Poincare Index Method Research Articles

How to find magnetic nulls and reconstruct field topology with MMS data?

AbstractIn this study, we apply a new method—the first‐order Taylor expansion (FOTE)—to find magnetic nulls and reconstruct magnetic field topology, in order to use it with the data from the forthcoming MMS mission. We compare this method with the previously used Poincare index (PI), and find that they are generally consistent, except that the PI method can only find a null inside the spacecraft (SC) tetrahedron, while the FOTE method can find a null both inside and outside the tetrahedron and also deduce its drift velocity. In addition, the FOTE method can (1) avoid limitations of the PI method such as data resolution, instrument uncertainty (Bz offset), and SC separation; (2) identify 3‐D null types (A, B, As, and Bs) and determine whether these types can degenerate into 2‐D (X and O); (3) reconstruct the magnetic field topology. We quantitatively test the accuracy of FOTE in positioning magnetic nulls and reconstructing field topology by using the data from 3‐D kinetic simulations. The influences of SC separation (0.05~1 di) and null‐SC distance (0~1 di) on the accuracy are both considered. We find that (1) for an isolated null, the method is accurate when the SC separation is smaller than 1 di, and the null‐SC distance is smaller than 0.25~0.5 di; (2) for a null pair, the accuracy is same as in the isolated‐null situation, except at the separator line, where the field is nonlinear. We define a parameter ξ ≡ |( λ1 + λ2 + λ3 )|/|λ|max in terms of the eigenvalues (λi) of the null to quantify the quality of our method—the smaller this parameter the better the results. Comparing to the previously used parameter (η≡|∇ ⋅ B|/|∇ × B|), ξ is more relevant for null identification. Using the new method, we reconstruct the magnetic field topology around a radial‐type null and a spiral‐type null, and find that the topologies are well consistent with those predicted in theory. We therefore suggest using this method to find magnetic nulls and reconstruct field topology with four‐point measurements, particularly from Cluster and the forthcoming MMS mission. For the MMS mission, this null‐finding algorithm can be used to trigger its burst‐mode measurements.

Statistical Descriptors for Fingerprint Matching

paper presents a novel algorithm for fingerprint matching using statistical descriptors. This fingerprint-matching algorithm overcomes the problems faced during matching of low quality fingerprint images. The steps of the algorithm include extraction of core point using Poincare index method, extraction of Region of Interest (ROI) around core point, and similarity evaluation of statistical descriptors using k-NN classifier. Statistical descriptors are computed from 16 Gray Level Co-occurrence Matrices (GLCM) from Extracted ROI. The proposed algorithm is evaluated on the FVC2002 DB2 database. The experimental results show the effectiveness of proposed algorithm. Computational efficiency is improved by considering the ROI of size 101  101 around the core point.

Singular Candidate Method: Improvement of Extended Relational Graph Method for Reliable Detection of Fingerprint Singularity

The singular points of fingerprints, viz. core and delta, are important referential points for the classification of fingerprints. Several conventional approaches such as the Poincare index method have been proposed; however, these approaches are not reliable with poor-quality fingerprints. This paper proposes a new core and delta detection employing singular candidate analysis and an extended relational graph. Singular candidate analysis allows the use both the local and global features of ridge direction patterns and realizes high tolerance to local image noise; this involves the extraction of locations where there is high probability of the existence of a singular point. Experimental results using the fingerprint image databases FVC2000 and FVC2002, which include several poor-quality images, show that the success rate of the proposed approach is 10% higher than that of the Poincare index method for singularity detection, although the average computation time is 15%-30% greater.