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selection subband adaptive filter (DS-NSAF)</title> <meta name="description" content="Search results for: dynamic selection subband adaptive filter (DS-NSAF)"> <meta name="keywords" content="dynamic selection subband adaptive filter (DS-NSAF)"> <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 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</div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: dynamic selection subband adaptive filter (DS-NSAF)</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7736</span> A New Sign Subband Adaptive Filter Based on Dynamic Selection of Subbands</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Shams%20Esfand%20Abadi">Mohammad Shams Esfand Abadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehrdad%20Zalaghi"> Mehrdad Zalaghi</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20ebrahimpour"> Reza ebrahimpour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a sign adaptive filter algorithm with the ability of dynamic selection of subband filters which leads to low computational complexity compared with conventional sign subband adaptive filter (SSAF) algorithm. Dynamic selection criterion is based on largest reduction of the mean square deviation at each adaption. We demonstrate that this simple proposed algorithm has the same performance of the conventional SSAF and somewhat faster than it. In the presence of impulsive interferences robustness of the simple proposed algorithm as well as the conventional SSAF and outperform the conventional normalized subband adaptive filter (NSAF) algorithm. Therefore, it is preferred for environments under impulsive interferences. Simulation results are presented to verify these above considerations very well have been achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acoustic%20echo%20cancellation%20%28AEC%29" title="acoustic echo cancellation (AEC)">acoustic echo cancellation (AEC)</a>, <a href="https://publications.waset.org/abstracts/search?q=normalized%20subband%20adaptive%20filter%20%28NSAF%29" title=" normalized subband adaptive filter (NSAF)"> normalized subband adaptive filter (NSAF)</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20selection%20subband%20adaptive%20filter%20%28DS-NSAF%29" title=" dynamic selection subband adaptive filter (DS-NSAF)"> dynamic selection subband adaptive filter (DS-NSAF)</a>, <a href="https://publications.waset.org/abstracts/search?q=sign%20subband%20adaptive%20filter%20%28SSAF%29" title=" sign subband adaptive filter (SSAF)"> sign subband adaptive filter (SSAF)</a>, <a href="https://publications.waset.org/abstracts/search?q=impulsive%20noise" title=" impulsive noise"> impulsive noise</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20filtering" title=" robust filtering"> robust filtering</a> </p> <a href="https://publications.waset.org/abstracts/20230/a-new-sign-subband-adaptive-filter-based-on-dynamic-selection-of-subbands" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20230.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">599</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">7735</span> Adaptive Filtering in Subbands for Supervised Source Separation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bruna%20Luisa%20Ramos%20Prado%20Vasques">Bruna Luisa Ramos Prado Vasques</a>, <a href="https://publications.waset.org/abstracts/search?q=Mariane%20Rembold%20Petraglia"> Mariane Rembold Petraglia</a>, <a href="https://publications.waset.org/abstracts/search?q=Antonio%20Petraglia"> Antonio Petraglia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper investigates MIMO (Multiple-Input Multiple-Output) adaptive filtering techniques for the application of supervised source separation in the context of convolutive mixtures. From the observation that there is correlation among the signals of the different mixtures, an improvement in the NSAF (Normalized Subband Adaptive Filter) algorithm is proposed in order to accelerate its convergence rate. Simulation results with mixtures of speech signals in reverberant environments show the superior performance of the proposed algorithm with respect to the performances of the NLMS (Normalized Least-Mean-Square) and conventional NSAF, considering both the convergence speed and SIR (Signal-to-Interference Ratio) after convergence. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20filtering" title="adaptive filtering">adaptive filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-rate%20processing" title=" multi-rate processing"> multi-rate processing</a>, <a href="https://publications.waset.org/abstracts/search?q=normalized%20subband%20adaptive%20filter" title=" normalized subband adaptive filter"> normalized subband adaptive filter</a>, <a href="https://publications.waset.org/abstracts/search?q=source%20separation" title=" source separation"> source separation</a> </p> <a href="https://publications.waset.org/abstracts/78300/adaptive-filtering-in-subbands-for-supervised-source-separation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78300.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">435</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">7734</span> A Subband BSS Structure with Reduced Complexity and Fast Convergence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salah%20Al-Din%20I.%20Badran">Salah Al-Din I. Badran</a>, <a href="https://publications.waset.org/abstracts/search?q=Samad%20Ahmadi"> Samad Ahmadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ismail%20Shahin"> Ismail Shahin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A blind source separation method is proposed; in this method, we use a non-uniform filter bank and a novel normalisation. This method provides a reduced computational complexity and increased convergence speed comparing to the full-band algorithm. Recently, adaptive sub-band scheme has been recommended to solve two problems: reduction of computational complexity and increase the convergence speed of the adaptive algorithm for correlated input signals. In this work, the reduction in computational complexity is achieved with the use of adaptive filters of orders less than the full-band adaptive filters, which operate at a sampling rate lower than the sampling rate of the input signal. The decomposed signals by analysis bank filter are less correlated in each subband than the input signal at full bandwidth, and can promote better rates of convergence. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blind%20source%20separation" title="blind source separation">blind source separation</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20complexity" title=" computational complexity"> computational complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=subband" title=" subband"> subband</a>, <a href="https://publications.waset.org/abstracts/search?q=convergence%20speed" title=" convergence speed"> convergence speed</a>, <a href="https://publications.waset.org/abstracts/search?q=mixture" title=" mixture "> mixture </a> </p> <a href="https://publications.waset.org/abstracts/33346/a-subband-bss-structure-with-reduced-complexity-and-fast-convergence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33346.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">579</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">7733</span> A Fast Convergence Subband BSS Structure</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salah%20Al-Din%20I.