Automatic code generation for microcontrollers from place-transition Petri net models

This paper describes a program for the automatic generation of code for Intel's 8051 microcontroller. The code is generated from a place-transition Petri net specification. Our goal is to minimize programming time. The code generated by our program has been observed to exactly match the net model. It has also been observed that no change is needed to be made to the generated code for its compilation to the target architecture. © 2011 IFAC.

Simulation of the flow through automatic valves of hermetic compressors by the immersed boundary method approach

The main goal of the present work is to verify the applicability of the Immersed Boundary Method together with the Virtual Physical Model to solve the flow through automatic valves of hermetic compressors. The valve was simplified to a two-dimensional radial diffuser, with diameter ratio of D/d = 1.5, and simulated for a one cycle of opening and closing process with a imposed velocity of 3.0 cm/s for the reed, dimensionless gap between disks in the range of 0.07 < s/d < 0.10, and inlet Reynolds number equal to 1500. The good results obtained showed that the methodology has great potential as project tool for this type of valve systems. © The Authors, 2011.

Automatic classification of fish germ cells through optimum-path forest

The spermatogenesis is crucial to the species reproduction, and its monitoring may shed light over some important information of such process. Thus, the germ cells quantification can provide useful tools to improve the reproduction cycle. In this paper, we present the first work that address this problem in fishes with machine learning techniques. We show here how to obtain high recognition accuracies in order to identify fish germ cells with several state-of-the-art supervised pattern recognition techniques. © 2011 IEEE.

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.

Structural refinement and photoluminescence properties of cube-like (Ca 1-xCu x)TiO 3 crystals synthesized by the microwave-hydrothermal method

In this work, (Ca 1-xCu x)TiO 3 crystals with (x = 0, 0.01 and 0.02), labeled as CTO, CCTO1 and CCTO2, were synthesized by the microwave-hydrothermal method at 140°C for 32 min. XRD patterns (Fig. 1), Rietveld refinement and FT-Raman spectroscopy indicated that these crystals present orthorhombic structure Pbnm. Micro-Raman and XANES spectra suggested that the substitution of Ca by Cu in A-site promoted a displacement of the [TiO6]-[TiO6] clusters adjacent from its symmetric center, which leads distortions on the [CaO 12] clusters neighboring and consequently cause the strains into the CaTiO3 lattice. FE-SEM images showed that these crystals have an irregular shape as cube like probably indicating an Ostwald-ripening and self-assemble as dominant mechanisms to crystals growth. The powders presented an intense PL blue-emission.

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.

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.

Automatic fiscal stabilizers

Includes bibliography

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.

Structural refinement, growth mechanism, infrared/Raman spectroscopies and photoluminescence properties of PbMoO4 crystals

Lead molybdate (PbMoO4) crystals were synthesized by the co-precipitation method at room temperature and then processed in a conventional hydrothermal (CH) system at low temperature (70 °C for different times). These crystals were structurally characterized by X-ray diffraction (XRD), Rietveld refinement, micro-Raman (MR) and Fourier transformed infrared (FT-IR) spectroscopies. Field emission scanning electron microscopy images were employed to observe the shape and monitor the crystal growth process. The optical properties were investigated by ultraviolet-visible (UV-Vis) absorption and photoluminescence (PL) measurements. XRD patterns and MR spectra indicate that these crystals have a scheelite-type tetragonal structure. Rietveld refinement data possibilities the evaluation of distortions in the tetrahedral [MoO 4] clusters. MR and FT-IR spectra exhibited a high mode ν1(Ag) ascribed to symmetric stretching vibrations as well as a large absorption band with two modes ν3(Eu and Au) related to anti-symmetric stretching vibrations in [MoO 4] clusters. Growth mechanisms were proposed to explain the stages involved for the formation of octahedron-like PbMoO4 crystals. UV-Vis absorption spectra indicate a reduction in optical band gap with an increase in the CH processing time. PL properties of PbMoO4 crystals have been elucidated using a model based on distortions of tetrahedral [MoO4] clusters due to medium-range intrinsic defects and intermediary energy levels (deep and shallow holes) within the band gap. © 2012 Elsevier Ltd. All rights reserved.

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.

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.

Photogrammetric refinement of laser-based building roof contours

In this paper a photogrammetric method is proposed for refining 3D building roof contours extracted from airborne laser scanning data. It is assumed that laser-derived planar faces of roofs are potentially accurate, while laser-derived building roof contours are not well defined. First, polygons representing building roof contours are extracted from a high-resolution aerial image. In the sequence, straight-line segments delimitating each building roof polygon are projected onto the corresponding laser-derived roof planes by using a new line-based photogrammetric model. Finally, refined 3D building roof contours are reconstructed by connecting every pair of photogrammetrically- projected adjacent straight lines. The obtained results showed that the proposed approach worked properly, meaning that the integration of image data and laser scanning data allows better results to be obtained, when compared to the results generated by using only laser scanning data. © 2013 IEEE.