An efficient cooperative technique for power-constrained multiuser wireless network

Cooperative communication plays an important role in wireless networks by improving network connectivity, spectrum efficiency, power, and communication reliability. Moreover, cooperative communication also facilitates the development of a well-organized approach in order to improve the quality of wireless terminals. Besides, it enables the utilisation of communication resources by allowing the nodes and pathways in a network to cooperate with one another via data transmissions. To control a wireless network, cooperative communication must manage its power to improve a network’s energy efficiency, capacity and reliability. When information is transmitted at a higher power, this decreases the lifespans of both the nodes and the network itself. Thus, controlling over the transmission of power is essential to obtain a sufficient level of bit-error-rate (BER) performance at the receiver. Relay nodes can improve system performance by reducing power consumption. Moreover, the decode-and-forward method is one of the best cooperative relay protocols that can be used to achieve better system performance in power constraints and BERs. In the present paper, system model containing source, destination and relay node is analysed. One cooperative scheme which including decode and forward is employed and investigated. At the experimental and simulation levels, the present paper showed that the power in the transmitters was observed and calculated in order to show the savings which are resulting from the use of relay nodes.     

Towards Improving Predictive Statistical Learning Model Accuracy by Enhancing Learning Technique

The accuracy of the statistical learning model depends on the learning technique used which in turn depends on the dataset’s values. In most research studies, the existence of missing values (MVs) is a vital problem. In addition, any dataset with MVs cannot be used for further analysis or with any data driven tool especially when the percentage of MVs are high. In this paper, the authors propose a novel algorithm for dealing with MVs depending on the feature selection (FS) of similarity classifier with fuzzy entropy measure. The proposed algorithm imputes MVs in cumulative order. The candidate feature to be manipulated is selected using similarity classifier with Parkash’s fuzzy entropy measure. The predictive model to predict MVs within the candidate feature is the Bayesian Ridge Regression (BRR) technique. Furthermore, any imputed features will be incorporated within the BRR equation to impute the MVs in the next chosen incomplete feature. The proposed algorithm was compared against some practical state-of-the-art imputation methods by conducting an experiment on four medical datasets which were gathered from several databases repository with MVs generated from the three missingness mechanisms. The evaluation metrics of mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R² score) were used to measure the performance. The results exhibited that performance vary depending on the size of the dataset, amount of MVs and the missingness mechanism type. Moreover, compared to other methods, the results showed that the proposed method gives better accuracy and less error in most cases.     

Context-aware multimodal recommendations of multimedia data in cyber situational awareness

Multimedia Tools and Applications - The current proliferation of large amounts of multimedia data creates an unprecedented challenge for security analysts in the context of Cyber Situational...     

Convective Poiseuille flow of Al2O3-EG nanofluid in a porous wavy channel with thermal radiation

In current article, convective Poiseuille boundary layer flow of ethylene glycol (C₂H₆O₂)-based nanofluid with suspended aluminum oxide (Al₂O₃) nanoparticles through a porous wavy channel has been examined. The impact of thermal radiation, Ohmic dissipation, electric field, and magnetic fields are also considered. The flow is due to constant pressure gradient in a wavy frame of reference. The governed momentum and thermal boundary layer equations is system of PDE’s, which are converted to system of ODE’s via suitable similarity transformations. The homotopy analysis method is applied to solve the governed flow problem. Convergence of series solutions is inspected through h-curves and residual errors norm, whereas the optimal value of convergence control parameter is obtained by means of genetic algorithm Nelder–Mead approach. The influence of numerous involving parameters like Hartmann number, Grashof number, Eckert number, electric parameter, radiation parameter, and porosity parameter on flow, heat transfer, skin friction coefficient and Nusselt number are illustrated through graphs and discussed briefly.

     

Mechanical,thermal and microstructural characteristics of cellulose fibre reinforced epoxy/organoclay nanocomposites

Epoxy nanocomposites reinforced with recycled cellulose fibres (RCFs) and organoclay platelets (30B) have been fabricated and investigated in terms of WAXS, TEM, mechanical properties and TGA. Results indicated that mechanical properties generally increased as a result of the addition of nanoclay into the epoxy matrix. The presence of RCF significantly enhanced flexural strength, fracture toughness, impact strength and impact toughness of the composites. However, the inclusion of 1 wt.% clay into RCF/epoxy composites considerably increased the impact strength and toughness. The presence of either nanoclay or RCF accelerated the thermal degradation of neat epoxy, but at high temperature, thermal stability was enhanced with increased char residue over neat resin. The failure micromechanisms and energy dissipative processes in these nanocomposites were discussed in terms of microstructural observations.     

Magnetocaloric Effect for La0.54Sr0.27Gd0.19MnO3 Nanoparticles at Room and Cryogenic Temperatures

The magnetic refrigerator (MR) has gained popularity due to its potential to improve the energy efficiency of refrigeration without the use of unsafe gas, as is the case with traditional gas compression techniques. Magnetocaloric lanthanum manganite investigation, particularly at room and cryogenic temperatures, shows favorable results for the development of MR. Previous thermodynamic models require a significant amount of time and effort to estimate the magnetocaloric effect (MCE). Consequently, we employ the phenomenological model (PM), which is simple and straightforward, requiring fewer parameters than many other modeling methods. We studied the magnetocaloric effect (MCE) of silica-coated La_0.54Sr_0.27Gd_0.19MnO₃ (LSGMO) nanoparticles via PM. According to PM results, MCE parameters were obtained as the consequences of the simulated magnetization of silica-coated LSGMO nanoparticles vs. temperature under 0.1 T a magnetic field. It is revealed that the MCE of silica-coated LSGMO nanoparticles covers a broad range of temperatures between 200 and 330 K. The comparison of MCE parameters for silica-coated LSGMO nanoparticles and some published works shows that silica-coated LSGMO nanoparticles are considerably larger than some of the MCE parameters in these published works. Finally, silica-coated LSGMO nanoparticles are suitable function materials in MR, especially at room and cryogenic temperatures, contributing to efficient MR.