%20Badran">Salah Al-Din I. Badran</a>, <a href="https://publications.waset.org/abstracts/search?q=Samad%20Ahmadi"> Samad Ahmadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ismail%20Shahin"> Ismail Shahin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A blind source separation method is proposed; in this method we use a non-uniform filter bank and a novel normalisation. This method provides a reduced computational complexity and increased convergence speed comparing to the full-band algorithm. Recently, adaptive sub-band scheme has been recommended to solve two problems: reduction of computational complexity and increase the convergence speed of the adaptive algorithm for correlated input signals. In this work the reduction in computational complexity is achieved with the use of adaptive filters of orders less than the full-band adaptive filters, which operate at a sampling rate lower than the sampling rate of the input signal. The decomposed signals by analysis bank filter are less correlated in each sub-band than the input signal at full bandwidth, and can promote better rates of convergence. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blind%20source%20separation" title="blind source separation">blind source separation</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20complexity" title=" computational complexity"> computational complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=subband" title=" subband"> subband</a>, <a href="https://publications.waset.org/abstracts/search?q=convergence%20speed" title=" convergence speed"> convergence speed</a>, <a href="https://publications.waset.org/abstracts/search?q=mixture" title=" mixture"> mixture</a> </p> <a href="https://publications.waset.org/abstracts/33150/a-fast-convergence-subband-bss-structure" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33150.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">555</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">7732</span> Design of Reconfigurable Fixed-Point LMS Adaptive FIR Filter </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Padmapriya">S. Padmapriya</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Lakshmi%20Prabha"> V. Lakshmi Prabha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, an efficient reconfigurable fixed-point Least Mean Square Adaptive FIR filter is proposed. The proposed architecture has two methods of operation: one is area efficient design and the other is optimized power. Pipelining of the adder blocks and partial product generator are used to achieve low area and reversible logic is used to obtain low power design. Depending upon the input samples and filter coefficients, one of the techniques is chosen. Least-Mean-Square adaptation is performed to update the weights. The architecture is coded using Verilog and synthesized in cadence encounter 0.18渭m technology. The synthesized results show that the area reduction ratio of the proposed when compared with conventional technique is about 1.2%. <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=carry%20select%20adder" title=" carry select adder"> carry select adder</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20mean%20square%20algorithm" title=" least mean square algorithm"> least mean square algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=reversible%20logic" title=" reversible logic"> reversible logic</a> </p> <a href="https://publications.waset.org/abstracts/11853/design-of-reconfigurable-fixed-point-lms-adaptive-fir-filter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11853.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">330</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">7731</span> Image Denoising Using Spatial Adaptive Mask Filter for Medical Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Sumalatha">R. Sumalatha</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20V.%20Subramanyam"> M. V. Subramanyam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In medical image processing the quality of the image is degraded in the presence of noise. Especially in ultra sound imaging and Magnetic resonance imaging the data was corrupted by signal dependent noise known as salt and pepper noise. Removal of noise from the medical images is a critical issue for researchers. In this paper, a new type of technique Adaptive Spatial Mask Filter (ASMF) has been proposed. The proposed filter is used to increase the quality of MRI and ultra sound images. Experimental results show that the proposed filter outperforms the implementation of mean, median, adaptive median filters in terms of MSE and PSNR. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=salt%20and%20pepper%20noise" title="salt and pepper noise">salt and pepper noise</a>, <a href="https://publications.waset.org/abstracts/search?q=ASMF" title=" ASMF"> ASMF</a>, <a href="https://publications.waset.org/abstracts/search?q=PSNR" title=" PSNR"> PSNR</a>, <a href="https://publications.waset.org/abstracts/search?q=MSE" title=" MSE"> MSE</a> </p> <a href="https://publications.waset.org/abstracts/3843/image-denoising-using-spatial-adaptive-mask-filter-for-medical-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3843.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">436</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">7730</span> Acoustic Echo Cancellation Using Different Adaptive Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Sharif">Hamid Sharif</a>, <a href="https://publications.waset.org/abstracts/search?q=Nazish%20Saleem%20Abbas"> Nazish Saleem Abbas</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Haris%20Jamil"> Muhammad Haris Jamil</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An adaptive filter is a filter that self-adjusts its transfer function according to an optimization algorithm driven by an error signal. Because of the complexity of the optimization algorithms, most adaptive filters are digital filters. Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications, including adaptive noise cancellation and echo cancellation. Acoustic echo cancellation is a common occurrence in today鈥檚 telecommunication systems. The signal interference caused by acoustic echo is distracting to both users and causes a reduction in the quality of the communication. In this paper, we review different techniques of adaptive filtering to reduce this unwanted echo. In this paper, we see the behavior of techniques and algorithms of adaptive filtering like Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Variable Step-Size Least Mean Square (VSLMS), Variable Step-Size Normalized Least Mean Square (VSNLMS), New Varying Step Size LMS Algorithm (NVSSLMS) and Recursive Least Square (RLS) algorithms to reduce this unwanted echo, to increase communication quality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20acoustic" title="adaptive acoustic">adaptive acoustic</a>, <a href="https://publications.waset.org/abstracts/search?q=echo%20cancellation" title=" echo cancellation"> echo cancellation</a>, <a href="https://publications.waset.org/abstracts/search?q=LMS%20algorithm" title=" LMS algorithm"> LMS algorithm</a>, <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=normalized%20least%20mean%20square%20%28NLMS%29" title=" normalized least mean square (NLMS)"> normalized least mean square (NLMS)</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20step-size%20least%20mean%20square%20%28VSLMS%29" title=" variable step-size least mean square (VSLMS)"> variable step-size least mean square (VSLMS)</a> </p> <a href="https://publications.waset.org/abstracts/167766/acoustic-echo-cancellation-using-different-adaptive-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167766.