Narula Institute of Technology

About: Narula Institute of Technology is a based out in . It is known for research contribution in the topics: Quantum dot cellular automaton & Cognitive radio. The organization has 288 authors who have published 490 publications receiving 2258 citations. The organization is also known as: NiT.

A complete virtual instrument for measuring and analyzing human stress in real time

Abstract: A virtual Instrument is designed, prototyped and developed for the real time measurement and recording of galvanic skin response (GSR), also known as electrodermal response to recognize human psycho physiological state under stressed condition. The virtual instrument also analyzes the galvanic skin response signals by statistical features. The Galvanic Skin Response (GSR) is a measure of skin conductivity which is extensively linked to human emotional condition during stress and activation. An emotional reaction under stressed condition often causes increased sweat gland activity in the palms of the hands and the soles of the feet, making the skin more conductive in these areas. The experiments are done under stress and no-stress conditions on 14 subjects of both the gender in the age group of 18 to 28 years old, mainly on university students. The experimental observations demonstrate a strong correlation between measured signals for galvanic skin response (GSR) and human physiological states. LabVIEW software is used for the front panel of the virtual instrument whereas MATLAB software is used for the calculation of features in the back end of the virtual instrument.

A new fuzzy rule based power management scheme for spectrum sharing in cognitive radio

Abstract: Cognitive radio is a technology for wireless communication in which either a network or a wireless node changes its transmission or reception parameters to communicate efficiently avoiding interference with licensed or unlicensed users The spectrum sharing network consisting of a pair of primary users (PUs) and a pair of cognitive users (CRs) in a fading channel The pair of PUs establishes a wireless link as the PU link The PU link and CR link utilize spectrum simultaneously with different priorities The PU link has a higher priority to utilize spectrum with respect to the CR link When the PU link utilizes spectrum, a desired quality of service (QoS) is given to be assured and the CR utilizes spectrum with an opportunistic power scale under this constraint, assuring the desired QoS on the PU link To compute an optimal opportunistic power scale for the CR link, a fuzzy-based opportunistic power control strategy is proposed based on the Mamdani fuzzy control model using three input variables: the PU's SNR and PU's interference channel gain, as well as Relative distance between PU's link and CR link The proposed system is found to give the satisfactory results

A scalable neural-network modular-array architecture for real-time multi-parameter damage detection in plate structures using single sensor output

Abstract: A scalable modular neural network array architecture has been proposed for real time damage detection in plate like structures for structural health monitoring applications. Damages in a plate like structure are simulated using finite element method of numeric system simulation. Various damage states are numerically simulated by varying Young's modulus of the material at various locations of the structure. Transient vibratory loads are applied at one end of the beam and picked at the other end by means of point sensors. The vibration signals thus obtained are then filtered and subjected to wavelet transform (WT) based multi resolution analysis (MRA) to extract features and identify them. The redundant features are removed and only the principal features are retained using principal component analysis (PCA). A large database of principal features (the feature base) corresponding to different damage scenarios is created. This feature base is used to train individual multi layer perceptron (MLP) networks to identify different parameters of the damage such as location and extent (Young's modulus). Individually trained MLP units are then organized and connected in parallel so that different damage parameters can be identified almost simultaneously, on being fed with new signal feature vectors. For a given case, damage classification success rate has been found to be encouraging. The main feature of this implementation is that it is scalable. That is, any number of trained MLP units capable of identifying a certain parameter of damages can be integrated into the architecture and theoretically it will take almost the same time to identify various damage parameters irrespective of their numbers.

Study of parametric optimization of the Cuckoo Search algorithm

Abstract: Cuckoo search (CS) is one of the latest and most efficient optimization techniques developed so far. Several attempts have been made in past in order to improve the efficiency of CS algorithm. In this paper we have tried to exploit several parameters of the CS algorithm in order to increase its efficiency. Cuckoo search is a metaheuristic optimization technique. Its parameters involve the Levy distribution factor beta (β) and the probability factor (P) with which solutions are replaced with new solutions. Hence for optimum values of the aforesaid parameters, efficiency of CS algorithm can be improved and can be used to solve optimization problems.

Quantitative Structure–Activity Relationship (QSAR) Study of Some DNA-Intercalating Anticancer Drugs

Abstract: Small molecular anticancer drugs bind reversibly to DNA and are used in chemotherapy. The experimental measurements of the inhibition activity of drugs are difficult, expensive, and time-consuming. So, quantitative structure–activity/property relationship method (QSAR) is adopted where the dependency of drug binding affinity with their structural features is exploited. To explore whether molecular descriptors can explain their DNA-binding affinity and toxicity, 23 antitumor molecules, which are being used clinically as drugs, are analyzed by rigorous statistical calculations. The 50% cancer cell growth inhibition constant (IC50) for any particular cancer cell line is obtained from the NCI/NIH database. Molecular descriptors (geometrical, physicochemical, and quantum chemical) are calculated for the molecules. A mathematical model is built to predict DNA-drug-binding constant and growth inhibitory concentration by multiple regression which can be useful for rational drug design.