Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal

Duplex and superduplex stainless steels are class of materials of a high importance for engineering purposes, since they have good mechanical properties combination and also are very resistant to corrosion. It is known as well that the chemical composition of such steels is very important to maintain some desired properties. In the past years, some works have reported that γ 2 precipitation improves the toughness of such steels, and its quantification may reveals some important information about steel quality. Thus, we propose in this work the automatic segmentation of γ 2 precipitation using two pattern recognition techniques: Optimum-Path Forest (OPF) and a Bayesian classifier. To the best of our knowledge, this if the first time that machine learning techniques are applied into this area. The experimental results showed that both techniques achieved similar and good recognition rates. © 2012 Taylor & Francis Group.

Measurement of the W boson mass with the D0 detector

We present a measurement of the W boson mass using data corresponding to 4.3fb -1 of integrated luminosity collected with the D0 detector during Run II at the Fermilab Tevatron pp̄ collider. With a sample of 1677394 W→eν candidate events, we measure M W=80.367±0. 026GeV. This result is combined with an earlier D0 result determined using an independent Run II data sample, corresponding to 1fb -1 of integrated luminosity, to yield M W=80.375±0.023GeV. © 2012 American Physical Society.

A semi-automatic method for indirect orientation of aerial images using ground control lines extracted from airborne laser scanner data

This paper presents a method for indirect orientation of aerial images using ground control lines extracted from airborne Laser system (ALS) data. This data integration strategy has shown good potential in the automation of photogrammetric tasks, including the indirect orientation of images. The most important characteristic of the proposed approach is that the exterior orientation parameters (EOP) of a single or multiple images can be automatically computed with a space resection procedure from data derived from different sensors. The suggested method works as follows. Firstly, the straight lines are automatically extracted in the digital aerial image (s) and in the intensity image derived from an ALS data-set (S). Then, correspondence between s and S is automatically determined. A line-based coplanarity model that establishes the relationship between straight lines in the object and in the image space is used to estimate the EOP with the iterated extended Kalman filtering (IEKF). Implementation and testing of the method have employed data from different sensors. Experiments were conducted to assess the proposed method and the results obtained showed that the estimation of the EOP is function of ALS positional accuracy.

Search for the standard model Higgs boson in ZH→ + -bb̄ production with the D0 detector in 9.7fb -1 of pp̄ collisions at √s=1.96TeV

We present a search for the standard model (SM) Higgs boson produced in association with a Z boson in 9.7fb -1 of pp̄ collisions collected with the D0 detector at the Fermilab Tevatron Collider at √s=1.96TeV. Selected events contain one reconstructed Z→e +e - or Z→μ +μ - candidate and at least two jets, including at least one jet identified as likely to contain a b quark. To validate the search procedure, we also measure the cross section for ZZ production in the same final state. It is found to be consistent with its SM prediction. We set upper limits on the ZH production cross section times branching ratio for H→bb̄ at the 95% C.L. for Higgs boson masses 90≤M H≤150GeV. The observed (expected) limit for M H=125GeV is 7.1 (5.1) times the SM cross section. © 2012 American Physical Society.

Search for the standard model Higgs boson in associated WH production in 9.7fb -1 of pp̄ collisions with the D0 detector

We present a search for the standard model Higgs boson in final states with a charged lepton (electron or muon), missing transverse energy, and two or three jets, at least one of which is identified as a b-quark jet. The search is primarily sensitive to WH→ νbb̄ production and uses data corresponding to 9.7fb -1 of integrated luminosity collected with the D0 detector at the Fermilab Tevatron pp̄ Collider at √s=1.96TeV. We observe agreement between the data and the expected background. For a Higgs boson mass of 125 GeV, we set a 95% C.L. upper limit on the production of a standard model Higgs boson of 5.2×σ SM, where σ SM is the standard model Higgs boson production cross section, while the expected limit is 4.7×σ SM. © 2012 American Physical Society.