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">80</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">7729</span> An Adaptive Hybrid Surrogate-Assisted Particle Swarm Optimization Algorithm for Expensive Structural Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiongxiong%20You">Xiongxiong You</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhanwen%20Niu"> Zhanwen Niu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Choosing an appropriate surrogate model plays an important role in surrogates-assisted evolutionary algorithms (SAEAs) since there are many types and different kernel functions in the surrogate model. In this paper, an adaptive selection of the best suitable surrogate model method is proposed to solve different kinds of expensive optimization problems. Firstly, according to the prediction residual error sum of square (PRESS) and different model selection strategies, the excellent individual surrogate models are integrated into multiple ensemble models in each generation. Then, based on the minimum root of mean square error (RMSE), the best suitable surrogate model is selected dynamically. Secondly, two methods with dynamic number of models and selection strategies are designed, which are used to show the influence of the number of individual models and selection strategy. Finally, some compared studies are made to deal with several commonly used benchmark problems, as well as a rotor system optimization problem. The results demonstrate the accuracy and robustness of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20selection" title="adaptive selection">adaptive selection</a>, <a href="https://publications.waset.org/abstracts/search?q=expensive%20optimization" title=" expensive optimization"> expensive optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=rotor%20system" title=" rotor system"> rotor system</a>, <a href="https://publications.waset.org/abstracts/search?q=surrogates%20assisted%20evolutionary%20algorithms" title=" surrogates assisted evolutionary algorithms"> surrogates assisted evolutionary algorithms</a> </p> <a href="https://publications.waset.org/abstracts/137516/an-adaptive-hybrid-surrogate-assisted-particle-swarm-optimization-algorithm-for-expensive-structural-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137516.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">141</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">7728</span> Design of Enhanced Adaptive Filter for Integrated Navigation System of FOG-SINS and Star Tracker</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nassim%20Bessaad">Nassim Bessaad</a>, <a href="https://publications.waset.org/abstracts/search?q=Qilian%20Bao"> Qilian Bao</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhao%20Jiangkang"> Zhao Jiangkang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The fiber optics gyroscope in the strap-down inertial navigation system (FOG-SINS) suffers from precision degradation due to the influence of random errors. In this work, an enhanced Allan variance (AV) stochastic modeling method combined with discrete wavelet transform (DWT) for signal denoising is implemented to estimate the random process in the FOG signal. Furthermore, we devise a measurement-based iterative adaptive Sage-Husa nonlinear filter with augmented states to integrate a star tracker sensor with SINS. The proposed filter adapts the measurement noise covariance matrix based on the available data. Moreover, the enhanced stochastic modeling scheme is invested in tuning the process noise covariance matrix and the augmented state Gauss-Markov process parameters. Finally, the effectiveness of the proposed filter is investigated by employing the collected data in laboratory conditions. The result shows the filter's improved accuracy in comparison with the conventional Kalman filter (CKF). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=inertial%20navigation" title="inertial navigation">inertial navigation</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20filtering" title=" adaptive filtering"> adaptive filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=star%20tracker" title=" star tracker"> star tracker</a>, <a href="https://publications.waset.org/abstracts/search?q=FOG" title=" FOG"> FOG</a> </p> <a href="https://publications.waset.org/abstracts/145618/design-of-enhanced-adaptive-filter-for-integrated-navigation-system-of-fog-sins-and-star-tracker" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145618.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">80</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">7727</span> Medical Images Enhancement Using New Dynamic Band Pass Filter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdellatif%20Baba">Abdellatif Baba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to facilitate medical images analysis by improving their quality and readability, we present in this paper a new dynamic band pass filter as a general and suitable operator for different types of medical images. Our objective is to enrich the details of any treated medical image to make it sufficiently clear enough to give an understood and simplified meaning even for unspecialized people in the medical domain. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=medical%20image%20enhancement" title="medical image enhancement">medical image enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20band%20pass%20filter" title=" dynamic band pass filter"> dynamic band pass filter</a>, <a href="https://publications.waset.org/abstracts/search?q=analysis%20improvement" title=" analysis improvement"> analysis improvement</a> </p> <a href="https://publications.waset.org/abstracts/14660/medical-images-enhancement-using-new-dynamic-band-pass-filter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14660.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">289</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">7726</span> Application of Adaptive Particle Filter for Localizing a Mobile Robot Using 3D Camera Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maysam%20Shahsavari">Maysam Shahsavari</a>, <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Jamalaldin%20Haddadi"> Seyed Jamalaldin Haddadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There are several methods to localize a mobile robot such as relative, absolute and probabilistic. In this paper, particle filter due to its simple implementation and the fact that it does not need to know to the starting position will be used. This method estimates the position of the mobile robot using a probabilistic distribution, relying on a known map of the environment instead of predicting it. Afterwards, it updates this estimation by reading input sensors and control commands. To receive information from the surrounding world, distance to obstacles, for example, a Kinect is used which is much cheaper than a laser range finder. Finally, after explaining the Adaptive Particle Filter method and its implementation in detail, we will compare this method with the dead reckoning method and show that this method is much more suitable for situations in which we have a map of the environment. <p class="card-text"><strong>Keywords:</strong> <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=localization" title=" localization"> localization</a>, <a href="https://publications.waset.org/abstracts/search?q=methods" title=" methods"> methods</a>, <a href="https://publications.waset.org/abstracts/search?q=odometry" title=" odometry"> odometry</a>, <a href="https://publications.waset.org/abstracts/search?q=kinect" title=" kinect "> kinect </a> </p> <a href="https://publications.waset.org/abstracts/53041/application-of-adaptive-particle-filter-for-localizing-a-mobile-robot-using-3d-camera-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53041.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">269</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">7725</span> 3D Guided Image Filtering to Improve Quality of Short-Time Binned Dynamic PET Images Using MRI Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tabassum%20Husain">Tabassum Husain</a>, <a href="https://publications.waset.org/abstracts/search?q=Shen%20Peng%20Li"> Shen Peng Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhaolin%20Chen"> Zhaolin Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper evaluates the usability of 3D Guided Image Filtering to enhance the quality of short-time binned dynamic PET images by using MRI images. Guided image filtering is an edge-preserving filter proposed to enhance 2D images. The 3D filter is applied on 1 and 5-minute binned images. The results are compared with 15-minute binned images and the Gaussian filtering. The guided image filter enhances the quality of dynamic PET images while also preserving important information of the voxels. