Greedy–knapsack algorithm for optimal downlink resource allocation in LTE networks

The long term evolution as a mobile broadband technology supports a wide domain of communication services with different requirements. Therefore, scheduling of all flows from various applications in overload states in which the requested amount of bandwidth exceeds the limited available spectrum resources is a challenging issue. Accordingly, in this paper, a greedy algorithm is presented to evaluate user candidates which are waiting for scheduling and select an optimal set of the users to maximize system performance, without exceeding available bandwidth capacity. The greedy–knapsack algorithm is defined as an optimal solution to the resource allocation problem, formulated based on the fractional knapsack problem. A compromise between throughput and QoS provisioning is obtained by proposing a class-based ranking function, which is a combination of throughput and QoS related parameters defined for each application. The simulation results show that the proposed method provides high performance in terms of throughput, loss and delay for different classes of QoS over the existing ones, especially under overload traffic.     

Two-dimensional Legendre wavelets for solving fractional Poisson equation with Dirichlet boundary conditions

In this paper, the two-dimensional Legendre wavelets are applied for numerical solution of the fractional Poisson equation with Dirichlet boundary conditions. In this way, a new operational matrix of fractional derivative for the Legendre wavelets is derived and then this operational matrix has been employed to obtain the numerical solution of the above-mentioned problem. The main characteristic behind this approach is that it reduces such problems to those of solving a system of algebraic equations which greatly simplifies the problem. The convergence of the two-dimensional Legendre wavelets expansion is investigated. Also the power of this manageable method is illustrated.     

Algorithm for solving product mix problem in two-constraint resources environment

The theory of constraints is an approach to production planning and control that emphasizes on the constraints in the system to increase throughput. One application in the theory of constraints is product mix decision. The objective of this paper is to present an algorithm to determine the product mix in a two-constraint resources environment. The theory of constraints solution could not reach optimum solution and has the risk of being infeasible when multiple constraint resources exist. The proposed algorithm is suitable for improving solutions obtained from theory of constraints and could provide throughput in product mix problems. Some alternatives are compared in this paper: the standard theory of constraints, integer linear programming, tabu search, hybrid tabu simulated annealing, and proposed algorithm solution. The numerical result shows the advantages of the proposed algorithm.     

An interactive algorithm for multi-objective flow shop scheduling with fuzzy processing time through resolution method and TOPSIS

This paper develops a method for solving a multi-objective flow shop scheduling in a fuzzy environment where processing times are fuzzy numbers. The objective functions are designed to simultaneously minimize the makespan (completion time), the mean flow time, and the machine idle time. For each objective function, a fuzzy subset in the decision space whose membership function represents the balance between feasibility degree of constraints and satisfaction degree of the goal is defined. Then, technique for order preference by similarity to an ideal solution (TOPSIS) method finds the nondominated solution in a multiple objective state. The TOPSIS method and the interactive resolution method are integrated in the proposed method to solve the multi-objective flow shop scheduling problem. One of the new contributions of this research is combining these two methods in solving this problem. The proposed algorithm provides a way to find a crisp solution for the fuzzy flow shop scheduling in a multi-objective state. Also, the proposed method yields a reasonable solution that represents the balance between the feasibility of a decision vector and the optimality for an objective function by the interactive participation of the decision maker in all steps of decision process. Application of the proposed method to flow shop scheduling is shown with two numerical examples. The results show that the algorithm could be applied for determining the most preferable sequence by finding a nondominated solution for different degrees of satisfaction of constraints, and with regard to objective value, where processing time is fuzzy.     

A multi-objective supply chain model integrated with location of distribution centers and supplier selection decisions

Today, in the competitive markets, supply chain works as an interrelating network of suppliers, manufacturers, distributors, and customers, to satisfy customer demands. Coordinating all parts of the supply chain is a multi-level process and needs important decisions that affect on the performance of each part and whole supply chain and some conflicting objectives should be considered. In this paper, the supplier selection problem is integrated with production decision and distributor location problems and a new mathematical model is proposed. Delivery time, total cost, and quality are the objective functions considered and weighting method is used to solve the multi-objective problem. A modified genetic algorithm is proposed to obtain Pareto optimal solution sets. Computational experiments are included for validation of proposed algorithm. The results show that the proposed algorithm gives high-quality solutions as well as better computational times.     

