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| 1 | Diffusion Based Channel Gains Estimation in WSN Using Fractional Order Strategies显示文摘In this study,it is proposed that the diffusion least mean square(LMS)algorithm can be improved by applying the fractional order signal processing methodologies.Application of Caputo’s fractional derivatives are considered in the optimization of cost function.It is suggested to derive a fractional order variant of the diffusion LMS algorithm.The applicability is tested for the estimation of channel parameters in a distributed environment consisting of randomly distributed sensors communicating through wireless medium.The topology of the network is selected such that a smaller number of nodes are informed.In the network,a random sleep strategy is followed to conserve the transmission power at the nodes.The proposed fractional ordermodified diffusionLMS algorithms are applied in the two configurations of combine-then-adapt and adapt-then-combine.The average squared error performance of the proposed algorithms along with its traditional counterparts are evaluated for the estimation of the Rayleigh channel parameters.Amathematical proof of convergence is provided showing that the addition of the nonlinear term resulting from fractional derivatives helps adjusts the autocorrelation matrix in such a way that the spread of its eigenvalues decreases.This increases the convergence as well as the steady state response even for the larger step sizes.Experimental results are shown for different number of nodes and fractional orders.The simulation results establish that the accuracy of the proposed scheme is far better than its classical counterparts,therefore,helps better solves the channel gains estimation problem in a distributed wireless environment.The algorithm has the potential to be applied in other applications related to learning and adaptation. | Nasir Mahmud Khokhar Muhammad Nadeem Majeed Syed Muslim Shah | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 2 | Flower Pollination Heuristics for Nonlinear Active Noise Control Systems显示文摘In this paper,a novel design of the flower pollination algorithm is presented for model identification problems in nonlinear active noise control systems.The recently introduced flower pollination based heuristics is implemented to minimize the mean squared error based merit/cost function representing the scenarios of active noise control system with linear/nonlinear and primary/secondary paths based on the sinusoidal signal,random and complex random signals as noise interferences.The flower pollination heuristics based active noise controllers are formulated through exploitation of nonlinear filtering with Volterra series.The comparative study on statistical observations in terms of accuracy,convergence and complexity measures demonstrates that the proposed meta-heuristic of flower pollination algorithm is reliable,accurate,stable as well as robust for active noise control system.The accuracy of the proposed nature inspired computing of flower pollination is in good agreement with the state of the art counterpart solvers based on variants of genetic algorithms,particle swarm optimization,backtracking search optimization algorithm,fireworks optimization algorithm along with their memetic combination with local search methodologies.Moreover,the central tendency and variation based statistical indices further validate the consistency and reliability of the proposed scheme mimic the mathematical model for the process of flower pollination systems. | Wasim Ullah Khan Yigang He Muhammad Asif Zahoor Raja Naveed Ishtiaq Chaudhary Zeshan Aslam Khan Syed Muslim Shah | 2021 | Computers, Materials & Continua2021，，4： | 0 |
| 3 | Fractional Processing Based Adaptive Beamforming Algorithm显示文摘Fractional order algorithms have shown promising results in various signal processing applications due to their ability to improve performance without significantly increasing complexity.The goal of this work is to inves-tigate the use of fractional order algorithm in the field of adaptive beam-forming,with a focus on improving performance while keeping complexity lower.The effectiveness of the algorithm will be studied and evaluated in this context.In this paper,a fractional order least mean square(FLMS)algorithm is proposed for adaptive beamforming in wireless applications for effective utilization of resources.This algorithm aims to improve upon existing beam-forming algorithms,which are inefficient in performance,by offering faster convergence,better accuracy,and comparable computational complexity.The FLMS algorithm uses fractional order gradient in addition to the standard ordered gradient in weight adaptation.The derivation of the algorithm is provided and supported by mathematical convergence analysis.Performance is evaluated through simulations using mean square error(MSE)minimization as a metric and compared with the standard LMS algorithm for various parameters.The results,obtained through Matlab simulations,show that the FLMS algorithm outperforms the standard LMS in terms of convergence speed,beampattern accuracy and scatter plots.FLMS outperforms LMS in terms of convergence speed by 34%.From this,it can be concluded that FLMS is a better candidate for adaptive beamforming and other signal processing applications. | Syed Asghar Ali Shah Tariqullah Jan Syed Muslim Shah Ruhul Amin Khalil Ahmad Sawalmeh Muhammad Anan | 2023 | Computers, Materials & Continua2023，，7： | 0 |
| 4 | Appearance Based Dynamic Hand Gesture Recognition Using 3D Separable Convolutional Neural Network显示文摘Appearance-based dynamic Hand Gesture Recognition(HGR)remains a prominent area of research in Human-Computer Interaction(HCI).Numerous environmental and computational constraints limit its real-time deployment.In addition,the performance of a model decreases as the subject’s distance from the camera increases.This study proposes a 3D separable Convolutional Neural Network(CNN),considering the model’s computa-tional complexity and recognition accuracy.The 20BN-Jester dataset was used to train the model for six gesture classes.After achieving the best offline recognition accuracy of 94.39%,the model was deployed in real-time while considering the subject’s attention,the instant of performing a gesture,and the subject’s distance from the camera.Despite being discussed in numerous research articles,the distance factor remains unresolved in real-time deployment,which leads to degraded recognition results.In the proposed approach,the distance calculation substantially improves the classification performance by reducing the impact of the subject’s distance from the camera.Additionally,the capability of feature extraction,degree of relevance,and statistical significance of the proposed model against other state-of-the-art models were validated using t-distributed Stochastic Neighbor Embedding(t-SNE),Mathew’s Correlation Coefficient(MCC),and the McNemar test,respectively.We observed that the proposed model exhibits state-of-the-art outcomes and a comparatively high significance level. | Muhammad Rizwan Sana Ul Haq Noor Gul Muhammad Asif Syed Muslim Shah Tariqullah Jan Naveed Ahmad | 2023 | Computers, Materials & Continua2023，，7： | 0 |
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