Small spherical viruses spontaneously encapsidate their genome into protein capsids. The encapsidation free energy and genome profile have been extensively studied using field theory techniques with Ground State Dominance Approximation (GSDA). In the thesis we employ the self-consistent field theory (SCFT) to calculate the exact genome field confined in a spherical viral shell and examine the validity of GSDA. Furthermore, we study the impact of N-terminal domains of capsid proteins and the secondary structure of RNA on the assembly efficiency in the regions where GSDA is valid. Additionally, we investigate the origin of icosahedral order (IO) of viral capsids. It is believed that large viruses grow their pentamers on IO vertices through an irreversible pathway. Using continuum elasticity theory, we study the growth of spherical cap with a stress-free boundary. We show that for large viruses, the scaffolding proteins or an inner core are essential for the nonlinear geometry of the capsid. The combined effect of nonlinearity and the free edge of the cap conspire to give rise to the configurations with IO defects during the assembly pathway.

In order to deal with recognition problem of the low-resolution and poor-quality images captured from traffic surveillance camera in intelligent transportation system, a vehicle logo super-resolution method using two-dimensional canonical correlation analysis(2D CCA)is presented. While numerous approaches have been proposed to do the recognition, they can only achieve good performance on the images with high resolution. In this paper, a novel combination of morphological operation and sliding window technique is adopted to detect the vehicle logo, and then using 2D CCA to do super-resolution reconstruction which improve performance significantly compared with other traditional methods. It analysis the performance of proposed approach on a large image dataset.