Effect of NBR on epoxy/glass prepregs properties

Solid acrylonitrile‐butadiene rubber (NBR) was used in epoxy resin for toughening and also for increasing the tack of epoxy/glass prepregs. The NBR used in this study was a rubber with 33% acrylonitrile content. The changes in thermal and mechanical properties such as glass transition temperature (T_g), curing characteristics and lap‐shear strength have been studied. For this purpose, three types of prepregs with two levels of NBR content of 3 and 5%, were prepared. Prepregs were made by solvent type impregnation apparatus. In this method, resin impregnates satin textile glass fiber under the controlled and constant condition of line speed and oven temperature. Prepregs were B‐staged for about 3%. The cure characterization, T_g and flow behavior were evaluated using differential scanning calorimetry and rheological analysis. Results showed that increasing the rubber content caused the following effects: (a) delay in gel time of prepregs, (b) increase in activation energy of prepregs, and (c) decrease in total heat of curing reaction. It is interesting that NBR increased the tack of epoxy/glass prepreg but, had no effect on its resin flow behavior. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Applying the noising method to find the best regression model

Regression analysis is one of the most applicable methods in statistical methodology used to find the best regression model according to the relationship among several variables in a system. The estimation of regression model, which is solved as a formulate optimization problem and making use of heuristic algorithms, is much simpler and faster than classic methods. Genetic algorithm (GA) as one of the heuristic algorithms had been used to solve this problem. In this paper, we extend the noising method as a recent combinatorial optimization problem to estimate the best regression model and evaluate its performances compared to GA. Also, in order to enhance the performance of our GA, we apply the Taguchi experimental design method to tune the parameters of the algorithm.     

Optimization of background suppression for arterial spin labeling perfusion imaging

Object  

To present an algorithm for optimization of background suppression pulse timing for arterial spin labeling (ASL) perfusion imaging.     

Spoofing detection in IEEE 802.15.4 networks based on received signal strength

The shared medium used in wireless networks makes them vulnerable to spoofing attacks, in which an adversary masquerades as one or more legitimate nodes to disturb normal operation of the network. In this paper we present a novel spoofing detection method for static IEEE 802.15.4 networks based on spatial correlation property of received signal strength (RSS). While most existing RSS based techniques directly process RSS values of the received frames and rely on multiple traffic air monitors (AMs) to provide an acceptable detection performance, we extract features of RSS streams to reduce data redundancy and provide a more distinguishable representation of the data. Our algorithm employs two features of RSS streams, summation of detailed coefficients (SDCs) in discrete Haar wavelet transform (DHWT) of the RSS streams and the ratio of out-of-bound frames. We show that in a typical scenario, a single AM with SDC as detection parameter, can theoretically outperform a system with 12 AMs which directly applies RSS values as detection parameter. Using ratio of out-of-bound frames facilitates detection of high rate attacks. In addition, we suggest adaptive learning of legitimate RSS values which enhances the robustness of the attack detector against environmental changes. Using both magnitude and frequency related features, we achieved high detection performance with a single AM; this enables development of preventive measures for spoofing attacks. The performance of our approach was evaluated through an IEEE 802.15.4 testbed in an office environment. Experimental results along with theoretical analysis show that the proposed method outperforms the existing RSS-based spoofing detection solutions. Using a single AM, we were able to attain 94.75% detection rate (DR) with 0.56% false positive rate (FPR). For 4 AMs, the results improved to 99% DR and 0% FPR.     

Highly Elastic Micropatterned Hydrogel for Engineering Functional Cardiac Tissue

Heart failure is a major international health issue. Myocardial mass loss and lack of contractility are precursors to heart failure. Surgical demand for effective myocardial repair is tempered by a paucity of appropriate biological materials. These materials should conveniently replicate natural human tissue components, convey persistent elasticity, promote cell attachment, growth and conformability to direct cell orientation and functional performance. Here, microfabrication techniques are applied to recombinant human tropoelastin, the resilience‐imparting protein found in all elastic human tissues, to generate photocrosslinked biological materials containing well‐defined micropatterns. These highly elastic substrates are then used to engineer biomimetic cardiac tissue constructs. The micropatterned hydrogels, produced through photocrosslinking of methacrylated tropoelastin (MeTro), promote the attachment, spreading, alignment, function, and intercellular communication of cardiomyocytes by providing an elastic mechanical support that mimics their dynamic mechanical properties in vivo. The fabricated MeTro hydrogels also support the synchronous beating of cardiomyocytes in response to electrical field stimulation. These novel engineered micropatterned elastic gels are designed to be amenable to 3D modular assembly and establish a versatile, adaptable foundation for the modeling and regeneration of functional cardiac tissue with potential for application to other elastic tissues.