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Electromyography (EMG) signals have been used to recognize various actions of hand movements, finger movements, and hand gestures. This paper aims to improve the classification accuracy of EMG signals while decreasing the number of features using the tree-seed algorithm. The dataset containing EMG signals utilized in this investigation is derived from a publicly accessible source. The rationale for selecting the tree-seed algorithm centers on its ability to enhance classification accuracy while minimizing the dimensionality of feature sets. The object function and treeseed algorithm’s nature avoids the results to have low accuracy with fewer features. The aim is not just to use a smaller number of features but also to achieve a higher accuracy rate. To ensure that selecting a smaller number of features does not decrease classification accuracy, the performance of all feature subsets was evaluated using the objective function. As a result, the number of selected features decreased, while the accuracy rate increased. The best accuracy improvement was observed, with the rate rising from 84.78% to 90.21% using the k-nearest neighbor (kNN) classifier with 50 out of 80 features. The maximum classification accuracy achieved was 99.75%, also using the kNN classifier. In this study, two different feature sets were compared using two different optimization algorithms in conjunction with four traditional machine learning algorithms to evaluate changes in classification accuracy. The classification accuracy and the improvements in accuracy, along with the number of selected features at the end of the iterations, have been reported. [ABSTRACT FROM AUTHOR]

In this article, a multiuser (MU) multiinput multioutput (MIMO) system is considered, which is essential to support a huge number of subscribers without consuming extra bandwidth or power. Dirty paper coding (DPC) for MU MIMO channel achieves the peak sum-rate for the MU multiple antenna system at the cost of high computational complexity. Both user and antenna scheduling with a population based meta-heuristic algorithm, i.e. binary flower pollination algorithm (binary FPA) has been demonstrated in this article to achieve system sum-rate comparable to DPC with very less computational complexity and time complexity. Moreover, binary FPA shows a significant improvement in system throughput/sum-rate performance as compared to other population based meta-heuristic algorithms like binary bat algorithm (binary BA) and binary genetic algorithm (binary GA). Furthermore, the proposed binary FPA algorithm successfully achieves higher system sum-rate as compared to random search scheme and different existing suboptimal scheduling algorithms from literature as well. The binary FPA has also better convergence rate and searching ability than both binary BA and binary GA techniques. The percentage deviation achieved by the proposed binary FPA algorithm is quite less than that of binary BA, binary GA, random search method, and existing suboptimal scheduling algorithms from the literature. The efficiency of binary FPA in all these fronts is verified using exhaustive simulation studies. [ABSTRACT FROM AUTHOR]

In graph theory, domination number and its variants such as total domination number are studied by many authors. Let the domination number and the total domination number of a graph G without isolated vertices be γ(G) and γt(G), respectively. Based on the inequality γt(G) ≤ 2γ(G), we investigate the graphs satisfying the upper bound, that is, graphs G with γt(G) = 2γ(G). In this paper, we present some new properties of such graphs and provide an algorithm which can determine whether γt(G) = 2γ(G) or not for a family of graphs not covered by the previous results in the literature. [ABSTRACT FROM AUTHOR]

The coupling of space-time block codes (STBCs) with the spatial modulation (SM) scheme can introduce a spatial diversity to the SM systems. This paper presents antenna selection techniques to provide an additional coding gain with the space-time block coded spatial modulation (STBC-SM). Specifically, the selection of optimal transmit antenna elements at the transmitter to send a spatially modulated space-time block code is studied. This paper studies the effects of applying the capacity optimized antenna selection (COAS) algorithm with the STBC-SM. The COAS algorithm has a suboptimal performance with a trade-off between complexity and bit-error-rate (BER) enhancement. Simulation results for STBC-SM with a spectral efficiency of 3 bits/s/Hz have been introduced with and without antenna selection. [ABSTRACT FROM AUTHOR]

In the paper, we present new theorems to show that a Hamiltonian path and circuit on an undirected graph can be formulated in terms of bases of polymatroids or extended polymatroids associated with submodular functions defined on subsets of the node-set of a given graph. In this way, we give a new formulation of the well-known traveling salesman problem including constraints in these terms. The main result in the paper states that using a special base of the polymatroid, a Hamiltonian path on an undirected graph can be solved effectively. Since the determination of a Hamiltonian circuit can be reduced to finding a Hamiltonian path between some node and its adjacent nodes, an efficient Hamiltonian path algorithm will lead to solving the Hamiltonian circuit problem. Finding some special base is the main problem in solving these NP -hard problems. [ABSTRACT FROM AUTHOR]

In this paper, Szabó's algorithm is used as the main tool to find locally symmetric left invariant Riemannian metrics on some 4-dimensional Lie groups. Locally symmetric left invariant Riemannian Lie groups constitute an important subclass of Riemannian Lie groups with zero-divergence Weyl-tensor the so-called C-manifolds. Some properties of the curvature operator of these 4-dimensional C-manifolds are studied. [ABSTRACT FROM AUTHOR]

