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estimation</title> <meta name="description" content="Search results for: background estimation"> <meta name="keywords" content="background estimation"> <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 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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="background estimation"> <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> 6389</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: background estimation</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6389</span> Optimization Modeling of the Hybrid Antenna Array for the DoA Estimation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Somayeh%20Komeylian">Somayeh Komeylian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The direction of arrival (DoA) estimation is the crucial aspect of the radar technologies for detecting and dividing several signal sources. In this scenario, the antenna array output modeling involves numerous parameters including noise samples, signal waveform, signal directions, signal number, and signal to noise ratio (SNR), and thereby the methods of the DoA estimation rely heavily on the generalization characteristic for establishing a large number of the training data sets. Hence, we have analogously represented the two different optimization models of the DoA estimation; (1) the implementation of the decision directed acyclic graph (DDAG) for the multiclass least-squares support vector machine (LS-SVM), and (2) the optimization method of the deep neural network (DNN) radial basis function (RBF). We have rigorously verified that the LS-SVM DDAG algorithm is capable of accurately classifying DoAs for the three classes. However, the accuracy and robustness of the DoA estimation are still highly sensitive to technological imperfections of the antenna arrays such as non-ideal array design and manufacture, array implementation, mutual coupling effect, and background radiation and thereby the method may fail in representing high precision for the DoA estimation. Therefore, this work has a further contribution on developing the DNN-RBF model for the DoA estimation for overcoming the limitations of the non-parametric and data-driven methods in terms of array imperfection and generalization. The numerical results of implementing the DNN-RBF model have confirmed the better performance of the DoA estimation compared with the LS-SVM algorithm. Consequently, we have analogously evaluated the performance of utilizing the two aforementioned optimization methods for the DoA estimation using the concept of the mean squared error (MSE). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DoA%20estimation" title="DoA estimation">DoA estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=Adaptive%20antenna%20array" title=" Adaptive antenna array"> Adaptive antenna array</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=LS-SVM%20optimization%20model" title=" LS-SVM optimization model"> LS-SVM optimization model</a>, <a href="https://publications.waset.org/abstracts/search?q=Radial%20basis%20function" title=" Radial basis function"> Radial basis function</a>, <a href="https://publications.waset.org/abstracts/search?q=and%20MSE" title=" and MSE"> and MSE</a> </p> <a href="https://publications.waset.org/abstracts/129058/optimization-modeling-of-the-hybrid-antenna-array-for-the-doa-estimation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129058.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">100</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">6388</span> Optical Flow Direction Determination for Railway Crossing Occupancy Monitoring</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zdenek%20Silar">Zdenek Silar</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20Dobrovolny"> Martin Dobrovolny</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article deals with the obstacle detection on a railway crossing (clearance detection). Detection is based on the optical flow estimation and classification of the flow vectors by K-means clustering algorithm. For classification of passing vehicles is used optical flow direction determination. The optical flow estimation is based on a modified Lucas-Kanade method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=background%20estimation" title="background estimation">background estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=direction%20of%20optical%20flow" title=" direction of optical flow"> direction of optical flow</a>, <a href="https://publications.waset.org/abstracts/search?q=K-means%20clustering" title=" K-means clustering"> K-means clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=objects%20detection" title=" objects detection"> objects detection</a>, <a href="https://publications.waset.org/abstracts/search?q=railway%20crossing%20monitoring" title=" railway crossing monitoring"> railway crossing monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=velocity%20vectors" title=" velocity vectors"> velocity vectors</a> </p> <a href="https://publications.waset.org/abstracts/1853/optical-flow-direction-determination-for-railway-crossing-occupancy-monitoring" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1853.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">518</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">6387</span> Design, Construction and Performance Evaluation of a HPGe Detector Shield</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Sharifi">M. Sharifi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Mirzaii"> M. Mirzaii</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Bolourinovin"> F. Bolourinovin</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Yousefnia"> H. Yousefnia</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Akbari"> M. Akbari</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Yousefi-Mojir"> K. Yousefi-Mojir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A multilayer passive shield composed of low-activity lead (Pb), copper (Cu), tin (Sn) and iron (Fe) was designed and manufactured for a coaxial HPGe detector placed at a surface laboratory for reducing background radiation and radiation dose to the personnel. The performance of the shield was evaluated and efficiency curves of the detector were plotted by using of the various standard sources in different distances. Monte Carlo simulations and a set of TLD chips were used for dose estimation in two distances of 20 and 40 cm. The results show that the shield reduced background spectrum and the personnel dose more than 95%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=HPGe%20shield" title="HPGe shield">HPGe shield</a>, <a href="https://publications.waset.org/abstracts/search?q=background%20count" title=" background count"> background count</a>, <a href="https://publications.waset.org/abstracts/search?q=personnel%20dose" title=" personnel dose"> personnel dose</a>, <a href="https://publications.waset.org/abstracts/search?q=efficiency%20curve" title=" efficiency curve"> efficiency curve</a> </p> <a href="https://publications.waset.org/abstracts/34295/design-construction-and-performance-evaluation-of-a-hpge-detector-shield" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34295.