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20PET%20images" title="dynamic PET images">dynamic PET images</a>, <a href="https://publications.waset.org/abstracts/search?q=guided%20image%20filter" title=" guided image filter"> guided image filter</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=information%20preservation%20filtering" title=" information preservation filtering"> information preservation filtering</a> </p> <a href="https://publications.waset.org/abstracts/152864/3d-guided-image-filtering-to-improve-quality-of-short-time-binned-dynamic-pet-images-using-mri-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152864.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">132</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">7724</span> Dynamic Process Monitoring of an Ammonia Synthesis Fixed-Bed Reactor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bothinah%20Altaf">Bothinah Altaf</a>, <a href="https://publications.waset.org/abstracts/search?q=Gary%20Montague"> Gary Montague</a>, <a href="https://publications.waset.org/abstracts/search?q=Elaine%20B.%20Martin"> Elaine B. Martin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study involves the modeling and monitoring of an ammonia synthesis fixed-bed reactor using partial least squares (PLS) and its variants. The process exhibits complex dynamic behavior due to the presence of heat recycling and feed quench. One limitation of static PLS model in this situation is that it does not take account of the process dynamics and hence dynamic PLS was used. Although it showed, superior performance to static PLS in terms of prediction, the monitoring scheme was inappropriate hence adaptive PLS was considered. A limitation of adaptive PLS is that non-conforming observations also contribute to the model, therefore, a new adaptive approach was developed, robust adaptive dynamic PLS. This approach updates a dynamic PLS model and is robust to non-representative data. The developed methodology showed a clear improvement over existing approaches in terms of the modeling of the reactor and the detection of faults. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ammonia%20synthesis%20fixed-bed%20reactor" title="ammonia synthesis fixed-bed reactor">ammonia synthesis fixed-bed reactor</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20partial%20least%20squares%20modeling" title=" dynamic partial least squares modeling"> dynamic partial least squares modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=recursive%20partial%20least%20squares" title=" recursive partial least squares"> recursive partial least squares</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20modeling" title=" robust modeling"> robust modeling</a> </p> <a href="https://publications.waset.org/abstracts/37994/dynamic-process-monitoring-of-an-ammonia-synthesis-fixed-bed-reactor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37994.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">393</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">7723</span> An Efficient Separation for Convolutive Mixtures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salah%20Al-Din%20I.%20Badran">Salah Al-Din I. Badran</a>, <a href="https://publications.waset.org/abstracts/search?q=Samad%20Ahmadi"> Samad Ahmadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Dylan%20Menzies"> Dylan Menzies</a>, <a href="https://publications.waset.org/abstracts/search?q=Ismail%20Shahin"> Ismail Shahin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes a new efficient blind source separation method; in this method we use a non-uniform filter bank and a new structure with different sub-bands. This method provides a reduced permutation and increased convergence speed comparing to the full-band algorithm. Recently, some structures have been suggested to deal with two problems: reducing permutation and increasing the speed of convergence of the adaptive algorithm for correlated input signals. The permutation problem is avoided with the use of adaptive filters of orders less than the full-band adaptive filter, which operate at a sampling rate lower than the sampling rate of the input signal. The decomposed signals by analysis bank filter are less correlated in each sub-band than the input signal at full-band, and can promote better rates of convergence. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Blind%20source%20separation" title="Blind source separation">Blind source separation</a>, <a href="https://publications.waset.org/abstracts/search?q=estimates" title=" estimates"> estimates</a>, <a href="https://publications.waset.org/abstracts/search?q=full-band" title=" full-band"> full-band</a>, <a href="https://publications.waset.org/abstracts/search?q=mixtures" title=" mixtures"> mixtures</a>, <a href="https://publications.waset.org/abstracts/search?q=sub-band" title=" sub-band"> sub-band</a> </p> <a href="https://publications.waset.org/abstracts/8254/an-efficient-separation-for-convolutive-mixtures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8254.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">7722</span> Phasor Measurement Unit Based on Particle Filtering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rithvik%20Reddy%20Adapa">Rithvik Reddy Adapa</a>, <a href="https://publications.waset.org/abstracts/search?q=Xin%20Wang"> Xin Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Phasor Measurement Units (PMUs) are very sophisticated measuring devices that find amplitude, phase and frequency of various voltages and currents in a power system. Particle filter is a state estimation technique that uses Bayesian inference. Particle filters are widely used in pose estimation and indoor navigation and are very reliable. This paper studies and compares four different particle filters as PMUs namely, generic particle filter (GPF), genetic algorithm particle filter (GAPF), particle swarm optimization particle filter (PSOPF) and adaptive particle filter (APF). Two different test signals are used to test the performance of the filters in terms of responsiveness and correctness of the estimates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=phasor%20measurement%20unit" title="phasor measurement unit">phasor measurement unit</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=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimisation" title=" particle swarm optimisation"> particle swarm optimisation</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20estimation" title=" state estimation"> state estimation</a> </p> <a href="https://publications.waset.org/abstracts/194127/phasor-measurement-unit-based-on-particle-filtering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194127.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">8</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">7721</span> Adaptive Kaman Filter for Fault Diagnosis of Linear Parameter-Varying Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rajamani%20Doraiswami">Rajamani Doraiswami</a>, <a href="https://publications.waset.org/abstracts/search?q=Lahouari%20Cheded"> Lahouari Cheded</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fault diagnosis of Linear Parameter-Varying (LPV) system using an adaptive Kalman filter is proposed. The LPV model is comprised of scheduling parameters, and the emulator parameters. The scheduling parameters are chosen such that they are capable of tracking variations in the system model as a result of changes in the operating regimes. The emulator parameters, on the other hand, simulate variations in the subsystems during the identification phase and have negligible effect during the operational phase. The nominal model and the influence vectors, which are the gradient of the feature vector respect to the emulator parameters, are identified off-line from a number of emulator parameter perturbed experiments. A Kalman filter is designed using the identified nominal model. As the system varies, the Kalman filter model is adapted using the scheduling variables. The residual is employed for fault diagnosis. The proposed scheme is successfully evaluated on simulated system as well as on a physical process control system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=identification" title="identification">identification</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20parameter-varying%20systems" title=" linear parameter-varying systems"> linear parameter-varying systems</a>, <a href="https://publications.waset.org/abstracts/search?q=least-squares%20estimation" title=" least-squares estimation"> least-squares estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title=" fault diagnosis"> fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=emulators" title=" emulators"> emulators</a> </p> <a href="https://publications.waset.org/abstracts/7656/adaptive-kaman-filter-for-fault-diagnosis-of-linear-parameter-varying-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7656.