"AVO-Polynom" Recognition Algorithm

Estimates Calculating Algorithms have a long story of application to recognition problems. Furthermore they have formed a basis for algebraic recognition theory. Yet use of ECA polynomials was limited to theoretical reasoning because of complexity of their construction and optimization. The new recognition method “AVO- polynom” based upon ECA polynomial of simple structure is described.

Self Evolving Character Recognition Using Genetic Operators

In this paper, a novel approach for character recognition has been presented with the help of genetic operators which have evolved from biological genetics and help us to achieve highly accurate results. A genetic algorithm approach has been described in which the biological haploid chromosomes have been implemented using a single row bit pattern of 315 values which have been operated upon by various genetic operators. A set of characters are taken as an initial population from which various new generations of characters are generated with the help of selection, crossover and mutation. Variations of population of characters are evolved from which the fittest solution is found by subjecting the various populations to a new fitness function developed. The methodology works and reduces the dissimilarity coefficient found by the fitness function between the character to be recognized and members of the populations and on reaching threshold limit of the error found from dissimilarity, it recognizes the character. As the new population is being generated from the older population, traits are passed on from one generation to another. We present a methodology with the help of which we are able to achieve highly efficient character recognition.

A stable graph-based representation for object recognition through high-order matching

Many Object recognition techniques perform some flavour of point pattern matching between a model and a scene. Such points are usually selected through a feature detection algorithm that is robust to a class of image transformations and a suitable descriptor is computed over them in order to get a reliable matching. Moreover, some approaches take an additional step by casting the correspondence problem into a matching between graphs defined over feature points. The motivation is that the relational model would add more discriminative power, however the overall effectiveness strongly depends on the ability to build a graph that is stable with respect to both changes in the object appearance and spatial distribution of interest points. In fact, widely used graph-based representations, have shown to suffer some limitations, especially with respect to changes in the Euclidean organization of the feature points. In this paper we introduce a technique to build relational structures over corner points that does not depend on the spatial distribution of the features. © 2012 ICPR Org Committee.

Human behaviour recognition in data-scarce domains

This paper presents the novel theory for performing multi-agent activity recognition without requiring large training corpora. The reduced need for data means that robust probabilistic recognition can be performed within domains where annotated datasets are traditionally unavailable. Complex human activities are composed from sequences of underlying primitive activities. We do not assume that the exact temporal ordering of primitives is necessary, so can represent complex activity using an unordered bag. Our three-tier architecture comprises low-level video tracking, event analysis and high-level inference. High-level inference is performed using a new, cascading extension of the Rao–Blackwellised Particle Filter. Simulated annealing is used to identify pairs of agents involved in multi-agent activity. We validate our framework using the benchmarked PETS 2006 video surveillance dataset and our own sequences, and achieve a mean recognition F-Score of 0.82. Our approach achieves a mean improvement of 17% over a Hidden Markov Model baseline.

Call recognition and individual identification of fish vocalizations based on automatic speech recognition: An example with the Lusitanian toadfish

The study of acoustic communication in animals often requires not only the recognition of species specific acoustic signals but also the identification of individual subjects, all in a complex acoustic background. Moreover, when very long recordings are to be analyzed, automatic recognition and identification processes are invaluable tools to extract the relevant biological information. A pattern recognition methodology based on hidden Markov models is presented inspired by successful results obtained in the most widely known and complex acoustical communication signal: human speech. This methodology was applied here for the first time to the detection and recognition of fish acoustic signals, specifically in a stream of round-the-clock recordings of Lusitanian toadfish (Halobatrachus didactylus) in their natural estuarine habitat. The results show that this methodology is able not only to detect the mating sounds (boatwhistles) but also to identify individual male toadfish, reaching an identification rate of ca. 95%. Moreover this method also proved to be a powerful tool to assess signal durations in large data sets. However, the system failed in recognizing other sound types.

Classificação de água de coco processada e natural por meio de HCA, PCA e teores de íons metálicos determinados por ICP OES

Inductively Coupled Plasma Optical Emission Spectrometry was used to determine Ca, Mg, Mn, Fe, Zn and Cu in samples of processed and natural coconut water. The sample preparation consisted in a filtration step followed by a dilution. The analysis was made employing optimized instrumental parameters and the results were evaluated using methods of Pattern Recognition. The data showed common concentration values for the analytes present in processed and natural samples. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) indicated that the samples of different kinds were statistically different when the concentrations of all the analytes were considered simultaneously.

