A novel approach for distributed application scheduling based on prediction of communication events

The evolution of commodity computing lead to the possibility of efficient usage of interconnected machines to solve computationally-intensive tasks, which were previously solvable only by using expensive supercomputers. This, however, required new methods for process scheduling and distribution, considering the network latency, communication cost, heterogeneous environments and distributed computing constraints. An efficient distribution of processes over such environments requires an adequate scheduling strategy, as the cost of inefficient process allocation is unacceptably high. Therefore, a knowledge and prediction of application behavior is essential to perform effective scheduling. In this paper, we overview the evolution of scheduling approaches, focusing on distributed environments. We also evaluate the current approaches for process behavior extraction and prediction, aiming at selecting an adequate technique for online prediction of application execution. Based on this evaluation, we propose a novel model for application behavior prediction, considering chaotic properties of such behavior and the automatic detection of critical execution points. The proposed model is applied and evaluated for process scheduling in cluster and grid computing environments. The obtained results demonstrate that prediction of the process behavior is essential for efficient scheduling in large-scale and heterogeneous distributed environments, outperforming conventional scheduling policies by a factor of 10, and even more in some cases. Furthermore, the proposed approach proves to be efficient for online predictions due to its low computational cost and good precision. (C) 2009 Elsevier B.V. All rights reserved.

Template-based quadrilateral mesh generation from imaging data

This paper describes a novel template-based meshing approach for generating good quality quadrilateral meshes from 2D digital images. This approach builds upon an existing image-based mesh generation technique called Imeshp, which enables us to create a segmented triangle mesh from an image without the need for an image segmentation step. Our approach generates a quadrilateral mesh using an indirect scheme, which converts the segmented triangle mesh created by the initial steps of the Imesh technique into a quadrilateral one. The triangle-to-quadrilateral conversion makes use of template meshes of triangles. To ensure good element quality, the conversion step is followed by a smoothing step, which is based on a new optimization-based procedure. We show several examples of meshes generated by our approach, and present a thorough experimental evaluation of the quality of the meshes given as examples.

Medical image retrieval based on complexity analysis

Texture is one of the most important visual attributes used in image analysis. It is used in many content-based image retrieval systems, where it allows the identification of a larger number of images from distinct origins. This paper presents a novel approach for image analysis and retrieval based on complexity analysis. The approach consists of a texture segmentation step, performed by complexity analysis through BoxCounting fractal dimension, followed by the estimation of complexity of each computed region by multiscale fractal dimension. Experiments have been performed with MRI database in both pattern recognition and image retrieval contexts. Results show the accuracy of the method and also indicate how the performance changes as the texture segmentation process is altered.

PLANT LEAF IDENTIFICATION BASED ON VOLUMETRIC FRACTAL DIMENSION

Texture is an important visual attribute used to describe the pixel organization in an image. As well as it being easily identified by humans, its analysis process demands a high level of sophistication and computer complexity. This paper presents a novel approach for texture analysis, based on analyzing the complexity of the surface generated from a texture, in order to describe and characterize it. The proposed method produces a texture signature which is able to efficiently characterize different texture classes. The paper also illustrates a novel method performance on an experiment using texture images of leaves. Leaf identification is a difficult and complex task due to the nature of plants, which presents a huge pattern variation. The high classification rate yielded shows the potential of the method, improving on traditional texture techniques, such as Gabor filters and Fourier analysis.

Fingerprint Segmentation

In this thesis, a new algorithm has been proposed to segment the foreground of the fingerprint from the image under consideration. The algorithm uses three features, mean, variance and coherence. Based on these features, a rule system is built to help the algorithm to efficiently segment the image. In addition, the proposed algorithm combine split and merge with modified Otsu. Both enhancements techniques such as Gaussian filter and histogram equalization are applied to enhance and improve the quality of the image. Finally, a post processing technique is implemented to counter the undesirable effect in the segmented image. Fingerprint recognition system is one of the oldest recognition systems in biometrics techniques. Everyone have a unique and unchangeable fingerprint. Based on this uniqueness and distinctness, fingerprint identification has been used in many applications for a long period. A fingerprint image is a pattern which consists of two regions, foreground and background. The foreground contains all important information needed in the automatic fingerprint recognition systems. However, the background is a noisy region that contributes to the extraction of false minutiae in the system. To avoid the extraction of false minutiae, there are many steps which should be followed such as preprocessing and enhancement. One of these steps is the transformation of the fingerprint image from gray-scale image to black and white image. This transformation is called segmentation or binarization. The aim for fingerprint segmentation is to separate the foreground from the background. Due to the nature of fingerprint image, the segmentation becomes an important and challenging task. The proposed algorithm is applied on FVC2000 database. Manual examinations from human experts show that the proposed algorithm provides an efficient segmentation results. These improved results are demonstrating in diverse experiments.

