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This paper explains the construction of a novel augmented hunger games search algorithm using a logarithmic spiral opposition-based learning technique. The proposed algorithm (LsOBL-HGS) is used as an efficient tool for both function optimization and controller design. To assess the performance of the algorithm for function optimization, benchmark functions from the CEC2017 test suite were employed and comparisons were made with available and good performing algorithms. In terms of controller design, the proposed LsOBL-HGS algorithm was utilized to design a FOPID controlled magnetic ball suspension system. Comparative assessments were also performed for FOPID controller design, as well using other state-of-the-art methods reported for the magnetic ball suspension system. The results showed that the proposed LsOBL-HGS algorithm has good capability for FOPID controller design employed in a magnetic ball suspension system as it provided an improvement of more than 13 % in terms of the transient response-related parameters and more than 34 % in terms of bandwidth compared to the best-reported approach used for comparisons. [ABSTRACT FROM AUTHOR]

Thousands of bursty and concurrent flows persistently overwhelm the bottleneck bandwidth. The quality of experience of low-latency applications (e.g. Web, AR/VR, IoT, VoIP, etc.) is inevitably influenced by the background applications (e.g. file-sharing, system updates) which tend to overfill the buffer of bottleneck links. To solve the above issues, Less-than-Best-Effort (LBE) protocols were proposed to support the data transmission of background applications. Therein, Low Extra Delay Background Transport (LEDBAT) is one of the most widely deployed LBE protocols. To achieve the low-priority feature, LEDBAT senders saturate the residual bandwidth of bottleneck links while keeping the low queuing delay. However, through the simulation, we observed that LEDBAT performs a severe throughput degradation in high-speed and lossy networks. To resolve this issue, an enhanced LEDBAT, named NLPC, is presented in this paper. NLPC introduces a variable gain value, which can dynamically adapt to the network congestion degree, to update the sending rate. Extensive simulations in NS-2 and some small-scale real-world experiments indicate that in high-speed and lossy networks, NLPC increases throughput up to 40% in comparison with LEDBAT, and maintains the low-priority feature as the other LBE protocols. [ABSTRACT FROM AUTHOR]

In nonparametric models, numerous penalization methods using a nonparametric series estimator have been developed for model selection and estimation. However, a penalization has been poorly understood combined with kernel smoothing. This can be attributed to the intrinsic technical and computational difficulties, which leads to different treatments from the developments of the penalized series estimators. Kernel smoothing is a popular and useful nonparametric estimation method; thus it is desirable to establish theoretical and computational analyses for penalized kernel smoothing. In this paper, we develop a novel penalized kernel quantile regression that has nice theoretical and computational properties in varying coefficient models. We show that the proposed method consistently identifies the partially linear structure of the varying coefficient model even when the number of covariates is allowed to increase with a sample size. We develop an efficient algorithm using the alternating direction method of multipliers with a computational convergence guarantee. We derive the plug-in bandwidth selection using high-dimensional kernel regression theory and the penalty parameter is selected by the proposed Bayesian Information Criterion. Our developments require novel high-dimensional kernel regression and computational analyses. The various simulations and real data analyses conducted in this study demonstrate the effectiveness and numerically verify the proposed method. • Propose a method that identifies the partially linear structure of the varying coefficient model. • Develop an efficient algorithm using the proximal alternating direction method of multipliers with a convergence guarantee. • Derive the novel plug-in bandwidth selection using high-dimensional kernel theory. [ABSTRACT FROM AUTHOR]

Development of 5G/F5G technology leads to massive applications accessing to backbone networks, which requires the backbone networks to be upgraded. Semi-filterless elastic optical network (semi-FEON) is a suitable technology to cheaply and gradually upgrade backbone networks. In semi-FEON, routing, modulation and spectrum assignment (RMSA) problem is one of the key issues. In this paper, we study the dynamic RMSA problem in semi-FEON and propose an RMSA algorithm. The algorithm includes three innovations: a K-shortest-subnet-paths (KSSP) algorithm is designed to search candidate paths in semi-FEON, a load-balancing-least-resources (LBLR) policy is introduced to re-sort the candidate paths, and a maximum-occupied-neighbors (MON) rule is proposed to assign spectrum resources to connection requests in semi-FEON. Simulation results show that the proposed KSSP-LBLR-MON algorithm outperforms the existing works in term of bandwidth blocking probability. Concretely, the improvement ratio is greater than 59.98% and 66.64% in German-Net and Henan-Net, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we employ a new multiresolution upscaling method for the data in porous media. The method employs an adaptive bandwidth of the kernel function as the multiresolution upscaling operator. We have implemented a hierarchical multiresolution upscaling strategy, which is based on maintaining the important fine scale model structures of the reservoir. In this method, bandwidth is a function of the variability of cell, which controls the upscaling process. In areas with a high variability of the considered feature, small bandwidth leads to maintaining the fine scale structures; and in homogeneous reservoir areas, large bandwidths will lead to coarse-sized cells. In this paper, we have used this proposed method for simultaneous upscaling of two features: density and neutron logs. Determination of optimal bandwidth or threshold for each feature is an important challenge in the bandwidth method. Due to different variabilities of two properties or features, simultaneous upscaling should be performed to capture the variability of both features in the model. The concept of simultaneous upscaling provides a common coarse model for two properties. To prepare the SUTF model, two minimum and maximum bandwidth approaches are used. The upscaling algorithm has been applied for density and neutron logs. Although the fine scale model contains 2290 cells, the number of coarsened cells in SUTF model based on the minimum bandwidth and maximum bandwidth approaches are 402 and 110, respectively. In addition to this new proposed method, the method of wavelet transform has also been used as a well-known method for upscaling, and finally, the results of the two methods are compared. By comparing the results of the two methods, we observe that the upscaling error in the kernel bandwidth method is less than that in the wavelet transform method. The advantage of the kernel bandwidth method over the wavelet transform is the reduction of the loss of the main information as well as the reduction of the numerical dispersion in the new proposed method compared to the wavelet transform method. These benefits have appeared at the upscaling error that is less in the proposed method. [ABSTRACT FROM AUTHOR]

