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作者： Da Rosa Righi, Rodrigo Rodrigues, Vinicius Facco De Nardin, Igor Fontana Da Costa, Cristiano André Alves, Marco Antonio Zanata Pillon, Maurício Aronne Applied Computing Graduate Program Unisinos University São Leopoldo Rio Grande do Sul Brazil Federal University of Paraná – UFPR Curitiba Paraná Brazil Santa Catarina State University – UDESC Joinville Santa Catarina Brazil

Elasticity is one of the most important features of cloud computing, referring to the ability to add or remove resources according to the needs of the application or service. Particularly for High Performance computing (HPC), elasticity can provide a better use of resources and also a reduction in the execution time of applications. Today, we observe the emergence of proactive initiatives to handle the elasticity and HPC duet, but they present at least one problem related to the need of a previous user experience, large processing time or completion of parameters. Concerning the aforesaid context, this paper presents ProElastic – a lightweight model that uses proactive elasticity to drive resource reorganisation decisions for HPC applications. Our idea is to explore both performance and adaptivity at middleware level in an effortless way at user perspective. The results showed performance gains and a competitive cost (application time consumed resources). Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Elasticity

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Application of future 6G technology to support heavy data traffic in highly mobile networks

Application of future 6G technology to support heavy data tr...

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2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH)

作者： Rafael Kunst Edison Pignaton Ting Zhou Honglin Hu Applied Computing Graduate Program University of Rio dos Sinos Valley (Unisinos) Sao Leopoldo Brazil Informatics Institute Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China

ISBN: (数字)9781728174075

ISBN: (纸本)9781728174082

SG technology is becoming a reality, with several deployments appearing around the world. Despite bringing improvements, 5G technology may not adequately support many envisaged applications that will demand large amounts of network resources. Thus, studies on the next generation, the 6G, are highly desirable. Observing this landscape, in this paper, we present a seminal study about the potential usage of 6G to support such highly demanding applications. Towards this study, we model a 6G system at the simulation level and conduct a case study exploring an application of high-definition video monitoring, applying the concepts of drone-swarm based surveillance. This scenario demands the transmission of large amounts of video data through the network. The acquired results present evidence that 6G can address these high network traffic demands.

关键词： 6G mobile communication 5G mobile communication Surveillance Telecommunication traffic Next generation networking

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Artificial neural network-based method to classify sedimentary rocks 12

Artificial neural network-based method to classify sedimenta...

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12th International Conference on Sensing Technology, ICST 2018

作者： Figueiredo, Rodrigo Marques Veronez, Mauricio Roberto Wohnrath, Francisco Manoel Da Silva, Marcio Rosa Gonzaga, Luiz Kupssinsku, Lucas Silveira Bordin, Fabiane Brum, Diego Cazarin, Caroline Lessio Graduate Program in Geology Unisinos University Saõ Leopoldo Brazil Advanced Visualization and Geoinformatics Laboratory Unisinos University Saõ Leopoldo Brazil Graduate Program in Electrical Engineering Unisinos University Saõ Leopoldo Brazil Graduate Program in Applied Computing Unisinos University Saõ Leopoldo Brazil Rio de Janeiro Brazil

ISBN: (纸本)9781538651476

In a geological study, an important step is to determine the type of sedimentary rock or its grain size. Such a determination requires accurate analysis in the field or in a laboratory. As the size of the study area grows, this activity can be time consuming and error prone because the number of specialists working under rigid criteria also increases. This paper proposes a novel methodology to classify grain size using unique wavelength reflectance data and artificial neural networks. The results indicate that the proposed method can be reliably used in the field. © 2018 IEEE.

关键词： Neural networks

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An Analysis of Machine Learning Classifiers in Breast Cancer Diagnosis

An Analysis of Machine Learning Classifiers in Breast Cancer...

