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检索条件"机构=Graduate Program on Applied Computing - UNISINOS"

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A Quantitative Analysis on Different Carbonate Indicators Based on Spaceborne Data in a Controlled Karst Area

A Quantitative Analysis on Different Carbonate Indicators Ba...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： M. Muller V. F. Sales D. C. Zanotta A. Marques T. T. Guimarães L. Bachi E. M. Souza D. Brum L. Gonzaga M. R. Veronez C. L. Cazarin Vizlab X-Reality & GeoInformatics Lab - Graduate Program in Applied Computing UNISINOS University Rio Grande do Sul Brazil State University of Maringa Parana Brazil CENPES - PETROBRAS Rio de Janeiro Brazil

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

New sensors aboard recently launched satellites have induced the development of several measures aimed to indicate the presence of many materials over the Earth. Karsts are places rich in carbonate rocks and present large economic and environmental importance. This paper aimed at assessing the performance and consistency of different carbonate estimators derived from orbital images acquired over a controlled karst area. Experiments were assisted by a multi-scaled reference data built through a high spatial resolution Unmanned Aerial Vehicle (UAV) image acquired over the selected area. Results show a considerable unconformity among selected measures and better performance presented by indices exploiting measures along visible and infrared spectral regions.

关键词： Rocks Remote sensing Earth Indexes Artificial satellites Thermal sensors Sensor phenomena and characterization

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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

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 m 2 , about 0.03% of the area.

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Modeling Of Mean Tropospheric Temperature From Convolutional Neural Networks

Modeling Of Mean Tropospheric Temperature From Convolutional...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Diego Brum Vinicius Francisco Rofatto Leonardo Scalco Rafaela de Oliveira Pena Luiz Gonzaga Daniel C. Zanotta Luiz Fernando Sapucci Mauricio Roberto Veronez VIZLAB—X-Reality and Geoinformatics Laboratory UNISINOS Sao Leopoldo RS Brazil Graduate Program on Applied Computing UNISINOS Sao Leopoldo RS Brazil Institute of Geography Federal University of Uberlandia Monte Carmelo MG Brazil Graduate Program in Agriculture and Geospatial Information Federal University of Uberlandia Monte Carmelo MG Brazil Brazilian Institute of Space Research (INPE) Cachoeira Paulista SP Brazil

The Mean Tropospheric Temperature (Tm) is important for the sake of the conversion between Zenith Path Delay (ZPD) to Precipitable Water Vapor (PWV) because of the proportionality constant that takes Tm to be calculated. In Brazil, Tm modeling’s been traditionally performed using Multiple Linear Regression Models (MLR). However, recent works suggest the use of Deep Learning methods to model Tm values. In this work, we propose models based on Convolutional Neural Networks (CNN) using radiosonde data from 1961 to 2010. The results show that CNN models can outperform the traditional methods considering different statistic metrics like R, standard deviation, and RMSE.

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Exploring cloud elasticity on developing an EPCGlobal-compliant middleware

Exploring cloud elasticity on developing an EPCGlobal-compli...

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IEEE International Conference on RFID

作者： Rodrigo da Rosa Righi Eduardo Souza dos Reis Gustavo Rostirolla Cristiano André da Costa Antonio Marcos Alberti Applied Computing Graduate Program Universidade do Vale do Rio dos Sinos (Unisinos) São Leopoldo RC Brazil Instituto Nacional de Telecomunições (Inatel) Santa Rita do Sapucaí MG Brazil

The digital universe is growing at significant rates in recent years. One of the main responsible for this sentence is the Internet of Things (IoT), which requires middlewares capable of handling this increase of data volume in real-time. Solutions modeled at software, hardware and/or architecture level present limitations to handle such load, facing a scalability problem in the IoT scope. In this context, this article presents a model named Eliot (Elasticity-driven Internet of Things) which combines both cloud and high performance computing to address the IoT scalability problem in EPCglobal-compliant architectures. Based on the Eliot model, we developed a prototype that can run as a plugin solution together with any current EPCglobal-compliant middleware. The results are encouraging, presenting significant performance gains when comparing both elastic and non-elastic executions.

