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.

Genetic Algorithm for FGP Model of a Multiobjective Bilevel Programming Problem in Uncertain Environment

Abstract: This chapter describes a Genetic Algorithm (GA) based Fuzzy Goal Programming (FGP) model to solve a Multiobjective Bilevel Programming Problem (MOBLPP) with a set of chance constraints within a structure of decentralized decision problems. To formulate the model, the chance constraints are converted first to their crisp equivalents to employ FGP methodology. Then, the tolerance membership functions associated with fuzzily described goals of the objective functions are defined to measure the degree of satisfaction of Decision Makers (DMs) with achievement of objective function values and also to obtain the degree of optimality of vector of decision variables controlled by upper-level DM in the decision system. In decision-making process, a GA scheme is adopted to solve the problem and thereby to obtain a proper solution for balancing execution powers of DMs in uncertain environment. A numerical example is provided to illustrate the method.

A Generalized Partial Canonical Correlation Model to Measure Contribution of Individual Drug Features Toward Side Effects Prediction

Abstract: Identification of potential drug-side effects is an open problem of importance for drug development. Side effects are related to a variety of interlinked aspects such as chemical properties of drugs, drug–target interactions, pathways involved, and many more. Existing statistical methods and machine learning models toward creating models that incorporate such features to predict adverse drug reactions. One of the challenges in these efforts is to disentangle the interdependence of features to identify the contribution of individual features toward specifying side effects. We present a partial canonical correlation analysis (PCCA) model that facilitates enumeration of contribution from individual drug features toward the prediction of a class of side effects, irrespective of interdependence on other features. The model is a combination of analytical and numerical strategies, and can be used to arrive at the most effective set of drug features starting from a range of available descriptors. For eye and nose related side effects, we demonstrate the implementation of our model for identification of best 2D chemical features that are closely linked with organ-specific adverse reactions. Despite the presence of a large number of drugs that are simultaneously associated with both the organs, the model could discern distinct drug features specifically linked to each class. With the availability of large amounts of data with an array of interdependent drug descriptors, such a model is of value in the drug discovery process as it enables in dealing with multidimensional drug features space.

A Comparative Analysis of PAPR in OFDM System for Different Parameters

Abstract: The Peak to Average Power Ratio (PAPR) plays an important role in Orthogonal Frequency Division Multiplexing (OFDM) in communication systems, especially in wireless cellular systems. This article illustrates the effectiveness of the CCDF curve to study the signal performance and effect of PAPR in orthogonal frequency division multiplexing. I performed some analysis of signal on power level basis and found that PAPR is an effective analysis tool to detect noise in signal.

Comparative Analysis of 6-T SRAM Cell in Terms of Power Using CMOS and DGMOS

Abstract: As we know for current industry, downscaling of MOSFET is a very essential factor, but due to downscaling, the performance of MOS degrades. For robust design, it is very much required to downscale the MOS length keeping the performance of MOSFET intact. The multigate MOS model has been introduced for keeping the MOS performance intact as well as reducing the channel length. In this paper, the comparison of 6-T single bit SRAM cell has been done using single-gate MOS and double-gate MOS in terms of power using CMOS technology and 90-nm channel length in SYMICA environment. SYMICA is an electronic design automation (EDA) tool for the analog and mixed-signal integrated circuit design. In this paper, we have simulated CMOS and DGMOS SRAM cell and compare the power dissipation of DGMOS SRAM cell and CMOS SRAM cell. It proves that the dynamic power consumption of DGMOS SRAM cell is much smaller than CMOS SRAM cell for a particular input voltage and channel length.

Effect of Audio Cue on Electrooculogram-Based Eye Movement Analysis of Visual Memory Recall

Abstract: Context aware ubiquitous computing systems are capable of assisting people by sensing human cognitive context. In this work, visual memory recall of human beings is identified by analysing their eye movements. Electrooculogram signals, potential difference produced in the surrounding region of eye socket for eye ball movement, are recorded to collect eye movement data. Electrooculogram signals while viewing ‘repeated’ and ‘non-repeated’ visual stimuli were classified for ‘with’ and ‘without’ audio cue sections. Adaptive autoregressive parameters, power spectral density, Hjorth parameters and wavelet coefficients are extracted from these signals as features. A combined feature space is formed comprising all four signal features. A maximum accuracy of 88.70 % is obtained on an average over five participating subjects using SVM-RBF classifier for ‘without audio’ visual memory recall. From this study, it is evident that this auditory effect leaves an impact on EOG signal patterns so that to make reduction in the recognition performance.