arXiv subjects

Nandita

Publications and source records attributed to Nandita.

3 recordsLinked to original sources

arXiv2023

Approximate Analytical Solution using Power Series Method for the Propagation of Blast Waves in a Rotational Axisymmetric non-ideal Gas

In this paper, the propagation of the blast (shock) waves in non-ideal gas atmosphere in rotational medium is studied using a power series method in cylindrical geometry. The flow variables are assumed to be varying according to the power law in the undisturbed medium with distance from the symmetry axis. To obtain the similarity solution, the initial density is considered as constant in the undisturbed medium. Approximate analytical solutions are obtained using Sakurai's method by extending the power series of the flow variables in power of ${\left( {\frac{{{a_0}}}{U}} \right)^2}$, where $U$ and $a_0$ are the speeds of the shock and sound, respectively, in undisturbed fluid. The strong shock wave is considered for the ratio ${\left( {\frac{{{a_0}}}{U}} \right)^2}$ which is considered to be a small quantity. With the aid of that method, the closed-form solutions for the zeroth-order approximation is given as well as first-order approximate solutions are discussed. Also, with the help of graphs behind the blast wave for the zeroth-order approximation, the distributions of variables such as density, radial velocity, pressure and azimuthal fluid velocity are analyzed. The results for the rotationally axisymmetric non-ideal gas environment are compared to those for the ideal gas atmosphere.

math-ph↗

arXiv2016

Blind Modulation Classification based on MLP and PNN

In this work, a pattern recognition system is investigated for blind automatic classification of digitally modulated communication signals. The proposed technique is able to discriminate the type of modulation scheme which is eventually used for demodulation followed by information extraction. The proposed system is composed of two subsystems namely feature extraction sub-system (FESS) and classifier sub-system (CSS). The FESS consists of continuous wavelet transform (CWT) for feature generation and principal component analysis (PCA) for selection of the feature subset which is rich in discriminatory information. The CSS uses the selected features to accurately classify the modulation class of the received signal. The proposed technique uses probabilistic neural network (PNN) and multilayer perceptron forward neural network (MLPFN) for comparative study of their recognition ability. PNN have been found to perform better in terms of classification accuracy as well as testing and training time than MLPFN. The proposed approach is robust to presence of phase offset and additive Gaussian noise.

cs.CV↗

arXiv2016

A Novel Fault Classification Scheme Based on Least Square SVM

This paper presents a novel approach for fault classification and section identification in a series compensated transmission line based on least square support vector machine. The current signal corresponding to one-fourth of the post fault cycle is used as input to proposed modular LS-SVM classifier. The proposed scheme uses four binary classifier; three for selection of three phases and fourth for ground detection. The proposed classification scheme is found to be accurate and reliable in presence of noise as well. The simulation results validate the efficacy of proposed scheme for accurate classification of fault in a series compensated transmission line.

eess.SY↗