A Novel Evolutionary-Based Cooperative Spectrum Sensing Mechanism for Cognitive Radio Networks

Cognitive radio (CR) is considered as a feasible intelligent technology for 4G wireless networks or self-organization networks and envisioned as a promising paradigm of exploiting intelligence for enhancing efficiency of underutilized spectrum bands. In CR, one of the main concerns is to reliably sense the presence of primary users, to attain protection against harmful interference caused by the potential spectrum access of secondary users (SUs). In this paper, evolutionary algorithms, namely, genetic algorithm (GA) and particle swarm optimization (PSO) are investigated. An imperialistic competitive algorithm (ICA) is proposed to minimize error detection at the common soft data fusion (SDF) center for structurally centralized cognitive radio network (CRN). By using these techniques, evolutionary operations are invoked to optimize the weighting coefficients applied on the sensing measurement components received from multiple cooperative SUs. The proposed method is compared with other evolutionary algorithms, as well as other conventional deterministic, such as maximal ratio combining- (MRC-), modified deflection coefficient- (MDC-), normal deflection coefficient- (NDC-) based SDF schemes and OR-rule HDF based. MATLAB simulations confirm the superiority of the ICA-based scheme over the PSO-, GA-based and other conventional schemes in terms of detection performance. In addition, the ICA-based scheme also shows promising convergence and time running performance as compared to other iterative-based schemes. This makes ICA an adequate solution to meet real-time requirements.     

Characterization of Epitaxial Indium Nitride Interlayers for Ohmic Contacts to Silicon Carbide

Ohmic contacts to n-type 4H- and 6H-SiC without postdeposition annealing were achieved using an interlayer of epitaxial InN beneath a layer of Ti. The InN films were grown by reactive dc magnetron sputtering at 450°C, whereas the Ti films were deposited by electron-beam evaporation at room temperature. The InN films were characterized by x-ray diffraction (XRD), secondary electron microscopy (SEM), cross-sectional transmission electron microscopy (TEM), and Hall-effect measurements. Both XRD and TEM observations revealed that the Ti and InN films have epitaxial relationships with the 6H-SiC substrate as follows: (0001)[]_Ti∥(0001)[]_InN∥(0001)[]_6H-SiC. The Ti/InN/SiC contacts displayed ohmic behavior, whereas Ti/SiC contacts (without an InN interlayer) were nonohmic. These results suggest that InN reduces the Schottky barrier height at the SiC surface via a small conduction-band offset and support previous reports of an electron accumulation layer at the surface of InN.     

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.     

On the prediction of solubility of alkane in carbon dioxide using the LSSVM algorithm

The increasing global energy demand and declination of oil reservoir in recent years cause the researchers attention focus on the enhancement of oil recovery approaches. One of the extensive applicable methods for enhancement of oil recovery, which has great efficiency and environmental benefits, is carbon dioxide injection. The CO₂ injection has various effects on the reservoir fluid, which causes enhancement of recovery. One of these effects is extraction of lighter components of crude oil, which straightly depends on solubility of hydrocarbons in carbon dioxide. In order to better understand of this parameter, in this study, Least squares support vector machine (LSSVM) algorithm was developed as a novel predictive tool to estimate solubility of alkane in CO₂ as function of carbon number of alkane, carbon dioxide density, pressure, and temperature. The predicting model outputs were compared with the extracted experimental solubility from literature statistically and graphically. The comparison showed the great ability and high accuracy of developed model in prediction of solubility.     

The effect of nanoparticles on wettability alteration for enhanced oil recovery: micromodel experimental studies and CFD simulation

The applications of nanotechnology in oilfields have attracted the attention of researchers to nanofluid injection as a novel approach for enhanced oil recovery. To better understand the prevailing mechanisms in such new displacement scenarios, micromodel experiments provide powerful tools to visually observe the way that nanoparticles may mobilize the trapped oil. In this work, the effect of silicon oxide nanoparticles on the alteration of wettability of glass micromodels was investigated in both experimental and numerical simulation approaches. The displacement experiments were performed on the original water-wet and imposed oil-wet (after aging in stearic acid/n-heptane solution) glass micromodels. The results of injection of nanofluids into the oil-saturated micromodels were then compared with those of the water injection scenarios. The flooding scenarios in the micromodels were also simulated numerically with the computational fluid dynamics (CFD) method. A good agreement between the experimental and simulation results was observed. An increase of 9% and 13% in the oil recovery was obtained by nanofluid flooding in experimental tests and CFD calculations, respectively.     

Experimental investigation into Fe3O4/SiO2 nanoparticle performance and comparison with other nanofluids in enhanced oil recovery

Nanofluids because of their surface characteristics improve the oil production from reservoirs by enabling different enhanced recovery mechanisms such as wettability alteration, interfacial tension (IFT) reduction, oil viscosity reduction, formation and stabilization of colloidal systems and the decrease in the asphaltene precipitation. To the best of the authors’ knowledge, the synthesis of a new nanocomposite has been studied in this paper for the first time. It consists of nanoparticles of both SiO₂ and Fe₃O₄. Each nanoparticle has its individual surface property and has its distinct effect on the oil production of reservoirs. According to the previous studies, Fe₃O₄ has been used in the prevention or reduction of asphaltene precipitation and SiO₂ has been considered for wettability alteration and/or reducing IFTs in enhanced oil recovery. According to the experimental results, the novel synthesized nanoparticles have increased the oil recovery by the synergistic effects of the formed particles markedly by activating the various mechanisms relative to the use of each of the nanoparticles in the micromodel individually. According to the results obtained for the use of this nanocomposite, understanding reservoir conditions plays an important role in the ultimate goal of enhancing oil recovery and the formation of stable emulsions plays an important role in oil recovery using this method.     

Disaster-survivable cloud-network mapping

Cloud-computing services are provided to consumers through a network of servers and network equipment. Cloud-network (CN) providers virtualize resources [e.g., virtual machine (VM) and virtual network (VN)] for efficient and secure resource allocation. Disasters are one of the worst threats for CNs as they can cause massive disruptions and CN disconnection. A disaster may also induce post-disaster correlated, cascading failures which can disconnect more CNs. Survivable virtual-network embedding (SVNE) approaches have been studied to protect VNs against single physical-link/-node and dual physical-link failures in communication infrastructure, but massive disruptions due to a disaster and their consequences can make SVNE approaches insufficient to guarantee cloud-computing survivability. In this work, we study the problem of survivable CN mapping from disaster. We consider risk assessment, VM backup location, and post-disaster survivability to reduce the risk of failure and probability of CN disconnection and the penalty paid by operators due to loss of capacity. We formulate the proposed approach as an integer linear program and study two scenarios: a natural disaster, e.g., earthquake and a human-made disaster, e.g., weapons-of-mass-destruction attack. Our illustrative examples show that our approach reduces the risk of CN disconnection and penalty up to 90 % compared with a baseline CN mapping approach and increases the CN survivability up to 100 % in both scenarios.