Measurement of the semileptonic charge asymmetry in B0 meson mixing with the D0 detector

We present a measurement of the semileptonic mixing asymmetry for B0 mesons, asld, using two independent decay channels: B0→μ +D -X, with D -→K +π -π -; and B0→μ +D *-X, with D * -→D ̄0π -, D ̄0→ K +π - (and charge conjugate processes). We use a data sample corresponding to 10.4fb -1 of pp̄ collisions at √s=1.96TeV, collected with the D0 experiment at the Fermilab Tevatron collider. We extract the charge asymmetries in these two channels as a function of the visible proper decay length of the B0 meson, correct for detector-related asymmetries using data-driven methods, and account for dilution from charge-symmetric processes using Monte Carlo simulation. The final measurement combines four signal visible proper decay length regions for each channel, yielding asld=[0.68±0.45(stat)±0.14(syst)]%. This is the single most precise measurement of this parameter, with uncertainties smaller than the current world average of B factory measurements. © 2012 American Physical Society.

Automatic segmentation of latent fingerprints

Latent fingerprints are routinely found at crime scenes due to the inadvertent contact of the criminals' finger tips with various objects. As such, they have been used as crucial evidence for identifying and convicting criminals by law enforcement agencies. However, compared to plain and rolled prints, latent fingerprints usually have poor quality of ridge impressions with small fingerprint area, and contain large overlap between the foreground area (friction ridge pattern) and structured or random noise in the background. Accordingly, latent fingerprint segmentation is a difficult problem. In this paper, we propose a latent fingerprint segmentation algorithm whose goal is to separate the fingerprint region (region of interest) from background. Our algorithm utilizes both ridge orientation and frequency features. The orientation tensor is used to obtain the symmetric patterns of fingerprint ridge orientation, and local Fourier analysis method is used to estimate the local ridge frequency of the latent fingerprint. Candidate fingerprint (foreground) regions are obtained for each feature type; an intersection of regions from orientation and frequency features localizes the true latent fingerprint regions. To verify the viability of the proposed segmentation algorithm, we evaluated the segmentation results in two aspects: a comparison with the ground truth foreground and matching performance based on segmented region. © 2012 IEEE.

Automatic visual dictionary generation through Optimum-Path Forest clustering

Image categorization by means of bag of visual words has received increasing attention by the image processing and vision communities in the last years. In these approaches, each image is represented by invariant points of interest which are mapped to a Hilbert Space representing a visual dictionary which aims at comprising the most discriminative features in a set of images. Notwithstanding, the main problem of such approaches is to find a compact and representative dictionary. Finding such representative dictionary automatically with no user intervention is an even more difficult task. In this paper, we propose a method to automatically find such dictionary by employing a recent developed graph-based clustering algorithm called Optimum-Path Forest, which does not make any assumption about the visual dictionary's size and is more efficient and effective than the state-of-the-art techniques used for dictionary generation. © 2012 IEEE.

Automatic landslide recognition through Optimum-Path Forest

In this paper we shed light over the problem of landslide automatic recognition using supervised classification, and we also introduced the OPF classifier in this context. We employed two images acquired from Geoeye-MS satellite at March-2010 in the northwest (high steep areas) and north sides (pipeline area) covering the area of Duque de Caxias city, Rio de Janeiro State, Brazil. The landslide recognition rate has been assessed through a cross-validation with 10 runnings. In regard to the classifiers, we have used OPF against SVM with Radial Basis Function for kernel mapping and a Bayesian classifier. We can conclude that OPF, Bayes and SVM achieved high recognition rates, being OPF the fastest approach. © 2012 IEEE.

Computer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials

The automatic characterization of particles in metallographic images has been paramount, mainly because of the importance of quantifying such microstructures in order to assess the mechanical properties of materials common used in industry. This automated characterization may avoid problems related with fatigue and possible measurement errors. In this paper, computer techniques are used and assessed towards the accomplishment of this crucial industrial goal in an efficient and robust manner. Hence, the use of the most actively pursued machine learning classification techniques. In particularity, Support Vector Machine, Bayesian and Optimum-Path Forest based classifiers, and also the Otsu's method, which is commonly used in computer imaging to binarize automatically simply images and used here to demonstrated the need for more complex methods, are evaluated in the characterization of graphite particles in metallographic images. The statistical based analysis performed confirmed that these computer techniques are efficient solutions to accomplish the aimed characterization. Additionally, the Optimum-Path Forest based classifier demonstrated an overall superior performance, both in terms of accuracy and speed. © 2012 Elsevier Ltd. All rights reserved.

