Precipitates segmentation from scanning electron microscope images through machine learning techniques

The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis. © 2011 Springer-Verlag Berlin Heidelberg.

Improving accuracy and speed of optimum-path forest classifier using combination of disjoint training subsets

The Optimum-Path Forest (OPF) classifier is a recent and promising method for pattern recognition, with a fast training algorithm and good accuracy results. Therefore, the investigation of a combining method for this kind of classifier can be important for many applications. In this paper we report a fast method to combine OPF-based classifiers trained with disjoint training subsets. Given a fixed number of subsets, the algorithm chooses random samples, without replacement, from the original training set. Each subset accuracy is improved by a learning procedure. The final decision is given by majority vote. Experiments with simulated and real data sets showed that the proposed combining method is more efficient and effective than naive approach provided some conditions. It was also showed that OPF training step runs faster for a series of small subsets than for the whole training set. The combining scheme was also designed to support parallel or distributed processing, speeding up the procedure even more. © 2011 Springer-Verlag.

A graph-based framework for thermal faceprint characterization

Thermal faceprint has been paramount in the last years. Since we can handle with face recognition using images acquired in the infrared spectrum, an unique individual's signature can be obtained through the blood vessels network of the face. In this work, we propose a novel framework for thermal faceprint extraction using a collection of graph-based techniques, which were never used to this task up to date. A robust method of thermal face segmentation is also presented. The experiments, which were conducted over the UND Collection C dataset, have showed promising results. © 2011 Springer-Verlag.

Wavelets and decision trees for target detection over sea surface using cosmo-skymed SAR data

We are investigating the combination of wavelets and decision trees to detect ships and other maritime surveillance targets from medium resolution SAR images. Wavelets have inherent advantages to extract image descriptors while decision trees are able to handle different data sources. In addition, our work aims to consider oceanic features such as ship wakes and ocean spills. In this incipient work, Haar and Cohen-Daubechies-Feauveau 9/7 wavelets obtain detailed descriptors from targets and ocean features and are inserted with other statistical parameters and wavelets into an oblique decision tree. © 2011 Springer-Verlag.

LUTS: A lightweight user-level transaction scheduler

Software Transactional Memory (STM) systems have poor performance under high contention scenarios. Since many transactions compete for the same data, most of them are aborted, wasting processor runtime. Contention management policies are typically used to avoid that, but they are passive approaches as they wait for an abort to happen so they can take action. More proactive approaches have emerged, trying to predict when a transaction is likely to abort so its execution can be delayed. Such techniques are limited, as they do not replace the doomed transaction by another or, when they do, they rely on the operating system for that, having little or no control on which transaction should run. In this paper we propose LUTS, a Lightweight User-Level Transaction Scheduler, which is based on an execution context record mechanism. Unlike other techniques, LUTS provides the means for selecting another transaction to run in parallel, thus improving system throughput. Moreover, it avoids most of the issues caused by pseudo parallelism, as it only launches as many system-level threads as the number of available processor cores. We discuss LUTS design and present three conflict-avoidance heuristics built around LUTS scheduling capabilities. Experimental results, conducted with STMBench7 and STAMP benchmark suites, show LUTS efficiency when running high contention applications and how conflict-avoidance heuristics can improve STM performance even more. In fact, our transaction scheduling techniques are capable of improving program performance even in overloaded scenarios. © 2011 Springer-Verlag.

FTMOntology: An ontology to fill the semantic gap between music, mood, personality, and human physiology

This paper presents a domain ontology, the FeelingTheMusic Ontology - FTMOntology. FTMOntology is designed to represent the complex domain of music and how it relates to other domains like mood, personality and physiology. This includes representing the main concepts and relations of music domain with each of the above-mentioned domains. The concepts and relations between music, mood, personality and physiology. The main contribution of this work is to model and relate these different domains in a consistent ontology. © 2011 Springer-Verlag.

A study on automatic methods based on mathematical morphology for Martian dust devil tracks detection

This paper presents three methods for automatic detection of dust devils tracks in images of Mars. The methods are mainly based on Mathematical Morphology and results of their performance are analyzed and compared. A dataset of 21 images from the surface of Mars representative of the diversity of those track features were considered for developing, testing and evaluating our methods, confronting their outputs with ground truth images made manually. Methods 1 and 3, based on closing top-hat and path closing top-hat, respectively, showed similar mean accuracies around 90% but the time of processing was much greater for method 1 than for method 3. Method 2, based on radial closing, was the fastest but showed worse mean accuracy. Thus, this was the tiebreak factor. © 2011 Springer-Verlag.

A Markov random field model for combining optimum-path forest classifiers using decision graphs and game strategy approach

The research on multiple classifiers systems includes the creation of an ensemble of classifiers and the proper combination of the decisions. In order to combine the decisions given by classifiers, methods related to fixed rules and decision templates are often used. Therefore, the influence and relationship between classifier decisions are often not considered in the combination schemes. In this paper we propose a framework to combine classifiers using a decision graph under a random field model and a game strategy approach to obtain the final decision. The results of combining Optimum-Path Forest (OPF) classifiers using the proposed model are reported, obtaining good performance in experiments using simulated and real data sets. The results encourage the combination of OPF ensembles and the framework to design multiple classifier systems. © 2011 Springer-Verlag.