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">499</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">7720</span> A Novel RLS Based Adaptive Filtering Method for Speech Enhancement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pogula%20Rakesh">Pogula Rakesh</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Kishore%20Kumar"> T. Kishore Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Speech enhancement is a long standing problem with numerous applications like teleconferencing, VoIP, hearing aids, and speech recognition. The motivation behind this research work is to obtain a clean speech signal of higher quality by applying the optimal noise cancellation technique. Real-time adaptive filtering algorithms seem to be the best candidate among all categories of the speech enhancement methods. In this paper, we propose a speech enhancement method based on Recursive Least Squares (RLS) adaptive filter of speech signals. Experiments were performed on noisy data which was prepared by adding AWGN, Babble and Pink noise to clean speech samples at -5dB, 0dB, 5dB, and 10dB SNR levels. We then compare the noise cancellation performance of proposed RLS algorithm with existing NLMS algorithm in terms of Mean Squared Error (MSE), Signal to Noise ratio (SNR), and SNR loss. Based on the performance evaluation, the proposed RLS algorithm was found to be a better optimal noise cancellation technique for speech signals. <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=adaptive%20noise%20canceller" title=" adaptive noise canceller"> adaptive noise canceller</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20squared%20error" title=" mean squared error"> mean squared error</a>, <a href="https://publications.waset.org/abstracts/search?q=noise%20reduction" title=" noise reduction"> noise reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=NLMS" title=" NLMS"> NLMS</a>, <a href="https://publications.waset.org/abstracts/search?q=RLS" title=" RLS"> RLS</a>, <a href="https://publications.waset.org/abstracts/search?q=SNR" title=" SNR"> SNR</a>, <a href="https://publications.waset.org/abstracts/search?q=SNR%20loss" title=" SNR loss"> SNR loss</a> </p> <a href="https://publications.waset.org/abstracts/16212/a-novel-rls-based-adaptive-filtering-method-for-speech-enhancement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16212.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">481</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">7719</span> Adaptive Optimal Controller for Uncertain Inverted Pendulum System: A Dynamic Programming Approach for Continuous Time System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dao%20Phuong%20Nam">Dao Phuong Nam</a>, <a href="https://publications.waset.org/abstracts/search?q=Tran%20Van%20Tuyen"> Tran Van Tuyen</a>, <a href="https://publications.waset.org/abstracts/search?q=Do%20Trong%20Tan"> Do Trong Tan</a>, <a href="https://publications.waset.org/abstracts/search?q=Bui%20Minh%20Dinh"> Bui Minh Dinh</a>, <a href="https://publications.waset.org/abstracts/search?q=Nguyen%20Van%20Huong"> Nguyen Van Huong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we investigate the adaptive optimal control law for continuous-time systems with input disturbances and unknown parameters. This paper extends previous works to obtain the robust control law of uncertain systems. Through theoretical analysis, an adaptive dynamic programming (ADP) based optimal control is proposed to stabilize the closed-loop system and ensure the convergence properties of proposed iterative algorithm. Moreover, the global asymptotic stability (GAS) for closed system is also analyzed. The theoretical analysis for continuous-time systems and simulation results demonstrate the performance of the proposed algorithm for an inverted pendulum system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=approximate%2Fadaptive%20dynamic%20programming" title="approximate/adaptive dynamic programming">approximate/adaptive dynamic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=ADP" title=" ADP"> ADP</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20optimal%20control%20law" title=" adaptive optimal control law"> adaptive optimal control law</a>, <a href="https://publications.waset.org/abstracts/search?q=input%20state%20stability" title=" input state stability"> input state stability</a>, <a href="https://publications.waset.org/abstracts/search?q=ISS" title=" ISS"> ISS</a>, <a href="https://publications.waset.org/abstracts/search?q=inverted%20pendulum" title=" inverted pendulum"> inverted pendulum</a> </p> <a href="https://publications.waset.org/abstracts/80356/adaptive-optimal-controller-for-uncertain-inverted-pendulum-system-a-dynamic-programming-approach-for-continuous-time-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80356.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">194</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">7718</span> Performance Analysis of 5G for Low Latency Transmission Based on Universal Filtered Multi-Carrier Technique and Interleave Division Multiple Access</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Asgharzadeh">A. Asgharzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Maroufi"> M. Maroufi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 5G mobile communication system has drawn more and more attention. The 5G system needs to provide three different types of services, including enhanced Mobile BroadBand (eMBB), massive machine-type communication (mMTC), and ultra-reliable and low-latency communication (URLLC). Universal Filtered Multi-Carrier (UFMC), Filter Bank Multicarrier (FBMC), and Filtered Orthogonal Frequency Division Multiplexing (f-OFDM) are suggested as a well-known candidate waveform for the coming 5G system. Themachine-to-machine (M2M) communications are one of the essential applications in 5G, and it involves exchanging of concise messages with a very short latency. However, in UFMC systems, the subcarriers are grouped into subbands but f-OFDM only one subband covers the entire band. Furthermore, in FBMC, a subband includes only one subcarrier, and the number of subbands is the same as the number of subcarriers. This paper mainly discusses the performance of UFMC with different parameters for the UFMC system. Also, paper shows that UFMC is the best choice outperforming OFDM in any case and FBMC in case of very short packets while performing similarly for long sequences with channel estimation techniques for Interleave Division Multiple Access (IDMA) systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=universal%20filtered%20multi-carrier%20technique" title="universal filtered multi-carrier technique">universal filtered multi-carrier technique</a>, <a href="https://publications.waset.org/abstracts/search?q=UFMC" title=" UFMC"> UFMC</a>, <a href="https://publications.waset.org/abstracts/search?q=interleave%20division%20multiple%20access" title=" interleave division multiple access"> interleave division multiple access</a>, <a href="https://publications.waset.org/abstracts/search?q=IDMA" title=" IDMA"> IDMA</a>, <a href="https://publications.waset.org/abstracts/search?q=fifth-generation" title=" fifth-generation"> fifth-generation</a>, <a href="https://publications.waset.org/abstracts/search?q=subband" title=" subband"> subband</a> </p> <a href="https://publications.waset.org/abstracts/125096/performance-analysis-of-5g-for-low-latency-transmission-based-on-universal-filtered-multi-carrier-technique-and-interleave-division-multiple-access" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/125096.