Accuracy of impression techniques for implants. Part 1 - Influence of transfer copings surface abrasion

Purpose: This study evaluated the influence of surface abrasion of transfer copings to obtain a precise master cast for a partially edentulous restoration with different inclinations. Materials and Methods: Replicas (N = 30) of a metal matrix (control group) containing two implants at 90° and 65° in relation to the benchtop were obtained using a polyether impression material and three impression techniques: square impression copings splint with dental floss and autopolymerizing acrylic resin (TRS), square impression copings abraded with aluminum oxide (TA), and square impression copings abraded with aluminum oxide and adhesive-coated (TAA). The replicas obtained in type V stone were digitalized, and the images were exported to AutoCAD software to perform the readings of possible degree alterations in implant inclinations. The results were submitted to analysis of variance (ANOVA) and Tukey test (α < 0.05). Results: Comparing the techniques with regard to the 90° implant inclination, no statistical difference was observed between the three techniques and the control group. Analyzing the three techniques with regard to the 65° implant inclination, no significant difference was seen between technique TA and the control group. Conclusions: Technique TA presented more accurate master casts than TRS and TAA techniques. The angulated implant (65°) tended to generate more imprecise master casts than implants perpendicular to the surface. © 2008 by The American College of Prosthodontists.

JSEG-based image segmentation in computer vision for agricultural mobile robot navigation

This project aims to apply image processing techniques in computer vision featuring an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained.

ANN statistical image recognition method for computer vision in agricultural mobile robot navigation

The main application area in this project, is to deploy image processing and segmentation techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. Thereby, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for image recognition. Hence, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave computational platforms, along with the application of customized Back-propagation Multilayer Perceptron (MLP) algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of segmented images in which reasonably accurate results were obtained. © 2010 IEEE.

Proposal of an On-demand Software Deployment System Based on Application Streaming, Virtualization Techniques and P2P Transport

This paper presents the work in progress of an on-demand software deployment system based on application virtualization concepts which eliminates the need of software installation and configuration on each computer. Some mechanisms were created, such as mapping of utilization of resources by the application to improve the software distribution and startup; a virtualization middleware which give all resources needed for the software execution; an asynchronous P2P transport used to optimizing distribution on the network; and off-line support where the user can execute the application even when the server is not available or when is out of the network. © Springer-Verlag Berlin Heidelberg 2010.

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.

Intrusion detection in computer networks using optimum-path forest clustering

Nowadays, organizations face the problem of keeping their information protected, available and trustworthy. In this context, machine learning techniques have also been extensively applied to this task. Since manual labeling is very expensive, several works attempt to handle intrusion detection with traditional clustering algorithms. In this paper, we introduce a new pattern recognition technique called Optimum-Path Forest (OPF) clustering to this task. Experiments on three public datasets have showed that OPF classifier may be a suitable tool to detect intrusions on computer networks, since it outperformed some state-of-the-art unsupervised techniques. © 2012 IEEE.

Influence of polishing procedures on the surface roughness of dental ceramics made by different techniques

The aim of this study was to evaluate the influence of 2 different surface polishing procedures - glazing (GZ) and manual polishing (MP) - on the roughness of ceramics processed by computer-aided design/computer-aided manufacturing (CAD/CAM) and conventional systems (stratification technique). Eighty ceramic discs (diameter: 8 mm, thickness: 1 mm) were prepared and divided among 8 groups (n = 10) according to the type of ceramic disc and polishing method: 4 GZ and 4 MP. Specimens were glazed according to each manufacturer's recommendations. Two silicone polishing points were used on the ceramic surface for manual polishing. Roughness was measured using a surface roughness tester. The roughness measurements were made along a distance of 2 mm on the sample surface and the speed of reading was 0.1 mm/s. Three measurements were taken for each sample. The data (μm) were statistically analyzed using analysis of variance (ANOVA) and Tukey's test (α = 0.05). Qualitative analysis was performed using scanning electron microscopy (SEM). The mean (± SD) roughness values obtained for GZ were: 1.1 ± 0.40 μm; 1.0 ± 0.31 μm; 1.6 ± 0.31 μm; and 2.2 ± 0.73 μm. For MP, the mean values were: 0.66 ± 0.13 μm; 0.43 ± 0.14 μm; 1.6 ± 0.55 μm; and 2.0 ± 0.63 μm. The mean roughness values were significantly affected by the ceramic type (P = 0.0001) and polishing technique (P = 0.0047). The SEM images confirmed the roughness data. The manually polished glass CAD/CAM ceramics promoted lower surface roughness than did the glazed feldspathic dental ceramics.

3D geovisualisation techniques applied in spatial data mining

The increase in the number of spatial data collected has motivated the development of geovisualisation techniques, aiming to provide an important resource to support the extraction of knowledge and decision making. One of these techniques are 3D graphs, which provides a dynamic and flexible increase of the results analysis obtained by the spatial data mining algorithms, principally when there are incidences of georeferenced objects in a same local. This work presented as an original contribution the potentialisation of visual resources in a computational environment of spatial data mining and, afterwards, the efficiency of these techniques is demonstrated with the use of a real database. The application has shown to be very interesting in interpreting obtained results, such as patterns that occurred in a same locality and to provide support for activities which could be done as from the visualisation of results. © 2013 Springer-Verlag.

A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks

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

Infrastructure Support for Controlled Experimentation with Software Testing and Regression Testing Techniques

Where the creation, understanding, and assessment of software testing and regression testing techniques are concerned, controlled experimentation is an indispensable research methodology. Obtaining the infrastructure necessary to support such experimentation, however, is difficult and expensive. As a result, progress in experimentation with testing techniques has been slow, and empirical data on the costs and effectiveness of techniques remains relatively scarce. To help address this problem, we have been designing and constructing infrastructure to support controlled experimentation with testing and regression testing techniques. This paper reports on the challenges faced by researchers experimenting with testing techniques, including those that inform the design of our infrastructure. The paper then describes the infrastructure that we are creating in response to these challenges, and that we are now making available to other researchers, and discusses the impact that this infrastructure has and can be expected to have.

