(1)H NMR Fingerprinting of Brazilian Commercial Gasoline: Pattern-Recognition Analyses for Origin Authentication Purposes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Recognition and distribution of Leptodactylus mystaceus (Anura; Leptodactylidae) in the State of São Paulo, Southeastern Brazil

Leptodactylus mystaceus, uma espécie com ampla distribuição geográfica pela América do Sul, é diagnosticada com base em exemplares do Estado de São Paulo, seu limite meridional de distribuição geográfica. Apresentamos aqui o primeiro registro da espécie para o Sudeste do Brasil, ampliando sua distribuição conhecida em cerca de 1.300 km ao sudeste. Também incluímos a descrição da vocalização de anúncio, informações sobre história natural, fotografia em vida e desenhos de caracteres morfológicos que auxiliam na identificação desta espécie.

Recognition of altered segments in Brazilian Sign Language

The purpose of this study was to verify discriminative control by segments of signs in adolescents with deafness who use Brazilian Sign Language (BSL). Four adolescent with bilateral deafness, with 3 years of BSL teaching, saw a video presenting a children's tale in BSL. After showing accurate understanding of the story, participants saw another video of the same story with 12 signs altered in one of their segments (hand configuration, place of articulation, or movement). They apparently did not detect the alterations. However, when the signs were presented in isolation in a matching-to-sample test, they virtually always selected the picture corresponding to the unaltered signs. Three participants selected an unfamiliar picture in 50% or more trials with an altered sign as a sample, showing that they could detect the majority of the altered signs.

Chemical communication, aggression, and conspecific recognition in the fish Nile tilapia

The chemical modulation of agonistic behavior and conspecific recognition were tested in juveniles of the fish Nile tilapia, Oreochromis niloticus (L.). After a 7-day isolation period, the fish were grouped (four individuals per aquarium) for 7 days. Then fish of alpha and beta ranks (previously matched for similar size) were paired in a neutral territory for analysis of their agonistic interaction. Pairs composed of alpha and beta fish were established with either fish from the same group (familiar) or from two different groups (unfamiliar). The pairs were tested in contiguous compartments, either with water exchange between the compartments or in the absence of water exchange. In each condition the fish were separated by a transparent glass partition. Twelve pairs were tested in each experimental condition. Fish behavior was videotaped and the following variables were analyzed: (a) frequency of and time spent in agonistic patterns, (b) latency to start fighting, and (c) duration of swimming. Water exchange between compartments decreased agonistic interactions. This effect, however, was more pronounced in pairs of fish coming from the same group (in this case, subordinate fish spent less time in confrontations than dominant ones). We conclude that chemical communication decreases aggression in this species by (1) inducing an alarm reaction and (2) increasing conspecific recognition (thus stabilizing the dominance hierarchy). (C) 1997 Elsevier B.V.

NATURAL PRODUCT SUBSTITUTION PATTERN AND SKELETAL RECOGNITION METHOD

This work describes a methodology for identification of skeletal types of diterpenes based on data base with 1500 compounds isolated from Asteraceae. One program named BOTOCSYS was built with the codification of the compounds and their botanical sources. An example of identification of a new substance is given.

Pattern recognition theory in nonlinear signal processing

A body of research has developed within the context of nonlinear signal and image processing that deals with the automatic, statistical design of digital window-based filters. Based on pairs of ideal and observed signals, a filter is designed in an effort to minimize the error between the ideal and filtered signals. The goodness of an optimal filter depends on the relation between the ideal and observed signals, but the goodness of a designed filter also depends on the amount of sample data from which it is designed. In order to lessen the design cost, a filter is often chosen from a given class of filters, thereby constraining the optimization and increasing the error of the optimal filter. To a great extent, the problem of filter design concerns striking the correct balance between the degree of constraint and the design cost. From a different perspective and in a different context, the problem of constraint versus sample size has been a major focus of study within the theory of pattern recognition. This paper discusses the design problem for nonlinear signal processing, shows how the issue naturally transitions into pattern recognition, and then provides a review of salient related pattern-recognition theory. In particular, it discusses classification rules, constrained classification, the Vapnik-Chervonenkis theory, and implications of that theory for morphological classifiers and neural networks. The paper closes by discussing some design approaches developed for nonlinear signal processing, and how the nature of these naturally lead to a decomposition of the error of a designed filter into a sum of the following components: the Bayes error of the unconstrained optimal filter, the cost of constraint, the cost of reducing complexity by compressing the original signal distribution, the design cost, and the contribution of prior knowledge to a decrease in the error. The main purpose of the paper is to present fundamental principles of pattern recognition theory within the framework of active research in nonlinear signal processing.