Color Segmentation using LVQ-Learning Vector Quantization

This thesis aims to present a color segmentation approach for traffic sign recognition based on LVQ neural networks. The RGB images were converted into HSV color space, and segmented using LVQ depending on the hue and saturation values of each pixel in the HSV color space. LVQ neural network was used to segment red, blue and yellow colors on the road and traffic signs to detect and recognize them. LVQ was effectively applied to 536 sampled images taken from different countries in different conditions with 89% accuracy and the execution time of each image among 31 images was calculated in between 0.726sec to 0.844sec. The method was tested in different environmental conditions and LVQ showed its capacity to reasonably segment color despite remarkable illumination differences. The results showed high robustness.

Real time video segmentation for recognising paint marks on bad wooden railway sleepers

Wooden railway sleeper inspections in Sweden are currently performed manually by a human operator; such inspections are based on visual analysis. Machine vision based approach has been done to emulate the visual abilities of human operator to enable automation of the process. Through this process bad sleepers are identified, and a spot is marked on it with specific color (blue in the current case) on the rail so that the maintenance operators are able to identify the spot and replace the sleeper. The motive of this thesis is to help the operators to identify those sleepers which are marked by color (spots), using an “Intelligent Vehicle” which is capable of running on the track. Capturing video while running on the track and segmenting the object of interest (spot) through this vehicle; we can automate this work and minimize the human intuitions. The video acquisition process depends on camera position and source light to obtain fine brightness in acquisition, we have tested 4 different types of combinations (camera position and source light) here to record the video and test the validity of proposed method. A sequence of real time rail frames are extracted from these videos and further processing (depending upon the data acquisition process) is done to identify the spots. After identification of spot each frame is divided in to 9 regions to know the particular region where the spot lies to avoid overlapping with noise, and so on. The proposed method will generate the information regarding in which region the spot lies, based on nine regions in each frame. From the generated results we have made some classification regarding data collection techniques, efficiency, time and speed. In this report, extensive experiments using image sequences from particular camera are reported and the experiments were done using intelligent vehicle as well as test vehicle and the results shows that we have achieved 95% success in identifying the spots when we use video as it is, in other method were we can skip some frames in pre-processing to increase the speed of video but the segmentation results we reduced to 85% and the time was very less compared to previous one. This shows the validity of proposed method in identification of spots lying on wooden railway sleepers where we can compromise between time and efficiency to get the desired result.

Traffic and Road Sign Recognition

This thesis presents a system to recognise and classify road and traffic signs for the purpose of developing an inventory of them which could assist the highway engineers’ tasks of updating and maintaining them. It uses images taken by a camera from a moving vehicle. The system is based on three major stages: colour segmentation, recognition, and classification. Four colour segmentation algorithms are developed and tested. They are a shadow and highlight invariant, a dynamic threshold, a modification of de la Escalera’s algorithm and a Fuzzy colour segmentation algorithm. All algorithms are tested using hundreds of images and the shadow-highlight invariant algorithm is eventually chosen as the best performer. This is because it is immune to shadows and highlights. It is also robust as it was tested in different lighting conditions, weather conditions, and times of the day. Approximately 97% successful segmentation rate was achieved using this algorithm.Recognition of traffic signs is carried out using a fuzzy shape recogniser. Based on four shape measures - the rectangularity, triangularity, ellipticity, and octagonality, fuzzy rules were developed to determine the shape of the sign. Among these shape measures octangonality has been introduced in this research. The final decision of the recogniser is based on the combination of both the colour and shape of the sign. The recogniser was tested in a variety of testing conditions giving an overall performance of approximately 88%.Classification was undertaken using a Support Vector Machine (SVM) classifier. The classification is carried out in two stages: rim’s shape classification followed by the classification of interior of the sign. The classifier was trained and tested using binary images in addition to five different types of moments which are Geometric moments, Zernike moments, Legendre moments, Orthogonal Fourier-Mellin Moments, and Binary Haar features. The performance of the SVM was tested using different features, kernels, SVM types, SVM parameters, and moment’s orders. The average classification rate achieved is about 97%. Binary images show the best testing results followed by Legendre moments. Linear kernel gives the best testing results followed by RBF. C-SVM shows very good performance, but ?-SVM gives better results in some case.

Improvement on the sales forecast accuracy for a fast growing company by the best combination of historical data usage and clients segmentation

Industrial companies in developing countries are facing rapid growths, and this requires having in place the best organizational processes to cope with the market demand. Sales forecasting, as a tool aligned with the general strategy of the company, needs to be as much accurate as possible, in order to achieve the sales targets by making available the right information for purchasing, planning and control of production areas, and finally attending in time and form the demand generated. The present dissertation uses a single case study from the subsidiary of an international explosives company based in Brazil, Maxam, experiencing high growth in sales, and therefore facing the challenge to adequate its structure and processes properly for the rapid growth expected. Diverse sales forecast techniques have been analyzed to compare the actual monthly sales forecast, based on the sales force representatives’ market knowledge, with forecasts based on the analysis of historical sales data. The dissertation findings show how the combination of both qualitative and quantitative forecasts, by the creation of a combined forecast that considers both client´s demand knowledge from the sales workforce with time series analysis, leads to the improvement on the accuracy of the company´s sales forecast.

