Quasi-interpolation on the Body Centered Cubic Lattice

This paper introduces a quasi-interpolation method for reconstruction of data sampled on the Body Centered Cubic (BCC) lattice. The reconstructions based on this quasi-interpolation achieve the optimal approximation order offered by the shifts of the quintic box spline on the BCC lattice. We also present a local FIR filter that is used to filter the data for quasi-interpolation. We document the improved quality and fidelity of reconstructions after employing the introduced quasi-interpolation method. Finally the resulting quasi-interpolation on the BCC sampled data are compared to the corresponding quasi-interpolation method on the Cartesian sampled data.     

Clustering the wireless Ad Hoc networks: A distributed learning automata approach

In Ad Hoc networks, the performance is significantly degraded as the size of the network grows. The network clustering by which the nodes are hierarchically organized on the basis of the proximity relieves this performance degradation. Finding the weakly connected dominating set (WCDS) is a promising approach for clustering the wireless Ad Hoc networks. Finding the minimum WCDS in the unit disk graph is an NP-Hard problem, and a host of approximation algorithms has been proposed. In this article, we first proposed a centralized approximation algorithm called DLA-CC based on distributed learning automata (DLA) for finding a near optimal solution to the minimum WCDS problem. Then, we propose a DLA-based clustering algorithm called DLA-DC for clustering the wireless Ad Hoc networks. The proposed cluster formation algorithm is a distributed implementation of DLA-CC, in which the dominator nodes and their closed neighbors assume the role of the cluster-heads and cluster members, respectively. In this article, we compute the worst case running time and message complexity of the clustering algorithm for finding a near optimal cluster-head set. We argue that by a proper choice of the learning rate of the clustering algorithm, a trade-off between the running time and message complexity of algorithm with the cluster-head set size (clustering optimality) can be made. The simulation results show the superiority of the proposed algorithms over the existing methods.     

A Learning Automata-Based Cognitive Radio for Clustered Wireless Ad-Hoc Networks

In current wireless networks, the radio systems are regulated by a fixed spectrum assignment strategy. This policy partitions the whole radio spectrum into a fixed number of radio ranges, each exclusively assigned to a specific user. Such a spectrum assignment strategy leads to an undesirable condition under which some systems only use a small portion of the allocated spectrum while the others have very serious spectrum insufficiency. The learning automata-based cognitive radio which is proposed in this paper is a highly potential technology to address the spectrum scarcity challenges in wireless ad hoc networks. This paper proposes a learning automata-based dynamic frame length TDMA scheme for slot assignment in clustered wireless ad-hoc networks with unknown traffic parameters, where the intra-cluster communications are scheduled by a TDMA scheme, and a CDMA scheme is overlaid on the TDMA to handle an interference-free inter-cluster communication. In this method, each cluster-head is responsible for a collision-free slot assignment within the cluster and determines the input traffic parameters of its own cluster members. It then takes these traffic parameters into consideration for an optimal channel access scheduling in the cluster. The medium access control layer in each cluster is based on a time division multiple access (TDMA) scheme, in which each host is assigned a fraction of the TDMA frame proportional to its traffic load. The simulation experiments show the superiority of our proposed slot assignment algorithm over the existing methods in terms of the channel utilization, control overhead, and throughput, specifically, under bursty traffic conditions.     

A learning automata-based heuristic algorithm for solving the minimum spanning tree problem in stochastic graphs

During the last decades, a host of efficient algorithms have been developed for solving the minimum spanning tree problem in deterministic graphs, where the weight associated with the graph edges is assumed to be fixed. Though it is clear that the edge weight varies with time in realistic applications and such an assumption is wrong, finding the minimum spanning tree of a stochastic graph has not received the attention it merits. This is due to the fact that the minimum spanning tree problem becomes incredibly hard to solve when the edge weight is assumed to be a random variable. This becomes more difficult if we assume that the probability distribution function of the edge weight is unknown. In this paper, we propose a learning automata-based heuristic algorithm to solve the minimum spanning tree problem in stochastic graphs wherein the probability distribution function of the edge weight is unknown. The proposed algorithm taking advantage of learning automata determines the edges that must be sampled at each stage. As the presented algorithm proceeds, the sampling process is concentrated on the edges that constitute the spanning tree with the minimum expected weight. The proposed learning automata-based sampling method decreases the number of samples that need to be taken from the graph by reducing the rate of unnecessary samples. Experimental results show the superiority of the proposed algorithm over the well-known existing methods both in terms of the number of samples and the running time of algorithm.     