In this paper, we propose a distributed algorithm that determines effective Overlapping Basic Service Set/Preamble Detection (OBSS/PD) threshold levels in each WiFi6 device to maximize the total throughput by increasing the spectral efficiency. Within WiFi6 standard, OBSS/PD mechanism is introduced to increase the overall efficiency of WiFi networks by tuning the receiver sensitivity as well as the transmission power. In a nutshell, the proposed algorithm, RACEBOT, tunes the hearing (i.e. reception) and speaking (i.e. transmission) parameters of each WiFi device individually for the betterment of the WiFi experience of all WiFi networks in a neighborhood. WiFi experience is not only affected by the spectral efficiency but also by the so-called rate selection algorithms that aims to select the ideal modulation and coding levels according to the ever-changing channel conditions. For higher flexibility, the proposed algorithm works agnostically to the selected rate selection mechanism. Moreover, RACEBOT is a distributed algorithm working independently in all WiFi devices in a given environment. We have conducted extensive simulations in NS3 to evaluate the performance of our proposed algorithm and compare its results with other prominent carrier sensitivity threshold algorithms, both pre-WiFi6 and post-WiFi6. Our results show that RACEBOT outperforms its competitors the RTOT and DSC algorithms in terms of aggregate throughput by 10%-20% in dense networks and 5%-10% in sparse networks considering modern rate selection algorithms such as Thompson. [ABSTRACT FROM AUTHOR]

As an important technology in next-generation networks, network virtualization has received more and more attention. Fault diagnosis is the crucial element for fault management and it is the process of inferring the exact failure in the network virtualization environment (NVE) from the set of observed symptoms. Although various traditional fault diagnosis algorithms have been proposed, the virtual network has some new characteristics, which include inaccessible fault information of the substrate network, inaccurate network observations, and a dynamic embedding relationship. To solve these challenges, a symptom-aware hybrid fault diagnosis (SAHFD) algorithm in the NVE is proposed in this paper. First, a multifactor Bayesian hierarchical model is proposed to denote the relationships between multiple factors in different layers. Second, the contribution degree is improved to locate the faults in the virtual layer and the active detection algorithm is introduced to filter some spurious faults in virtual layer fault diagnosis. Then, in substrate layer fault diagnosis, the active detection algorithm is introduced to solve the problem of incomplete network observations. Finally, a heuristic greedy algorithm is proposed to select appropriate actions based on minimum weight set covering method with minimum cost. Simulation results show that, compared with other algorithms, the SAHFD algorithm has a higher accuracy rate, lower false positive rate, and better environmental adaptability in the NVE. [ABSTRACT FROM AUTHOR]

Named data networking (NDN), as a future Internet architecture that is a promising alternative to TCP/IP networks, has the new features of connectionless, in-network cache, and hop-by-hop forwarding, which makes the congestion control algorithms of traditional TCP/IP networks unable to be directly applied to NDN. In addition, since the optimal size of the sending window cannot be determined, the existing window-based congestion control algorithms generally use the AIMD-like window adjustment algorithm, which cannot achieve the optimal throughput. In this paper, we propose a precise feedback-based, multipath-aware congestion control algorithm PFECC, which is inspired by Accel-Brake Control algorithm. PFECC considers the influence of Interest flows, uses a fair queuing algorithm at the intermediate node to calculate the target rate of each flow, and gives accurate feedback on each dequeued data packet. The consumer changes the size of the sending window according to the feedback to quickly converge to the target bandwidth. To fully exploit the hop-by-hop adaptive forwarding feature of NDN, each downstream node timely senses the congestion trend of the upstream link to split the forwarding rate of Interest to avoid congestion. Simulation results show that PFECC can effectively reduce transmission delay, treat each flow fairly, and converge to the best throughput faster. [ABSTRACT FROM AUTHOR]

For some industrial converter applications such as battery chargers, a DC/DC converter is needed to step down the high DC (direct current) voltages generated by PWM (pulse with modulated) rectifiers to lower voltage levels such as 110V/220V. These both complicate the design and decrease the efficiency. In this paper, a novel topology that includes a step-down transformer and a novel control algorithm is proposed. The proposed current, which is synchronized, look-up table based, sinusoidal PWM control method (CS-LUT-SPWM method) using switching frequency-oriented synchronization (SWFOS) results in a very fast PWM generation and stable operation. Finally, a new thyristor-based start-up circuit providing safe operation when there is energy black out is proposed. A 32-kW converter has been designed, built, and tested to prove the concepts. The proposed converter has an efficiency higher than 94%, an input current total harmonic distortion (THDi) less than 5%, and a power factor closer to 0.99. Theoretical calculations and experimented results show that proposed converter has better efficiency (94%-96%) compared to classical PWM boost rectifiers with a series DC/DC converter topology (92%) in the literature. Fast PWM control algorithm, less complexity, low switching noises, one-stage conversion circuit, and novel start-up circuit are other advantages of the proposed converter. [ABSTRACT FROM AUTHOR]

In supervised classification, obtaining nonlinear separating functions from an algorithm is crucial for prediction accuracy. This paper analyzes the polyhedral conic functions (PCF) algorithm that generates nonlinear separating functions by only solving simple subproblems. Then, a revised version of the algorithm is developed that achieves better generalization and fast training while maintaining the simplicity and high prediction accuracy of the original PCF algorithm. This is accomplished by making the following modifications to the subproblem: extension of the objective function with a regularization term, relaxation of a hard constraint set and introduction of a new error term. Experimental results show that the modifications provide %12 better generalization on average and up to 10x faster training. This paper also contributes to the literature by providing detailed comparisons of the other classification algorithms that use polyhedral conic functions for the first time. [ABSTRACT FROM AUTHOR]