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">456</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">6386</span> Simulation of 3-D Direction-of-Arrival Estimation Using MUSIC Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Duckyong%20Kim">Duckyong Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jong%20Kang%20Park"> Jong Kang Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Jong%20Tae%20Kim"> Jong Tae Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> DOA (Direction of Arrival) estimation is an important method in array signal processing and has a wide range of applications such as direction finding, beam forming, and so on. In this paper, we briefly introduce the MUSIC (Multiple Signal Classification) Algorithm, one of DOA estimation methods for analyzing several targets. Then we apply the MUSIC algorithm to the two-dimensional antenna array to analyze DOA estimation in 3D space through MATLAB simulation. We also analyze the design factors that can affect the accuracy of DOA estimation through simulation, and proceed with further consideration on how to apply the system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DOA%20estimation" title="DOA estimation">DOA estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=MUSIC%20algorithm" title=" MUSIC algorithm"> MUSIC algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20spectrum" title=" spatial spectrum"> spatial spectrum</a>, <a href="https://publications.waset.org/abstracts/search?q=array%20signal%20processing" title=" array signal processing"> array signal processing</a> </p> <a href="https://publications.waset.org/abstracts/88658/simulation-of-3-d-direction-of-arrival-estimation-using-music-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88658.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">379</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">6385</span> Lithium-Ion Battery State of Charge Estimation Using One State Hysteresis Model with Nonlinear Estimation Strategies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Farag">Mohammed Farag</a>, <a href="https://publications.waset.org/abstracts/search?q=Mina%20Attari"> Mina Attari</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Andrew%20Gadsden"> S. Andrew Gadsden</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeid%20R.%20Habibi"> Saeid R. Habibi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Battery state of charge (SOC) estimation is an important parameter as it measures the total amount of electrical energy stored at a current time. The SOC percentage acts as a fuel gauge if it is compared with a conventional vehicle. Estimating the SOC is, therefore, essential for monitoring the amount of useful life remaining in the battery system. This paper looks at the implementation of three nonlinear estimation strategies for Li-Ion battery SOC estimation. One of the most common behavioral battery models is the one state hysteresis (OSH) model. The extended Kalman filter (EKF), the smooth variable structure filter (SVSF), and the time-varying smoothing boundary layer SVSF are applied on this model, and the results are compared. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=state%20of%20charge%20estimation" title="state of charge estimation">state of charge estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=battery%20modeling" title=" battery modeling"> battery modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=one-state%20hysteresis" title=" one-state hysteresis"> one-state hysteresis</a>, <a href="https://publications.waset.org/abstracts/search?q=filtering%20and%20estimation" title=" filtering and estimation"> filtering and estimation</a> </p> <a href="https://publications.waset.org/abstracts/68017/lithium-ion-battery-state-of-charge-estimation-using-one-state-hysteresis-model-with-nonlinear-estimation-strategies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68017.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">444</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">6384</span> Frequency Offset Estimation Schemes Based on ML for OFDM Systems in Non-Gaussian Noise Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Keunhong%20Chae">Keunhong Chae</a>, <a href="https://publications.waset.org/abstracts/search?q=Seokho%20Yoon"> Seokho Yoon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, frequency offset (FO) estimation schemes robust to the non-Gaussian noise environments are proposed for orthogonal frequency division multiplexing (OFDM) systems. First, a maximum-likelihood (ML) estimation scheme in non-Gaussian noise environments is proposed, and then, the complexity of the ML estimation scheme is reduced by employing a reduced set of candidate values. In numerical results, it is demonstrated that the proposed schemes provide a significant performance improvement over the conventional estimation scheme in non-Gaussian noise environments while maintaining the performance similar to the estimation performance in Gaussian noise environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frequency%20offset%20estimation" title="frequency offset estimation">frequency offset estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum-likelihood" title=" maximum-likelihood"> maximum-likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=non-Gaussian%20noise%0D%0Aenvironment" title=" non-Gaussian noise environment"> non-Gaussian noise environment</a>, <a href="https://publications.waset.org/abstracts/search?q=OFDM" title=" OFDM"> OFDM</a>, <a href="https://publications.waset.org/abstracts/search?q=training%20symbol" title=" training symbol"> training symbol</a> </p> <a href="https://publications.waset.org/abstracts/9430/frequency-offset-estimation-schemes-based-on-ml-for-ofdm-systems-in-non-gaussian-noise-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9430.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">353</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">6383</span> Parameters Estimation of Multidimensional Possibility Distributions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sergey%20Sorokin">Sergey Sorokin</a>, <a href="https://publications.waset.org/abstracts/search?q=Irina%20Sorokina"> Irina Sorokina</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Yazenin"> Alexander Yazenin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present a solution to the Maxmin u/E parameters estimation problem of possibility distributions in m-dimensional case. Our method is based on geometrical approach, where minimal area enclosing ellipsoid is constructed around the sample. Also we demonstrate that one can improve results of well-known algorithms in fuzzy model identification task using Maxmin u/E parameters estimation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=possibility%20distribution" title="possibility distribution">possibility distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=parameters%20estimation" title=" parameters estimation"> parameters estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=Maxmin%20u%5CE%20estimator" title=" Maxmin u\E estimator"> Maxmin u\E estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20model%20identification" title=" fuzzy model identification"> fuzzy model identification</a> </p> <a href="https://publications.waset.org/abstracts/16751/parameters-estimation-of-multidimensional-possibility-distributions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16751.