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">134</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">7717</span> Linear MIMO Model Identification Using an Extended Kalman Filter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Matthew%20C.%20Best">Matthew C. Best</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Linear Multi-Input Multi-Output (MIMO) dynamic models can be identified, with no a priori knowledge of model structure or order, using a new Generalised Identifying Filter (GIF). Based on an Extended Kalman Filter, the new filter identifies the model iteratively, in a continuous modal canonical form, using only input and output time histories. The filter鈥檚 self-propagating state error covariance matrix allows easy determination of convergence and conditioning, and by progressively increasing model order, the best fitting reduced-order model can be identified. The method is shown to be resistant to noise and can easily be extended to identification of smoothly nonlinear systems. <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=Kalman%20filter" title=" Kalman filter"> Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20model" title=" linear model"> linear model</a>, <a href="https://publications.waset.org/abstracts/search?q=MIMO" title=" MIMO"> MIMO</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20order%20reduction" title=" model order reduction"> model order reduction</a> </p> <a href="https://publications.waset.org/abstracts/24532/linear-mimo-model-identification-using-an-extended-kalman-filter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24532.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">594</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">7716</span> The Effect of Feature Selection on Pattern Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chih-Fong%20Tsai">Chih-Fong Tsai</a>, <a href="https://publications.waset.org/abstracts/search?q=Ya-Han%20Hu"> Ya-Han Hu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of feature selection (or dimensionality reduction) is to filter out unrepresentative features (or variables) making the classifier perform better than the one without feature selection. Since there are many well-known feature selection algorithms, and different classifiers based on different selection results may perform differently, very few studies consider examining the effect of performing different feature selection algorithms on the classification performances by different classifiers over different types of datasets. In this paper, two widely used algorithms, which are the genetic algorithm (GA) and information gain (IG), are used to perform feature selection. On the other hand, three well-known classifiers are constructed, which are the CART decision tree (DT), multi-layer perceptron (MLP) neural network, and support vector machine (SVM). Based on 14 different types of datasets, the experimental results show that in most cases IG is a better feature selection algorithm than GA. In addition, the combinations of IG with DT and IG with SVM perform best and second best for small and large scale datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20classification" title=" pattern classification"> pattern classification</a>, <a href="https://publications.waset.org/abstracts/search?q=dimensionality%20reduction" title=" dimensionality reduction"> dimensionality reduction</a> </p> <a href="https://publications.waset.org/abstracts/5047/the-effect-of-feature-selection-on-pattern-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5047.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">669</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">7715</span> Weighted Rank Regression with Adaptive Penalty Function</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> The use of regularization for statistical methods has become popular. The least absolute shrinkage and selection operator (LASSO) framework has become the standard tool for sparse regression. However, it is well known that the LASSO is sensitive to outliers or leverage points. We consider a new robust estimation which is composed of the weighted loss function of the pairwise difference of residuals and the adaptive penalty function regulating the tuning parameter for each variable. Rank regression is resistant to regression outliers, but not to leverage points. By adopting a weighted loss function, the proposed method is robust to leverage points of the predictor variable. Furthermore, the adaptive penalty function gives us good statistical properties in variable selection such as oracle property and consistency. We develop an efficient algorithm to compute the proposed estimator using basic functions in program R. We used an optimal tuning parameter based on the Bayesian information criterion (BIC). Numerical simulation shows that the proposed estimator is effective for analyzing real data set and contaminated data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20penalty%20function" title="adaptive penalty function">adaptive penalty function</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20penalized%20regression" title=" robust penalized regression"> robust penalized regression</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20selection" title=" variable selection"> variable selection</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20rank%20regression" title=" weighted rank regression"> weighted rank regression</a> </p> <a href="https://publications.waset.org/abstracts/79449/weighted-rank-regression-with-adaptive-penalty-function" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79449.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">7714</span> Design and Simulation of Unified Power Quality Conditioner based on Adaptive Fuzzy PI Controller</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Brahim%20Ferdi">Brahim Ferdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Samira%20Dib"> Samira Dib</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The unified power quality conditioner (UPQC), a combination of shunt and series active power filter, is one of the best solutions towards the mitigation of voltage and current harmonics problems in distribution power system. PI controller is very common in the control of UPQC. However, one disadvantage of this conventional controller is the difficulty in tuning its gains (Kp and Ki). To overcome this problem, an adaptive fuzzy logic PI controller is proposed. The controller is composed of fuzzy controller and PI controller. According to the error and error rate of the control system and fuzzy control rules, the fuzzy controller can online adjust the two gains of the PI controller to get better performance of UPQC. Simulations using MATLAB/SIMULINK are carried out to verify the performance of the proposed controller. The results show that the proposed controller has fast dynamic response and high accuracy of tracking the current and voltage references. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20fuzzy%20PI%20controller" title="adaptive fuzzy PI controller">adaptive fuzzy PI controller</a>, <a href="https://publications.waset.org/abstracts/search?q=current%20harmonics" title=" current harmonics"> current harmonics</a>, <a href="https://publications.waset.org/abstracts/search?q=PI%20controller" title=" PI controller"> PI controller</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20harmonics" title=" voltage harmonics"> voltage harmonics</a>, <a href="https://publications.waset.org/abstracts/search?q=UPQC" title=" UPQC"> UPQC</a> </p> <a href="https://publications.waset.org/abstracts/16996/design-and-simulation-of-unified-power-quality-conditioner-based-on-adaptive-fuzzy-pi-controller" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16996.