An Analysis of Energy Efficiency Improvement Through Wireless Energy Transfer in Wireless Sensor Network

In a wireless sensor network, wireless Energy transfer is a demanding technology for the energy difficulties in recent times. The foremost disadvantage of presentation is limited duration because WSN contains only restricted battery energy at a node. Therefore, we anticipated cluster-related wireless energy transfer in this document. The foremost intention of the method is to augment the duration of the sensor network through charging by the help of this wireless power transfer technology. So that, mobile charging vehicle (MCV) is established to move within the network and charge the sensor node battery wireless. The sensor nodes in the network are collected as a cluster for energy efficiency. Here, the cluster head is chosen for each one cluster in the network which is based on the rank metric value. Suppose, if one node in the network is reducing its energy, then the CH will send charge request and route ID to the MCV. Afterward, the MCV recognize the node by means of the exacting route and establish to charge the node. The reproduction consequences illustrate that the network lifetime of our anticipated method is enhanced than obtainable method.

     

Contrast Enhancement of Retinal Images Using Green Plan Masking and Whale Optimization Algorithm

Wireless Personal Communications - In this study, a coplanar waveguide fed patch antenna is proposed for Wireless Local Area Network (WLAN) and Worldwide Interoperability for Microwave Access...     

A Comprehensive Survey on Autonomous Driving Cars: A Perspective View

Wireless Personal Communications - Over the past decades Machine Learning and Deep Learning algorithm played a vital part in the development of Autonomous Vehicle. It is indeed for the perception...     

Forest optimization algorithm-based feature selection using classifier ensemble

Features selection is the process of choosing the relevant subset of features from the high-dimensional dataset to enhance the performance of the classifier. Much research has been carried out in the present world for the process of feature selection. Algorithms such as Naïve Bayes (NB), decision tree, and genetic algorithm are applied to the high-dimensional dataset to select the relevant features and also to increase the computational speed. The proposed model presents a solution for selection of features using ensemble classifier algorithms. The proposed algorithm is the combination of minimum redundancy and maximum relevance (mRMR) and forest optimization algorithm (FOA). Ensemble-based algorithms such as support vector machine (SVM), K-nearest neighbor (KNN), and NB is further used to enhance the performance of the classifier algorithm. The mRMR-FOA is used to select the relevant features from the various datasets and 21% to 24% improvement is recorded in the feature selection. The ensemble classifier algorithms further improves the performance of the algorithm and provides accuracy of 96%.     

Seebeck Coefficient Measurement and Its Narrow Band Model Interpretation in FeTe<Subscript>0.5</Subscript>Se<Subscript>0.5</Subscript> Superconductor

We have measured Seebeck coefficient (S) of FeTe_0.5Se_0.5 superconducting sample from 10 to 300 K. The variation of Seebeck coefficient with temperature of this system was found to be very anomalous, and the overall experimental observation of the S(T) was studied in the outline of a narrow-band model. In high-temperature region, the Seebeck coefficient is almost independent of temperature. Further, from the study of high-temperature magnitude of S, sample undergoes a change in sign in the Seebeck coefficient, wherein, appearance of a negative peak around 22 K and subsequently, its Seebeck coefficient goes to zero in the superconducting transition temperature regime around 11 to 13 K. It is revealed that the bandwidth and a small asymmetry involved in narrow bands give a realistic explanation to the anomalous temperature dependence of Seebeck coefficient of FeTe_0.5Se_0.5 system.     

Application of Modified Bellman-Ford Algorithm for Cooperative Communication

Wireless Personal Communications - Network lifetime of mobile ad hoc networks is limited by the battery power of the mobile nodes. Energy saving can be achieved by operating the nodes in power save...     

Structural Transformation in Cubic Zirconia

The structure of CaO-stabilized cubic zirconia has been investigated by X-ray diffraction at high temperatures and pressures up to 1000°C and ∼35 GPa, using a diamond-anvil cell interfaced with aYAG laser heating system. At ∼1000°C and 15 GPa, the cubic phase transforms directly into an orthorhombic phase with the PbCl₂ structure. This high-pressure phase is quenchable. The zero-pressure lattice parameters are a=0.3327±0.0004 nm, b=0.5566±0.0003 nm, and c =0.6487±0.0005 nm, the volume change being 10.5%.     

Vertical motion of a sphere in an electrically conducting,stratified, diffusive medium

Summary The vertical (upwards) flow of an incompressible, Newtonian, viscous, stratified, diffusive, electrically conducting fluid embedded with a uniform magnetic field that is applied in the vertical direction, past a non-conducting sphere is considered. Quasisteady approximation is made allowing for a time dependent buoyancy force. Method of matched asymptotic expansions is employed to obtain the drag exerted on the sphere, for small values of a stratification parameter such that 1,R _e ^1/3, ^–1/3,P _e ^2/3 andR _m =O(^2/3). The drag is computed for certain typical values of the magnetic interaction parameter and diffusivity parameter. The results reveal the fact that while the stratification and magnetic field have the enhancing effect over the drag, the effect of diffusivity is to decrease it.With 3 Figures     

Deconvolution of damping forces with a nonlinear microresonator

We report a fully electrical microcantilever device that utilizes capacitance for both actuation and detection and show that it can characterize various gases with a bare silicon microcantilever. We find the motion of the cantilever as it rings down when the oscillating force is removed, by measuring the voltage induced by the oscillating capacitance in the microcantilever∕counterelectrode system. The ringdown waveform was analyzed using an iterative numerical algorithm to calculate the oscillator motion, modeling the cantilever∕electrode capacitance to calculate the electrostatic force. We find that nonlinearity in the motion of the cantilever is not necessarily a disadvantage. After calibration, we simultaneously measure viscosity and density of several gaseous mixtures, yielding viscosities within ±2% and densities within ±6% of NIST values.