This paper develops a finite-time dynamic surface control (DSC) scheme for nonlinear systems with mismatched uncertainties via a high-order sliding mode(HOSM) observer. By designing a second-order terminal sliding surface based on the estimated signals, an observer-based sliding mode control (SMC) is designed to counteract the mismatched uncertainties in each step of backstepping. The proposed DSC scheme exhibits the following two attractive features. One is the application of HOSM observer to deal with mismatched system uncertainty functions. This is very different from the traditional approximator-based adaptive methods in dealing with high-order uncertain nonlinear systems. The other is the finite-time convergence of the provided algorithm, which guarantees the transient performance of tracking signals. Especially, the finite convergence time is explicitly given in the controller design and stability analysis. Simulation results of numerical example illustrates that the proposed approach shows better control performance than traditional approximators-based adaptive methods. [ABSTRACT FROM AUTHOR]

In order to recover missing samples in a nonstationary signal, this paper employs a time-signal analysis and filtering method. The instantaneous frequency of a multicomponent signal is first estimated by employing a robust and computationally efficient method. Then the time-frequency filtering is performed using a dechirping operation to recover missing samples. These steps are repeated until convergence. The proposed method achieves better performance than the state of art methods both in terms of the accuracy of the recovered signal and computational efficiency. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimating the time series of a gene expression using nonlinear Bayesian filtering algorithms. The response of gene regulatory networks (GRNs) to functional requirements in the cell and environmental conditions evolves over time. Dynamic biological processes such as cancer progression and treatment recovery depend on the collected genetic profiles. These processes are behind genetic interactions that rewire over the course of time. The GRN was formulated as a nonlinear and non-Gaussian dynamic system defined by the gene measurement model and the unknown state is an evolution of the gene model. However, the GRN has a high dimensional space where most of nonlinear Bayesian filtering algorithms are ineffective in high dimensional spaces. Therefore, many authors have introduced various techniques to overcome what has become known as the curse of dimensionality. This paper presents a comparative study between extended Kalman filter, unscented Kalman filter and derivatives of particle filters, in tracking the evolution of gene expression over time. Application of the nonlinear Bayesian filtering algorithms to estimate the evolution of gene expression from synthetic and real data, shows that the unscented particle filter (UKFPF) provides promising and robust results compared to other filters. Furthermore, UKF-PF provides an alternative solution to the problem of modeling gene regulatory networks. [ABSTRACT FROM AUTHOR]

In this paper, a post-pruning method known as zero-one loss function pruning (ZOLFP) that is based on zero-one loss function is introduced. The proposed ZOLFP method minimizes the expected loss, rather than evaluating the misclassification error rate of a node and its subtree. The subtree is pruned when expected loss of the node is less than or equal to the sum of the loss of its leaves. The experimental results demonstrate that ZOLFP method outperforms Un-pruned C4.5 Decision Tree (UDT-C4.5) algorithm, reduced error pruning (REP), and minimum error pruning (MEP) in terms of performance accuracy in all used datasets. It is also shown that the complexity of the proposed method ZOLFP is not more than the complexity of REP and MEP methods. Furthermore, the results show that ZOLFP method achieves satisfactory results compared to REP, MEP, and UDT-C4.5 algorithms in terms of precision score, recall score, true positive rate, false positive rate, F-measure, and area under ROC scores during the experiment process. [ABSTRACT FROM AUTHOR]

Message recovery is an important property in Nyberg-Rueppel type digital signature algorithms. However, the security of Nyberg-Rueppel type digital signature algorithms depends on the hard problems which might be vulnerable to quantum attacks. Therefore, quantum resistant Nyberg-Rueppel type digital signature algorithms with message recovery property are needed. Since NTRU-based cryptosystems are one of the best studied quantum-resistant schemes, using traditional NTRU encryption scheme has several advantages on the message recovery property. In this paper, we define Nyberg-Rueppel type of NTRU digital signature algorithm. It is carried out by combining NTRU-based encryption and signature algorithms. In the proposed scheme, efficient message recovery property is achieved with the help of NTRU. Then, we compare the computational cost of our Nyberg-Rueppel type signature scheme with the others in terms of the arithmetic complexity. According to the asymptotic complexity results, the proposed scheme has better arithmetic complexity than Nyberg-Rueppel type schemes. We also discuss the security properties of the proposed scheme by modifying attacks on Nyberg-Rueppel type algorithms and lattice-based algorithms. [ABSTRACT FROM AUTHOR]

A point-to-point (P2P) communication system based on the CPM-DSSS scheme ensures reliability, security, and antijamming capabilities. However, for reliable detection of data carrier synchronization of CPM-DSSS based system is one of the requirements. This paper presents a joint algorithm for carrier frequency offset (CFO) and carrier phase offset (CPO) estimation for CPM-DSSS based P2P system. The results indicate that the proposed CFO estimator is unbiased and can accurately estimate a wide range of offsets. Moreover, the proposed algorithm is compared with another research work. The results show that the proposed CFO and CPO estimation algorithm outperforms its counterpart with a performance improvement of about 9 dB and above, respectively. The effectiveness of the proposed estimator in the Stanford University Interim (SUI)-1 multipath fading channel is also demonstrated. Finally, the proposed estimator's bit-error-rate (BER) performance and computational complexity are also given for the CPM-DSSS P2P system. [ABSTRACT FROM AUTHOR]