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">470</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">6382</span> A Packet Loss Probability Estimation Filter Using Most Recent Finite Traffic Measurements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pyung%20Soo%20Kim">Pyung Soo Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Eung%20Hyuk%20Lee"> Eung Hyuk Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Mun%20Suck%20Jang"> Mun Suck Jang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A packet loss probability (PLP) estimation filter with finite memory structure is proposed to estimate the packet rate mean and variance of the input traffic process in real-time while removing undesired system and measurement noises. The proposed PLP estimation filter is developed under a weighted least square criterion using only the finite traffic measurements on the most recent window. The proposed PLP estimation filter is shown to have several inherent properties such as unbiasedness, deadbeat, robustness. A guideline for choosing appropriate window length is described since it can affect significantly the estimation performance. Using computer simulations, the proposed PLP estimation filter is shown to be superior to the Kalman filter for the temporarily uncertain system. One possible explanation for this is that the proposed PLP estimation filter can have greater convergence time of a filtered estimate as the window length M decreases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=packet%20loss%20probability%20estimation" title="packet loss probability estimation">packet loss probability estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20memory%20filter" title=" finite memory filter"> finite memory filter</a>, <a href="https://publications.waset.org/abstracts/search?q=infinite%20memory%20filter" title=" infinite memory filter"> infinite memory filter</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a> </p> <a href="https://publications.waset.org/abstracts/9519/a-packet-loss-probability-estimation-filter-using-most-recent-finite-traffic-measurements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9519.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">674</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">6381</span> Time Delay Estimation Using Signal Envelopes for Synchronisation of Recordings</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sergei%20Aleinik">Sergei Aleinik</a>, <a href="https://publications.waset.org/abstracts/search?q=Mikhail%20Stolbov"> Mikhail Stolbov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, a method of time delay estimation for dual-channel acoustic signals (speech, music, etc.) recorded under reverberant conditions is investigated. Standard methods based on cross-correlation of the signals show poor results in cases involving strong reverberation, large distances between microphones and asynchronous recordings. Under similar conditions, a method based on cross-correlation of temporal envelopes of the signals delivers a delay estimation of acceptable quality. This method and its properties are described and investigated in detail, including its limits of applicability. The method’s optimal parameter estimation and a comparison with other known methods of time delay estimation are also provided. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cross-correlation" title="cross-correlation">cross-correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=delay%20estimation" title=" delay estimation"> delay estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20envelope" title=" signal envelope"> signal envelope</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a> </p> <a href="https://publications.waset.org/abstracts/2280/time-delay-estimation-using-signal-envelopes-for-synchronisation-of-recordings" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2280.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">485</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">6380</span> VaR Estimation Using the Informational Content of Futures Traded Volume</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amel%20Oueslati">Amel Oueslati</a>, <a href="https://publications.waset.org/abstracts/search?q=Olfa%20Benouda"> Olfa Benouda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> New Value at Risk (VaR) estimation is proposed and investigated. The well-known two stages Garch-EVT approach uses conditional volatility to generate one step ahead forecasts of VaR. With daily data for twelve stocks that decompose the Dow Jones Industrial Average (DJIA) index, this paper incorporates the volume in the first stage volatility estimation. Afterwards, the forecasting ability of this conditional volatility concerning the VaR estimation is compared to that of a basic volatility model without considering any trading component. The results are significant and bring out the importance of the trading volume in the VaR measure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Garch-EVT" title="Garch-EVT">Garch-EVT</a>, <a href="https://publications.waset.org/abstracts/search?q=value%20at%20risk" title=" value at risk"> value at risk</a>, <a href="https://publications.waset.org/abstracts/search?q=volume" title=" volume"> volume</a>, <a href="https://publications.waset.org/abstracts/search?q=volatility" title=" volatility"> volatility</a> </p> <a href="https://publications.waset.org/abstracts/56021/var-estimation-using-the-informational-content-of-futures-traded-volume" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56021.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">285</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">6379</span> Depth Estimation in DNN Using Stereo Thermal Image Pairs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmet%20Faruk%20Akyuz">Ahmet Faruk Akyuz</a>, <a href="https://publications.waset.org/abstracts/search?q=Hasan%20Sakir%20Bilge">Hasan Sakir Bilge</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Depth estimation using stereo images is a challenging problem in computer vision. Many different studies have been carried out to solve this problem. With advancing machine learning, tackling this problem is often done with neural network-based solutions. The images used in these studies are mostly in the visible spectrum. However, the need to use the Infrared (IR) spectrum for depth estimation has emerged because it gives better results than visible spectra in some conditions. At this point, we recommend using thermal-thermal (IR) image pairs for depth estimation. In this study, we used two well-known networks (PSMNet, FADNet) with minor modifications to demonstrate the viability of this idea. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=thermal%20stereo%20matching" title="thermal stereo matching">thermal stereo matching</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20networks" title="deep neural networks">deep neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title="CNN">CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=Depth%20estimation" title="Depth estimation">Depth estimation</a> </p> <a href="https://publications.waset.org/abstracts/140133/depth-estimation-in-dnn-using-stereo-thermal-image-pairs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/140133.