IFTrace: Video segmentation of deformable objects using the Image Foresting Transform

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

Effect of thermocycling on hardness, absorption, solubility and colour change of soft liners

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Colour change of soft denture liners after storage in coffee and coke

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

Increase in severity of molar-incisor hypomineralization and its relationship with the colour of enamel opacity: a prospective cohort study

Background. Predicting risk of posteruptive enamel breakdown (PEB) of molar-incisor hypomineralization (MIH) opacity is a difficult but important clinical task. Therefore, there is a need to evaluate these aspects through longitudinal studies.Objective. The aim of this longitudinal study was to analyse the relationship between colours of MIH opacity of children aged 6-12 (baseline) and other clinical and demographic variables involved in the increase in severity of MIH.Materials and methods. A blinded prospective 18-month follow-up was conducted with 147 individuals presenting mild MIH. Tooth-based incidence of increase in severity of MIH (PEB or atypical restorations) was used as dependent measurement. Enamel opacities were recorded according to colour shades of white, yellow and brown, allowing assessment of susceptibility to structural loss over time, according to colour of MIH opacity. Poisson regression models were used to adjust the results for demographic and clinical variables.Results. Brown and yellow MIH opacities were at higher risk for PEB and atypical restorations than those of white ones, even after adjustment for clinical and demographic variables.Conclusion. Teeth presenting mild MIH severity associated with yellow and brown enamel opacities were at high risk for increase in severity of MIH than lighter ones. This result could help clinicians determine a risk-based treatment for children with MIH.

A square-wave voltammetric method for analysing the colour marker quinizarine in petrol and diesel fuels

This work presents an electroanalytical method based on square-wave voltammetry (SWV) for the determination of quinizarine (QNZ) in a mixture of Britton-Robinson buffer 0.08 mol L-1 with 30% of acetonitrile. The QNZ was oxidized at glassy carbon electrode in and the well-defined peak at +0.45 V vs. Ag/AgCl can be used for its determination as colour marker in fuel samples. All parameters were optimized and analytical curves can be constructed for QNZ concentrations ranging from 2.0 x 10(-6) mol L-1 to 1.4 x 10(-5) mol L-1, using f = 60 Hz and E-sw = 25 mV. The method offers a limit detection of 4.12 x 10(-7) mol L-1 and a standard deviation of 4.5% when six measurements of 1.25 x 10(-5) mol L-1 are compared. The method was successfully applied for determining QNZ in gasoline and diesel oil and the obtained results showed good agreement with those reported previously. (c) 2006 Elsevier Ltd. All rights reserved.

The Bauru Group: A continental Cretaceous unit in Brazil - Concepts based on micropaleontological, oxygen isotope and stratigraphical data

The integration of outcrop and subsurface information, including micropaleontological data, facies and sequence stratigraphic studies, and oxygen isotope analysis, allow us to present a new stratigraphic model for the Cretaceous continental deposits of the Bauru Group, Brazil. Thirty-eight fossil taxa were recovered from these deposits, including 29 species of ostracodes and 9 species of charophytes. Seven of these ostracode species and three subspecies are new and formally described here. The associations of Chara barbosai - Ilyocypris cf. riograndensis, found in the Adamantina Formation, and Amblyochara sp. - Neuquenocypris minor mineira nov. subsp., found in the Marília Formation. Ponte Alta Member, represent two distinct groups that are respectively Turonian-Santonian and Maastrichtian (probably Late Maastrichtian) in age. Therefore, a hiatus, encompassing more than 11 Ma, separates those two formations. From bottom to top, four depositional cycles were recognized in the Bauru Group in western São Paulo: cycles 1 and 2 belong to Caiuá Formation (fluvio-lacustrine and lacustrine deposits in the Presidente Prudente region), cycle 3 to the Santo Anastácio and lower Adamantina Formation (respectively fluvial and lacustrine deposits), and cycle 4 to the upper Adamantina Formation (fluvio-lacustrine facies). An erosional unconformity separates the Caiuá and Santo Anastácio Formations (between cycles 2 and 3). The Marília Formation is a distinct unit from the underlying succession; it does not occur in western São Paulo, but is found in restricted areas of São Paulo, Minas Gerais, Mato Grosso do Sul and Goiás States. During the deposition of the Bauru Group (Aptian? to Maastrichtian) the climate was hot and arid-semiarid. Shallow lakes underwent fluctuations in expansion (wet phases) and contraction (dry phases), as well as variations in salinity. During the deposition of the Adamantina Formation (Turonian-Santonian) there were long, dry periods that caused segmentation of large lakes (due to topographic irregularities in the basaltic substrate) and sometimes exposures of the lake floors; when flooded these lake floors were colonized by extensive meadows of single species of charophytes. Small ephemeral ponds, that were hydrochemically unstable and colonized by multiple species of charophytes, were the depositional sites for the marls and mudstones of Ponte Alta Member (Maastrichtian, Late Maastrichtian?). Our micropaleontological age control, combined with the Late Cretaceous ages of volcanic ashes found in the southeastern Brazil coastal basins, and the stratigraphic position of analcimites from the Jaboticabal-SP region, suggest a Late Coniacian-Santonian age for important magmatic events occurred in the interior of Brazil (north-central São Paulo State, Triângulo Mineiro, and southwestern Goiás State).