关键词： Elasticity Internet of things Monitoring Cloud computing Radiofrequency identification Computer architecture Load modeling

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Immersive Paleontological Experience Through Virtual and Augmented Reality Representation

Immersive Paleontological Experience Through Virtual and Aug...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Gustavo C. Almeida Daniel C. Zanotta Taina T. Guimaraes Ademir Marques Rodrigo S. Horodyski Luiz Gonzaga Maurício R. Veronez Vinícius C. Souza Vizlab — X-Reality and GeoInformatics Lab UNISINOS São Leopoldo RS Brazil Graduate Program in Applied Computing at Vale do Rio dos Sinos University São Leopoldo RS Brazil Rio Grande do Sul Geological History Museum UNISINOS São Leopoldo RS Brazil

Virtual reality systems have been extending their usability in many areas that could not be contemplated before. One of these fields is paleontology, which can now use virtual reality to build realistic models of real fossils with many goals, including preservation of originals, optimized visualization, or even for restoring missing parts. In this paper we present an immersive system where a entire scenario is reconstructed using digital photogrammetry and Mosis LAB application. The system has shown useful for many applications from touristic purposes and paleontological studies, since the immersive system provides an increased sense of manipulation, as well as the possibility of detailed inspection both inside and outside structures.

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An ontology-based repository for a spontaneous social network

An ontology-based repository for a spontaneous social networ...

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International Conference on Computational Aspects of Social Networks, CASON

作者： Bryan Garber da Silva Cristiano André da Costa Dante Zaupa Gustavo Freitas Universidade do Vale do Rio dos Sinos - UNISINOS São Leopoldo Brazil Applied Computing Graduate Program - PIPCA Universidade do Vale do Rio dos Sinos - UNISINOS São Leopoldo Brazil CNPq Technological and Inovation Fellow Universidade do Vale do Rio dos Sinos – UNISINOS São Leopoldo Brazil Universidade do Vale do Rio dos Sinos – UNISINOS São Leopoldo Brazil

With the latest technological advancements and the popularization of mobile devices nowadays, software development has evolved towards mobility. One of the main uses of mobile devices is to access social networks. They are becoming important tools in people's life. In this context, the Mingle project has the objective of creating a spontaneous social networks that is mainly accessed by mobile devices, such as smartphones. Mingle uses the concepts of ubiquitous computing to create a notion of spontaneity, i.e, providing a social network adapted to the persons specific location. This article has the goal of presenting an ontology and the persistence model for managing the information inside a specific location (called “Cell” in our model). To evaluate our proposal, two technics were applied: lexical evaluation and application-level evaluation. The first assessment employs the Levenshtein edit distance and compares Mingle with other popular ontologies in the field, while the second one evaluates if we deal with all Mingle requirements.

关键词： Ontologies Social network services Prototypes Servers Knowledge based systems Mobile handsets Libraries

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Evaluation of Resampling Techniques to Provide Better Synthesized Input Data to Super-Resolution Deep Learning Model Training

Evaluation of Resampling Techniques to Provide Better Synthe...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Vinicius Sales Ademir Marques Graciela Racolte Anderson Nunes Tainá Guimaraes Daniel Zanotta André Spigolon Luiz Gonzaga Mauricio Roberto Veronez Vizlab - X-Reality and GeoInformatics Lab UNISINOS-São Leopoldo RS Brazil Graduate Program in Applied Computing at Vale do Rio dos Sinos University São Leopoldo RS Brazil CENPES - PETROBRAS Rio de Janeiro Brazil

