Core Point Pixel-Level Localization by Fingerprint Features in Spatial Domain

Singular point detection is a primary step in fingerprint recognition, especially for fingerprint alignment and classification. But in present there are still some problems and challenges such as more false-positive singular points or inaccurate reference point localization. This paper proposes an accurate core point localization method based on spatial domain features of fingerprint images from a completely different viewpoint to improve the fingerprint core point displacement problem of singular point detection. The method first defines new fingerprint features, called furcation and confluence, to represent specific ridge/valley distribution in a core point area, and uses them to extract the innermost Curve of ridges. The summit of this Curve is regarded as the localization result. Furthermore, an approach for removing false Furcation and Confluence based on their correlations is developed to enhance the method robustness. Experimental results show that the proposed method achieves satisfactory core localization accuracy in a large number of samples

Constraints on the slope of the dark halo mass function by microlensing observables

We investigate the dark halo lens mass function (MF) for a wide class of spheroidal non singular isothermal models comparing observed and observable microlensing quantities for MACHO observations towards LMC and taking into account the detection efficiency. We evaluate the microlensing observable quantities, i.e. observable optical depth, number of events and mean duration, for models with homogenous power - law MF changing the upper and lower mass limits and the flattening of the dark halo. By applying the simple technique of the inverse problem method we are then able to get some interesting constraints on the slope $\alpha$ of the MF and on the dark halo mass fraction f made out by MACHOs consistently with previous results.Comment: 10 LaTex pages, 2 postscript figures, accepted on 21/5/2001 for pubblication on A&A; title changed, completely revised version : a new definition of observable optical depth is used and all the MACHO results from 5.7 years of observations are used to constrain the slope of the dark halo mass functio

Data-driven pattern identification and outlier detection in time series

We address the problem of data-driven pattern identification and outlier detection in time series. To this end, we use singular value decomposition (SVD) which is a well-known technique to compute a low-rank approximation for an arbitrary matrix. By recasting the time series as a matrix it becomes possible to use SVD to highlight the underlying patterns and periodicities. This is done without the need for specifying user-defined parameters. From a data mining perspective, this opens up new ways of analyzing time series in a data-driven, bottom-up fashion. However, in order to get correct results, it is important to understand how the SVD-spectrum of a time series is influenced by various characteristics of the underlying signal and noise. In this paper, we have extended the work in earlier papers by initiating a more systematic analysis of these effects. We then illustrate our findings on some real-life data

Optimisation of Artefact Removal Algorithm for Microwave Imaging of the Axillary Region Using Experimental Prototype Signals

Microwave Imaging (MWI) has the potential to aid breast cancer staging through the detection of Axillary Lymph Nodes (ALNs). This type of system can present some challenges, mainly due to the irregular axillary surface. The optimisation of the artefact removal algorithm to successfully remove the surface reflections is of great importance. In this paper, we propose using Singular Value Decomposition (SVD) as an artefact removal algorithm and study the effect of choosing different subsets of antenna positions for artefact removal on imaging results using experimental signals. We show that different subsets of antenna positions affect the results and in some cases prevent the targets detection. Our analysis allowed us to find an optimal combination of parameters which results in Signal-to-Clutter Ratio higher than 2.77 dB and Location Error lower than 14.9 mm for three different experimental tests. These results are relevant for the development of dedicated algorithms for ALN-MWI application.info:eu-repo/semantics/publishedVersio

Algorithms for Large Scale Problems in Eigenvalue and Svd Computations and in Big Data Applications

As ”big data” has increasing influence on our daily life and research activities, it poses significant challenges on various research areas. Some applications often demand a fast solution of large, sparse eigenvalue and singular value problems; In other applications, extracting knowledge from large-scale data requires many techniques such as statistical calculations, data mining, and high performance computing. In this dissertation, we develop efficient and robust iterative methods and software for the computation of eigenvalue and singular values. We also develop practical numerical and data mining techniques to estimate the trace of a function of a large, sparse matrix and to detect in real-time blob-filaments in fusion plasma on extremely large parallel computers. In the first work, we propose a hybrid two stage SVD method for efficiently and accurately computing a few extreme singular triplets, especially the ones corresponding to the smallest singular values. The first stage achieves fast convergence while the second achieves the final accuracy. Furthermore, we develop a high-performance preconditioned SVD software based on the proposed method on top of the state-of-the-art eigensolver PRIMME. The method can be used with or without preconditioning, on parallel computers, and is superior to other state-of-the-art SVD methods in both efficiency and robustness. In the second study, we provide insights and develop practical algorithms to accomplish efficient and accurate computation of interior eigenpairs using refined projection techniques in non-Krylov iterative methods. By analyzing different implementations of the refined projection, we propose a new hybrid method to efficiently find interior eigenpairs without compromising accuracy. Our numerical experiments illustrate the efficiency and robustness of the proposed method. In the third work, we present a novel method to estimate the trace of matrix inverse that exploits the pattern correlation between the diagonal of the inverse of the matrix and that of some approximate inverse. We leverage various sampling and fitting techniques to fit the diagonal of the approximation to that of the inverse. Our method may serve as a standalone kernel for providing a fast trace estimate or as a variance reduction method for Monte Carlo in some cases. An extensive set of experiments demonstrate the potential of our method. In the fourth study, we provide first results on applying outlier detection techniques to effectively tackle the fusion blob detection problem on extremely large parallel machines. We present a real-time region outlier detection algorithm to efficiently find and track blobs in fusion experiments and simulations. Our experiments demonstrated we can achieve linear time speedup up to 1024 MPI processes and complete blob detection in two or three milliseconds

Characterization of the Inner Knot of the Crab: The Site of the Gamma-ray Flares?

One of the most intriguing results from the gamma-ray instruments in orbit has been the detection of powerful flares from the Crab Nebula. These flares challenge our understanding of pulsar wind nebulae and models for particle acceleration. We report on the portion of a multiwavelength campaign using Keck, HST, and Chandra concentrating on a small emitting region, the Crab's inner knot, located a fraction of an arcsecond from the pulsar. We find that the knot's radial size, tangential size, peak flux, and the ratio of the flux to that of the pulsar are correlated with the projected distance of the knot from the pulsar. A new approach, using singular value decomposition for analyzing time series of images, was introduced yielding results consistent with the more traditional methods while some uncertainties were substantially reduced. We exploit the characterization of the knot to discuss constraints on standard shock-model parameters that may be inferred from our observations assuming the inner knot lies near to the shocked surface. These include inferences as to wind magnetization, shock shape parameters such as incident angle and poloidal radius of curvature, as well as the IR/optical emitting particle enthalpy fraction. We find that while the standard shock model gives good agreement with observation in many respects, there remain two puzzles: (a) The observed angular size of the knot relative to the pulsar--knot separation is much smaller than expected; (b) The variable, yet high degree of polarization reported is difficult to reconcile with a highly relativistic downstream flow.Comment: 46 pages, 14 figures, submitted to the Astrophysical Journa