In this paper, we obtain the existence--uniqueness of solution to the second-order linear Fredholm integrodifferential equation (FIDE) with Dirichlet boundary condition by hybridizable discontinuous Galerkin (HDG) method. A key property of these methods is to eliminate all internal degrees of freedom and to construct a linear system that only includes globally coupled unknowns at the element interfaces. After designing and implementing HDG algorithm, we provide some necessary and sufficient conditions based on the stabilization parameter and kernel function to guarantee the existence-uniqueness of the approximate solution. Then, some numerical examples are carried out to assess the performance of the present method. When comparing with existing some methods in literature, the experimental studies verify the reliability and feasibility of the HDG method for the problem under consideration. [ABSTRACT FROM AUTHOR]

Distributed multiple organizations interact with each other. If the domains employ role-based access control, one method for interaction between domains is role-mapping. However, it may violate constraints in the domains such as role hierarchy, separation of duty, and cardinality. Therefore, autonomy of the domains is lost. This paper proposes secure interoperation in multidomain environments. For this purpose, a cross-domain is created by foreign permission assignment. In an effort to maintain the autonomy of every domain, several rules are defined formally. Then, a decentralized scheme is used to provide permission mapping between domains. At the next stage, the proposed crossdomain is specified using Alloy, the first logic language. Subsequently, validity of the rules is analyzed through Alloy analyzer. [ABSTRACT FROM AUTHOR]

Clustering ensemble selection has shown high efficiency in the improvement of the quality of clustering solutions. This technique comprises two important metrics: diversity and quality. It has been empirically proved that ensembles of higher effectiveness can be achieved through taking into consideration the diversity and quality simultaneously. However, the relationships between these two metrics in base clusterings have remained uncertain. This paper suggests a new hierarchical selection algorithm using a diversity/quality measure based on the Jaccard similarity measure. In the proposed algorithm, the selection of the subsets of the clustering partitions is done based on their diversity measures. The proposed diversity measure (in two types of pair-wise diversity and hybrid diversity) is applied to the proposed algorithm. Hypergraph-partitioning algorithm (HGPA), cluster-based similarity partition algorithm (CSPA), and meta-clustering algorithm (MCLA) were used to obtain the consensus solution and cluster ensemble selection results with a hierarchical method. The experimental results on 14 datasets showed that selecting a subset of base clusterings using the proposed algorithm led to more accurate results compared to those of the full ensemble. The effectiveness and robustness of the proposed algorithm were demonstrated in comparison with the full ensemble. The comparative results showed that the proposed method by new diversity measure outperformed the full ensemble. [ABSTRACT FROM AUTHOR]

Several remote sensing applications require high-spatial-high-spectral resolution multispectral (MS) images. However, most MS sensors provide low-spatial-high-spectral resolution MS images together with high-spatial-low-spectral resolution panchromatic (PAN) bands. In order to increase the spatial resolution of MS bands to the resolution of PAN images and to obtain high-spatial/spectral resolution MS bands, either MS and PAN images are fused (i.e., pansharpening) or super resolution (SR) is performed using MS bands only. Nevertheless, existing methods do not utilize the available temporal and spatial information together. In this paper, we propose a multiframe SR algorithm using high-spatial/spectral resolution MS images (i.e., pansharpened), taking advantage of both spatial and temporal data, in order to exceed the spatial resolution of the available PAN bands. We first employ a wavelet-based pansharpening method on a set of MS and PAN images captured at different times. Then, we utilize these pansharpened MS bands in a wavelet-based multiframe SR scheme. The proposed method reveals the inter-wavelet-subband relationship of multitemporal images for SR. We demonstrate our results with comparisons on a Landsat 7 ETM+ dataset. [ABSTRACT FROM AUTHOR]

In this paper, a novel hybrid Taguchi-vortex search algorithm (HTVS) is proposed for solving global optimization problems. Taguchi orthogonal approximation and vortex search algorithm (VS) are hybridized in presenting method. In HTVS, orthogonal arrays in the Taguchi method are trained and obtained better solutions are used to find global optima in VS. Thus, HTVS has better relation between exploration and exploitation, and it exhibits more powerful approximation to find global optimum value. Proposed HTVS algorithm is applied to sixteen well-known benchmark optimization test functions with different dimensions. The results are compared with the Taguchi orthogonal array approximation (TOAA), vortex search algorithm, grey wolf optimizer (GWO), sine cosine algorithm (SCA), moth-flame optimization algorithm (MFO), whale optimization algorithm (WOA) and salp swarm algorithm (SSA). In order to compare the effectiveness of HTVS statistically, Wilcoxon signed-rank test (WSRT) is used in this study. Furthermore, HTVS is applied to two different real engineering problems having some constraints (tension/compression spring design and pressure vessel design). All obtained results suggested that HTVS can find optimal or very close to optimal results. Moreover, it has good computational ability and fast convergence behavior as well. [ABSTRACT FROM AUTHOR]

Recently, the hysteresis motors have a special significance in the nuclear industries. This is because these motors have some advantages such as low noise, high mechanical strength and group feeding ability. They also have some disadvantages that make some limitations for related industries. These disadvantages include low synchronization torque, low power factor, low efficiency, and hunting. One solution to reduce these disadvantages is to combine the hysteresis motor with the PM motor. This however requires a correct and flexible design procedure as well as an appropriate choice of the machine structure. Accordingly, this paper aims to present a new design algorithm for a disc-type permanent magnet hysteresis synchronous (PMHS) hybrid motor. The proposed design algorithm not only tries to retain the positive features of the both motors in the resulted hybrid motor, but it also gives the user the option of determining the percentage of the combination of the two motors. In other words, the user can determine the contribution of each motor to the output power of the resulted motor. The proposed algorithm is finally validated through fabricating a prototype disc-type PMHS motor and evaluating its performance by simulation and experiment. [ABSTRACT FROM AUTHOR]