A Controlled Experiment Assessing Test Case Prioritization Techniques via Mutation Faults

Regression testing is an important part of software maintenance, but it can also be very expensive. To reduce this expense, software testers may prioritize their test cases so that those that are more important are run earlier in the regression testing process. Previous work has shown that prioritization can improve a test suite’s rate of fault detection, but the assessment of prioritization techniques has been limited to hand-seeded faults, primarily due to the belief that such faults are more realistic than automatically generated (mutation) faults. A recent empirical study, however, suggests that mutation faults can be representative of real faults. We have therefore designed and performed a controlled experiment to assess the ability of prioritization techniques to improve the rate of fault detection techniques, measured relative to mutation faults. Our results show that prioritization can be effective relative to the faults considered, and they expose ways in which that effectiveness can vary with characteristics of faults and test suites. We also compare our results to those collected earlier with respect to the relationship between hand-seeded faults and mutation faults, and the implications this has for researchers performing empirical studies of prioritization.

JVM-based Techniques for Improving Java Observability

Observability measures the support of computer systems to accurately capture, analyze, and present (collectively observe) the internal information about the systems. Observability frameworks play important roles for program understanding, troubleshooting, performance diagnosis, and optimizations. However, traditional solutions are either expensive or coarse-grained, consequently compromising their utility in accommodating today’s increasingly complex software systems. New solutions are emerging for VM-based languages due to the full control language VMs have over program executions. Existing such solutions, nonetheless, still lack flexibility, have high overhead, or provide limited context information for developing powerful dynamic analyses. In this thesis, we present a VM-based infrastructure, called marker tracing framework (MTF), to address the deficiencies in the existing solutions for providing better observability for VM-based languages. MTF serves as a solid foundation for implementing fine-grained low-overhead program instrumentation. Specifically, MTF allows analysis clients to: 1) define custom events with rich semantics ; 2) specify precisely the program locations where the events should trigger; and 3) adaptively enable/disable the instrumentation at runtime. In addition, MTF-based analysis clients are more powerful by having access to all information available to the VM. To demonstrate the utility and effectiveness of MTF, we present two analysis clients: 1) dynamic typestate analysis with adaptive online program analysis (AOPA); and 2) selective probabilistic calling context analysis (SPCC). In addition, we evaluate the runtime performance of MTF and the typestate client with the DaCapo benchmarks. The results show that: 1) MTF has acceptable runtime overhead when tracing moderate numbers of marker events; and 2) AOPA is highly effective in reducing the event frequency for the dynamic typestate analysis; and 3) language VMs can be exploited to offer greater observability.

A computer-controlled color vision test for children based on the Cambridge Colour Test

The present study aimed at providing conditions for the assessment of color discrimination in children using a modified version of the Cambridge Colour Test (CCT, Cambridge Research Systems Ltd., Rochester, UK). Since the task of indicating the gap of the Landolt C used in that test proved counterintuitive and/or difficult for young children to understand, we changed the target Stimulus to a patch of color approximately the size of the Landolt C gap (about 7 degrees Of Visual angle at 50 cm from the monitor). The modifications were performed for the CCT Trivector test which measures color discrimination for the protan, deutan and tritan confusion lines. Experiment I Sought to evaluate the correspondence between the CCT and the child-friendly adaptation with adult subjects (n = 29) with normal color vision. Results showed good agreement between the two test versions. Experiment 2 tested the child-friendly software with children 2 to 7 years old (n = 25) using operant training techniques for establishing and maintaining the subjects` performance. Color discrimination thresholds were progressively lower as age increased within the age range tested (2 to 30 years old), and the data-including those obtained for children-fell within the range of thresholds previously obtained for adults with the CCT. The protan and deutan thresholds were consistently lower than tritan thresholds, a pattern repeatedly observed in adults tested with the CCT. The results demonstrate that the test is fit for assessment of color discrimination in young children and may be a useful tool for the establishment of color vision thresholds during development.

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.

Design and control techniques of optical networks

The world of communication has changed quickly in the last decade resulting in the the rapid increase in the pace of peoples’ lives. This is due to the explosion of mobile communication and the internet which has now reached all levels of society. With such pressure for access to communication there is increased demand for bandwidth. Photonic technology is the right solution for high speed networks that have to supply wide bandwidth to new communication service providers. In particular this Ph.D. dissertation deals with DWDM optical packet-switched networks. The issue introduces a huge quantity of problems from physical layer up to transport layer. Here this subject is tackled from the network level perspective. The long term solution represented by optical packet switching has been fully explored in this years together with the Network Research Group at the department of Electronics, Computer Science and System of the University of Bologna. Some national as well as international projects supported this research like the Network of Excellence (NoE) e-Photon/ONe, funded by the European Commission in the Sixth Framework Programme and INTREPIDO project (End-to-end Traffic Engineering and Protection for IP over DWDM Optical Networks) funded by the Italian Ministry of Education, University and Scientific Research. Optical packet switching for DWDM networks is studied at single node level as well as at network level. In particular the techniques discussed are thought to be implemented for a long-haul transport network that connects local and metropolitan networks around the world. The main issues faced are contention resolution in a asynchronous variable packet length environment, adaptive routing, wavelength conversion and node architecture. Characteristics that a network must assure as quality of service and resilience are also explored at both node and network level. Results are mainly evaluated via simulation and through analysis.