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.

Transmission system expansion planning by a branch-and-bound algorithm

A branch and bound (B& B) algorithm using the DC model, to solve the power system transmission expansion planning by incorporating the electrical losses in network modelling problem is presented. This is a mixed integer nonlinear programming (MINLP) problem, and in this approach, the so-called fathoming tests in the B&B algorithm were redefined and a nonlinear programming (NLP) problem is solved in each node of the B& B tree, using an interior-point method. Pseudocosts were used to manage the development of the B&B tree and to decrease its size and the processing time. There is no guarantee of convergence towards global optimisation for the MINLP problem. However, preliminary tests show that the algorithm easily converges towards the best-known solutions or to the optimal solutions for all the tested systems neglecting the electrical losses. When the electrical losses are taken into account, the solution obtained using the Garver system is better than the best one known in the literature.

The selective neurotoxin DSP-4 impairs the noradrenergic projections from the locus coeruleus to the inferior colliculus in rats

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

Oriented texture classification based on self-organizing neural network and Hough transform

This paper presents a technique for oriented texture classification which is based on the Hough transform and Kohonen's neural network model. In this technique, oriented texture features are extracted from the Hough space by means of two distinct strategies. While the first operates on a non-uniformly sampled Hough space, the second concentrates on the peaks produced in the Hough space. The described technique gives good results for the classification of oriented textures, a common phenomenon in nature underlying an important class of images. Experimental results are presented to demonstrate the performance of the new technique in comparison, with an implemented technique based on Gabor filters.

Electric and magnetic properties of polymer electrolyte/carbon black composites

Measurements of 1H Nuclear Magnetic Resonance (NMR) relaxation times, Electron Paramagnetic Resonance (EPR) and AC Impedance Spectroscopy (IS) are reported for composites based on PEO8:LiClO4 and carbon black (CB), prepared by two methods: solvent and fusion processing. Three nuclear relaxation processes were identified for 1H nuclei: (i) belonging to the polymer chains in the amorphous phase, loosely bound to the CB particles, whose dynamics is almost the same as for unfilled polymer, (ii) belonging to the polymer chains which are tightly attached to the CB particles, and (iii) belonging to the crystalline phase in the loose polymer chain. The paramagnetic electronic susceptibility of the composite samples, measured by EPR, was interpreted by assuming a contribution of localized spin states that follow a Curie law, and a Pauli-like contribution of delocalized spins. A significant change of the EPR linewidth was observed at 40 K, which is the temperature where the Curie and Pauli susceptibilities equally contribute to the paramagnetic electronic susceptibility. The electrical properties are very sensitive to the preparation methods of the composites, which conditions the interaction between carbon particle-carbon particle and carbon particle-polymer chain. Classical statistic models to describe the conductivity in these media were not satisfactory. © 1998 Published by Elsevier Science B.V. All rights reserved.

Respiratory and storage behavior of fresh cut 'Tommy Atkins' mango

The respiratory and storage behavior of fresh cut 'Tommy Atkins' mango, naturally ripened (NR) or with use of ethylene (RE), were studied. Fruits were selected, washed and disinfected (200 mgCl.L-1) and stored for 12 hours at 10°C. After this period, they were processed under hygienic conditions at 10°C, packaged in polyethylene terephthalate (PET) trays or in styrofoam trays wrapped with stretchable polyvinyl chloride (PVC) film and stored for up to 15 days at 3°C. The products were evaluated regarding the evolution of internal atmosphere in the packing (O2 and CO 2), development of weight, appearance, shelf life and consumer acceptability. The respiratory rate was measured before and after processing every two hours. The yield of 'Tommy Atkins' mango to produce fresh cut product was 48.09±0.95%. Increase of the respiration rate of both mango samples was verified one hour after the preparation (NR = 17.75 mL CO 2.kg-1.h-1; RE = 28.29 mL CO 2.kg-1.h-1), followed by stabilization at 3.76 and 8.07 mL CO2/kg.h, respectively. The percentage of O2 in packages was stable in all treatments, 15-20% in PVC trays, 18-20% in PET tray. The percentage of CO2 was steady around 1.5-2.5%. The products lost fresh mass during the storage, from 0.06% to 0.30% for PET trays and from 0.15% to 1.61% for trays covered with PVC. The appearance was considered appropriate for commercialization until the 13th day, whereas product from mangoes ripened with application of ethylene was for 11 days, presenting browning in the external surface. The naturally ripened mango presented the best flavor and consumer preference in relation to the mango ripened with application of ethylene for 11 days of storage. The control of hygienic conditions during the production and storage was good and with safety for until 10 days.

COX24 codes for a mitochondrial protein required for processing of the COX1 transcript

In most strains of Saccharomyces cerevisiae the mitochondrial gene COX1, for subunit 1 of cytochrome oxidase, contains multiple exons and introns. Processing of COX1 primary transcript requires accessory proteins factors, some of which are encoded by nuclear genes and others by reading frames residing in some of the introns of the COX1 and COB genes. Here we show that the low molecular weight protein product of open reading frame YLR204W, for which we propose the name COX24, is also involved in processing of COX1 RNA intermediates. The growth defect of cox24 mutants is partially rescued in strains harboring mitochondrial DNA lacking introns. Northern blot analyses of mitochondrial transcripts indicate cox24 null mutants to be blocked in processing of introns aI2 and aI3. The dependence of intron aI3 excision on Cox24p is also supported by the growth properties of the cox24 mutant harboring mitochondrial DNA with different intron compositions. The intermediate phenotype of the cox24 mutant in the background of intronless mitochondrial DNA, however, suggests that in addition to its role in splicing of the COX1 pre-mRNA, Cox24p still has another function. Based on the analysis of a cox14-cox24 double mutant, we propose that the other function of Cox24p is related to translation of the COX1 mRNA. © 2006 by The American Society for Biochemistry and Molecular Biology, Inc.