Low-power consumption is becoming a crucial concern that cannot be neglected in system-on-chip (SoC) designs. Low-power solutions help designers to provide a powerful methodology to analyze, estimate, and optimize today’s power concerns. Early estimation of power at a high level reduces the redesign cycle and turn-around time. Power dissipation is a function of input patterns and their characteristics. This paper describes a power estimation technique by using predefined statistical characteristics-based input patterns, which gives the average power dissipation of individual intellectual property (IP) blocks and interconnects/buses in an SoC system design. During the power estimation phase, the discrete water cycle algorithm is implemented to generate optimized input patterns. Then Monte-Carlo zero-delay simulation approach is used for each individual IP block and buses at a high level. Total system power is the simple addition of average powers of IP blocks and buses. In experiments of entire IP-based SoC system with buses, our modified macromodel gives an average error of 7.91%. Accuracy of the proposed model is improved as compared to our previous model. [ABSTRACT FROM AUTHOR]

An adaptive algorithm for evaluation of singular and near singular integrals in 3D is presented. The algorithm is based on successive adaptive/selective subdivisions of the element until a prescribed error criteria is met. For evaluating the integrals in each subdivision, Gauss quadrature is applied. The method is computationally simple, memory efficient and can be applied for both triangular and quadrilateral elements, including the elements with nonplanar and/or curved surfaces. To assess the method, several examples are discussed. It has shown that the algorithm performs well for singular and near-singular integral examples presented in the paper and evaluates the integrals with very high accuracy. [ABSTRACT FROM AUTHOR]

This paper investigates a multiple sensor fusion based drift compensation technique for a mecanum wheeled mobile robot platform. The mobile robot is equipped with high-precision encoders integrated to the wheels and four accelerometers placed on its chassis. The proposed algorithm combines the information from the encoders and the acceleration sensors to estimate the total drift in the acceleration dimension. The inner loop controller is designed utilizing a disturbance-observer-based acceleration control structure which is blind against the slipping motion of the wheels. The estimated drift acceleration from the sensor fusion is then mapped back to the joint space of the robot and used as additional compensation over the existing controllers. The proposed algorithm is tested on a series of experiments. The results of the experiments are also compared with those of a recent study in order to provide a benchmark evaluation. The enhanced tracking performance yielding towards smaller error magnitudes in the experiments illustrate the efficacy and success of the proposed control architecture in attenuating the positioning drift of mecanum wheeled robots. [ABSTRACT FROM AUTHOR]

The trend of intellectualization and complication of mechanical equipment makes the demand for intelligent diagnostic methods more and more intense in industry. In view of the difficulty of obtaining mechanical fault samples and the requirement of clear and reliable diagnosis results, intelligent diagnosis methods need to adapt to the learning of small samples and have the interpretability of white box model. In this paper, inspired by biological immunity, an intelligent fault diagnosis method was proposed--optimizing b-cell pool clonal selection classification algorithm (OBPCSCA). The OBPCSCA provides a method to construct unique B-cell pools corresponding to specific antigen pools, and uses greedy strategy to generate memory B-cell pools. The experimental comparison with AIRS and AICSL on four UCI benchmark data sets shows that the OBPCSCA has a better balance between the number of memory cells and the accuracy of classification. In particular, compared with AIRS, the OBPCSCA can greatly reduce the number of memory B-cells on the premise of ensuring high classification accuracy. In comparison with the top general classifiers, the OBPCSCA has certain competitiveness in these four data sets. Finally, the algorithm was applied to the bearing data set of Case Western Reserve University for fault diagnosis, and the results showed effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Tick data is a large volume of data, related to a phenomenon such as stock market or weather change, with data values changing rapidly over time. An important issue is to store tick data table in a way that it occupies minimum storage space while at the same time it can provide fast execution of queries. In this paper, a mathematical model is proposed to partition tick data tables into clusters with the aim of minimizing the required storage space. The genetic algorithm is then used to solve the mathematical model which is indeed a clustering model. The proposed method has been evaluated on a real-world weather tick dataset and compared to the storage-optimizing hierarchical agglomerative clustering (SOHAC) algorithm. The experiments show that our proposed method substantially outperforms SOHAC in achieving smaller values for compression ratio while reducing the execution time for small number of clusters. [ABSTRACT FROM AUTHOR]

Complex systems are large scale and involve numerous uncertainties, which means that such systems tend to be expensive to operate. Further, it is difficult to analyze systems of this kind in a real environment, and for this reason agent-based modeling and simulation techniques are used instead. Based on estimation methods, modeling and simulation techniques establish an output set against the existing input set. However, as the data set in a given complex systems becomes very large, it becomes impossible to use estimation methods to create the output set desired. Therefore, a new mechanism is needed to optimize data sets in this context. In this paper, the adaptive modified artificial bee colony algorithm is shown to be successful in optimizing the numerical test function and complex system parameter data sets. Moreover, the results show that this algorithm can be successfully adapted to a given problem. Specifically, this algorithm can be more successful in optimizing problem solving than either the artificial bee colony algorithm or the modified artificial bee colony algorithm. The adaptive modified artificial bee colony algorithm performs a search in response to feedback received from the simulation in run-time. Because of its adaptability, the adaptive modified artificial bee colony algorithm is of great importance for its ability to find solutions to multiple kinds of problems across numerous fields. [ABSTRACT FROM AUTHOR]

