Abstract

Abstract

Abstract

Abstract

Abstract
The present paper describes the Portuguese National Brain Imaging Network designed to join R&D efforts of four Portuguese universities (Aveiro, Coimbra, Minho and Porto) in this emergent scientific area. This is an open initiative, already funded in 81.3% of its predicted investment (similar to 4.3 million E) for the first 5 years of operation, opened to the participation of other national institutions. This area of neuroscience uses several types of datasets from different medical imaging modalities and biosignals. MRI/MRS and fMRI volumes along with high-resolution EEG are our main targets for the first 5 years of operation and can easily reach the GByte size for a patient study. The Brain Imaging Network Grid (BING) will provide the support to a "neuroscientist-friendly" web portal where neuroscientists can submit brain imaging datasets for different analysis protocols. We will focus the present paper on the description of the consortium, its objectives and the network and Grid services architecture designs that will provide both the computational resources and the federated large data repository for the Portuguese national wide neuroscience scientific community.

Abstract
Automatic biometric identification based on fingerprints is still one of the most reliable identification method in criminal and forensic applications. A critical step in fingerprint analysis without human intervention is to automatically and reliably extract singular points from the input fingerprint images. These singular points (cores and deltas) not only represent the characteristics of local ridge patterns but also determine the topological structure (i.e., fingerprint type) and largely influence the orientation field. Poincaré Index-based methods are one of the most common for singular points detection. However, these methods usually result in many spurious detections. Therefore, we propose an enhanced version of the method presented by Zhou et al. [13] that introduced a feature called DORIC to improve the detection. Our principal contribution lies in the adoption of a smoothed orientation field and in the formulation of a new algorithm to analyze the DORIC feature. Experimental results show that the proposed algorithm is accurate and robust, giving better results than the best reported results so far, with improvements in the range of 5% to 7%. © 2009 IEEE.