Concerning the collinearity problem between the regressors in functional Magnetic Resonance Imaging (fMRI) model, a method of orthogonalization was proposed. Firstly, the regressors of interest and the regressors to be orthogonalized were determined. Then, the related part with regressos of interest was removed from the regressors to be orthogonalized, and the collinear regressors of the model were orthogonally decomposed into independent parts to eliminate the effect of collinearity. The influence of orthogonalization on General Linear Model (GLM) was also discussed and analysed. Finally, the experiments were carried out through some synthetic data and a current popular fMRI data analysis software package-Functional magnetic resonance imaging of the brain Software Library (FSL).The experimental results show that, the method of orthogonalization can eliminate the collinearity in the model and improve the significance of the regressors of interest to achieve accurate brain functional localization. The proposed method of orthogonalization can be used for the basic research and clinical treatment of brain.

A design for a new self-routing multicast network which can realize arbitrary multicast assignments between its inputs and outputs without any blocking was proposed.The network design used a recursive decomposition approach and was based on the binary splitting concept.It was recursively constructed by cascading a binary splitting network and two half-size multicast networks.The multicast assignments were copyed and binary splitted by the first Omega network and then routing through the N×N Omega×Omega-1 networks and the N/2×N/2 Omega×Omega-1 networks and so on.All kinds of Omega×Omega-1 networks have different size,but they can be connected and routing parallel.This new multicast network uses O（Nlog2N） logic gates,and has O（log2N） depth and O（log2N） routing time.It is compared favorably with the previously proposed multicast networks.