Next generation ethernet passive optical network (NG-EPON) is a promising technology to cater huge bandwidth and efficient distribution demands of the future Internet services. IEEE working group has been doing efforts for the standardization of NG-EPON. Four wavelength channels of 25Gbps each are supported by NG-EPON for the transmission of optical network unit (ONU) traffic towards optical line terminal (OLT). Dynamic wavelength and bandwidth allocation (DWBA) algorithms are needed for the efficient arbitration of bandwidth resources between subscribers. In this paper, we have proposed a DWBA algorithm named as efficient bandwidth management algorithm (EBMA) for NG-EPON. EBMA has been designed to manage the diversified traffic requirements of different ONUs. EBMA performance is evaluated comparatively with modified IPACT (M-IPACT) and first fit (FF-DWBA) algorithms. EBMA for NG-EPON proves to be better on the basis of packet delay, packet drop ratio and grant utilization. Simulation results showed improved performance of EBMA in comparison with M-IPACT and FF-DWBA algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of finding a common solution to split generalized mixed equilibrium problem and fixed point problem for quasi-φ-nonexpansive mappings in 2-uniformly convex and uniformly smooth Banach space E1 and a smooth, strictly convex, and reflexive Banach space E2 . An iterative algorithm with Armijo linesearch rule for solving the problem is presented and its strong convergence theorem is established. The convergence result is obtained without using the hybrid method which is mostly used when strong convergence is desired. Finally, numerical experiments are presented to demonstrate the practicability, efficiency, and performance of our algorithm in comparison with other existing algorithms in the literature. Our results extend and improve many recent results in this directio [ABSTRACT FROM AUTHOR]

Accurate indoor localization technologies are currently in high demand in wireless sensor networks, which strongly drive the development of various wireless applications including healthcare monitoring, patient tracking and endoscopic capsule localization. The precise position determination requires exact estimation of the time varying characteristics of wireless channels. In this paper, we address this issue and propose a three-phased scheme, which employs an optimal single stage TDOA/FDOA/AOA indoor localization based on spatial sparsity. The first contribution is to formulate the received unknown signals from the emitter as a compressive sensing problem. Then, we solve an l1 minimization problem to localize the emitter's position. To combat the nonstationary behavior of wireless channels between sensor nodes, the results of our proposed localization algorithm are finally fused using a novel fusion method based on the adaptive normal hedge algorithm. To improve the accuracy of the estimated location, an optimal set of weighed coefficients are derived through introducing a new loss function. Monte Carlo simulation results show that the accuracy of the proposed localization framework is superior compared to the existing indoor localization schemes in low SNR regimes. [ABSTRACT FROM AUTHOR]

In this paper, we present a direct quantum adaptation of the classical shifted power method. The method is very similar to the iterative phase estimation algorithm; however, it does not require any initial estimate of an eigenvector, and as in the classical case its convergence and the required number of iterations are directly related to the eigengap. If the amount of the gap is in the order of 1/poly(n), then the algorithm can converge to the dominant eigenvalue in O(poly(n)) time. The method can be potentially used for solving any eigenvalue related problem and finding minimum/maximum of a data set in lieu of Grover's search algorithm. In addition, if the solution space of an optimization problem with n parameters is encoded as the eigenspace of an 2n dimensional unitary operator in O(poly(n)) time and the eigengap is not too small, then the solution for such a problem can be found in O(poly(n)). As an example, using the quantum gates, we show how to generate the solution space of the quadratic unconstrained binary optimization as the eigenvectors of a diagonal unitary matrix and find the solution for the problem. [ABSTRACT FROM AUTHOR]

Floorplanning is a fundamental design step in the physical design of printed circuit boards (PCBs) and integrated circuits (ICs), as it handles the complexity of layout design. From a computational point of view, the floorplanning problem is an NP hard problem, and the size of the search space grows exponentially with increasing numbers of modules. Thus, the algorithm used is an essential factor for speed and quality of the floorplanning process. Although polynomial-time floorplanning algorithms can be implemented when solution space is limited to slicing floorplans, optimal solutions often exist only in the nonslicing floorplan search space. Various stochastic algorithms such as simulated annealing (SA), the genetic algorithm (GA), and the relay race algorithm (RRA) can be used with nonslicing floorplans. In this paper, a modified relay race algorithm (MRRA) is proposed. Based on the experimental results utilizing MCNC benchmarks, MRRA improved both solution quality and run time for area optimization when compared with SA, GA, and RRA. [ABSTRACT FROM AUTHOR]