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">279</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">6378</span> Parameter Estimation of Induction Motors by PSO Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Mohammadi">A. Mohammadi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Asghari"> S. Asghari</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Aien"> M. Aien</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Rashidinejad"> M. Rashidinejad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> After emergent of alternative current networks and their popularity, asynchronous motors became more widespread than other kinds of industrial motors. In order to control and run these motors efficiently, an accurate estimation of motor parameters is needed. There are different methods to obtain these parameters such as rotor locked test, no load test, DC test, analytical methods, and so on. The most common drawback of these methods is their inaccuracy in estimation of some motor parameters. In order to remove this concern, a novel method for parameter estimation of induction motors using particle swarm optimization (PSO) algorithm is proposed. In the proposed method, transient state of motor is used for parameter estimation. Comparison of the simulation results purtuined to the PSO algorithm with other available methods justifies the effectiveness of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=induction%20motor" title="induction motor">induction motor</a>, <a href="https://publications.waset.org/abstracts/search?q=motor%20parameter%20estimation" title=" motor parameter estimation"> motor parameter estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=PSO%20algorithm" title=" PSO algorithm"> PSO algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=analytical%20method" title=" analytical method"> analytical method</a> </p> <a href="https://publications.waset.org/abstracts/15433/parameter-estimation-of-induction-motors-by-pso-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15433.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">633</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">6377</span> Online Pose Estimation and Tracking Approach with Siamese Region Proposal Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cheng%20Fang">Cheng Fang</a>, <a href="https://publications.waset.org/abstracts/search?q=Lingwei%20Quan"> Lingwei Quan</a>, <a href="https://publications.waset.org/abstracts/search?q=Cunyue%20Lu"> Cunyue Lu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human pose estimation and tracking are to accurately identify and locate the positions of human joints in the video. It is a computer vision task which is of great significance for human motion recognition, behavior understanding and scene analysis. There has been remarkable progress on human pose estimation in recent years. However, more researches are needed for human pose tracking especially for online tracking. In this paper, a framework, called PoseSRPN, is proposed for online single-person pose estimation and tracking. We use Siamese network attaching a pose estimation branch to incorporate Single-person Pose Tracking (SPT) and Visual Object Tracking (VOT) into one framework. The pose estimation branch has a simple network structure that replaces the complex upsampling and convolution network structure with deconvolution. By augmenting the loss of fully convolutional Siamese network with the pose estimation task, pose estimation and tracking can be trained in one stage. Once trained, PoseSRPN only relies on a single bounding box initialization and producing human joints location. The experimental results show that while maintaining the good accuracy of pose estimation on COCO and PoseTrack datasets, the proposed method achieves a speed of 59 frame/s, which is superior to other pose tracking frameworks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=pose%20estimation" title=" pose estimation"> pose estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=pose%20tracking" title=" pose tracking"> pose tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=Siamese%20network" title=" Siamese network"> Siamese network</a> </p> <a href="https://publications.waset.org/abstracts/112839/online-pose-estimation-and-tracking-approach-with-siamese-region-proposal-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112839.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">6376</span> Robust Adaptation to Background Noise in Multichannel C-OTDR Monitoring Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andrey%20V.%20Timofeev">Andrey V. Timofeev</a>, <a href="https://publications.waset.org/abstracts/search?q=Viktor%20M.%20Denisov"> Viktor M. Denisov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A robust sequential nonparametric method is proposed for adaptation to background noise parameters for real-time. The distribution of background noise was modelled like to Huber contamination mixture. The method is designed to operate as an adaptation-unit, which is included inside a detection subsystem of an integrated multichannel monitoring system. The proposed method guarantees the given size of a nonasymptotic confidence set for noise parameters. Properties of the suggested method are rigorously proved. The proposed algorithm has been successfully tested in real conditions of a functioning C-OTDR monitoring system, which was designed to monitor railways. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=guaranteed%20estimation" title="guaranteed estimation">guaranteed estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=multichannel%20monitoring%20systems" title=" multichannel monitoring systems"> multichannel monitoring systems</a>, <a href="https://publications.waset.org/abstracts/search?q=non-asymptotic%20confidence%20set" title=" non-asymptotic confidence set"> non-asymptotic confidence set</a>, <a href="https://publications.waset.org/abstracts/search?q=contamination%20mixture" title=" contamination mixture"> contamination mixture</a> </p> <a href="https://publications.waset.org/abstracts/28516/robust-adaptation-to-background-noise-in-multichannel-c-otdr-monitoring-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28516.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">430</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">6375</span> Characteristic Function in Estimation of Probability Distribution Moments </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vladimir%20S.%20Timofeev">Vladimir S. Timofeev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article the problem of distributional moments estimation is considered. The new approach of moments estimation based on usage of the characteristic function is proposed. By statistical simulation technique, author shows that new approach has some robust properties. For calculation of the derivatives of characteristic function there is used numerical differentiation. Obtained results confirmed that author’s idea has a certain working efficiency and it can be recommended for any statistical applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=characteristic%20function" title="characteristic function">characteristic function</a>, <a href="https://publications.waset.org/abstracts/search?q=distributional%20moments" title=" distributional moments"> distributional moments</a>, <a href="https://publications.waset.org/abstracts/search?q=robustness" title=" robustness"> robustness</a>, <a href="https://publications.waset.org/abstracts/search?q=outlier" title=" outlier"> outlier</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20estimation%20problem" title=" statistical estimation problem"> statistical estimation problem</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20simulation" title=" statistical simulation"> statistical simulation</a> </p> <a href="https://publications.waset.org/abstracts/11779/characteristic-function-in-estimation-of-probability-distribution-moments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11779.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">504</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">6374</span> Considering the Reliability of Measurements Issue in Distributed Adaptive Estimation Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wael%20M.