• Providing efficient routing and content management for ICN. • It enables bandwidth and cost-based routing for mobile and social sensing applications. • Caching is an important feature of ICN. • Cache replacement policy proposed to improve ICN performance. Information-Centric Network (ICN) is an essential networking model which contains several future Internet research activities. This paper considers ICN architecture for mobile and social sensing data. Routing and caching are among the most critical features of ICN, which improves the performance of efficiency of network and content sharing by fulfilling user requirements with cached content. This paper introduces a new routing model for information-centric network based on least bandwidth and cost (LBC) with two cache replacement policies. The cache replacement policies find and replace the content in the free cache space. Time aware Least Recently Used (TLRU) and Least Frequent Recently Used (LFRU) are employed as a cache replacement policy. The proposed algorithm implemented and compared, with the previous algorithm in terms of the following metrics success rate, hop count and delay. The results of the experiments show that the proposed algorithm is more effective compared to other algorithms. [Display omitted] [ABSTRACT FROM AUTHOR]

• A practical model based on kernel density estimation is presented. • An improved appropriate kernel bandwidth determination method is presented through the integration of optimal bandwidth selection method based on the rule of thumb, instead of the complicated quintic equation in the previous work, the initial bandwidth selection method and the correcting bandwidth selection method. • A new strategy to correct the measurements according to the hidden component of the measurement error is applied to PKDE and further enhances its robustness to bad data. • A comprehensive performance test of all the proposed models and algorithms to other methods is thoroughly performed using multiple IEEE test systems and practical systems. This paper presented a practical state estimation model and algorithm based on the kernel density theory (PKDE), which owns better performance than the existing methods. First, a PKDE was presented, which transforms the calculation of the kernel bandwidth of each measurement to that of each state variable. Therefore, the number of kernel bandwidths required to be determined is greatly reduced as the number of state variables are much less than that of the measurements. The feasibility of the PKDE model is supported by a proposition which is strictly proved. Second, an approach for choosing the appropriate kernel bandwidth was presented through the integration of the optimal bandwidth method based on the rule of thumb, the initial bandwidth selection method and the correcting bandwidth selection of the state variables. Third, PKDE's robustness to bad data is further enhanced by a strategy to correct the measurements according to the hidden component of the measurement error. The simulation results finally verify the effectiveness of the proposed models and algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates a novel distributed fusion system for target tracking in three dimensional surveillance using multiple asynchronous BO sensors by simultaneously addressing the problems of target mis-detection and clutter disturbance as well as the out-of-sequence (OOS) information fusion. The proposed distributed fusion architecture consists of the local tracking and distributed fusion, wherein, the refined BO measurements output from the local tracking in each BO sensor are transmitted to the fusion center for subsequent distributed fusion. In the local tracking, instead of kinematic state, the target pseudo state is defined and tracked by operating the proposed pseudo state-integrated probabilistic data association (PS-IPDA) algorithm with an approximately linear pseudo state evolution model, resulting in greatly refined BO sensor measurements. In the fusion center, the sequential asynchronous IPDA-general one-step retrodiction update (SAIPDA-GORU) algorithm is proposed to effectively fuse the transmitted origin-unknown OOS refined measurements (OOSRMs) with the central tracks. The SAIPDA-GORU propagates the central track hybrid state to the OOS time by deploying a general one-step retrodiction technique, and mathematically formulates the OOSRMs-to-retrodicted central track association probabilities utilized to facilitate the fusing of currently filtered central track with OOSRMs. Numerical results show that the proposed PS-IPDA algorithm reduces the errors of target bearings measurement by around a half and eliminates more than 60% of the clutter measurements contained in raw detections, saving significant communication bandwidth between local sensors and fusion center. Furthermore, the proposed SAIPDA-GORU method tangibly improves the estimation accuracy and false track discrimination over the existing discarding method, with the potential to be implemented in real-time. [ABSTRACT FROM AUTHOR]