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Conferencia Latinoamericana En Informatica (CLEI)

作者： Fabiano Teixeira João Luis Zeni Montenegro Cristiano André da Costa Rodrigo da Rosa Righi Software Innovation Laboratory - SOFTWARELAB Applied Computing Graduate Program Universidade do Vale do Rio dos Sinos - UNISINOS Av Unisinos 950 Sao Leopoldo 93022-750 Brazil

ISBN: (数字)9781728155746

ISBN: (纸本)9781728155753

In the field of assisted cancer diagnosis, it is expected that the involvement of machine learning in diseases will give doctors a second opinion and help them to make a faster / better determination. There are a huge number of studies in this area using traditional machine learning methods and in other cases, using deep learning for this purpose. This article aims to evaluate the predictive models of machine learning classification regarding the accuracy, objectivity, and reproducible of the diagnosis of malignant neoplasm with fine needle aspiration. Also, we seek to add one more class for testing in this database as recommended in previous studies. We present six different classification methods: Multilayer Perceptron, Decision Tree, Random Forest, Support Vector Machine and Deep Neural Network for evaluation. For this work, we used at University of Wisconsin Hospital database which is composed of thirty values which characterize the properties of the nucleus of the breast mass. As we showed in result sections, DNN classifier has a great performance in accuracy level (92%), indicating better results in relation to traditional models. Random forest 50 and 100 presented the best results for the ROC curve metric, considered an excellent prediction when compared to other previous studies published.

关键词： Machine learning Breast cancer Support vector machines Neural networks Forestry

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3D data acquisition using stereo camera 38

3D data acquisition using stereo camera

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38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018

作者： Kirsten, Evandro Inocencio, Leonardo Campos Veronez, Maurício Roberto Da Silveira, Luiz Gonzaga Bordin, Fabiane Marson, Fernando Pinho Advanced Visualization and Geoinformatics Laboratory UNISINOS Saõ Leopoldo RS Brazil Graduate Program on Applied Computing UNISINOS Av. UNISINOS 950 Saõ Leopoldo RS93022-750 Brazil

ISBN: (纸本)9781538671504

Computer vision systems allow digital reconstruction of targets by capturing information through remote sensors such as video cameras and scanners. In this context, the objective of this work was to evaluate the capacity and quality of three-dimensional reconstruction of static targets using the ZED stereoscopic camera. For this goal, we took images of several environments and objects with different surfaces, textures, lighting, distances and acquisition speeds. The results were compared with high-density and high precision point clouds obtained from the targets using a Leica Viva TS15 total station. The data were processed in the CloudCompare software to calculate the displacement between the models generated by the camera and the total station. Under certain circumstances, this technology is able to reconstruct three-dimensional objects and environments with an error of a few centimeters. © 2018 IEEE.

关键词： Computer vision

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Analysis of positional and geometric accuracy of objects in survey with unmanned aerial vehicle (UAV) 38

Analysis of positional and geometric accuracy of objects in ...

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38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018

作者： Soares, Gabriel Inocencio, Leonardo Campos Veronez, Maurício Roberto Da Silveira, Luiz Gonzaga Bordin, Fabiane Marson, Fernando Pinho Advanced Visualization and Geoinformatics Laboratory UNISINOS São Leopoldo RS Brazil Graduate Program on Applied Computing UNISINOS Av. UNISINOS 950 São Leopoldo RS93022-000 Brazil

ISBN: (纸本)9781538671504

This study aimed the analysis of the positional and geometric accuracy of objects in orthomosaics obtained through different unmanned aerial vehicle (UAV) data processing software covering an area located within Universidade do Vale do Rio dos Sinos - unisinos in São Leopoldo, RS. A total of nine ground control points (GCP) and twenty checkpoints were surveyed in order register and classify the processed orthomosaics according to the cartographic accuracy standard - Padrão de Exatidão Cartográfica (PEC). Four software was employed to process the UAV data: Pix4D mapper, Agisoft PhotoScan, Menci APS and Bentley Context Capture. The results obtained from each software were compared and identified the smallest distortions when processing with and without ground control points. The flight was executed at a height of 90m with 60% sidelap and 80% overlap using an ST800 UAV equipped with a Sony NEX-7 small format non-metric camera with 24 megapixels resolution. The software GeoPEC was used to classify the orthomosaics according to PEC. For data processed with ground control points all orthomosaics were classified “Class A” in 1/500 scale, however, only Menci APS did not present a trend line via t-student test. On the other hand, Menci APS presented the worst results without the ground control points. In processing with GCP, all orthomosaics obtained optimum results with an approximated error of 2,5 m2 about 0,03% of the area. © 2018 IEEE

关键词： Cartographic Accuracy Standard Ground Control Points Orthomosaic Unmanned Aerial Vehicle

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3D DATA ACQUISITION USING STEREO CAMERA

3D DATA ACQUISITION USING STEREO CAMERA

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IEEE International Geoscience and Remote Sensing Symposium

