Simple Theoretical Analysis of the Fowler–Nordehim Field Emission from Quantum Confined Optoelectronic Materials

Ghatak, K. P.; Bose, P. K.; Bhattacharya, Sitangshu; Bhattacharjee, A.; de, Dominique; Ghosh, S. K.; Debbarma, Sukanta; Paitya, Nitai

doi:10.1166/qm.2013.1029

Cited by 8 publications

(5 citation statements)

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“…There are several studies available in which phenomena related to the onset of convection in a rotating porous medium have been investigated by many researchers including. [23][24][25][26][27][28][29] For the observed abnormal behavior of magnetic and non-magnetic nanoparticles in the base fluid leading to higher thermal conductivity, the study of modern Quantum theory [54][55][56][57] along with appropriate mathematical model was attempted. But of the several attempts to account for the abnormal thermal behavior of the nanofluids, the most promising one was performed by Buongiorno in 2006, who came out with a mathematical model based on thermophoresis and the Brownian diffusive motion of the nanoparticles.…”

Section: Introductionmentioning

confidence: 99%

Convective Transport in a Binary Nanofluid Saturated Porous Layer: A Nonlinear Approach

Agarwal¹,

Rana²

2015

j comput theor nanosci

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Nonlinear analysis of a binary nanofluid saturated rotating porous layer is performed in the present article. While the porous medium complies with the Brinkman model, Brownian motion and thermoporesis of nanoparticles introduce additional nonlinear effects for the heat transport in the nanofluid. The thermal energy equations include the diffusion and cross-diffusion terms. For a flat fluid layer contained between two free boundaries, an exact analytic solution is obtained using one term Galerkin method. The critical Rayleigh number for the linear onset of instability is determined numerically. Nonlinear analysis using a truncated Fourier series, has been done in terms of the Nusselt numbers'. The behavior of the concentration and thermal Nusselt numbers is investigated numerically by solving the finite amplitude equations. The results obtained have been presented graphically and discussed in detail.

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Section: Introductionmentioning

confidence: 99%

Convective Transport in a Binary Nanofluid Saturated Porous Layer: A Nonlinear Approach

Agarwal¹,

Rana²

2015

j comput theor nanosci

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“…It seems that facial image recognition being regarded as a fundamental field of biometrics which has been applied to various areas. [1][2][3][4] Generally, there are two main steps in a typical face recognition system. 5 In the first step, methods of feature extraction are usually used to derive an effective representation of the facial images.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition via Multilinear Principal Component Analysis and Two-Dimensional Extreme Learning Machine

Zhang¹,

Lin²,

Chen³

2015

Jnl of Comp & Theo Nano

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Because of face images are naturally two-dimensional data, there have been several 2D feature extraction methods to deal with facial images while there are few 2D effective classifiers. In this work, by using a linear tensor projection, a new two-dimensional classifier based on extreme learning machine is introduced. Due to the proposed algorithm can classify matrix data directly without vectorizing them, the intrinsic structure information of the input data can be reserved. In addition, discriminative features sets are generated using MPCA to ascertain classification accuracy. Finally, Extensive experiments are carried out on two face databases and the results are compared against state-of-the-art techniques. It is demonstrated that the proposed algorithm MPCA plus 2D-ELM achieves better recognition performance.

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“…1 There is an increasing interest in the multilinear subspace analysis and many methods have been proposed to operate directly on these tensorial data during the past several years. [2][3][4][5] Such as multilinear principal component analysis (MPCA), 6 which is a tensor extension of principal component analysis (PCA), 7 applies PCA transformation on each mode of tensors. By the same way, multilinear discriminant analysis (MDA), 8 general tensor discriminant analysis (GTDA), 9 and tensor subspace analysis (TSA), 10 respectively apply linear discriminant analysis (LDA), 11 maximum scatter difference (MSD), 12 and locality preserving projections (LPP) 13 transform each mode of tensors.…”

Section: Introductionmentioning

confidence: 99%

Extended Extreme Learning Machine for Biometric Signal Classification

Fan¹,

Lin²,

Chen³

2015

Jnl of Comp & Theo Nano

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Recently, there are several multilinear methods have been proposed for tensorial data dimensionality reduction (feature extraction). However, there are few new algorithms for tensorial signals classification. To solve this problem, in this paper, a novel classifier as a tensor extension of extreme learning machine for multi-dimensional data recognition is introduced. Due to the proposed solution can classify tensorial data directly without vectorizing them, the intrinsic structure information of the input data can be reserved. Moreover, compared with the traditional ELM, much fewer parameters need to be calculated through the proposed tensor based classifier. Extensive experiments are carried out on different databases, and the experiment results are compared against state-of-theart techniques. It is demonstrated that the new tensor based classifier can get better recognition performance with an extremely fast learning speed.

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Simple Theoretical Analysis of the Fowler–Nordehim Field Emission from Quantum Confined Optoelectronic Materials

Cited by 8 publications

References 0 publications

Convective Transport in a Binary Nanofluid Saturated Porous Layer: A Nonlinear Approach

Convective Transport in a Binary Nanofluid Saturated Porous Layer: A Nonlinear Approach

Face Recognition via Multilinear Principal Component Analysis and Two-Dimensional Extreme Learning Machine

Extended Extreme Learning Machine for Biometric Signal Classification

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