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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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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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Assisted Multi-Frame Approach for Super-Resolution in UAV Photogrammetric Images

Assisted Multi-Frame Approach for Super-Resolution in UAV Ph...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Daniel Zanotta Vinicius Sales Ademir Marques Andre Luiz Durante Spigolon Luiz Gonzaga Maurício 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 PETROBRAS - Petrobras Research and Development Center (CENPES) RJ Brazil

Improvement in spatial resolution of remote sensing images will always be a hot-topic since the sharpening of image objects is mandatory for many appications. Even thought image instruments have received great improvements in the recent years, super-resolution techniques are welcome to increase even more the level of quality of the data for further interpretations. In this paper we present a multi-frame super-resolution approach that uses a set of UAV images acquired at the same spot, but with slight different perspectives caused by random fluctuations. The small off-sets between consecutive pixels of the low spatial resolution images are considered at subpixel level to feed an arithmetic system of equations able to produce high resolution pixels, which are the unknowns of the system. The results are soundness and could show visual enhancement. However, further developments is need to in deep undertanding and possible advancement of the designed approach.

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Evaluating a New Approach to Data Fusion in Wearable Physiological Sensors for Stress Monitoring 9th

Evaluating a New Approach to Data Fusion in Wearable Physiol...

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9th Brazilian Conference on Intelligent Systems, BRACIS 2020

作者： Rodrigues, Clarissa Fröhlich, William R. Jabroski, Amanda G. Rigo, Sandro J. Rodrigues, Andreia de Castro, Elisa Kern Applied Computing Graduate Program UNISINOS São Leopoldo Brazil São Leopoldo Brazil Universidade Lusíada Lisboa Portugal

ISBN: (纸本)9783030613792

The physiological signs are a reliable source to identify stress states, and wearable sensors provide precise identification of physiological signs associated with the stress occurrence. The literature review shows that the use of physiological signs as a source for stress patterns identification is still a critical investigation subject. Few studies evaluate the effect of combining several different signals and the implications of the data acquisition procedures and details. This article’s objective is to investigate the possible integration of data obtained from heart rate variability, electrocardiographic, electrodermal activity, and electromyography to detect stress patterns, considering a new experimental protocol to data acquisition. The data acquisition involved the Trier Social Stress Test, wearable sensor monitoring, and complementary stress perception instruments, resulting in a publicly available dataset. This dataset was evaluated using different machine learning classifiers, considering the obtained annotated data and exploring different physiological features and their combinations. © 2020, Springer Nature Switzerland AG.

关键词： Data acquisition

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Proposal of a Method for Wildlife-Vehicle Collisions Risk Assessment Based on Geographic Information Systems and Deep Learning

Proposal of a Method for Wildlife-Vehicle Collisions Risk As...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Diego Brum Marianne Müller Mauricio R. Veronez Eniuce M. de Souza Luiz Gonzaga Claudio J. A. Nhanga Guilherme T. Conrado Natália Procksch Julia Dias Fabio Viegas Guilherme Cauduro Vanessa S. Silva Gefersom C. Lima Izidoro Amaral Caroline M. Carvalho Larissa Oliveira Gonçalves Graduate Program on Applied Computing - UNISINOS - São Leopoldo RS Brazil Vizlab X-Reality & GeoInformatics Lab UNISINOS - São Leopoldo RS Brazil Graduate Program on Biology - UNISINOS - São Leopoldo RS Brazil Graduate Program on Ecology - UFRGS - Porto Alegre RS Brazil

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

This work proposes a deep learning and GIS based workflow to assess the influence of highway barriers on wildlife collisions. Our work consists of using Convolutional Neural Networks to classify images extracted automatically from Google Street View to determine the type of barrier, and using geoprocessing tools to estimate parameters as barrier length and location. The method was applied in a real dataset, classifying correctly the barriers in the road-kill points with accuracy of 84.44%. Statistical tests were used to evaluate the influence of each type of barrier on the road-kills.

