Development of a CAD system for automatic classification of microcalcifications based on FPGA

This paper presents a Computer Aided Diagnosis (CAD) system that automatically classifies microcalcifications detected on digital mammograms into one of the five types proposed by Michele Le Gal, a classification scheme that allows radiologists to determine whether a breast tumor is malignant or not without the need for surgeries. The developed system uses a combination of wavelets and Artificial Neural Networks (ANN) and is executed on an Altera DE2-115 Development Kit, a kit containing a Field-Programmable Gate Array (FPGA) that allows the system to be smaller, cheaper and more energy efficient. Results have shown that the system was able to correctly classify 96.67% of test samples, which can be used as a second opinion by radiologists in breast cancer early diagnosis. (C) 2013 The Authors. Published by Elsevier B.V.

Structure of large two-neutron halos in light exotic nuclei

The structure of three-body halo nuclei formed by two neutrons and a core (nnc) is studied using zero-range interactions. The halo wave function can be completely parameterized only by the s-wave scattering lengths and two-neutron separation energy. The sizes and the neutron-neutron correlation function of Li-11 and Be-14 are calculated and compared to experimental data. A general classification scheme for three-body halos with two identical particles is discussed as well as the critical conditions to allow excited Efimov states.

Screening Brazilian C gasoline quality: Application of the SIMCA chemometric method to gas chromatographic data

A total of 2400 samples of commercial Brazilian C gasoline were collected over a 6-month period from different gas stations in the São Paulo state, Brazil, and analysed with respect to 12 physicochemical parameters according to regulation 309 of the Brazilian Government Petroleum, Natural Gas and Biofuels Agency (ANP). The percentages (v/v) of hydrocarbons (olefins, aromatics and saturated) were also determined. Hierarchical cluster analysis (HCA) was employed to select 150 representative samples that exhibited least similarity on the basis of their physicochemical parameters and hydrocarbon compositions. The chromatographic profiles of the selected samples were measured by gas chromatography with flame ionisation detection and analysed using soft independent modelling of class analogy (SIMCA) method in order to create a classification scheme to identify conform gasolines according to ANP 309 regulation. Following the optimisation of the SIMCA algorithm, it was possible to classify correctly 96% of the commercial gasoline samples present in the training set of 100. In order to check the quality of the model, an external group of 50 gasoline samples (the prediction set) were analysed and the developed SIMCA model classified 94% of these correctly. The developed chemometric method is recommended for screening commercial gasoline quality and detection of potential adulteration. (c) 2007 Elsevier B.V. All rights reserved.

Sistema embarcado para suporte ao diagnóstico de microcalcificações em mamografias digitais

Pós-graduação em Engenharia Elétrica - FEIS

A organização do conhecimento e a representação do conhecimento no domínio da arquivística

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

The Relationship between Authors and Main Thematic Categories in the Field of Knowledge Organization: A Bibliometric Approach

This is a study about the relationships between authors and the main thematic categories in the papers published in the last five International ISKO Conferences, held between 2002 and 2010. The aim is to map the domain as ISKO conferences are considered the most representative forum in the field. The published papers are considered to indicate the relationships between authors and themes. The Classification Scheme for Knowledge Organization Error! Bookmark not defined Literature (CSKOL) was used to categorize the papers. The theoretical and methodological foundations of the study can be found in the concept of domain analysis proposed by Hjorland. The analysis of the papers (n=146) led to the identification of the most productive authors, the networks representing the relationships between the authors as also the categories that constitute the primary areas of research.

Questing mass dimension 1 spinor fields

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Simulation results and applications of an advection bounded scheme to practical flows

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A Fast Large Scale Iris Database Classification with Optimum-Path Forest Technique: A Case Study

Majority of biometric researchers focus on the accuracy of matching using biometrics databases, including iris databases, while the scalability and speed issues have been neglected. In the applications such as identification in airports and borders, it is critical for the identification system to have low-time response. In this paper, a graph-based framework for pattern recognition, called Optimum-Path Forest (OPF), is utilized as a classifier in a pre-developed iris recognition system. The aim of this paper is to verify the effectiveness of OPF in the field of iris recognition, and its performance for various scale iris databases. This paper investigates several classifiers, which are widely used in iris recognition papers, and the response time along with accuracy. The existing Gauss-Laguerre Wavelet based iris coding scheme, which shows perfect discrimination with rotary Hamming distance classifier, is used for iris coding. The performance of classifiers is compared using small, medium, and large scale databases. Such comparison shows that OPF has faster response for large scale database, thus performing better than more accurate but slower Bayesian classifier.

Shape-based features for cat ganglion retinal cells classification

This article presents a quantitative and objective approach to cat ganglion cell characterization and classification. The combination of several biologically relevant features such as diameter, eccentricity, fractal dimension, influence histogram, influence area, convex hull area, and convex hull diameter are derived from geometrical transforms and then processed by three different clustering methods (Ward's hierarchical scheme, K-means and genetic algorithm), whose results are then combined by a voting strategy. These experiments indicate the superiority of some features and also suggest some possible biological implications.