Learning automata based multi‐agent system algorithms for finding optimal policies in Markov games

Markov games, as the generalization of Markov decision processes to the multi‐agent case, have long been used for modeling multi‐agent systems (MAS). The Markov game view of MAS is considered as a sequence of games having to be played by multiple players while each game belongs to a different state of the environment. In this paper, several learning automata based multi‐agent system algorithms for finding optimal policies in Markov games are proposed. In all of the proposed algorithms, each agent residing in every state of the environment is equipped with a learning automaton. Every joint‐action of the set of learning automata in each state corresponds to moving to one of the adjacent states. Each agent moves from one state to another and tries to reach the goal state. The actions taken by learning automata along the path traversed by the agent are then rewarded or penalized based on the comparison of the average reward received by agent per move along the path with a dynamic threshold. In the second group of the proposed algorithms, the concept of entropy has been imported into learning automata based multi‐agent systems to improve the performance of the algorithms. To evaluate the performance of the proposed algorithms, computer experiments have been conducted. The results of experiments have shown that the proposed algorithms perform better than the existing algorithms in terms of speed and accuracy of reaching the optimal policy. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

An adaptive call admission algorithm for cellular networks

In this paper, we first propose a new continuous action-set learning automaton and theoretically study its convergence properties and show that it converges to the optimal action. Then we give an adaptive and autonomous call admission algorithm for cellular mobile networks, which uses the proposed learning automaton to minimize the blocking probability of the new calls subject to the constraint on the dropping probability of the handoff calls. The simulation results show that the performance of the proposed algorithm is close to the performance of the limited fractional guard channel algorithm for which we need to know all the traffic parameters in advance.     

Weighted Steiner Connected Dominating Set and its Application to Multicast Routing in Wireless MANETs

In this paper, we first propose three centralized learning automata-based heuristic algorithms for approximating a near optimal solution to the minimum weight Steiner connected dominating set (WSCDS) problem. Finding the Steiner connected dominating set of the network graph is a promising approach for multicast routing in wireless ad-hoc networks. Therefore, we present a distributed implementation of the last approximation algorithm proposed in this paper (Algorithm III) for multicast routing in wireless mobile ad-hoc networks. The proposed WSCDS algorithms are compared with the well-known existing algorithms and the obtained results show that Algorithm III outperforms the others both in terms of the dominating set size and running time. Our simulation experiments also show the superiority of the proposed multicast routing algorithm over the best previous methods in terms of the packet delivery ratio, multicast route lifetime, and end-to-end delay.     

Utilizing Acidic Sprays for Eliminating Salmonella enterica on Raw Almonds

ABSTRACT In‐shell or shelled almonds inoculated with Salmonella were sprayed with water, acetic acid, citric acid, acidified sodium chlorite, peroxyacetic acid, or a mixture of citric, hydrochloric, and phosphoric acids before testing for Salmonella on xylose‐lysine‐deoxycholate (XLD) agar and tryptic soy agar (TSA). Spraying acids on in‐shell almonds reduced about 0.48 to 1.88 and 0.22 to 0.67 log colony‐forming units (CFU)/g of Salmonella on XLD agar and TSA counts, respectively, on the in‐shell almonds but had no effect on the edible portion of the almonds (shelled almonds). When spraying acids on shelled almonds, a single spray application (1.6 mL acid solution/25 g of shelled almonds) with 1 min holding caused 0.72 to 1.93 and 0.38 to 1.35 log CFU/ g reductions of Salmonella counts on XLD agar and TSA, respectively. Increasing the holding time to 5 min did not enhance the reductions. When spraying shelled almonds, increasing the number of application (1 to 3 times) enhanced Salmonella reduction. Except for the peroxyacetic acids, increasing total holding time (5 to 120 min) improved the efficacies. Furthermore, increasing acid concentrations improved the efficacies of acetic, citric, and peroxyacetic acid treatments. Estimated 5‐log reductions on both TSA and XLD counts can be achieved under laboratory conditions using 10% citric acid by (1) the combination of shelling, 1 spraying, and 3 d of storage, (2) the combination of shelling, 2 sprayings, and 1 d of storage, or (3) the combination of shelling and 3 sprayings. Acidic sprays may be utilized for enhancing the microbiological safety of raw nuts.     

Effect of graphene nanoplatelets presence on the morphology,structure, and thermal properties of polypropylene in fiber melt‐spinning process

Melt spinning of graphene nanoplatelets (GnPs)‐polypropylene (PP) nanocomposite fibers are reported for the first time. PP/GnPs fibers were spun with a pilot‐plant spinning machine with varying concentration of GnPs by mixing PP/GnPs masterbatch with PP. The effect of inclusion of GnPs on the morphology and crystalline structure of PP fibers was investigated. The thermal stability of the fibers was also evaluated by thermogravimetric analysis. The light microscopy images showed that the GnPs are uniformly distributed over the PP matrix. The differential scanning calorimetry (DSC) results revealed that presence of GnPs affects both the melting and crystallization behaviors. The melting peaks of PP/GnPs nanocomposite fibers were broader than that of neat PP fibers, indicating a broader crystal size distribution in PP/GnPs nanocomposite fibers as compared to the neat PP fibers. Besides, an obvious increment in the crystallization peak temperature was observed in GnPs‐PP nanocomposite fibers. The wide‐angle X‐ray diffraction spectra (WAXD) results showed that the crystal type of nanocomposite fibers did not change and was still the α‐monoclinic crystal form. Moreover, the morphology of spherulites demonstrated that GnPs increased the nucleation sites in the nanocomposite fibers which in turn restricted the crystal growth of PP chains. This finding supported the DSC and WAXD results. Activation energies were calculated by Horowitz and Metzger's method as 77.87 and 105.41 kJ/mol for neat PP and PP/0.2 wt% GnPs fibers, respectively, suggesting an increase in the thermal stability of GnPs‐PP nanocomposite fibers. POLYM. COMPOS., 36:367–375, 2015. © 2014 Society of Plastics Engineers