of deep learning has led to intricate and resource-intensive deep neural networks widely used in computer vision tasks. However, their complexity results in high computational demands and memory usage, hindering real-time application. To address this, research focuses on model compression techniques. The paper provides an overview of recent advancements in compressing neural networks and categorizes the various methods into four main approaches: network pruning, quantization, network decomposition, and knowledge distillation. This paper aims to provide a comprehensive outline of both the advantages and limitations of each method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20compression" title="model compression">model compression</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20network" title=" deep neural network"> deep neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=pruning" title=" pruning"> pruning</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20distillation" title=" knowledge distillation"> knowledge distillation</a>, <a href="https://publications.waset.org/abstracts/search?q=quantization" title=" quantization"> quantization</a>, <a href="https://publications.waset.org/abstracts/search?q=low-rank%20decomposition" title=" low-rank decomposition"> low-rank decomposition</a> </p> <a href="https://publications.waset.org/abstracts/185803/exploring-deep-neural-network-compression-an-overview" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185803.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">43</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">1122</span> 0.13-µm Complementary Metal-Oxide Semiconductor Vector Modulator for Beamforming System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20S.%20Kim">J. S. Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a 0.13-µm Complementary Metal-Oxide Semiconductor (CMOS) vector modulator for beamforming system. The vector modulator features a 360° phase and gain range of -10 dB to 10 dB with a root mean square phase and amplitude error of only 2.2° and 0.45 dB, respectively. These features make it a suitable for wireless backhaul system in the 5 GHz industrial, scientific, and medical (ISM) bands. It draws a current of 20.4 mA from a 1.2 V supply. The total chip size is 1.87x1.34 mm². <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CMOS" title="CMOS">CMOS</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20modulator" title=" vector modulator"> vector modulator</a>, <a href="https://publications.waset.org/abstracts/search?q=beamforming" title=" beamforming"> beamforming</a>, <a href="https://publications.waset.org/abstracts/search?q=802.11ac" title=" 802.11ac"> 802.11ac</a> </p> <a href="https://publications.waset.org/abstracts/67880/013-m-complementary-metal-oxide-semiconductor-vector-modulator-for-beamforming-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67880.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">210</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">1121</span> Using Support Vector Machines for Measuring Democracy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tommy%20Krieger">Tommy Krieger</a>, <a href="https://publications.waset.org/abstracts/search?q=Klaus%20Gruendler"> Klaus Gruendler </a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present a novel approach for measuring democracy, which enables a very detailed and sensitive index. This method is based on Support Vector Machines, a mathematical algorithm for pattern recognition. Our implementation evaluates 188 countries in the period between 1981 and 2011. The Support Vector Machines Democracy Index (SVMDI) is continuously on the 0-1-Interval and robust to variations in the numerical process parameters. The algorithm introduced here can be used for every concept of democracy without additional adjustments, and due to its flexibility it is also a valuable tool for comparison studies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=democracy" title="democracy">democracy</a>, <a href="https://publications.waset.org/abstracts/search?q=democracy%20index" title=" democracy index"> democracy index</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines" title=" support vector machines"> support vector machines</a> </p> <a href="https://publications.waset.org/abstracts/31697/using-support-vector-machines-for-measuring-democracy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31697.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">378</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">1120</span> Core Loss Influence on MTPA Current Vector Variation of Synchronous Reluctance Machine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Huai-Cong%20Liu">Huai-Cong Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Tae%20Chul%20Jeong"> Tae Chul Jeong</a>, <a href="https://publications.waset.org/abstracts/search?q=Ju%20Lee"> Ju Lee </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this study was to develop an electric circuit method (ECM) to ascertain the core loss influence on a Synchronous Reluctance Motor (SynRM) in the condition of the maximum torque per ampere (MTPA). SynRM for fan usually operates on the constant torque region, at synchronous speed the MTPA control is adopted due to current vector. However, finite element analysis (FEA) program is not sufficient exactly to reflect how the core loss influenced on the current vector. This paper proposed a method to calculate the current vector with consideration of core loss. The precision of current vector by ECM is useful for MTPA control. The result shows that ECM analysis is closer to the actual motor’s characteristics by testing with a 7.5kW SynRM drive System. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=core%20loss" title="core loss">core loss</a>, <a href="https://publications.waset.org/abstracts/search?q=SynRM" title=" SynRM"> SynRM</a>, <a href="https://publications.waset.org/abstracts/search?q=current%20vector" title=" current vector"> current vector</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20saturation" title=" magnetic saturation"> magnetic saturation</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20torque%20per%20ampere%20%28MTPA%29" title=" maximum torque per ampere (MTPA)"> maximum torque per ampere (MTPA)</a> </p> <a href="https://publications.waset.org/abstracts/25312/core-loss-influence-on-mtpa-current-vector-variation-of-synchronous-reluctance-machine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25312.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">530</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">1119</span> A Word-to-Vector Formulation for Word Representation </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sandra%20Rizkallah">Sandra Rizkallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Amir%20F.%20Atiya"> Amir F. Atiya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work presents a novel word to vector representation that is based on embedding the words into a sphere, whereby the dot product of the corresponding vectors represents the similarity between any two words. Embedding the vectors into a sphere enabled us to take into consideration the antonymity between words, not only the synonymity, because of the suitability to handle the polarity nature of words. For example, a word and its antonym can be represented as a vector and its negative. Moreover, we have managed to extract an adequate vocabulary. The obtained results show that the proposed approach can capture the essence of the language, and can be generalized to estimate a correct similarity of any new pair of words. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title="natural language processing">natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20to%20vector" title=" word to vector"> word to vector</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20similarity" title=" text similarity"> text similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title=" text mining"> text mining</a> </p> <a href="https://publications.waset.org/abstracts/81808/a-word-to-vector-formulation-for-word-representation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81808.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">275</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">1118</span> The Boundary Element Method in Excel for Teaching Vector Calculus and Simulation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Stephen%20Kirkup">Stephen Kirkup</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses the implementation of the boundary element method (BEM) on an Excel spreadsheet and how it can be used in teaching vector calculus and simulation. There are two separate spreadheets, within which Laplace equation is solved by the BEM in two dimensions (LIBEM2) and axisymmetric three dimensions (LBEMA). The main algorithms are implemented in the associated programming language within Excel, Visual Basic for Applications (VBA). The BEM only requires a boundary mesh and hence it is a relatively accessible method. The BEM in the open spreadsheet environment is demonstrated as being useful as an aid to teaching and learning. The application of the BEM implemented on a spreadsheet for educational purposes in introductory vector calculus and simulation is explored. The development of assignment work is discussed, and sample results from student work are given. The spreadsheets were found to be useful tools in developing the students’ understanding of vector calculus and in simulating heat conduction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=boundary%20element%20method" title="boundary element method">boundary element method</a>, <a href="https://publications.waset.org/abstracts/search?q=Laplace%E2%80%99s%20equation" title=" Laplace’s equation"> Laplace’s equation</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20calculus" title=" vector calculus"> vector calculus</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=education" title=" education"> education</a> </p> <a href="https://publications.waset.org/abstracts/95383/the-boundary-element-method-in-excel-for-teaching-vector-calculus-and-simulation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95383.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">1117</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">1116</span> Performance of Total Vector Error of an Estimated Phasor within Local Area Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Abdolkhalig">Ahmed Abdolkhalig</a>, <a href="https://publications.waset.org/abstracts/search?q=Rastko%20Zivanovic"> Rastko Zivanovic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper evaluates the Total Vector Error of an estimated Phasor as define in IEEE C37.118 standard within different medium access in Local Area Networks (LAN). Three different LAN models (CSMA/CD, CSMA/AMP, and Switched Ethernet) are evaluated. The Total Vector Error of the estimated Phasor has been evaluated for the effect of Nodes Number under the standardized network Band-width values defined in IEC 61850-9-2 communication standard (i.e. 0.1, 1, and 10 Gbps). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=phasor" title="phasor">phasor</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20area%20network" title=" local area network"> local area network</a>, <a href="https://publications.waset.org/abstracts/search?q=total%20vector%20error" title=" total vector error"> total vector error</a>, <a href="https://publications.waset.org/abstracts/search?q=IEEE%20C37.118" title=" IEEE C37.118"> IEEE C37.118</a>, <a href="https://publications.waset.org/abstracts/search?q=IEC%2061850" title=" IEC 61850"> IEC 61850</a> </p> <a href="https://publications.waset.org/abstracts/5655/performance-of-total-vector-error-of-an-estimated-phasor-within-local-area-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5655.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">311</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">1115</span> Volume Density of Power of Multivector Electric Machine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aldan%20A.%20Sapargaliyev">Aldan A. Sapargaliyev</a>, <a href="https://publications.waset.org/abstracts/search?q=Yerbol%20A.%20Sapargaliyev"> Yerbol A. Sapargaliyev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Since the invention, the electric machine (EM) can be defined as oEM – one-vector electric machine, as it works due to one-vector inductive coupling with use of one-vector electromagnet. The disadvantages of oEM are large size and limited efficiency at low and medium power applications. This paper describes multi-vector electric machine (mEM) based on multi-vector inductive coupling, which is characterized by the increased surface area of the inductive coupling per EM volume, with a reduced share of inefficient and energy-consuming part of the winding, in comparison with oEM’s. Particularly, it is considered, calculated and compared the performance of three different electrical motors and their power at the same volumes and rotor frequencies. It is also presented the result of calculation of correlation between power density and volume for oEM and mEM. The method of multi-vector inductive coupling enables mEM to possess 1.5-4.0 greater density of power per volume and significantly higher efficiency, in comparison with today’s oEM, especially in low and medium power applications. mEM has distinct advantages, when used in transport vehicles such as electric cars and aircrafts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electric%20machine" title="electric machine">electric machine</a>, <a href="https://publications.waset.org/abstracts/search?q=electric%20motor" title=" electric motor"> electric motor</a>, <a href="https://publications.waset.org/abstracts/search?q=electromagnet" title=" electromagnet"> electromagnet</a>, <a href="https://publications.waset.org/abstracts/search?q=efficiency%20of%20electric%20motor" title=" efficiency of electric motor"> efficiency of electric motor</a> </p> <a href="https://publications.waset.org/abstracts/67282/volume-density-of-power-of-multivector-electric-machine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67282.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">338</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">1114</span> Support Vector Regression with Weighted Least Absolute Deviations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kang-Mo%20Jung">Kang-Mo Jung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Least squares support vector machine (LS-SVM) is a penalized regression which considers both fitting and generalization ability of a model. However, the squared loss function is very sensitive to even single outlier. We proposed a weighted absolute deviation loss function for the robustness of the estimates in least absolute deviation support vector machine. The proposed estimates can be obtained by a quadratic programming algorithm. Numerical experiments on simulated datasets show that the proposed algorithm is competitive in view of robustness to outliers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=least%20absolute%20deviation" title="least absolute deviation">least absolute deviation</a>, <a href="https://publications.waset.org/abstracts/search?q=quadratic%20programming" title=" quadratic programming"> quadratic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=robustness" title=" robustness"> robustness</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=weight" title=" weight"> weight</a> </p> <a href="https://publications.waset.org/abstracts/23674/support-vector-regression-with-weighted-least-absolute-deviations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23674.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">527</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=vector%20quantization%20%28VQ%29&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=vector%20quantization%20%28VQ%29&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=vector%20quantization%20%28VQ%29&page=4">4</a></li> <li class="page-item"><a class="page-link" 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