Global maximum operating point (GMOP) tracking is an important requirement of solar photovoltaic (PV) systems under partial shading conditions (PSCs). Though the perturb and observe algorithm is simple and effective, it fails to recognize the GMOP. This paper explores the application of the firefly algorithm (FA) to the maximum power point tracking (MPPT) problem of PV systems. In order to determine the shortest path to reach the GMOP under various PSCs, a new fast convergence firefly algorithm (FA) is proposed. Additionally, the change in firefly position is limited to a maximum value identified based on the characteristics of the PSC. The fast convergence method is guaranteed to find the GMOP, avoiding the local operating point obstacle through a repeated space search technique. Using MATLAB, the algorithm is implemented on a model PV system. An experimental 300-W PV system is developed to validate the operating point of the PV system under various PSCs. The proposed method is tested on a 5-kW solar power plant. The results demonstrate that the proposed MPPT algorithm outperforms particle swarm optimization, FA-based MPPTs, and other methods available in the literature. [ABSTRACT FROM AUTHOR]

While deduplication brings the advantage of significant space savings in storage, it nevertheless incurs the overhead of maintaining huge metadata. Updating such huge metadata during the data migration that arises due to load balancing activity results in significant overhead. In order to reduce this metadata update overhead, this paper proposes a suitable alternate index that tracks the data blocks even when they migrate across the nodes without explicitly storing the location information. In addition, a virtual server-based load balancing (VSLB) algorithm has been proposed in order to reduce the migration overhead. The experimental results indicate that the proposed index reduces the metadata update overhead by 74% when compared to the existing index. Furthermore, VSLB reduces the migration overhead by 33% when compared to the existing ID reassignment-based load balancing approach. [ABSTRACT FROM AUTHOR]

This paper proposes a minimal length difference algorithm for construction of a line in an image by solving the problem of optimal contour approximation. In this algorithm, a method for finding interest points is proposed, and the object matching (classification) is done by mapping interest points onto a vector space. In cases where the lines in the representation of the images are not smooth, the algorithm converges rapidly. The results of the experiments showed that for convergence of the contour simplification, there were 5-6 iterations for n = 13. To check how close the curve approximation calculated by the algorithm above, the researchers have calculated the length of the curve simplification manually. This length was then compared to the length of the original curve. The results showed that the length of the simplified curve grew rapidly to 92%-95% of the original curve length. The further increase in the number of points does not affect this indicator. According to the obtained results, the relative difference and the relative difference distance are good metrics to match objects. [ABSTRACT FROM AUTHOR]

In this paper, a new method to deal with automatic speaker verification based on band-limited phaseonly correlation (BLPOC) is proposed. The aim of this study is to validate the use of the BLPOC function as a new limited-data automatic speaker verification technique. Although some speaker verification techniques have high accuracy, efficiency usually depends on the extraction of complex theoretical information from speech signals and the amount of the data for training the algorithms. The BLPOC function is a high-accuracy biometric technique traditionally implemented in human identification by fingerprints (through image-matching). When applying the BLPOC function in automatic speaker verification through the proposed algorithms (under limited-data conditions), a 98.24% true acceptance rate (TAR) and 87.17% true rejection rate (TRR) in a custom database (and 93.75% TAR and 67.05% TRR in the ELSDSR database) were obtained. The proposed algorithm is a theoretically simple method for automatic speaker verification whose main advantage is that it can provide identification under limited-data conditions. In this sense, the BLPOC function could be applicable in other limited-data biometric identifications by sound signals. [ABSTRACT FROM AUTHOR]

Modern power systems are highly complicated and nonlinear in nature. Accurate estimation of the power system states (voltage-magnitude and phase-angle) is required for the secure operation of the power system. The presence of bad-data measurements in meters has made this estimation process challenging. An efficient estimator should detect and eliminate the effect of bad data during the estimation process. Least measurement rejected (LMR) is a robust estimator that has been found successful in dealing with various categories of bad data. The performance of LMR depends upon the proper selection of a tolerance for each measurement. This paper presents a novel approach for tolerance value selection to improve the capability of handling different single and multiple bad-data scenarios successfully. The performance of this updated LMR (ULMR) is compared with weighted least squares, weighted least absolute value, and two versions of LMR from the literature. IEEE 30-bus and 118-bus systems are used to demonstrate the robustness of the proposed estimator under different bad-measurement (single and multiple) scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we investigate approximation properties of the Stancu type generalization of the α-Bernstein operator. We obtain a recurrence relation for moments and the rate of convergence by means of moduli of continuity. Also, we present Voronovskaya and Grüss--Voronovskaya type asymptotic results for these operators. Finally, the study contains numerical considerations regarding the constructed operators based on Maple algorithms. [ABSTRACT FROM AUTHOR]

A numerical semigroup is perfect if it does not have isolated gaps. In this paper we will order the perfect numerical semigroups with a fixed multiplicity. This ordering allows us to give an algorithm procedure to obtain them. We also study the perfect monoid, which is a subset of N that can be expressed as an intersection of perfect numerical semigroups, and we present the perfect monoid generated by a subset of N. We give an algorithm to calculate it. We study the perfect closure of a numerical semigroup, as well as the perfect numerical semigroup with maximal embedding dimension, in particular Arf and saturated numerical semigroups. [ABSTRACT FROM AUTHOR]