Hyperspectral images often have low spatial resolution due to the sensor sizes required to capture the required spectral response. Super-resolution (SR) techniques try to mitigate this by injecting more detail in the upscaled image, either with numerical methods or deep learning and Convolution Neural Networks. In the deep learning methods, the models learn image details by inferring a high-resolution (HR) image from a synthetic low-resolution (LR) image that simulates the natural degradation of sensors by applying resampling (to reduce the image detail) and noising (to add small errors and interference). Often disregarded in the literature, the resampling method applied to generate the synthetic image can impact greatly the deep learning model training. This work, evaluate several resampling techniques to measure this impact using the Harvard hyperspectral dataset. Results showed that the Lanczos filter was the best among eight other resampling methods. The Nemenyi and Friedman ranking statistical tests also indicated that the Cubic-Spline, Bicubic, and RMS achieved good results.

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Clustering Fracture Data in 3D Outcrop Models with Hierarchical Agglomerative Clustering and Fisher Statistics

Clustering Fracture Data in 3D Outcrop Models with Hierarchi...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Graciela Racolte Ademir Marques Vinicius Ferreira Sales Daniel Capella Zanotta Delano Menecucci Ibanez Mauricio Roberto Veronez Luiz Gonzaga Vizlab — X-Reality and GeoInformatics Lab UNISINOS São Leopoldo RS Brazil Graduate Program in Applied Computing at Vale do Rio dos Sinos University São Leopoldo RS Brazil CENPES PETROBRAS Rio de Janeiro Brazil

Most fracture properties, such as orientation and density, are acquired by interpreting data obtained at the wellbores, while fracture properties between wells are typically derived from seismic data. However, this information is sparse or has low resolution leading to the study and analysis of outcrops. The data acquisition in outcrops is facilitated by its 3D representation in Digital Outcrop Models (DOM) obtained from LiDAR and Photogrammetry. It also allows virtual interpretation and fracture detection. In either case, the 3D fracture data need to be clustered in fracture sets to allow the statistical analysis necessary to upscale and resample the DFN. This clusterization is carried out by methods like k-means and fuzzy, however with caveats, like the prior definition of the number of clusters. In this work, we propose the use of agglomerative hierarchical clustering to cluster 3D fracture data obtained by a KD-Tree segmentation algorithm. Results showed that when compared to k-means, the proposed method presented more compact clusters and balance when considering Fisher’s statistics of dispersion.

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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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Indirect Calibration of Hyperspectral Images Using Rock Samples

Indirect Calibration of Hyperspectral Images Using Rock Samp...

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Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Daniel C. Zanotta Leonardo D. Araújo Marcelo K. De Souza Delano M. Ibanez Luiz G. Silveira Maurício R. Veronez Vizlab — X-Reality and GeoInformatics Lab UNISINOS São Leopoldo RS Brazil Graduate Program in Applied Computing at Vale do Rio dos Sinos University São Leopoldo RS Brazil PETROBRAS - Petrobras Research and Development Center (CENPES) RJ Brazil

ISBN: (数字)9798331513139

ISBN: (纸本)9798331513146

This paper presents a methodology for the indirect calibration of hyperspectral images using radiometric measurements from field samples. While hyperspectral imaging (HSI) is essential in remote sensing, its accuracy depends on precise calibration, which is often challenging in the field. Most approaches rely on a single standard panel used as absolute reference and the empirical line method. This often leads to mistakes and uncertainties in the analysis. In this paper we propose an alternative that utilizes radiometric data from rock samples with known spectral properties to indirectly calibrate hyperspectral images. The results show specific improvements in accuracy, particularly in the shape of the curves, compared to standard calibration method. The calibrated images offer better material differentiation and more reliable spectral signatures, which are crucial for geological mapping and mineral exploration. This approach can enhance the reliability of hyperspectral data analysis in geological and environmental studies, especially in situations where time and logistics are challenging.

关键词： Accuracy Uncertainty Coal Signal processing Rocks Radiometry Calibration Minerals Standards Hyperspectral imaging

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