25 anos de quimiometria no Brasil

Chemometric activities in Brazil are described according to three phases: before the existence of microcomputers in the 1970s, through the initial stages of microcomputer use in the 1980s and during the years of extensive microcomputer applications of the ´90s and into this century. Pioneering activities in both the university and industry are emphasized. Active research areas in chemometrics are cited including experimental design, pattern recognition and classification, curve resolution for complex systems and multivariate calibration. New trends in chemometrics, especially higher order methods for treating data, are emphasized.

Concepção e implementação de um sistema para analise cinematica de movimentos humanos

Universidade Estadual de Campinas. Faculdade de Educação Física

Medida da visão em candelas: description of a psychophysical technique to quantify luminous intensity

OBJETIVO: Desenvolver um método e um dispositivo para quantificar a visão em candela (cd). Os estudos de medida da visão são importantes para todas as ciências visuais. MÉTODOS: É um estudo teórico e experimental. Foram descritos os detalhes do método psicofísico e da calibração do dispositivo. Foram realizados testes preliminares em voluntários. RESULTADOS: É um teste psicofísico simples e com resultado expresso em unidades do sistema internacional de medidas. Com a descrição técnica será possível reproduzir o experimento em outros centros de pesquisa. CONCLUSÃO: Os resultados aferidos em intensidade luminosa (cd) são uma opção para estudo visual. Esses resultados possibilitarão extrapolar medidas para modelos matemáticos e para simular efeitos individuais com dados aberrométricos.

Cellular Renewal and Improvement of Local Cell Effector Activity in Peritoneal Cavity in Response to Infectious Stimuli

The peritoneal cavity (PerC) is a singular compartment where many cell populations reside and interact. Despite the widely adopted experimental approach of intraperitoneal (i.p.) inoculation, little is known about the behavior of the different cell populations within the PerC. To evaluate the dynamics of peritoneal macrophage (Mempty set) subsets, namely small peritoneal Mempty set (SPM) and large peritoneal Mempty set (LPM), in response to infectious stimuli, C57BL/6 mice were injected i.p. with zymosan or Trypanosoma cruzi. These conditions resulted in the marked modification of the PerC myelo-monocytic compartment characterized by the disappearance of LPM and the accumulation of SPM and monocytes. In parallel, adherent cells isolated from stimulated PerC displayed reduced staining for beta-galactosidase, a biomarker for senescence. Further, the adherent cells showed increased nitric oxide (NO) and higher frequency of IL-12-producing cells in response to subsequent LPS and IFN-gamma stimulation. Among myelo-monocytic cells, SPM rather than LPM or monocytes, appear to be the central effectors of the activated PerC; they display higher phagocytic activity and are the main source of IL-12. Thus, our data provide a first demonstration of the consequences of the dynamics between peritoneal Mempty set subpopulations by showing that substitution of LPM by a robust SPM and monocytes in response to infectious stimuli greatly improves PerC effector activity.

Leukotriene B(4) amplifies NF-kappa B activation in mouse macrophages by reducing SOCS1 inhibition of MyD88 expression

Activation of NF-kappa B and 5-lipoxygenase-mediated (5-LO-mediated) biosynthesis of the lipid mediator leukotriene B(4) (LTB(4)) are pivotal components of host defense and inflammatory responses. However, the role of LTB(4) in mediating innate immune responses elicited by specific TLR ligands and cytokines is unknown. Here we have shown that responses dependent on MyD88 (an adaptor protein that mediates signaling through all of the known TLRs, except TLR3, as well as IL-1 beta and IL-18) are reduced in mice lacking either 5-LO or the LTB(4) receptor BTL1, and that macrophages from these mice are impaired in MyD88-dependent activation of NF-kappa B. This macrophage defect was associated with lower basal and inducible expression of MyD88 and reflected impaired activation of STAT1 and overexpression of the STAT1 inhibitor SOCS1. Expression of MyD88 and responsiveness to the TLR4 ligand LPS were decreased by Stat1 siRNA silencing in WT macrophages and restored by Socs1 siRNA in 5-LO-deficient macrophages. These results uncover a pivotal role in macrophages for the GPCR BLT1 in regulating activation of NF-kappa B through Stat1-dependent expression of MyD88.