%20Bazzi">Wael M. Bazzi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amir%20Rastegarnia"> Amir Rastegarnia</a>, <a href="https://publications.waset.org/abstracts/search?q=Azam%20Khalili"> Azam Khalili</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we consider the issue of reliability of measurements in distributed adaptive estimation problem. To this aim, we assume a sensor network with different observation noise variance among the sensors and propose new estimation method based on incremental distributed least mean-square (IDLMS) algorithm. The proposed method contains two phases: I) Estimation of each sensors observation noise variance, and II) Estimation of the desired parameter using the estimated observation variances. To deal with the reliability of measurements, in the second phase of the proposed algorithm, the step-size parameter is adjusted for each sensor according to its observation noise variance. As our simulation results show, the proposed algorithm considerably improves the performance of the IDLMS algorithm in the same condition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20filter" title="adaptive filter">adaptive filter</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20estimation" title=" distributed estimation"> distributed estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%0D%0Anetwork" title=" sensor network"> sensor network</a>, <a href="https://publications.waset.org/abstracts/search?q=IDLMS%20algorithm" title=" IDLMS algorithm"> IDLMS algorithm</a> </p> <a href="https://publications.waset.org/abstracts/27648/considering-the-reliability-of-measurements-issue-in-distributed-adaptive-estimation-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27648.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">634</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">6373</span> State Estimation of a Biotechnological Process Using Extended Kalman Filter and Particle Filter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Simutis">R. Simutis</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Galvanauskas"> V. Galvanauskas</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Levisauskas"> D. Levisauskas</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Repsyte"> J. Repsyte</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Grincas"> V. Grincas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with advanced state estimation algorithms for estimation of biomass concentration and specific growth rate in a typical fed-batch biotechnological process. This biotechnological process was represented by a nonlinear mass-balance based process model. Extended Kalman Filter (EKF) and Particle Filter (PF) was used to estimate the unmeasured state variables from oxygen uptake rate (OUR) and base consumption (BC) measurements. To obtain more general results, a simplified process model was involved in EKF and PF estimation algorithms. This model doesn’t require any special growth kinetic equations and could be applied for state estimation in various bioprocesses. The focus of this investigation was concentrated on the comparison of the estimation quality of the EKF and PF estimators by applying different measurement noises. The simulation results show that Particle Filter algorithm requires significantly more computation time for state estimation but gives lower estimation errors both for biomass concentration and specific growth rate. Also the tuning procedure for Particle Filter is simpler than for EKF. Consequently, Particle Filter should be preferred in real applications, especially for monitoring of industrial bioprocesses where the simplified implementation procedures are always desirable. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biomass%20concentration" title="biomass concentration">biomass concentration</a>, <a href="https://publications.waset.org/abstracts/search?q=extended%20Kalman%20filter" title=" extended Kalman filter"> extended Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20filter" title=" particle filter"> particle filter</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20estimation" title=" state estimation"> state estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=specific%20growth%20rate" title=" specific growth rate"> specific growth rate</a> </p> <a href="https://publications.waset.org/abstracts/12940/state-estimation-of-a-biotechnological-process-using-extended-kalman-filter-and-particle-filter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12940.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">430</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">6372</span> Estimation of Fuel Cost Function Characteristics Using Cuckoo Search</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20R.%20Al-Rashidi">M. R. Al-Rashidi</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20M.%20El-Naggar"> K. M. El-Naggar</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20F.%20Al-Hajri"> M. F. Al-Hajri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The fuel cost function describes the electric power generation-cost relationship in thermal plants, hence, it sheds light on economical aspects of power industry. Different models have been proposed to describe this relationship with the quadratic function model being the most popular one. Parameters of second order fuel cost function are estimated in this paper using cuckoo search algorithm. It is a new population based meta-heuristic optimization technique that has been used in this study primarily as an accurate estimation tool. Its main features are flexibility, simplicity, and effectiveness when compared to other estimation techniques. The parameter estimation problem is formulated as an optimization one with the goal being minimizing the error associated with the estimated parameters. A case study is considered in this paper to illustrate cuckoo search promising potential as a valuable estimation and optimization technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cuckoo%20search" title="cuckoo search">cuckoo search</a>, <a href="https://publications.waset.org/abstracts/search?q=parameters%20estimation" title=" parameters estimation"> parameters estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=fuel%20cost%20function" title=" fuel cost function"> fuel cost function</a>, <a href="https://publications.waset.org/abstracts/search?q=economic%20dispatch" title=" economic dispatch"> economic dispatch</a> </p> <a href="https://publications.waset.org/abstracts/25377/estimation-of-fuel-cost-function-characteristics-using-cuckoo-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25377.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">581</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">6371</span> ML-Based Blind Frequency Offset Estimation Schemes for OFDM Systems in Non-Gaussian Noise Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Keunhong%20Chae">Keunhong Chae</a>, <a href="https://publications.waset.org/abstracts/search?q=Seokho%20Yoon"> Seokho Yoon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes frequency offset (FO) estimation schemes robust to the non-Gaussian noise for orthogonal frequency division multiplexing (OFDM) systems. A maximum-likelihood (ML) scheme and a low-complexity estimation scheme are proposed by applying the probability density function of the cyclic prefix of OFDM symbols to the ML criterion. From simulation results, it is confirmed that the proposed schemes offer a significant FO estimation performance improvement over the conventional estimation scheme in non-Gaussian noise environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frequency%20offset" title="frequency offset">frequency offset</a>, <a href="https://publications.waset.org/abstracts/search?q=cyclic%20prefix" title=" cyclic prefix"> cyclic prefix</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum-likelihood" title=" maximum-likelihood"> maximum-likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=non-Gaussian%0D%0Anoise" title=" non-Gaussian noise"> non-Gaussian noise</a>, <a href="https://publications.waset.org/abstracts/search?q=OFDM" title=" OFDM"> OFDM</a> </p> <a href="https://publications.waset.org/abstracts/10266/ml-based-blind-frequency-offset-estimation-schemes-for-ofdm-systems-in-non-gaussian-noise-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10266.