Oropharyngeal dysphagia identification using wavelets and optimum path forest

The swallowing disturbers are defined as oropharyngeal dysphagia when present specifies signals and symptoms that are characterized for alterations in any phases of swallowing. Early diagnosis is crucial for the prognosis of patients with dysphagia and the potential to diagnose dysphagia in a noninvasive manner by assessing the sounds of swallowing is a highly attractive option for the dysphagia clinician. This study proposes a new framework for oropharyngeal dysphagia identification, having two main contributions: a new set of features extract from swallowing signal by discrete wavelet transform and the dysphagia classification by a novel pattern classifier called OPF. We also employed the well known SVM algorithm in the dysphagia identification task, for comparison purposes. We performed the experiments in two sub-signals: the first was the moment of the maximal peak (MP) of the signal and the second is the swallowing apnea period (SAP). The OPF final accuracy obtained were 85.2% and 80.2% for the analyzed signals MP and SAP, respectively, outperforming the SVM results. ©2008 IEEE.

Pitch detection algorithms based on zero-cross rate and autocorrelation function for musical notes

This paper discusses two pitch detection algorithms (PDA) for simple audio signals which are based on zero-cross rate (ZCR) and autocorrelation function (ACF). As it is well known, pitch detection methods based on ZCR and ACF are widely used in signal processing. This work shows some features and problems in using these methods, as well as some improvements developed to increase their performance. © 2008 IEEE.

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.

Remote Sensing Detection of atmospheric pollutants using Lidar, Sodar and correlation with air quality data in an industrial area

Optical remote sensing techniques have obvious advantages for monitoring gas and aerosol emissions, since they enable the operation over large distances, far from hostile environments, and fast processing of the measured signal. In this study two remote sensing devices, namely a Lidar (Light Detection and Ranging) for monitoring the vertical profile of backscattered light intensity, and a Sodar (Acoustic Radar, Sound Detection and Ranging) for monitoring the vertical profile of the wind vector were operated during specific periods. The acquired data were processed and compared with data of air quality obtained from ground level monitoring stations, in order to verify the possibility of using the remote sensing techniques to monitor industrial emissions. The campaigns were carried out in the area of the Environmental Research Center (Cepema) of the University of São Paulo, in the city of Cubatão, Brazil, a large industrial site, where numerous different industries are located, including an oil refinery, a steel plant, as well as fertilizer, cement and chemical/petrochemical plants. The local environmental problems caused by the industrial activities are aggravated by the climate and topography of the site, unfavorable to pollutant dispersion. Results of a campaign are presented for a 24- hour period, showing data of a Lidar, an air quality monitoring station and a Sodar. © 2011 SPIE.

Employment, growth and reforms in Jamaica

Includes bibliography

Improving image classification through descriptor combination

The efficiency in image classification tasks can be improved using combined information provided by several sources, such as shape, color, and texture visual properties. Although many works proposed to combine different feature vectors, we model the descriptor combination as an optimization problem to be addressed by evolutionary-based techniques, which compute distances between samples that maximize their separability in the feature space. The robustness of the proposed technique is assessed by the Optimum-Path Forest classifier. Experiments showed that the proposed methodology can outperform individual information provided by single descriptors in well-known public datasets. © 2012 IEEE.

The natural gas industry and its regulation in Latin America

Includes bibliography

Frugivory and Seed Dispersal by Northern Pigtailed Macaques (Macaca leonina), in Thailand

Tropical rain forest conservation requires a good understanding of plant-animal interactions. Seed dispersal provides a means for plant seeds to escape competition and density-dependent seed predators and pathogens and to colonize new habitats. This makes the role and effectiveness of frugivorous species in the seed dispersal process an important topic. Northern pigtailed macaques (Macaca leonina) may be effective seed dispersers because they have a diverse diet and process seeds in several ways (swallowing, spitting out, or dropping them). To investigate the seed dispersal effectiveness of a habituated group of pigtailed macaques in Khao Yai National Park, Thailand, we examined seed dispersal quantity (number of fruit species eaten, proportion in the diet, number of feces containing seeds, and number of seeds processed) and quality (processing methods used, seed viability and germination success, habitat type and distance from parent tree for the deposited seeds, and dispersal patterns) via focal and scan sampling, seed collection, and germination tests. We found thousands of seeds per feces, including seeds up to 58 mm in length and from 88 fruit species. Importantly, the macaques dispersed seeds from primary to secondary forests, via swallowing, spitting, and dropping. Of 21 species, the effect of swallowing and spitting was positive for two species (i. e., processed seeds had a higher % germination and % viability than control seeds), neutral for 13 species (no difference in % germination or viability), and negative (processed seeds had lower % germination and viability) for five species. For the final species, the effect was neutral for spat-out seeds but negative for swallowed seeds. We conclude that macaques are effective seed dispersers in both quantitative and qualitative terms and that they are of potential importance for tropical rain forest regeneration. © 2013 Springer Science+Business Media New York.