Feature selection environment for genomic applications

Background: Feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e., for dimensionality reduction). There are many genomic and proteomic applications that rely on feature selection to answer questions such as selecting signature genes which are informative about some biological state, e. g., normal tissues and several types of cancer; or inferring a prediction network among elements such as genes, proteins and external stimuli. In these applications, a recurrent problem is the lack of samples to perform an adequate estimate of the joint probabilities between element states. A myriad of feature selection algorithms and criterion functions have been proposed, although it is difficult to point the best solution for each application. Results: The intent of this work is to provide an open-source multiplataform graphical environment for bioinformatics problems, which supports many feature selection algorithms, criterion functions and graphic visualization tools such as scatterplots, parallel coordinates and graphs. A feature selection approach for growing genetic networks from seed genes ( targets or predictors) is also implemented in the system. Conclusion: The proposed feature selection environment allows data analysis using several algorithms, criterion functions and graphic visualization tools. Our experiments have shown the software effectiveness in two distinct types of biological problems. Besides, the environment can be used in different pattern recognition applications, although the main concern regards bioinformatics tasks.

Improvements on ICA mixture models for image pre-processing and segmentation

Today several different unsupervised classification algorithms are commonly used to cluster similar patterns in a data set based only on its statistical properties. Specially in image data applications, self-organizing methods for unsupervised classification have been successfully applied for clustering pixels or group of pixels in order to perform segmentation tasks. The first important contribution of this paper refers to the development of a self-organizing method for data classification, named Enhanced Independent Component Analysis Mixture Model (EICAMM), which was built by proposing some modifications in the Independent Component Analysis Mixture Model (ICAMM). Such improvements were proposed by considering some of the model limitations as well as by analyzing how it should be improved in order to become more efficient. Moreover, a pre-processing methodology was also proposed, which is based on combining the Sparse Code Shrinkage (SCS) for image denoising and the Sobel edge detector. In the experiments of this work, the EICAMM and other self-organizing models were applied for segmenting images in their original and pre-processed versions. A comparative analysis showed satisfactory and competitive image segmentation results obtained by the proposals presented herein. (C) 2008 Published by Elsevier B.V.

Exploring the emission intensities of ICP OES aided by chemometrics in the geographical discrimination of mineral waters

A rapid method for classification of mineral waters is proposed. The discrimination power was evaluated by a novel combination of chemometric data analysis and qualitative multi-elemental fingerprints of mineral water samples acquired from different regions of the Brazilian territory. The classification of mineral waters was assessed using only the wavelength emission intensities obtained by inductively coupled plasma optical emission spectrometry (ICP OES), monitoring different lines of Al, B, Ba, Ca, Cl, Cu, Co, Cr, Fe, K, Mg, Mn, Na, Ni, P, Pb, S, Sb, Si, Sr, Ti, V, and Zn, and Be, Dy, Gd, In, La, Sc and Y as internal standards. Data acquisition was done under robust (RC) and non-robust (NRC) conditions. Also, the combination of signal intensities of two or more emission lines for each element were evaluated instead of the individual lines. The performance of two classification-k-nearest neighbor (kNN) and soft independent modeling of class analogy (SIMCA)-and preprocessing algorithms, autoscaling and Pareto scaling, were evaluated for the ability to differentiate between the various samples in each approach tested (combination of robust or non-robust conditions with use of individual lines or sum of the intensities of emission lines). It was shown that qualitative ICP OES fingerprinting in combination with multivariate analysis is a promising analytical tool that has potential to become a recognized procedure for rapid authenticity and adulteration testing of mineral water samples or other material whose physicochemical properties (or origin) are directly related to mineral content.

Sensorless synchronization method for thin-film direct optical broadband monitoring system

Optical monitoring systems are necessary to manufacture multilayer thin-film optical filters with low tolerance on spectrum specification. Furthermore, to have better accuracy on the measurement of film thickness, direct monitoring is a must. Direct monitoring implies acquiring spectrum data from the optical component undergoing the film deposition itself, in real time. In making film depositions on surfaces of optical components, the high vacuum evaporator chamber is the most popular equipment. Inside the evaporator, at the top of the chamber, there is a metallic support with several holes where the optical components are assembled. This metallic support has rotary motion to promote film homogenization. To acquire a measurement of the spectrum of the film in deposition, it is necessary to pass a light beam through a glass witness undergoing the film deposition process, and collect a sample of the light beam using a spectrometer. As both the light beam and the light collector are stationary, a synchronization system is required to identify the moment at which the optical component passes through the light beam.