Showing papers by "Narula Institute of Technology published in 2010"

and states of two electron atoms under Debye plasma screening

Abstract: Extensive non-relativistic variational calculations for estimating the energy values of 2 pnd ( D o 1 , 3 ) states [ n = 3 – 6 ] of two electron atoms (He, Li + , Be 2 + ) and 2 pnp ( P e 1 ) [ n = 3 – 8 ] and 2 pnp ( P e 3 ) states [ n = 2 – 7 ] of Be 2 + under weakly coupled plasma screening have been performed using explicitly correlated Hylleraas type basis. The modified energy eigenvalues of P e 1 , 3 states arising from two p electrons of Be 2 + ion and D o 1 , 3 states due to 2 pnd configuration of Li + and Be 2 + ion in the Debye plasma environment are being reported for the first time. The effect of plasma has been incorporated through the Debye screening model. The system tends towards gradual instability and the number of bound states reduces with increasing plasma coupling strength. The wavelengths for 2 pn ′ p ( P e 1 ) [ n ′ = 3 – 8 ] → 2 pnd ( D o 1 ) [ n = 3 – 6 ] and 2 pn ′ p ( P e 3 ) [ n ′ = 2 – 8 ] → 2 pnd ( D o 3 ) [ n = 3 – 6 ] transitions in plasma embedded two electron atoms have also been reported.

An Efficient Single-Fault Detection Technique for Micro-fluidic Based Biochips

Abstract: This paper presents a new fault detection technique for digital micro-fluidic based biochips. Due to recent advances in microfluidics, digital micro-fluidic biochips are revolutionizing laboratory procedures, e.g., biosensing, clinical diagnostics etc. Because of the underlying mixed-technology and mixed energy domains, biochips exhibit unique failure mechanisms and defects. Off-line and on-line test techniques are required to ensure system dependability. In this paper, a new technique is presented to traverse all the cells and cell boundary, for the purpose of detecting a single fault within the bio-chip. Experimental results show that the proposed technique improves the fault detection time to a great extent.

A genetic algorithm approach to fuzzy quadratic bilevel programming

Abstract: This article presents a genetic algorithm (GA) based fuzzy goal programming (FGP) procedure for modelling and solving quadratic bilevel programming problems (QBLPPs) of a hierarchical decision organization. In the FGP model formulation, the concept of tolerance membership functions for measuring the degree of satisfaction of the decision makers (DMs) regarding goal achievement of the fuzzy objectives and the degree of optimality of the decision vector controlled by the upper-level DM are defined in the decision making horizon. In the goal achievement function of the model, minimization of the under-deviations of the defined membership goals from the highest membership value (unity) on the basis of the assigned priorities is considered. In the solution process, sensitivity analysis on different priority structures by using an GA scheme is made to reach an ideal-point dependent solution in the decision environment. The potential use of the approach is illustrated by a numerical example.

Metastable bound D1,3o states below N=3 ionization threshold of He+

Abstract: The applicability of Ritz-variational principle for 3dnf (D1,3o) states lying between N=2 and N=3 ionization threshold of singly ionized helium is discussed in this communication. Probable existence of such a state for O6+ within the planetary atmosphere in the polar regions of Jupiter is discussed.

A genetic algorithm based stochastic simulation approach to chance constrained interval valued multiobjective decision making problems

Abstract: This article presents how the stochastic simulation through genetic algorithm (GA) can be used to modeling and solving chance constrained interval valued multiobjective decision making (MODM) problems. In the proposed method, a stochastic simulation approach to the chance constraints is employed for interval valued goal representation of the objectives as well as decision identification through the use of an GA method in an inexact decision making context. In the executable goal programming (GP) model of the problem, both the aspects of the GP, minsum GP and minmax GP [1], are addressed within goal achievement function for minimizing possible regrets associated with the deviational variables of the defined goals for goal achievement within the target intervals specified in the decision making environment. A numerical example is solved and a comparison is made with the conventional GP approach.

A genetic algorithm method to fuzzy goal programming formulation based on penalty function for academic personnel management in university system

Abstract: This article demonstrates how the GA approach can be efficiently used to the penalty function based fuzzy goal programming (FGP) formulation of academic personnel planning problems in university management system. In the proposed approach, requirement of total full-time teaching staff and allocation of pay-roll budget to each of academic departments are fuzzily described. The recruitment of minimum number of teaching and non-teaching staff and maintaining of certain ratios of part-time teaching staff and non-teaching staff individually with full-time teaching staff, and a ratio of total number of students with total teaching staff in each department for smooth functioning of the academic activities of the departments are considered as constraints in the academic planning horizon. In the model formulation of the problem, the concept of penalty functions for measuring the degree of achievement of membership goals in different ranges for the defined fuzzy goals and thereby arriving at a satisfactory decision is considered. In the solution process, an GA scheme is employed in an iterative manner to achieve the fuzzy goals on the basis of their assigned priorities in the decision making environment. A case example of the University of Kalyani, West Bengal (W.B), India is considered to illustrate the potential use of the approach.

A genetic algorithm based goal programming approach to Interval-valued multilevel programming problems

Abstract: This article presents a goal programming (GP) procedure for solving Interval-valued multilevel programming (IVMLP) problems by using genetic algorithm (GA) in a large hierarchical decision making and planning organization. In the proposed approach, first the individual best and least solutions of the objectives of the decision makers (DMs) located at different hierarchical decision levels are determined by using an GA method. Then, the target interval for achievement of each of the objectives as well as the target interval of the decision vector controlled by the upper-level DM is defined in the inexact decision making environment. In the model formulation of the problem, first the interval valued objectives and control vectors are transformed into the conventional form of goal by using interval arithmetic technique and then introducing under- and over-deviational variables to each of them. In the solution process, both the aspects of minsum and minmax GP formulation are adopted to minimize the lower bounds of the regret intervals for goal achievement within the specified interval from the optimistic point of view and thereby distribution of proper decision powers to the DMs of the hierarchical levels. The potential use of the approach is illustrated by a numerical example.

Performance analysis of a fuzzy logic based adaptive call admission control over heterogeneous wireless networks

Abstract: This paper propose a novel call admission control (CAC) scheme that considers real-time and non-real-time nature of a call before allocating resources for its progress in a network. The scheme accumulates the benefits of different CAC schemes for best optimization of the available resources. It has combined the three major schemes- reserve guard channel, buffer based and prioritization. Application of fuzzy logic made the scheme adaptive by dynamic control of the threshold resource parameters using fuzzy logic. Simulation results show that the scheme outperforms the existing CAC schemes in terms of QoS.