关键词： Roads Wildlife Deep learning Sociology Geographic information systems Tools Software

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A multi-agent system for optimizing physiological collection based on adaptive strategies

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Smart Health 2021年 19卷

作者： Aranda, Jorge Arthur Schneider Bavaresco, Rodrigo Simon da Silva Machado, Roger de Carvalho, Juliano Varella Corrêa, Adenauer Yamin Barbosa, Jorge Luis Victória Applied Computing Graduate Program (PPGCA) of University of Vale do Rio dos Sinos (UNISINOS) Av. Unisinos 950 São Leopoldo 93.022-750 RS Brazil Creative and Technological Sciences Institute of Feevale University ERS 239 2755 Novo Hamburgo RS 93525-075 RS Brazil Computer Science Graduate Program of Federal University of Pelotas Gomes Carneiro 1 Pelotas 96010-610 RS Brazil

Wearable devices have emerged from the evolution of communication and information technology, along with the miniaturization of electronic components. These devices monitor the user's physiology on a periodic basis and generally have low battery autonomy. This article proposes the Odin system, a multi-agent system that performs real-time analysis of physiological data in order to adapt the collection according to the user's health condition. Two types of adaptation were proposed in terms of adapting the collection. The first, wait-time adaptation (WTA), consists of collecting physiological data through a sensor defined as the main sensor, where the frequency of the collection is based on the user's condition. Meanwhile, the second, paused sensors adaptation (PSA), manages the activation status of secondary sensors when the main sensor suggests a possible risk to the user's health. An evaluation was implemented through two steps. First, a simulation was performed with requests control to optimize the parameters of the data collection. These results showed an increase of 214% in battery life in the adaptive scenario compared to one with no adaptivity. Second, Odin's adaptivity was evaluated in terms of a real dataset, which allowed for the reduction of the number of requests. This reduction optimized the battery consumption, with a 66% improvement over data collection with no adaptivity. © 2020 Elsevier Inc.

关键词： Adaptive systems Embedded systems Healthcare Wearable Devices

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Perspectives on risk prioritization of data center vulnerabilities using rank aggregation and multi-objective optimization

arXiv

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arXiv 2022年

作者： Grisci, Bruno Iochins Kuhn, Gabriela Colombelli, Felipe Matter, Vítor Kehl Lima, Leomar Heinen, Karine Pegoraro, Mauricio Borges, Marcio Rigo, Sandro José Barbosa, Jorge Luis Victória da Rosa Righi, Rodrigo da Costa, Cristiano André de Oliveira Ramos, Gabriel Software Innovation Laboratory - Softwarelab Graduate Program in Applied Computing Universidade do Vale do Rio dos Sinos Av. Unisinos 950 RS São Leopoldo93022-750 Brazil Institute of Informatics Federal University of Rio Grande do Sul Av. Bento Gonçalves 9500 RS Porto Alegre91501-970 Brazil Dell Technologies Brazil Av. Industrial Belgraf 400 Eldorado do Sul RS 92990-000 Brazil

Nowadays, data has become an invaluable asset to entities and companies, and keeping it secure represents a major challenge. Data centers are responsible for storing data provided by software applications. Nevertheless, the number of vulnerabilities has been increasing every day. Managing such vulnerabilities is essential for building a reliable and secure network environment. Releasing patches to fix security flaws in software is a common practice to handle these vulnerabilities. However, prioritization becomes crucial for organizations with an increasing number of vulnerabilities since time and resources to fix them are usually limited. This review intends to present a survey of vulnerability ranking techniques and promote a discussion on how multi-objective optimization could benefit the management of vulnerabilities risk prioritization. The state-of-the-art approaches for risk prioritization were reviewed, intending to develop an effective model for ranking vulnerabilities in data centers. The main contribution of this work is to point out multi-objective optimization as a not commonly explored but promising strategy to prioritize vulnerabilities, enabling better time management and increasing security. Copyright © 2022, The Authors. All rights reserved.

关键词： Multiobjective optimization

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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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A linguistic approach to short sentences keywords identification for a question answering system

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International Journal of Web Engineering and Technology 2019年第4期14卷 367-382页

作者： Araujo, Denis Rigo, Sandro José Schmitt, Bruna Koch Hentges, Alencar Applied Computing Graduate Program Universidade do Vale do Rio dos Sinos - UNISINOS São Leopoldo Brazil

One of the aims of question answering systems is to identify which words are more relevant to understand the users' needs. Known approaches involve the identification of the users' intentions through a set of previously built related sentences. Some limitations of these approaches are the lack of flexibility and limited selection options. In this paper, we present an approach based on computational linguistics to identify the keywords in short sentences for question answering systems. The main contribution of our approach is related to the new way we use the information generated by the natural language processing tools to identify the keywords of the sentences, by profoundly exploring the linguistic information to select the keywords of the questions. Besides, we emphasise the generalisation and the simplicity of our algorithm. The efficiency of our method was proved by the performance of 0.9776 in precision, recall value of 0.9962, resulting in an F1 score of 0.9868 reached in the validation experiment using QALD-7 as a gold standard. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Natural language processing systems

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