Virtual instrumentation: A practical approach to control and supervision process

This work describes a control and supervision application takes into account the virtual instrumentation advantages to control and supervision industrial manufacturing stations belonging to the modular production system MPS® by Festo. These stations integrate sensors, actuators, conveyor belt and other industrial elements. The focus in this approach was to replace the use of programmable logic controllers by a computer equipped with a software application based on Labview and, together, performs the functions of traditional instruments and PLCs. The manufacturing stations had their processes modeled and simulated in Petri nets. After the models were implemented in Labview environment. Tests and previous similar works in MPS® installed in Automation Laboratory, at UNESP Sorocaba campus, showed the materials and methods used in this work allow the successful use of virtual instrumentation. The results indicate the technology as an advantageous approach for the automation of industrial processes, with gains in flexibility and reduction in project cost. © 2011 IEEE.

Domain reduction using GRASP construction phase for transmission expansion planning problem

This paper proposes a new strategy to reduce the combinatorial search space of a mixed integer linear programming (MILP) problem. The construction phase of greedy randomized adaptive search procedure (GRASP-CP) is employed to reduce the domain of the integer variables of the transportation model of the transmission expansion planning (TM-TEP) problem. This problem is a MILP and very difficult to solve specially for large scale systems. The branch and bound (BB) algorithm is used to solve the problem in both full and the reduced search space. The proposed method might be useful to reduce the search space of those kinds of MILP problems that a fast heuristic algorithm is available for finding local optimal solutions. The obtained results using some real test systems show the efficiency of the proposed method. © 2012 Springer-Verlag.

A malware detection system inspired on the human immune system

Malicious programs (malware) can cause severe damage on computer systems and data. The mechanism that the human immune system uses to detect and protect from organisms that threaten the human body is efficient and can be adapted to detect malware attacks. In this paper we propose a system to perform malware distributed collection, analysis and detection, this last inspired by the human immune system. After collecting malware samples from Internet, they are dynamically analyzed so as to provide execution traces at the operating system level and network flows that are used to create a behavioral model and to generate a detection signature. Those signatures serve as input to a malware detector, acting as the antibodies in the antigen detection process. This allows us to understand the malware attack and aids in the infection removal procedures. © 2012 Springer-Verlag.

How bioinformatics enables livestock applied sciences in the genomic era

This review paper presents the three main approaches currently used in livestock genomic sciences where the bioinfomatics plays a critical role. They are named as Genomic Selection (GS), Genome Wide Association Study (GWAS) and Signatures of Selection (SS). The subsides for the construction of this article were generated in a current project (started in 2011), so called Zebu Genome Consortium (ZGC), which joins researchers from different institutions and countries, aiming to scientifically explore genomic information of Bos taurus indicus cattle breeds and deliver useful information to breeders and academic community, specially from the tropical regions of the world. © 2012 Springer-Verlag.

Vectorized algorithms for quadtree construction and descent

This paper presents vectorized methods of construction and descent of quadtrees that can be easily adapted to message passing parallel computing. A time complexity analysis for the present approach is also discussed. The proposed method of tree construction requires a hash table to index nodes of a linear quadtree in the breadth-first order. The hash is performed in two steps: an internal hash to index child nodes and an external hash to index nodes in the same level (depth). The quadtree descent is performed by considering each level as a vector segment of a linear quadtree, so that nodes of the same level can be processed concurrently. © 2012 Springer-Verlag.

A proposed neural control for the trajectory tracking of a nonholonomic mobile robot with disturbances

In this paper, a trajectory tracking control problem for a nonholonomic mobile robot by the integration of a kinematic neural controller (KNC) and a torque neural controller (TNC) is proposed, where both the kinematic and dynamic models contains disturbances. The KNC is a variable structure controller (VSC) based on the sliding mode control theory (SMC), and applied to compensate the kinematic disturbances. The TNC is a inertia-based controller constituted of a dynamic neural controller (DNC) and a robust neural compensator (RNC), and applied to compensate the mobile robot dynamics, and bounded unknown disturbances. Stability analysis with basis on Lyapunov method and simulations results are provided to show the effectiveness of the proposed approach. © 2012 Springer-Verlag.

Teaching and learning data structures supported by computers: An experiment using CADILAG tool

This paper aims to present the use of a learning object (CADILAG), developed to facilitate understanding data structure operations by using visual presentations and animations. The CADILAG allows visualizing the behavior of algorithms usually discussed during Computer Science and Information System courses. For each data structure it is possible visualizing its content and its operation dynamically. Its use was evaluated an the results are presented. © 2012 AISTI.