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">556</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">7713</span> An Overview of Adaptive Channel Equalization Techniques and Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Navdeep%20Singh%20Randhawa">Navdeep Singh Randhawa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wireless communication system has been proved as the best for any communication. However, there are some undesirable threats of a wireless communication channel on the information transmitted through it, such as attenuation, distortions, delays and phase shifts of the signals arriving at the receiver end which are caused by its band limited and dispersive nature. One of the threat is ISI (Inter Symbol Interference), which has been found as a great obstacle in high speed communication. Thus, there is a need to provide perfect and accurate technique to remove this effect to have an error free communication. Thus, different equalization techniques have been proposed in literature. This paper presents the equalization techniques followed by the concept of adaptive filter equalizer, its algorithms (LMS and RLS) and applications of adaptive equalization technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=channel%20equalization" title="channel equalization">channel equalization</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20equalizer" title=" adaptive equalizer"> adaptive equalizer</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20mean%20square" title=" least mean square"> least mean square</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/9270/an-overview-of-adaptive-channel-equalization-techniques-and-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9270.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">450</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">7712</span> Dynamic Gabor Filter Facial Features-Based Recognition of Emotion in Video Sequences</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20Hari%20Prasath">T. Hari Prasath</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Ithaya%20Rani"> P. Ithaya Rani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the world of visual technology, recognizing emotions from the face images is a challenging task. Several related methods have not utilized the dynamic facial features effectively for high performance. This paper proposes a method for emotions recognition using dynamic facial features with high performance. Initially, local features are captured by Gabor filter with different scale and orientations in each frame for finding the position and scale of face part from different backgrounds. The Gabor features are sent to the ensemble classifier for detecting Gabor facial features. The region of dynamic features is captured from the Gabor facial features in the consecutive frames which represent the dynamic variations of facial appearances. In each region of dynamic features is normalized using Z-score normalization method which is further encoded into binary pattern features with the help of threshold values. The binary features are passed to Multi-class AdaBoost classifier algorithm with the well-trained database contain happiness, sadness, surprise, fear, anger, disgust, and neutral expressions to classify the discriminative dynamic features for emotions recognition. The developed method is deployed on the Ryerson Multimedia Research Lab and Cohn-Kanade databases and they show significant performance improvement owing to their dynamic features when compared with the existing methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=detecting%20face" title="detecting face">detecting face</a>, <a href="https://publications.waset.org/abstracts/search?q=Gabor%20filter" title=" Gabor filter"> Gabor filter</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-class%20AdaBoost%20classifier" title=" multi-class AdaBoost classifier"> multi-class AdaBoost classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=Z-score%20normalization" title=" Z-score normalization"> Z-score normalization</a> </p> <a href="https://publications.waset.org/abstracts/85005/dynamic-gabor-filter-facial-features-based-recognition-of-emotion-in-video-sequences" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85005.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">278</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">7711</span> An Efficient FPGA Realization of Fir Filter Using Distributed Arithmetic </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Iruleswari">M. Iruleswari</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Jeyapaul%20Murugan"> A. Jeyapaul Murugan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most fundamental part used in many Digital Signal Processing (DSP) application is a Finite Impulse Response (FIR) filter because of its linear phase, stability and regular structure. Designing a high-speed and hardware efficient FIR filter is a very challenging task as the complexity increases with the filter order. In most applications the higher order filters are required but the memory usage of the filter increases exponentially with the order of the filter. Using multipliers occupy a large chip area and need high computation time. Multiplier-less memory-based techniques have gained popularity over past two decades due to their high throughput processing capability and reduced dynamic power consumption. This paper describes the design and implementation of highly efficient Look-Up Table (LUT) based circuit for the implementation of FIR filter using Distributed arithmetic algorithm. It is a multiplier less FIR filter. The LUT can be subdivided into a number of LUT to reduce the memory usage of the LUT for higher order filter. Analysis on the performance of various filter orders with different address length is done using Xilinx 14.5 synthesis tool. The proposed design provides less latency, less memory usage and high throughput. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=finite%20impulse%20response" title="finite impulse response">finite impulse response</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20arithmetic" title=" distributed arithmetic"> distributed arithmetic</a>, <a href="https://publications.waset.org/abstracts/search?q=field%20programmable%20gate%20array" title=" field programmable gate array"> field programmable gate array</a>, <a href="https://publications.waset.org/abstracts/search?q=look-up%20table" title=" look-up table"> look-up table</a> </p> <a href="https://publications.waset.org/abstracts/51854/an-efficient-fpga-realization-of-fir-filter-using-distributed-arithmetic" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51854.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">457</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">7710</span> Designing Intelligent Adaptive Controller for Nonlinear Pendulum Dynamical System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Ghasemi">R. Ghasemi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20R.%20Rahimi%20Khoygani"> M. R. Rahimi Khoygani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes the designing direct adaptive neural controller to apply for a class of a nonlinear pendulum dynamic system. The radial basis function (RBF) neural adaptive controller is robust in presence of external and internal uncertainties. Both the effectiveness of the controller and robustness against disturbances are importance of this paper. The simulation results show the promising performance of the proposed controller. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neural%20controller" title="adaptive neural controller">adaptive neural controller</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20dynamical" title=" nonlinear dynamical"> nonlinear dynamical</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=RBF" title=" RBF"> RBF</a>, <a href="https://publications.waset.org/abstracts/search?q=driven%20pendulum" title=" driven pendulum"> driven pendulum</a>, <a href="https://publications.waset.org/abstracts/search?q=position%20control" title=" position control "> position control </a> </p> <a href="https://publications.waset.org/abstracts/13745/designing-intelligent-adaptive-controller-for-nonlinear-pendulum-dynamical-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13745.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">482</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">7709</span> Neural Adaptive Controller for a Class of Nonlinear Pendulum Dynamical System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Reza%20Rahimi%20Khoygani">Mohammad Reza Rahimi Khoygani</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20Ghasemi"> Reza Ghasemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, designing direct adaptive neural controller is applied for a class of a nonlinear pendulum dynamic system. The radial basis function (RBF) is used for the Neural network (NN). The adaptive neural controller is robust in presence of external and internal uncertainties. Both the effectiveness of the controller and robustness against disturbances are the merits of this paper. The promising performance of the proposed controllers investigates in simulation results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20control" title="adaptive control">adaptive control</a>, <a href="https://publications.waset.org/abstracts/search?q=pendulum%20dynamical%20system" title=" pendulum dynamical system"> pendulum dynamical system</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=adaptive%20neural%20controller" title=" adaptive neural controller"> adaptive neural controller</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20dynamical" title=" nonlinear dynamical"> nonlinear dynamical</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=RBF" title=" RBF"> RBF</a>, <a href="https://publications.waset.org/abstracts/search?q=driven%20pendulum" title=" driven pendulum"> driven pendulum</a>, <a href="https://publications.waset.org/abstracts/search?q=position%20control" title=" position control "> position control </a> </p> <a href="https://publications.waset.org/abstracts/13649/neural-adaptive-controller-for-a-class-of-nonlinear-pendulum-dynamical-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13649.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">670</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">7708</span> Diffusion Adaptation Strategies for Distributed Estimation Based on the Family of Affine Projection Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Shams%20Esfand%20Abadi">Mohammad Shams Esfand Abadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Ranjbar"> Mohammad Ranjbar</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20Ebrahimpour"> Reza Ebrahimpour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work presents the distributed processing solution problem in a diffusion network based on the adapt then combine (ATC) and combine then adapt (CTA)selective partial update normalized least mean squares (SPU-NLMS) algorithms. Also, we extend this approach to dynamic selection affine projection algorithm (DS-APA) and ATC-DS-APA and CTA-DS-APA are established. The purpose of ATC-SPU-NLMS and CTA-SPU-NLMS algorithm is to reduce the computational complexity by updating the selected blocks of weight coefficients at every iteration. In CTA-DS-APA and ATC-DS-APA, the number of the input vectors is selected dynamically. Diffusion cooperation strategies have been shown to provide good performance based on these algorithms. The good performance of introduced algorithm is illustrated with various experimental results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=selective%20partial%20update" title="selective partial update">selective partial update</a>, <a href="https://publications.waset.org/abstracts/search?q=affine%20projection" title=" affine projection"> affine projection</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20selection" title=" dynamic selection"> dynamic selection</a>, <a href="https://publications.waset.org/abstracts/search?q=diffusion" title=" diffusion"> diffusion</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20distributed%20networks" title=" adaptive distributed networks"> adaptive distributed networks</a> </p> <a href="https://publications.waset.org/abstracts/20231/diffusion-adaptation-strategies-for-distributed-estimation-based-on-the-family-of-affine-projection-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20231.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">707</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">7707</span> Gaussian Particle Flow Bernoulli Filter for Single Target Tracking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hyeongbok%20Kim">Hyeongbok Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Lingling%20Zhao"> Lingling Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaohong%20Su"> Xiaohong Su</a>, <a href="https://publications.waset.org/abstracts/search?q=Junjie%20Wang"> Junjie Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Bernoulli filter is a precise Bayesian filter for single target tracking based on the random finite set theory. The standard Bernoulli filter often underestimates the number of targets. This study proposes a Gaussian particle flow (GPF) Bernoulli filter employing particle flow to migrate particles from prior to posterior positions to improve the performance of the standard Bernoulli filter. By employing the particle flow filter, the computational speed of the Bernoulli filters is significantly improved. In addition, the GPF Bernoulli filter provides a more accurate estimation compared with that of the standard Bernoulli filter. Simulation results confirm the improved tracking performance and computational speed in two- and three-dimensional scenarios compared with other algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bernoulli%20filter" title="Bernoulli filter">Bernoulli 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=particle%20flow%20filter" title=" particle flow filter"> particle flow filter</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20finite%20sets" title=" random finite sets"> random finite sets</a>, <a href="https://publications.waset.org/abstracts/search?q=target%20tracking" title=" target tracking"> target tracking</a> </p> <a href="https://publications.waset.org/abstracts/162210/gaussian-particle-flow-bernoulli-filter-for-single-target-tracking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162210.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">92</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=dynamic%20selection%20subband%20adaptive%20filter%20%28DS-NSAF%29&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=dynamic%20selection%20subband%20adaptive%20filter%20%28DS-NSAF%29&page=3">3</a></li> <li class="page-item"><a class="page-link" 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href="https://publications.waset.org/abstracts/search?q=dynamic%20selection%20subband%20adaptive%20filter%20%28DS-NSAF%29&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=dynamic%20selection%20subband%20adaptive%20filter%20%28DS-NSAF%29&page=10">10</a></li> <li class="page-item disabled"><span class="page-link">...</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=dynamic%20selection%20subband%20adaptive%20filter%20%28DS-NSAF%29&page=257">257</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=dynamic%20selection%20subband%20adaptive%20filter%20%28DS-NSAF%29&page=258">258</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=dynamic%20selection%20subband%20adaptive%20filter%20%28DS-NSAF%29&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> 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