Heritage Institute of Technology

About: Heritage Institute of Technology is a based out in . It is known for research contribution in the topics: Steganography & Support vector machine. The organization has 581 authors who have published 1045 publications receiving 8345 citations.

Effective GIS Mobile Query System

Abstract: In this paper two new algorithms for GIS Mobile query (point-in-area function) are proposed. This algorithm search the status of a given co-ordinate point with respect to an area (described as a set of co-ordinates without any geometric relationship). In these algorithms computational complexity are reduced. One of the algorithms is much closed to the accuracy of exhaustive search.

On invex programming problem in Hilbert spaces

Abstract: In this paper we introduce the invex programming problem in Hilbert space. The requisite theory has been established to characterize the solution of such class of problems.

Optimized Flexible Power Selection for Opportunistic Underlay Cognitive Radio Networks

Abstract: A flexible transmit power selection concept for underlay cognitive users is proposed in this paper. We have employed an opportunistic, sensing based spectrum sharing method. Besides the power constraint to avoid interference at PU, the transmit power constraints of secondary user is also considered. Received Signal Strength Indicator based carrier selection method has been adopted. To resolve hidden terminal problem, twin scan concept is used at both ends (secondary transmitter and receiver) with same carrier frequency. Secondary transmitter selects suitable carrier frequency to initiate communication with the minimum power level as defined by the proposed algorithm. If received signal strength at the corresponding secondary receiver is below the predefined required receiver threshold, then power level is stepped up automatically. To maximize secondary user channel capacity, we have considered flexible power selection strategy as per channel state information. If the cognitive receiver is unable to recover the received information, even with the peak transmit power, it will again perform the frequency scanning operation. This is repeated till the best result is achieved. A power control circuit is designed to check the power selection concept.

GA-based optimal feature weight and parameter selection of NPPC for tea quality estimation

Abstract: Electronic nose (e-nose) is an artificial olfaction system that is being widely used in many industries. E-noses detect smells with the help of electronic signals produced by a number of sensors. The important part of an efficient e-nose system is to recognize these electronic signals accurately by some pattern classification algorithm. Recently developed nonparallel plane proximal classifier (NPPC) has shown its effectiveness in pattern classification task using kernel trick. In general the performance of such classifier depends on the values of optimal parameter set as well as the feature set. In this research work we have studied the effect of simultaneous parameter and feature weight selection on the accuracy of black tea quality estimation employing multiclass one vs. one NPPC. In order to choose the model parameters we have used genetic algorithm (GA). Experimental results show that GA-based tuning and feature weighting scheme increases the performance of NPPC by ∼ 2% in the problem of black tea quality prediction.