Refractive indices and order parameters of two liquid crystals

Refractive indices have been measured throughout the nematic phase of 4-n-pentyl-4'cyanobiphenyl (5CB) and the smectic A and nematic phases of 4-n-octyl-4'-cyanobiphenyl (8CB). The Vuks and Neugebauer methods of calculating the order parameter are compared. Without knowledge of the molecular polarisabilities it is only possible to calculate a quantity proportional to the order parameter, and within this limitation it is found that the two methods give identical results. The order parameter is scaled using the extrapolation method suggested by Haller [14]. Using a suitable average of the refractive indices and the density data of Gannon and Faber [9], it is shown that the Lorentz-Lorenz relation is obeyed over a 2 % density range in 5CB.

Comparison of performance parameters for conventional and localized surface plasmon resonance graphene biosensors

This paper investigates the enhancement of the sensitivity and adsorption efficiency of a localized surface plasmon resonance (LSPR) biosensor that includes a layer of graphene sheet on top of the gold layer. For this purpose, biomolecular interactions of biotin-streptavidin with the graphene layer on the gold thin film are monitored. The performance of the LSPR graphene biosensor is theoretically and numerically assessed in terms of sensitivity and adsorption efficiency under varying conditions, including the thickness of biomolecule layer, number of graphene layers and operating wavelength. Enhanced sensitivity and improved adsorption efficiency are obtained for the LSPR graphene biosensor in comparison with its conventional counterpart. It is found that the LSPR graphene biosensor has better sensitivity with lower operating wavelength and larger number of graphene layers.

Automatic parameters selection for eigenfaces

In this paper, we investigate the parameters selection for Eigenfaces. Our focus is on the eigenvectors and threshold selection issues. We will propose a systematic approach in selecting the eigenvectors based on relative errors of the eigenvalues for the covariance matrix. In addition, we have proposed a method for selecting the classification threshold that utilizes the information obtained from the training data set. Experimentation was conducted on two benchmark face databases, ORL and AMP, with results indicating that the proposed automatic eigenvectors and threshold selection methods produce better recognition performance in terms of precision and recall rates. Furthermore, we show that the eigenvector selection method outperforms energy and stretching dimension methods in terms of selected number of eigenvectors and computation cost.