Integral Representation of Polynomial Xn (x;a,b)

Abstract: In the present paper we have obtained fine difference formula, contour integral representation, real integral representation, infinite single integral representation, finite single integral representation, finite double integral representation, finite double integral representation of polynomial ࢔ࢄ .(࢈ ,ࢇ ;࢞) Keywords: Finite difference, single integral representation, contour integral representation, simple generating relation, double integral representation</jats:p

Design And Analysis Of Slot Loaded Rectangular Patch Antenna For Wireless Communication System

A multiple-input multiple-output (MIMO) technique has been considered one of the most promising technologies to enhance the performance of wireless communication systems with high-speed transmission rates. A MIMO system utilizing several antenna components is more advantageous than a single-input single-output (SISO) system in terms of increasing channel capacity and reducing transmitting power. Conventional universal serial bus (USB) dongles are attractive for providing plug and- play functionality in mobile communication devices such as laptops. Future wireless USB dongles should be capable of accommodating higher data rates than the current systems owing to the advent of various multimedia services. Up to date, most MIMO antenna systems with more than two antennas are three-dimensional rather than planar. However, the basic problem with the MIMO systems is the requirement of electrically small antennas which usually have several constraints. Hence, these antennas are considered in our project work

Microbial Glucuronans and Succinoglycans

International audienc

Application of advanced algorithms for enhancement in machining performance of Inconel 718

366-376Inconel 718 is the most promising nickel-based alloy finding wide usage in engineering applications because of its good mechanical properties. However, this alloy is difficult to machine and results in poor surface quality after machining. Optimization of parameters is essential for improving machining performance of this costly and hard to cut material. The research discusses estimation of optimum parameters using teaching-learning based optimization (TLBO) and compares them to those obtained by genetic algorithm (GA) in turning of Inconel 718. The parameters cutting speed, feed rate and depth of cut are selected as independent variables. The experiments are designed using central composite design of response surface methodology for the modelling of turning process. Surface roughness, tool flank wear and cutting temperature are selected as response parameters for minimization. The adequacy of modified models developed by response surface methodology are tested and then utilized for formulation of multi-objective optimization function. The function is solved by GA and TLBO. After comparing optimization results, the best algorithm is used for confirmation test. Convergence of TLBO algorithm is much faster as compared to GA even though there is very little difference in the optimum values of parameters