作者： Evandro Kirsten Leonardo Campos Inocencio Mauricio Roberto Veronez Luiz Gonzaga da Silveira Fabiane Bordin Fernando Pinho Marson UNISINOS - São Leopoldo Advanced Visualization & Geoinformatics Laboratory RS Brazil Graduate Program on Applied Computing - UNISINOS São Leopoldo RS Brazil

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Pipel: Exploiting resource reorganisation to optimise performance of pipeline-structured applications in the cloud

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International Journal of Computational Systems Engineering 2019年第1期5卷 1-17页

作者： Meyer, Vinicius Rodrigues, Vinicius Facco da Rosa Righi, Rodrigo da Costa, Cristiano André Galante, Guilherme Both, Cristiano Bonato Applied Computing Graduate Program Unisinos University Av. Unisinos 950 - Cristo Rei Rio Grande do Sul São Leopoldo Brazil Western Paraná State University UNIOESTE R. Universitária 2069 - Jardim Universitário Paraná Cascavel Brazil Federal University of Health Sciences of Porto Alegre - UFCSPA R. Sarmento Leite 245 - Centro Histórico Rio Grande do Sul Porto Alegre Brazil

Workflow has become a standard for many scientific applications that are characterized by a collection of processing elements. Particularly, a pipeline application is a type of workflow that receives a set of tasks, which must pass through all processing elements in a linear fashion. However, the strategy of using a fixed number of resources can cause under- or over-provisioning situations, besides not fitting irregular demands. In this context, our idea is to deploy the pipeline application in the cloud, so executing it with a feature that differentiates cloud from other distributed systems: resource elasticity. Thus, we propose Pipel: a reactive elasticity model that uses lower and upper load thresholds and the CPU metric to on-the-fly select the most appropriated number of compute nodes for each stage along the pipeline execution. The results were promising, presenting an average gain of 38% in the application time when comparing non-elastic and elastic executions. © 2019 Inderscience Enterprises Ltd.

关键词： Pipelines

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MOSIS - Multi-outcrop sharing & interpretation system 37

MOSIS - Multi-outcrop sharing & interpretation system

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37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017

作者： Gonzaga, Luiz Veronez, Mauricio Roberto Alves, Demetrius Nunes Bordin, Fabiane Kannenberg, Gabriel Lanzer Marson, Fernando P. Tognoli, Francisco M.W. Inocencio, Leonardo C. Graduate Program on Applied Computing UNISINOS São Leopoldo RS Brazil Graduate Program on Geology UNISINOS São Leopoldo RS Brazil Advanced Visualization and Geoinformatics Laboratory UNISINOS São Leopoldo RS Brazil

ISBN: (纸本)9781509049516

The use of LiDAR and multiples digital images jointly with 3-D reconstruction techniques for creating 3-D models of natural outcrops and surfaces studies have increased dramatically in the last few years. These techniques have provided an enormous amount of data for interpretation by geoscientists. However, these researchers have no available software capable of offering a user experience comparable to the fieldwork. The majority of solutions have considered desktop systems, which presents inherent limitations due to the 2-D characteristics of displays and loss of immersion into the 3-D model, or up until expensive and complex stereoscopic based approaches to improve the 3-D user experience do not offer well suitable solutions. To address these limitations, this paper presents a low-cost completely disruptive solution for processing, visualizing, sharing and directly handling Digital Outcrop Models with the support of a full interpretation toolset, the MOSIS System. The proposed system provides a fully immersive computational environment, capable of teleporting virtually geoscientists to the fieldwork, giving an awareness of being there physically with an extensible toolset for the DOM's interpretation. Besides, desktop, web and mobile versions of MOSIS have been under development and fulfill the lack of tools for digital outcrop modeling. © 2017 IEEE.

关键词： Three-dimensional displays Geology Data visualization Solid modeling Tools Visualization Standards

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Analysis of Positional and Geometric Accuracy of Objects in Survey with Unmanned Aerial Vehicle (UAV)

Analysis of Positional and Geometric Accuracy of Objects in ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Gabriel Soares Leonardo Campos Inocencio Maurício Roberto Veronez Luiz Gonzaga da Silveira Fabiane Bordin Fernando Pinho Marson UNISINOS Advanced Visualization & Geoinformatics Laboratory São Leopoldo RS Brazil Graduate Program on Applied Computing - UNISINOS São Leopoldo RS Brazil São Leopoldo RS Brazil Universidade do Vale do Rio dos Sinos Sao Leopoldo RS BR

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