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">476</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">6370</span> Design of Transmit Beamspace and DOA Estimation in MIMO Radar</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Ilakkiya">S. Ilakkiya</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Merline"> A. Merline</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A multiple-input multiple-output (MIMO) radar systems use modulated waveforms and directive antennas to transmit electromagnetic energy into a specific volume in space to search for targets. This paper deals with the design of transmit beamspace matrix and DOA estimation for multiple-input multiple-output (MIMO) radar with collocated antennas.The design of transmit beamspace matrix is based on minimizing the difference between a desired transmit beampattern and the actual one while enforcing the constraint of uniform power distribution across the transmit array elements. Rotational invariance property is established at the transmit array by imposing a specific structure on the beamspace matrix. Semidefinite programming and spatial-division based design (SDD) are also designed separately. In MIMO radar systems, DOA estimation is an essential process to determine the direction of incoming signals and thus to direct the beam of the antenna array towards the estimated direction. This estimation deals with non-adaptive spectral estimation and adaptive spectral estimation techniques. The design of the transmit beamspace matrix and spectral estimation techniques are studied through simulation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20and%20non-adaptive%20spectral%20estimation" title="adaptive and non-adaptive spectral estimation">adaptive and non-adaptive spectral estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=direction%20of%20arrival%20estimation" title=" direction of arrival estimation"> direction of arrival estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=MIMO%20radar" title=" MIMO radar"> MIMO radar</a>, <a href="https://publications.waset.org/abstracts/search?q=rotational%20invariance%20property" title=" rotational invariance property"> rotational invariance property</a>, <a href="https://publications.waset.org/abstracts/search?q=transmit" title=" transmit"> transmit</a>, <a href="https://publications.waset.org/abstracts/search?q=receive%20beamforming" title=" receive beamforming "> receive beamforming </a> </p> <a href="https://publications.waset.org/abstracts/30032/design-of-transmit-beamspace-and-doa-estimation-in-mimo-radar" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30032.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">519</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">6369</span> Comparative Analysis of Two Approaches to Joint Signal Detection, ToA and AoA Estimation in Multi-Element Antenna Arrays</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Olesya%20Bolkhovskaya">Olesya Bolkhovskaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexey%20Davydov"> Alexey Davydov</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Maltsev"> Alexander Maltsev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper two approaches to joint signal detection, time of arrival (ToA) and angle of arrival (AoA) estimation in multi-element antenna array are investigated. Two scenarios were considered: first one, when the waveform of the useful signal is known a priori and, second one, when the waveform of the desired signal is unknown. For first scenario, the antenna array signal processing based on multi-element matched filtering (MF) with the following non-coherent detection scheme and maximum likelihood (ML) parameter estimation blocks is exploited. For second scenario, the signal processing based on the antenna array elements covariance matrix estimation with the following eigenvector analysis and ML parameter estimation blocks is applied. The performance characteristics of both signal processing schemes are thoroughly investigated and compared for different useful signals and noise parameters. <p class="card-text"><strong>Keywords:</strong> <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=signal%20detection" title=" signal detection"> signal detection</a>, <a href="https://publications.waset.org/abstracts/search?q=ToA" title=" ToA"> ToA</a>, <a href="https://publications.waset.org/abstracts/search?q=AoA%20estimation" title=" AoA estimation"> AoA estimation</a> </p> <a href="https://publications.waset.org/abstracts/11917/comparative-analysis-of-two-approaches-to-joint-signal-detection-toa-and-aoa-estimation-in-multi-element-antenna-arrays" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11917.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">497</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">6368</span> A New IFO Estimation Scheme for Orthogonal Frequency Division Multiplexing Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Keunhong%20Chae">Keunhong Chae</a>, <a href="https://publications.waset.org/abstracts/search?q=Seokho%20Yoon"> Seokho Yoon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We address a new integer frequency offset (IFO) estimation scheme with an aid of a pilot for orthogonal frequency division multiplexing systems. After correlating each continual pilot with a predetermined scattered pilot, the correlation value is again correlated to alleviate the influence of the timing offset. From numerical results, it is demonstrated that the influence of the timing offset on the IFO estimation is significantly decreased. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=estimation" title="estimation">estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=integer%20frequency%20offset" title=" integer frequency offset"> integer frequency offset</a>, <a href="https://publications.waset.org/abstracts/search?q=OFDM" title=" OFDM"> OFDM</a>, <a href="https://publications.waset.org/abstracts/search?q=timing%20offset" title=" timing offset"> timing offset</a> </p> <a href="https://publications.waset.org/abstracts/22778/a-new-ifo-estimation-scheme-for-orthogonal-frequency-division-multiplexing-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22778.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">568</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">6367</span> 6D Posture Estimation of Road Vehicles from Color Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yoshimoto%20Kurihara">Yoshimoto Kurihara</a>, <a href="https://publications.waset.org/abstracts/search?q=Tad%20Gonsalves"> Tad Gonsalves</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=6D%20posture%20estimation" title="6D posture estimation">6D posture estimation</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=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=AlexNet" title=" AlexNet"> AlexNet</a> </p> <a href="https://publications.waset.org/abstracts/138449/6d-posture-estimation-of-road-vehicles-from-color-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138449.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">155</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">6366</span> An Approach to Noise Variance Estimation in Very Low Signal-to-Noise Ratio Stochastic Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Miljan%20B.