An ideal fusion method preserves the spectral information in fused image without spatial distortion. The PCA is believed to be a well-known pan-sharpening approach and being widely used for its efficiency and high spatial resolution. However, it can distort the spectral characteristics of multispectral images. The current paper tries to present a new fusion method based on the same concept. In the conventional standard PCA method, PCA transform is applied to spectral bands of multispectral images, but we applied the PCA transform to pixel blocks instead. Since PCA coefficients are extracted from statistical properties of the image, it is more consistent with type and texture of remotely sensed image compared to other kernels such as wavelets. After that, a new hybrid algorithm is proposed which uses both the spatial PCA and the spectral PCA method to improve the quality of the merged images. Visual and statistical analyses show that the proposed algorithm clearly improves the merging quality in terms of RASE, ERGAS, SAM, correlation coefficient and UIQI; compared to fusion methods such as IHS, Brovey, PCA, HPF, and HPM.

To improve drug selectivity toward target cells, one interesting approach for drug delivery is to use polymer nanoparticles. A two-layered ONIOM Becke3-LYP: UFF calculation was carried out to study the structural and thermodynamic properties of the interaction between acetamide derivatives and the PCA-PEG-PCA copolymers. The Interaction enthalpies and the Gibbs free energies between acetamide derivatives as anti-HIV and polymeric nanoparticles in the gas and solution phases were calculated. The structure as well as the thermodynamics of optimized complexes was discussed from the biological point of view. In the gas phase, substitutes of phenyl, flurophenyl, 4-acetyl-2-bromophenyl, and 3-methyl-acetate-thiophen-2-yl had the highest energies, and in the water phase, the enthalpies and Gibbs free energies of the interaction for most compounds were almost identical. In the both phases the interaction is relatively weak and copolymers can be used for drug delivery.

Face recognition is an essential field of image processing and computer vision. In this paper, we have developed a facial recognition system that can detect and recognize the face of a person by comparing the characteristics, and features of the face to those of known faces. Our approach considers the face recognition problem as an intrinsically two-dimensional recognition problem rather than requiring recovery of three-dimensional geometry, considering that eigenvectors generally describe human faces in the face space. The system works by projecting face images onto a feature space that spans the significant variations among known face images that are called eigenvectors (or principal components of the face set). Our technique can learn and recognize new faces in an unsupervised style—this approach is based on eigenfaces and principal component analysis (PCA).

In this paper, an efficient method is proposed for load estimation of distribution networks with limited real-time data based on principal component analysis. The principal components of load variables are first detected off-line from historical data. Next, the interrelations between the load variables are developed using a new concept that we call Inverse PCA (IPCA) method; this leads to some proper models devised so that the distribution network becomes observable. Finally, through incorporation of a certain finite set of real time data measurements, the loads of all network nodes in real-time are estimated with a desired degree of accuracy via the IPCA method. A case study on a real network is considered in the paper to highlight better the merit of the proposed method. The experimental results easily show that the proposed method outperforms the previously cited techniques.