Particle swarm optimization for ensembling generation for evidential k-nearest-neighbour classifier

The problem addressed in this paper concerns the ensembling generation for evidential k-nearest-neighbour classifier. An efficient method based on particle swarm optimization (PSO) is here proposed. We improve the performance of the evidential k-nearest-neighbour (EkNN) classifier using a random subspace based ensembling method. Given a set of random subspace EkNN classifier, a PSO is used for obtaining the best parameters of the set of evidential k-nearest-neighbour classifiers, finally these classifiers are combined by the “vote rule”. The performance improvement with respect to the state-of-the-art approaches is validated through experiments with several benchmark datasets.

Fusion of color spaces for ear authentication

In this work, we propose a local approach for 2D ear authentication based on an ensemble of matchers trained on different color spaces. This is the first work that proposes to exploit the powerful properties of color analysis for improving the performance of an ear matcher.The method described is based on the selection of color spaces from which a set of Gabor features are extracted. The selection is performed using the sequential forward floating selection where the fitness function is related to the optimization of the ear recognition performance. Finally, the matching step is performed by means of the combination by the sum rule of several 1-nearest neighbor classifiers constructed on different color components.The effectiveness of the proposed method is demonstrated using the Notre-Dame EAR data set. Particularly interesting are the results obtained by the new approach in terms of rank-1 (∼84%), rank-5 (∼93%) and area under the ROC curve (∼98.5%), which are better than those obtained by other state-of-the-art 2D ear matchers.     

Evolved feature weighting for random subspace classifier.

The problem addressed in this letter concerns the multiclassifier generation by a random subspace method (RSM). In the RSM, the classifiers are constructed in random subspaces of the data feature space. In this letter, we propose an evolved feature weighting approach: in each subspace, the features are multiplied by a weight factor for minimizing the error rate in the training set. An efficient method based on particle swarm optimization (PSO) is here proposed for finding a set of weights for each feature in each subspace. The performance improvement with respect to the state-of-the-art approaches is validated through experiments with several benchmark data sets.     

Local binary patterns for a hybrid fingerprint matcher

In this work, we present a novel hybrid fingerprint matcher system based on local binary patterns. The two fingerprints to be matched are first aligned using their minutiae, then the images are decomposed in several overlapping sub-windows, each sub-window is convolved with a bank of Gabor filters and, finally, the invariant local binary patterns histograms are extracted from the convolved images.Extensive experiments conducted over the four FVC2002 fingerprint databases show the effectiveness of the proposed hybrid approach with respect to the well-known Tico's minutiae matcher and other image-based approaches. Moreover, a BioHashing approach have been designed using the proposed fixed-length feature vector and very interesting performance has been obtained by combining it with the Tico's minutiae matcher.     

Boundary stabilisation to non-stationary solutions for deterministic and stochastic parabolic-type equations

In this work, we design explicit, finite-dimensional boundary feedback laws for stabilisation to trajectories for parabolic-type equations. The simple form of the feedback allows to write the solution of the corresponding closed-loop equation in a mild formulation via a kernel; then, taking advantage of this, the stability is shown. As an application, null stabilisation for stochastic parabolic-type equations is deduced as well. As far as we know, the present work is the first result on boundary feedback stabilisation to trajectories and for stochastic parabolic-type equations, with stability guaranteed independent of how large the level of the noise is.     

Nanostructured Biomaterials with Controlled Properties Synthesis and Characterization

Magnetic nanoparticles were obtained using an adjusted Massart method and were covered in a layer-by-layer technique with hydrogel-type biocompatible shells, from chitosan and hyaluronic acid. The synthesized nanocomposites were characterized using dynamic light scattering, transmission electron microscopy, and Fourier transformed infrared spectroscopy. Biocompatibility of magnetic nanostructures was determined by MTT (3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide) cell proliferation assay, swelling tests, and degradation tests. In addition, interaction of hydrogel-magnetic nanoparticles with microorganisms was studied. The possibility of precise nanoparticles size control, as long as the availability of bio-compatible covering, makes them suitable for biomedical applications.     

A multi-matcher for ear authentication

In this work we propose a local approach of 2D ear authentication. A multi-matcher system is proposed where each matcher is trained using features extracted from a single sub-window of the whole 2D image. The features are extracted by the convolution of each sub-window with a bank of Gabor Filters, then their dimensionality is reduced by Laplacian EigenMaps. The best matchers, corresponding to the most discriminative sub-windows, are selected by running the Sequential Forward Floating Selection (SFFS). Our experiments, carried out on a database of 114 people, show that combining only few (ten) sub-windows in the fusion step it is possible to achieve a very low Equal Error Rate.     

MKL-tree: an index structure for high-dimensional vector spaces

In this work, a novel hierarchical data structure for high dimensional data indexing is proposed. MKL-tree is based on dimensionality reduction operated by means of the MKL transform, a multi-space generalization of the KL transform. A local dimensionality reduction is performed at each node of the tree, allowing more selective features to be extracted and thus increasing the discriminating power of the index. The mathematical foundation for nodes and leaves representation and for the techniques aimed to manage the structure is detailed. Moreover, the algorithms for bulk loading MKL-tree (i.e., for creating the tree given a large number of objects simultaneously), for updating and splitting nodes after the insertion of new objects and for performing similarity searches are described. Results are reported for the comparison of MKL-tree with other well-known access methods in terms of I/O and CPU costs and precision of the result in the execution of similarity queries.     

Deformation of loaded micropolar elastic cylinders

In this paper we solve the general problem of deformation of a composite elastic cylinder. We consider the case when the cross-section of the cylinder is occupied by different inhomogeneous and anisotropic micropolar elastic materials. The elastic coefficients are assumed to be independent of the axial coordinate and are discontinuous, in general, over the surface of separation of the materials. The cylinder is subjected to body loads and to tractions on the lateral surface, and to appropriate stress and couple-stress resultants over its ends.