Aircraft interior failure pattern recognition utilizing text mining and neural networks

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Recognition of Cretaceous, Paleocene, and Neogene tectonic reactivation through apatite fission-track analysis in Precambrian areas of southeast Brazil: Association with the opening of the south Atlantic Ocean

Apatite fission-track analysis was used for the determination of thermal histories and ages in Precambrian areas of southeast Brazil. Together with geological and geomorphologic information, these ages enable us to quantify the thermal histories and timing of Mesozoic and Cenozoic epirogenic and tectonic processes. The collected samples are from different geomorphologic blocks: the high Mantiqueira mountain range (HMMR) with altitude above 1000 m, the low Mantiqueira mountain range (LMMR) under 1000 m, the Serra do Mar mountain range (SMMR), the Jundiá and Atlantic Plateaus, and the coastline, all of which have distinct thermal histories. During the Aptian (∼120 Ma), there was an uplift of the HMMR, coincident with opening of the south Atlantic Ocean. Its thermal history indicates heating (from ∼60 to∼80 °C) until the Paleocene, when rocks currently exposed in the LMMR reached temperatures of ∼100 °C. In this period, the Serra do Mar rift system and the Japi erosion surface were formed. The relief records the latter. During the Late Cretaceous, the SMMR was uplifted and probably linked to its origin; in the Tertiary, it experienced heating from ∼60 to ∼90 °C, then cooling that extends to the present. The SMMR, LMMR, and HMMR were reactivated mainly in the Paleocene, and the coastline during the Paleogene. These processes are reflected in the sedimentary sequences and discordances of the interior and continental margin basins. © 2002 Elsevier Science Ltd. All rights reserved.

Evolvable hardware applied to voice recognition

This paper presents some results of the application on Evolvable Hardware (EHW) in the area of voice recognition. Evolvable Hardware is able to change inner connections, using genetic learning techniques, adapting its own functionality to external condition changing. This technique became feasible by the improvement of the Programmable Logic Devices. Nowadays, it is possible to have, in a single device, the ability to change, on-line and in real-time, part of its own circuit. This work proposes a reconfigurable architecture of a system that is able to receive voice commands to execute special tasks as, to help handicapped persons in their daily home routines. The idea is to collect several voice samples, process them through algorithms based on Mel - Ceptrais theory to obtain their numerical coefficients for each sample, which, compose the universe of search used by genetic algorithm. The voice patterns considered, are limited to seven sustained Portuguese vowel phonemes (a, eh, e, i, oh, o, u).

Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition

Structural health monitoring (SHM) is related to the ability of monitoring the state and deciding the level of damage or deterioration within aerospace, civil and mechanical systems. In this sense, this paper deals with the application of a two-step auto-regressive and auto-regressive with exogenous inputs (AR-ARX) model for linear prediction of damage diagnosis in structural systems. This damage detection algorithm is based on the. monitoring of residual error as damage-sensitive indexes, obtained through vibration response measurements. In complex structures there are. many positions under observation and a large amount of data to be handed, making difficult the visualization of the signals. This paper also investigates data compression by using principal component analysis. In order to establish a threshold value, a fuzzy c-means clustering is taken to quantify the damage-sensitive index in an unsupervised learning mode. Tests are made in a benchmark problem, as proposed by IASC-ASCE with different damage patterns. The diagnosis that was obtained showed high correlation with the actual integrity state of the structure. Copyright © 2007 by ABCM.

Fusion of fingerprint recognition methods for robust human identification

Biometrics is one of the biggest tendencies in human identification. The fingerprint is the most widely used biometric. However considering the automatic fingerprint recognition a completely solved problem is a common mistake. The most popular and extensively used methods, the minutiae-based, do not perform well on poor-quality images and when just a small area of overlap between the template and the query images exists. The use of multibiometrics is considered one of the keys to overcome the weakness and improve the accuracy of biometrics systems. This paper presents the fusion of a minutiae-based and a ridge-based fingerprint recognition method at rank, decision and score level. The fusion techniques implemented leaded to a reduction of the Equal Error Rate by 31.78% (from 4.09% to 2.79%) and a decreasing of 6 positions in the rank to reach a Correct Retrieval (from rank 8 to 2) when assessed in the FVC2002-DB1A database. © 2008 IEEE.

Routine libraries for pattern recognition in quasispecies.

The results obtained through biological research usually need to be analyzed using computational tools, since manual analysis becomes unfeasible due to the complexity and size of these results. For instance, the study of quasispecies frequently demands the analysis of several, very lengthy sequences of nucleotides and amino acids. Therefore, bioinformatics tools for the study of quasispecies are constantly being developed due to different problems found by biologists. In the present study, we address the development of a software tool for the evaluation of population diversity in quasispecies. Special attention is paid to the localization of genome regions prone to changes, as well as of possible hot spots.

Robust and fast vowel recognition using optimum-path forest

The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is required. In this work, we present an algorithm for AVR based on the Optimum-Path Forest (OPF), which is an emergent pattern recognition technique recently introduced in literature. Adopting a supervised training procedure and using speech tags from two public datasets, we observed that OPF has outperformed ANNs, SVMs, plus other classifiers, in terms of training time and accuracy. ©2010 IEEE.

ANN statistical image recognition method for computer vision in agricultural mobile robot navigation

The main application area in this project, is to deploy image processing and segmentation techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. Thereby, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for image recognition. Hence, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave computational platforms, along with the application of customized Back-propagation Multilayer Perceptron (MLP) algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of segmented images in which reasonably accurate results were obtained. © 2010 IEEE.

Pattern recognition structured heuristics methods for image processing in mobile robot navigation

In this project, the main focus is to apply image processing techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained. ©2010 IEEE.