Nature‐inspired meta‐heuristic algorithms for solving the load balancing problem in the software‐defined network

The growth of the networks has difficult network management. Recently, a concept called software‐defined network (SDN) has been proposed to address this issue, which makes network management more adaptable. Control and forwarding planes are separated in SDN. The control plane is a centralized logical controller that controls the network. The forwarding plane that consists of transfer devices is responsible for transmitting packets. Because the network resources are limited, optimizing the use of resources in the networks is an important issue. Load balancing improves the balanced distribution of loads across multiple resources in order to maximize the reliability and network resources efficiency. SDN controllers can create an optimal load balancing compared to traditional networks because they have a network global view. The load‐balancing problem can be solved using many different nature‐inspired meta‐heuristic techniques because it has the NP‐complete nature. Hence, for solving load balancing problem in SDN, nature‐inspired meta‐heuristic techniques are important methods. However, to the best of our knowledge, there is not a survey or systematic review on studying these matters. Accordingly, in the area of the load balancing in the SDN, this paper reviews systematically the nature‐inspired meta‐heuristic techniques. Also, this study demonstrates advantages and disadvantages regarded of the chosen nature‐inspired meta‐heuristic techniques and considers their algorithms metrics. Moreover, to apply better load balancing techniques in the future, the important challenges of these techniques have been investigated.     

A New Algorithm for the Elimination of Common Subexpressions in Hardware Implementation of Digital Filters by Using Genetic Programming

A new algorithm based on Genetic Programming (GP) for the problem of optimization of Multiple constant Multiplication (MCM) by Common Subexpression Elimination(CSE) is developed. This method is used for hardware optimization for DSP systems. A solution based on Genetic Programming is shown in this paper. The performance of the technique is demonstrated in one- and multi-dimensional digital filters with constant coefficients.     

A rapid smart approach for reliability and failure mode analysis of memories and large systems

This paper presents a novel system simulation methodology based on the known Monte Carlo technique, used for reliability and failure mode analysis of complex and large systems. The presented approach, called “state-merging and assorted random-testing” (SMART), is particularly applicable to systems involving different types of clusters of identical components, and is ideally suited for simulation of huge memories and similar systems. Simulators based on this approach are insensitive to the number of system components, system reliability or the number of associated spares or standby units, and thus they afford an extremely small simulation time compared to the accelerated Monte Carlo simulation time.     

Modeling the heat of vaporization of petroleum fractions and pure hydrocarbons

The present contribution was aimed to estimate the vaporization enthalpy of petroleum fractions and pure hydrocarbons by using the combination of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) called GA-ANFIS. This tool can approximate the vaporization enthalpy as a function of the specific gravity, molecular weight, and boiling point temperature with high accuracy based on 122 data gathered from the previously published literature. Furthermore, results from the proposed model have been compared with different correlations and its acceptable predictive ability against other correlations was proved in order to the estimation of the vaporization enthalpy. The percentage of absolute relative deviation and R-squared (R²) was 1.64% and 0.9967%, respectively. This tool is simple to use and can be of considerable help for petroleum engineers to have an accurate estimation of vaporization enthalpy of hydrocarbon fractions of pure hydrocarbons.     

Two-stage thermocatalytic upgrading of fuel oil to olefins and fuels over a nanoporous hierarchical acidic catalyst

A two-stage thermocatalytic upgrading process using a novel catalyst was investigated to produce light olefins and liquid fuels from fuel oil. The upgraded oil from the first thermal stage demonstrated lower viscosity and higher crackability compared to the virgin feedstock. In the next step, the vapor-phase catalytic cracking of the upgraded fraction was implemented over a novel nanoporous composite catalyst, characterized by the XRD, FTIR, NH₃- TPD, and N₂ physisorption techniques. In total, more than 55?wt% of light olefins, particularly propylene (25.5?wt%) together with 25.4?wt% and 32.5?wt% of gasoline and diesel fuel were obtained in this process.     

Optimal visual sensor placement for coverage based on target location profile

The proper placement of visual sensors across a sensor field for covering targets with arbitrary location and orientation is a mission-critical decision in surveillance applications. The specifics of sensor deployment in these applications not only determine the maximum achievable coverage, but it also affects the quality of the target’s appearance in cameras for subsequent use in vision tasks. However, the inaccuracies inherent in localization techniques and the lack of knowledge regarding the target orientation render existing proposals insufficient for real-life scenarios. In this paper, we address both challenges. First, we extend the conventional point representation of targets with a circular model to account for full-angle coverage of targets with known location yet with unknown orientation from all directions. We then assume, in the absence of precise location information, a trajectory profile of targets could instead be generated through the importance sampling of the environment, indicating spots where the target is most likely located. This profile-based abstraction enables us to capture the uncertainty in target’s location by encircling every agglomeration of proximal samples within one cluster. Each cluster can then be viewed as a virtual macroscopic circular target for which we formulate the coverage problem in terms of a Binary Integer Programming (BIP) model. We have also taken into account the presence of obstruction in between multiple targets by calculating the angles of view of the sensors in an occlusion-dependant manner, effectively determining optimal placement for maximal instead of full-angle coverage. Evaluation results derived from our simulation experiments reveal that the proposed mechanism can effectively achieve high coverage accuracy with minimum number of deployed sensors.     

A partial-SOI LDMOSFET with triangular buried-oxide for breakdown voltage improvement

In this paper, a near-triangular buried-oxide partial silicon-on-insulator (TB-PSOI) lateral double-diffused MOS field-effect transistor is proposed. The electric field and electrostatic potential in this structure are modified by the gradual buried-oxide thickness increase. The modification includes the addition of a new peak in the electric field in comparison to that of the conventional PSOI. To assess the efficiency of the proposed structure, its breakdown voltage is compared with that of conventional PSOI using two-dimensional simulations. A comparative study is performed in terms of silicon-film and buried-oxide layer thicknesses, drift region and buried-oxide layer lengths, and drift region doping concentrations. The study shows that under the same drain current, the breakdown voltage of TB-PSOI is nearly two times higher than its PSOI counterpart (108% improvement). Simulation results show that the three-stepped oxide layer closely follows the TB-PSOI structure with a breakdown voltage improvement of 96% compared to that of the PSOI structure.     

Effect of light,acidified processing and storage on carbohydrates and other nutrients in mung bean sprouts

Fresh 4-day-old etiolated mung bean seedlings were analysed for proteins, carbohydrates and lipids, and vitamins A and C. Exposure of the sprouts to artificial light for 24 h increased the content of most constituents, especially provitamin A. Changes in the composition of the seedlings after preservation and storage for 6 months were followed. The sprouts were either canned or bottled, and then kept at various storage temperatures, regardless of storage conditions, hydrolysis of complex carbohydrates took place. There is some evidence that the reducing sugars released during storage underwent a Maillard reaction, leading to browning of the sprouts.     

Local oscillator chain for 1.55 to 1.75THz with 100-/spl mu/W peak power

We report on the design and performance of a fix-tuned /spl times/2/spl times/3/spl times/3 frequency multiplier chain that covers 1.55-1.75THz. The chain is nominally pumped with 100mW at W-band. At 120K the measured output power is larger than 4/spl mu/W across the band with a peak power of 100/spl mu/W at 1.665THz. A similar chain operated at room temperature produced a peak power of 21/spl mu/W. These power levels now make it possible to deploy multipixel heterodyne imaging arrays in this frequency range.