The principal function of circuit breakers (CBs) is to isolate a portion of the power network from the rest of it in a timely manner following an opening command. Any failure in the opening operation of CBs, especially following a fault condition, will almost certainly result in a catastrophic event. Therefore, the issue of failure detection of CBs is essential and has a vital role in power system protection. This paper presents a novel power-based algorithm for failure detection of CBs. The power loss of CBs due to an arcing event increases as the arcing time gets longer. The arcing time of CBs increases with defective operations originating from any failure or malfunction for different reasons. The proposed method uses the power difference between the input and output terminals of CBs to calculate the power loss due to an arcing event, which can then be used to estimate the arcing energy. A trip signal is initiated whenever the estimated arcing energy for a single opening operation reaches a predefined trip level, which shows a failure in CB operation. This predefined trip value is related to the integration of the maximum power capacity of the CB during an opening operation time. Computer simulation studies conducted to analyze the performance of the proposed technique demonstrate that the algorithm is capable of discriminating between normal and abnormal arcing energies and thus identifying malfunctions or failures in the opening operations of CBs. [ABSTRACT FROM AUTHOR]

In this paper, we present an efficient retrieval algorithm for encrypted speech based on an inverse fast Fourier transform and measurement matrix. Our approach improves query performance, as well as retrieval efficiency and accuracy, compared to existing content-based encrypted speech retrieval methods. Our proposed algorithm constructs a perceptual hash scheme using perceptual hash sequences from original speech files. By classifying the sequences and applying run-length compression, we decrease the cloud storage required for the hash index. We secure the speech database by encrypting it with Henon chaos scrambling, which offers excellent resistance to attacks. Experimental results show that the robustness, discrimination, and feature extraction efficiency of our proposed method are better than the existing alternatives, with good recall and precision ratios and with high retrieval efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Identification schemes are used to verify identities of parties and signatures. Recently, systems based on multivariate polynomials have been preferred in identification schemes due to their resistance against quantum attacks. In this paper, we propose a quantum secure 3-pass identification scheme based on multivariate quadratic polynomials. We compare the proposed scheme with the previous ones in view of memory requirements, communication length, and computation time. We define an efficiency metric by using impersonation probability and computation time. According to the comparison results, the proposed one has the same computation time as that of Monteiro et al. and reduces impersonation probability compared to the work of Sakumoto et al. We also propose a new signature scheme constructed from the proposed identification scheme. In addition, we compare the signature scheme with the previous schemes in view of signature and key sizes. We improve the signature size compared to that given in previous work by Chen et al. [ABSTRACT FROM AUTHOR]

This paper discusses the effect of nonuniform varying communication delay on distributed consensus algorithms in discrete time. After introducing the delayed mathematical model, we first investigate the ergodicity of the delayed system using the properties of scrambling matrices. Subsequently, the effect of nonuniform varying delay on convergence is examined. It is shown theoretically that nonuniform delay is not detrimental to the convergence rate of the algorithm for directed acyclic graphs. The results are also illustrated with several numerical examples. [ABSTRACT FROM AUTHOR]

This paper describes the design of an automatic parallelization framework. The kernel supplied at its front end was suggested as an instrument for parallel potential assessment. It was used to measure the maximum achievable speedups in the major set of the CHStone benchmark suite programs. In such framework, we suggested the liberation of parallelism incrementally. We proposed a data dependency heuristic-based transformation method to make true dependences dissociation. We generated an internal representation (IR2), where the Banerjee test conditions are met. Two among three of Banerjee test conditions came to be committed. In shared memory many/multicore platforms, the third condition could be satisfied by privatization. We would be able to choose the safe and the opportune pairwise (mapping-privatization) scheme among a number of threads mapping scenarios that become available in the IR2 structure. Instrumentation on a subset of CHStone benchmark was carried out as a validity proof of our proposal, and the results confirmed that our framework kernel is robust. [ABSTRACT FROM AUTHOR]

Images play an essential part in our daily lives and the performance of various imaging applications is dependent on the user's quality of experience. No-reference image quality assessment (NR-IQA) has gained importance to assess the perceived quality, without using any prior information of the nondistorted version of the image. Different NR-IQA techniques that utilize natural scene statistics classify the distortion type based on groups of features and then these features are used for estimating the image quality score. However, every type of distortion has a different impact on certain sets of features. In this paper, a new feature selection algorithm is proposed for distortion identification based image verity and integration evaluation that selects distinct feature groups for each distortion type. The selection procedure is based on the contribution of each feature on the Spearman rank order correlation constant (SROCC) score. Only those feature groups are used in the prediction model that have majority features with SROCC score greater than mean SROCC score of all the features. The proposed feature selection algorithm for NR-IQA shows better performance in comparison to state-of-the-art NR-IQA techniques and other feature selection algorithms when evaluated on three commonly used databases. [ABSTRACT FROM AUTHOR]

Let Tn and Sn be the full transformation semigroup and the symmetric group on Xn = {1, ..., n}, respectively. For n, m ∈ Z+ with m ≤ n - 1 let T(n,m) = {α ∈ Tn : Xmα = Xm}. In this paper we research generating sets and the rank of T(n,m). In particular, we prove that ... for 1 ≤ m ≤ n - 1. [ABSTRACT FROM AUTHOR]

In this paper, we obtain a Grobner-Shirshov (noncommutative Gröbner) basis for the singular part of the Brauer semigroup. It gives an algorithm for getting normal forms and hence an algorithm for solving the word problem in these semigroups. [ABSTRACT FROM AUTHOR]

The aim of this paper is to find new iterative Newton-like schemes inspired by the modified Newton iterative algorithm and prove that these iterations are faster than the existing ones in the literature. We further investigate their behavior and finally illustrate the results by numerical examples. [ABSTRACT FROM AUTHOR]