%20Petrovi%C4%87">Miljan B. Petrović</a>, <a href="https://publications.waset.org/abstracts/search?q=Du%C5%A1an%20B.%20Petrovi%C4%87"> Dušan B. Petrović</a>, <a href="https://publications.waset.org/abstracts/search?q=Goran%20S.%20Nikoli%C4%87"> Goran S. Nikolić</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes a method for AWGN (Additive White Gaussian Noise) variance estimation in noisy stochastic signals, referred to as Multiplicative-Noising Variance Estimation (MNVE). The aim was to develop an estimation algorithm with minimal number of assumptions on the original signal structure. The provided MATLAB simulation and results analysis of the method applied on speech signals showed more accuracy than standardized AR (autoregressive) modeling noise estimation technique. In addition, great performance was observed on very low signal-to-noise ratios, which in general represents the worst case scenario for signal denoising methods. High execution time appears to be the only disadvantage of MNVE. After close examination of all the observed features of the proposed algorithm, it was concluded it is worth of exploring and that with some further adjustments and improvements can be enviably powerful. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=noise" title="noise">noise</a>, <a href="https://publications.waset.org/abstracts/search?q=signal-to-noise%20ratio" title=" signal-to-noise ratio"> signal-to-noise ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20signals" title=" stochastic signals"> stochastic signals</a>, <a href="https://publications.waset.org/abstracts/search?q=variance%20estimation" title=" variance estimation"> variance estimation</a> </p> <a href="https://publications.waset.org/abstracts/39515/an-approach-to-noise-variance-estimation-in-very-low-signal-to-noise-ratio-stochastic-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39515.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">386</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">6365</span> A Mathematical Model of Power System State Estimation for Power Flow Solution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20Benhamida">F. Benhamida</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Graa"> A. Graa</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Benameur"> L. Benameur</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Ziane"> I. Ziane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The state estimation of the electrical power system operation state is very important for supervising task. With the nonlinearity of the AC power flow model, the state estimation problem (SEP) is a nonlinear mathematical problem with many local optima. This paper treat the mathematical model for the SEP and the monitoring of the nonlinear systems of great dimensions with an application on power electrical system, the modelling, the analysis and state estimation synthesis in order to supervise the power system behavior. in fact, it is very difficult, to see impossible, (for reasons of accessibility, techniques and/or of cost) to measure the excessive number of the variables of state in a large-sized system. It is thus important to develop software sensors being able to produce a reliable estimate of the variables necessary for the diagnosis and also for the control. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=power%20system" title="power system">power system</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20estimation" title=" state estimation"> state estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=robustness" title=" robustness"> robustness</a>, <a href="https://publications.waset.org/abstracts/search?q=observability" title=" observability"> observability</a> </p> <a href="https://publications.waset.org/abstracts/36293/a-mathematical-model-of-power-system-state-estimation-for-power-flow-solution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36293.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">523</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">6364</span> Optimal Secondary Prevention and Background Risk</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Anouar%20Razgallah">Mohamed Anouar Razgallah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper examines in the context of a one-period model the impact of background risk on the optimal secondary prevention. We conduct our study based on various configurations of the background risk. We intend to show that in most cases the level of secondary prevention effort varied after the introduction of background risk, however, in very few cases this level remains constant. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=secondary%20prevention" title="secondary prevention">secondary prevention</a>, <a href="https://publications.waset.org/abstracts/search?q=primary%20prevention" title=" primary prevention"> primary prevention</a>, <a href="https://publications.waset.org/abstracts/search?q=background%20risk" title=" background risk"> background risk</a>, <a href="https://publications.waset.org/abstracts/search?q=ecomomics" title=" ecomomics"> ecomomics</a> </p> <a href="https://publications.waset.org/abstracts/18430/optimal-secondary-prevention-and-background-risk" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18430.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">426</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6363</span> Towards an Intelligent Ontology Construction Cost Estimation System: Using BIM and New Rules of Measurement Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20H.%20Abanda">F. H. Abanda</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Kamsu-Foguem"> B. Kamsu-Foguem</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20H.%20M.%20Tah"> J. H. M. Tah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Construction cost estimation is one of the most important aspects of construction project design. For generations, the process of cost estimating has been manual, time-consuming and error-prone. This has partly led to most cost estimates to be unclear and riddled with inaccuracies that at times lead to over- or under-estimation of construction cost. The development of standard set of measurement rules that are understandable by all those involved in a construction project, have not totally solved the challenges. Emerging Building Information Modelling (BIM) technologies can exploit standard measurement methods to automate cost estimation process and improves accuracies. This requires standard measurement methods to be structured in ontologically and machine readable format; so that BIM software packages can easily read them. Most standard measurement methods are still text-based in textbooks and require manual editing into tables or Spreadsheet during cost estimation. The aim of this study is to explore the development of an ontology based on New Rules of Measurement (NRM) commonly used in the UK for cost estimation. The methodology adopted is Methontology, one of the most widely used ontology engineering methodologies. The challenges in this exploratory study are also reported and recommendations for future studies proposed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BIM" title="BIM">BIM</a>, <a href="https://publications.waset.org/abstracts/search?q=construction%20projects" title=" construction projects"> construction projects</a>, <a href="https://publications.waset.org/abstracts/search?q=cost%20estimation" title=" cost estimation"> cost estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=NRM" title=" NRM"> NRM</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology" title=" ontology"> ontology</a> </p> <a href="https://publications.waset.org/abstracts/16960/towards-an-intelligent-ontology-construction-cost-estimation-system-using-bim-and-new-rules-of-measurement-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16960.