Micro-raman spectroscopic characterization of a CR-39 detector

Characterization by micro-Raman spectroscopy of polymeric materials used as nuclear track detectors reveals physico-chemical and morphological information on the material's molecular structure. In this work, the nuclear track detector poly(allyl diglycol carbonate), or Columbia Resin 39 (CR-39), was characterized according to the fluence of alpha particles produced by a 226Ra source and chemical etching time. Therefore, damage of the CR-39 chemical structure due to the alpha-particle interaction with the detector was analyzed at the molecular level. It was observed that the ionization and molecular excitation of the CR-39 after the irradiation process entail cleavage of chemical bonds and formation of latent track. In addition, after the chemical etching, there is also loss of polymer structure, leading to the decrease of the group density C-O-C (∼888 cm-1), CH=CH (∼960 cm -1), C-O (∼1110 cm-1), C-O-C (∼1240 cm -1), C-O (∼1290 cm-1), C-O (∼1741 cm -1), -CH2- (∼2910 cm-1), and the main band -CH2- (∼2950 cm-1). The analyses performed after irradiation and chemical etching led to a better understanding of the CR-39 molecular structure and better comprehension of the process of the formation of the track, which is related to chemical etching kinetics. Copyright © 2013 Society for Applied Spectroscopy.

Use of algorithms for semi-automatic quantification of pulmonary fibrosis and emphysema

Obtaining a semi-automatic quantification of pathologies found in the lung, through images of high resolution computed tomography (HRCT), is of great importance to aid in medical diagnosis. Paraccocidioidomycosis (PCM) is a systemic disease that affects the lung and even after effective treatment leaves sequels such as pulmonary fibrosis and emphysema. It is very important to the area of tropical diseases that the lung injury be quantified more accurately. In this stud, we propose the development of algorithms in computational environment Matlab® able to objectively quantify lung diseases such as fibrosis and emphysema. The program consists in selecting the region of interest (ROI), and through the use of density masks and filters, obtaining the lesion area quantification in relation to the healthy area of the lung. The proposed method was tested on 15 exams of HRCT of patients with confirmed PCM. To prove the validity and effectiveness of the method, we used a virtual phantom, also developed in this research. © 2013 Springer-Verlag.

Measurements of differential jet cross sections in proton-proton collisions at √s=7 TeV with the CMS detector

Measurements of inclusive jet and dijet production cross sections are presented. Data from LHC proton-proton collisions at √s=7 TeV, corresponding to 5.0 fb-1 of integrated luminosity, have been collected with the CMS detector. Jets are reconstructed up to rapidity 2.5, transverse momentum 2 TeV, and dijet invariant mass 5 TeV, using the anti-k T clustering algorithm with distance parameter R=0.7. The measured cross sections are corrected for detector effects and compared to perturbative QCD predictions at next-to-leading order, using five sets of parton distribution functions. © 2013 CERN.

The advertisement call of dendropsophus tritaeniatus (Bokermann, 1965) (Anura: Hylidae)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals

Secondary phases such as Laves and carbides are formed during the final solidification stages of nickel based superalloy coatings deposited during the gas tungsten arc welding cold wire process. However, when aged at high temperatures, other phases can precipitate in the microstructure, like the γ″ and δ phases. This work presents a new application and evaluation of artificial intelligent techniques to classify (the background echo and backscattered) ultrasound signals in order to characterize the microstructure of a Ni-based alloy thermally aged at 650 and 950 °C for 10, 100 and 200 h. The background echo and backscattered ultrasound signals were acquired using transducers with frequencies of 4 and 5 MHz. Thus with the use of features extraction techniques, i.e.; detrended fluctuation analysis and the Hurst method, the accuracy and speed in the classification of the secondary phases from ultrasound signals could be studied. The classifiers under study were the recent optimum-path forest (OPF) and the more traditional support vector machines and Bayesian. The experimental results revealed that the OPF classifier was the fastest and most reliable. In addition, the OPF classifier revealed to be a valid and adequate tool for microstructure characterization through ultrasound signals classification due to its speed, sensitivity, accuracy and reliability. © 2013 Elsevier B.V. All rights reserved.