Institution

# Budge Budge Institute of Technology

Education•Baj Baj, India•

About: Budge Budge Institute of Technology is a(n) education organization based out in Baj Baj, India. It is known for research contribution in the topic(s): Steganography & Plasmon. The organization has 65 authors who have published 121 publication(s) receiving 744 citation(s). The organization is also known as: BBIT.

Topics: Steganography, Plasmon, Surface plasmon, Triangular function, Piecewise

##### Papers

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TL;DR: An environmentally friendly method has been applied for the preparation of reduced graphene oxide (RGO) by using polyphenols that contained a phytoextract of Mangifera indica L. along with Solanum tuberosum L. as mentioned in this paper.

Abstract: An environmentally friendly method has been applied for the preparation of reduced graphene oxide (RGO). This method was developed by using polyphenols that contained a phytoextract of Mangifera indica L. along with Solanum tuberosum L. as reducing agents since they are non-toxic and naturally available. The phytoextracts used in the production of RGO was set between 60 and 70 °C. Graphene oxide (GO) was prepared by modified Hummer’s method as reported in earlier findings. Structural and morphological studies demonstrate that the part of the oxygen functionalities in GO can be removed by following green reduction. Characterizations of the resulting product have been done by X-ray diffraction, FTIR, UV–vis and Raman spectroscopy. FESEM, TEM, EDX spectrum, TGA, DLS and Zeta potential measurements of the samples have also been carried out to study the morphological, thermal and surface charge characteristics. Electrical conductivity was also measured to check the extent of reduction of GO to RGO.

64 citations

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TL;DR: In this paper, a generalized neuron model is used for forecasting the short term electricity price of Australian electricity market, the preprocessing of the input parameters is accomplished using wavelet transform for better representation of the low and high frequency components, the free parameters of the generalized neurons model are tuned using environment adaptation method algorithm for increasing the generalization ability and efficacy of the model.

Abstract: The liberalization of the power markets gained a remarkable momentum in the context of trading electricity as a commodity. With the upsurge in restructuring of the power markets, electricity price plays a dominant role in the current deregulated market scenario which is majorly influenced by the economics being governed. In the deregulated environment price forecasting is an important aspect for the power system planning. The problem of price forecasting can be entirely viewed as a signal processing problem with proper estimation of model parameters, modeling of uncertainties, etc. Among the different existing models the artificial neural network based models have gained wide popularity due their black box structure but it too has its own limitations. In the proposed work in order to overcome the limitations of the classical artificial neural network model, generalized neuron model is used for forecasting the short term electricity price of Australian electricity market. The pre-processing of the input parameters is accomplished using wavelet transform for better representation of the low and high frequency components. The free parameters of the generalized neuron model are tuned using environment adaptation method algorithm for increasing the generalization ability and efficacy of the model.

51 citations

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TL;DR: In this article, the authors derived mathematical model of solar PV, in terms of two unknown, namely, series resistance and ideality factor, and then, using combination of analytical method, simulated annealing method, and derived model, a new parameter estimation technique has been proposed.

Abstract: The accuracy in electrical model parameters of solar photovoltaic (PV), such as photon current, the diode dark saturation current, series resistance, shunt resistance, and diode ideality factor, are desirable to predict the real performance characteristics of solar PV under varying environment conditions. First, this paper derives mathematical model of solar PV, in terms of two unknown, namely, series resistance and ideality factor. Then, using combination of analytical method, simulated annealing method, and derived model, a new parameter estimation technique has been proposed. Finally, performance indices, such as PV characteristics curve, relative maximum power error, root mean square deviation, and normalized root mean square deviation are estimated for the various solar PV panels, using proposed and existing methods, to reveal the effectiveness of the proposed method. Also, experimental data have been considered for the validation. Finally, through the comparative analysis of the results, it is revealed that the proposed method offers solar PV characteristics more closer to the real characteristics than the other existing methods.

46 citations

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TL;DR: In this paper, a new well-behaved charged anisotropic solution of the field equations was discovered, which can represent a physically possible configuration with the inclusion of some net electric charge, i.e. the solution can become a wellbehaved solution with decreasing sound speed radially outward.

Abstract: In the present paper, we discover a new well-behaved charged anisotropic solution of Einstein–Maxwell’s field equations. We ansatz the metric potential g00 of the form given by Maurya el al. (Eur. Phys. J. C 76(2) (2016) 693) with n = 2. In their paper, it is mentioned that for n = 2, the solution is not well-behaved for neutral configuration as the speed of sound is nondecreasing radially outward. However, the solution can represent a physically possible configuration with the inclusion of some net electric charge, i.e. the solution can become a well-behaved solution with decreasing sound speed radially outward for a charged configuration. Due to the inclusion of electric charge, the solution leads to a very stiff equation-of-state (EoS) with the velocity of sound at the center vr02 = 0.819, vt02 = 0.923 and the compactness parameter u = 0.823 is close to the Buchdahl limit 0.889. This stiff EoS support a compact star configuration of mass 5.418M⊙ and radius of 10.1km.

45 citations

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TL;DR: In this paper, a new well-behaved charged anisotropic solution of the field equations was discovered, which can represent a physically possible configuration with the inclusion of some net electric charged i.e. the solution can become a wellbehaved solution with decreasing sound speed radially outward.

Abstract: In the present article, we discover a new well-behaved charged anisotropic solution of Einstein-Maxwell's field equations. We ansatz the metric potential $g_{00}$ of the form given by Maurya el al. (arXiv:1607.05582v1) with $n=2$. In their article it is mentioned that for $n=2$ the solution is not well-behaved for neutral configuration as the speed of sound is non-decreasing radially outward. However, the solution can represent a physically possible configuration with the inclusion of some net electric charged i.e. the solution can become a well-behaved solution with decreasing sound speed radially outward for a charged configuration. Due to the inclusion of electric charged the solution leads to a very stiff equation of state (EoS) with the velocity of sound at the center $v_{r0}^2=0.819, ~v_{t0}^2=0.923$ and the compactness parameter $u=0.823$ is closed to the Buchdahl limit 0.889. This stiff EoS support a compact star configuration of mass $5.418M_\odot$ and radius of $10.1 km$.

34 citations

##### Authors

Showing all 65 results

Name | H-index | Papers | Citations |
---|---|---|---|

Supriya Dutta | 16 | 37 | 823 |

Mainak Adhikari | 12 | 42 | 472 |

Arpita Adhikari | 12 | 41 | 410 |

Anish Deb | 10 | 79 | 441 |

Rishabh Dev Shukla | 9 | 16 | 301 |

Sumit Chabri | 8 | 23 | 284 |

Sourav Sadhukhan | 7 | 9 | 175 |

Sarosij Adak | 7 | 24 | 132 |

Labakanta Mandal | 7 | 15 | 133 |

Asish Kumar Mukhopadhyay | 7 | 34 | 132 |

Subhadeep Metya | 6 | 19 | 129 |

Sumita Banerjee | 6 | 8 | 120 |

Krishnendu Mukherjee | 6 | 13 | 91 |

Sandip Chanda | 5 | 22 | 93 |

A. Mitra | 5 | 9 | 96 |