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">551</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">6362</span> Hybrid Subspace Approach for Time Delay Estimation in MIMO Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mojtaba%20Saeedinezhad">Mojtaba Saeedinezhad</a>, <a href="https://publications.waset.org/abstracts/search?q=Sarah%20Yousefi"> Sarah Yousefi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a hybrid subspace approach for Time Delay Estimation (TDE) in multivariable systems. While several methods have been proposed for time delay estimation in SISO systems, delay estimation in MIMO systems were always a big challenge. In these systems the existing TDE methods have significant limitations because most of procedures are just based on system response estimation or correlation analysis. We introduce a new hybrid method for TDE in MIMO systems based on subspace identification and explicit output error method; and compare its performance with previously introduced procedures in presence of different noise levels and in a statistical manner. Then the best method is selected with multi objective decision making technique. It is shown that the performance of new approach is much better than the existing methods, even in low signal-to-noise conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=system%20identification" title="system identification">system identification</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20delay%20estimation" title=" time delay estimation"> time delay estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=ARX" title=" ARX"> ARX</a>, <a href="https://publications.waset.org/abstracts/search?q=OE" title=" OE"> OE</a>, <a href="https://publications.waset.org/abstracts/search?q=merit%20ratio" title=" merit ratio"> merit ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=multi%20variable%20decision%20making" title=" multi variable decision making"> multi variable decision making</a> </p> <a href="https://publications.waset.org/abstracts/48722/hybrid-subspace-approach-for-time-delay-estimation-in-mimo-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48722.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">346</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6361</span> Online Battery Equivalent Circuit Model Estimation on Continuous-Time Domain Using Linear Integral Filter Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cheng%20Zhang">Cheng Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=James%20Marco"> James Marco</a>, <a href="https://publications.waset.org/abstracts/search?q=Walid%20Allafi"> Walid Allafi</a>, <a href="https://publications.waset.org/abstracts/search?q=Truong%20Q.%20Dinh"> Truong Q. Dinh</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20D.%20Widanage"> W. D. Widanage</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Equivalent circuit models (ECMs) are widely used in battery management systems in electric vehicles and other battery energy storage systems. The battery dynamics and the model parameters vary under different working conditions, such as different temperature and state of charge (SOC) levels, and therefore online parameter identification can improve the modelling accuracy. This paper presents a way of online ECM parameter identification using a continuous time (CT) estimation method. The CT estimation method has several advantages over discrete time (DT) estimation methods for ECM parameter identification due to the widely separated battery dynamic modes and fast sampling. The presented method can be used for online SOC estimation. Test data are collected using a lithium ion cell, and the experimental results show that the presented CT method achieves better modelling accuracy compared with the conventional DT recursive least square method. The effectiveness of the presented method for online SOC estimation is also verified on test data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electric%20circuit%20model" title="electric circuit model">electric circuit model</a>, <a href="https://publications.waset.org/abstracts/search?q=continuous%20time%20domain%20estimation" title=" continuous time domain estimation"> continuous time domain estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20integral%20filter%20method" title=" linear integral filter method"> linear integral filter method</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20and%20SOC%20estimation" title=" parameter and SOC estimation"> parameter and SOC estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=recursive%20least%20square" title=" recursive least square"> recursive least square</a> </p> <a href="https://publications.waset.org/abstracts/67718/online-battery-equivalent-circuit-model-estimation-on-continuous-time-domain-using-linear-integral-filter-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67718.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">383</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">6360</span> Electrical Load Estimation Using Estimated Fuzzy Linear Parameters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bader%20Alkandari">Bader Alkandari</a>, <a href="https://publications.waset.org/abstracts/search?q=Jamal%20Y.%20Madouh"> Jamal Y. Madouh</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20M.%20Alkandari"> Ahmad M. Alkandari</a>, <a href="https://publications.waset.org/abstracts/search?q=Anwar%20A.%20Alnaqi"> Anwar A. Alnaqi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new formulation of fuzzy linear estimation problem is presented. It is formulated as a linear programming problem. The objective is to minimize the spread of the data points, taking into consideration the type of the membership function of the fuzzy parameters to satisfy the constraints on each measurement point and to insure that the original membership is included in the estimated membership. Different models are developed for a fuzzy triangular membership. The proposed models are applied to different examples from the area of fuzzy linear regression and finally to different examples for estimating the electrical load on a busbar. It had been found that the proposed technique is more suited for electrical load estimation, since the nature of the load is characterized by the uncertainty and vagueness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20regression" title="fuzzy regression">fuzzy regression</a>, <a href="https://publications.waset.org/abstracts/search?q=load%20estimation" title=" load estimation"> load estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20linear%20parameters" title=" fuzzy linear parameters"> fuzzy linear parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=electrical%20load%20estimation" title=" electrical load estimation"> electrical load estimation</a> </p> <a href="https://publications.waset.org/abstracts/18341/electrical-load-estimation-using-estimated-fuzzy-linear-parameters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18341.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">540</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=background%20estimation&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=background%20estimation&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=background%20estimation&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=background%20estimation&page=5">5</a></li> <li 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