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Observing Network Performance and Congestion on Managing Assets with RFID and Cloud computing

Journal of Computer and Communications 2017年第9期5卷 43-66页

作者： Leandro Andrioli Rodrigo da Rosa Righi Cristiano André da Costa Lucas Graebin Applied Computing Graduate Program Universidade do Vale do Rio dos Sinos (Unisinos) S&#227 o Leopoldo RS Brazil

Today, we observe that more and more, radio frequency identification (RFID) technology has been used to identify and track objects in enterprises and institutions. In addition, we also perceive the growing adoption of cloud computing, either public or private, to process and store data from the objects. In this context, the literature does not present an initiative that looks into the network on enterprise-cloud interactions, so neglecting network performance and congestion information when transmitting data to the cloud. Thus, we are presenting a model named ACMA—Automatic Control and Management of Assets. ACMA employs context awareness to control and monitor corporate assets in companies with multiple units. ACMA provides a centralized point of access in the cloud in which interested actors can get online data about each corporate asset. In particular, our scientific contribution consists in considering network congestion to control dynamically the data updating interval from sensors to the cloud. The idea is to search for reliability and integrity of operations, without losing or corrupting data when updating the information to cloud. Thus, this article describes the ACMA model, its architecture, algorithms and features. In addition, we describe the evaluation methodology and the results obtained through experiments and simulations based on the developed prototype.

关键词： Internet of Things RFID Cloud computing Network Congestion Adaptivity Assets

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Optimizing a Homomorphic Filter for Illumination Compensation In Face Recognition Using Population-Based Algorithms

Optimizing a Homomorphic Filter for Illumination Compensatio...

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Workshop of Computer Vision

作者： Guilherme Felippe Plichoski Chidambaram Chidambaram Rafael Stubs Parpinelli Graduate Program in Applied Computing State University of Santa Catarina Joinville Santa Catarina Brazil

ISBN: (纸本)9781538614525

Face recognition (FR) systems based on populationbased heuristics algorithms is common nowadays. Depends on the approach, there is a need for tuning parameters in preprocessing step using optimization algorithms. Regarding FR challenges, the illumination variation is one of the critical factors. The homomorphic filter (HF) is one of the methods that can compensate ination aiding to stabilize the face images obtained under different lighting conditions. However, the HF requires some optimal parameters which can be possibly determined through optimization process. Based on this context, in this paper, we attempt to investigate the application of two population-based algorithms, the Jaya and the Particle Swarm Optimization (PSO), in order to optimize the HF parameters in CMU-PIE and BIOINFO face databases. The high performance of both algorithms shows that they are suitable to compensate the illumination variation achieving 100% and 91.3% recognition rates on CMUPIE and BIO-INFO, respectively.

关键词： Lighting Optimization Mathematical model Hafnium Face Face recognition Particle swarm optimization

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A Trust Reputation Architecture for Cloud computing Environment

A Trust Reputation Architecture for Cloud Computing Environm...

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ACS/IEEE International Conference on Computer Systems and Applications

作者： Luís Felipe Bilecki Adriano Fiorese Graduate Program in Applied Computing (MCA) Santa Catarina State University (UDESC) Joinville Brazil

Cloud computing provides computational resources (processing, storage, software, network) to users in a scalable and dynamic way over the Internet. The use of these resources are impacted by issues related to privacy, security and trust. In this sense, this paper presents a trust reputation architecture applied in the cloud computing environment. The reputed trust is based on two trust indicators: objective and subjective ones. The objective trust indicator is based on the historical QoS indicators of a cloud provider. Then, the subjective trust indicator is composed of the users' feedbacks to the cloud providers. In order to evaluate the proposed architecture, simulations were performed using a P2P Network Simulator in order to emulate cloud providers and their clients. The evaluation results show the architecture applicability with low overhead.

关键词： Cloud computing Quality of service Computer architecture Monitoring Security Computational modeling Measurement

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Doctrine based multi-resolution HLA distributed simulation 17

Doctrine based multi-resolution HLA distributed simulation

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32nd Annual ACM Symposium on applied computing, SAC 2017

作者： Paul, Ricardo L. Nunes, Raul C. Oliveira, Vanderlan D. Kunde, Diogo Computer Science Graduate Program Fed. Univ. of Santa Maria Santa Maria RS Brazil Applied Computing Dpt. Fed. Univ. of Santa Maria Santa Maria RS Brazil

ISBN: (纸本)9781450344869

In distributed simulation, the purpose of multi-resolution methods is to allow simulation integration getting consis- tent views of different resolutions. In these methods, chal- lenges in the treatment of aggregation and disaggregation can be found, such as synchronization of events and objects, and data reliability when occurs a change of resolu- tion. Doctrines are the basis for operations involving different resources (human and material) with different roles to succeed in operation, and multi-resolution in distributed simulation can take advantage of this factor to ensure the reliability of aggregation and disaggregation processes. The aim of this paper is to propose a new strategy for treatment of multi-resolution, which builds converting rules from doctrine dynamic rules. The multi-resolution converter uses dynamic rules to describe aspects of the doctrine in the ag- gregation and disaggregation processes, increasing the reliability of multi-resolution distributed simulations. This strat- egy was implemented on a High Level Architecture (HLA) federation with virtual and constructive Commercial Off- The-Shelf (COTS) simulators. The experiments showed the exibility of this strategy on managing different doctrines. © 2017 ACM.

关键词： Reliability

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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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The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels (vol 400, pg 1619, 2022)

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LANCET 2022年第10365期400卷 1766-1766页

作者： Romanello, M. Di Napoli, C. Drummond, P. Institute for Global Health University College London London UK School of Agriculture Policy and Development University of Reading Reading UK Institute for Sustainable Resources University College London London UK Department of Global Health Centre for Health and the Global Environment University of Washington Seattle WA USA UCL Energy Institute University College London London UK Department of Health Sciences University of York York UK Department of Meteorology University of Reading Reading UK Institute for Risk and Disaster Reduction University College London London UK School of Earth and Environment University of Leeds Leeds UK Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK School of Population Health University of Melbourne Melbourne VIC Australia Department of Earth System Science Tsinghua University Beijing China Mercator Research Institute on Global Commons and Climate Change Berlin Germany Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute of Environmental Sciences University of Geneva Geneva Switzerland Cardiovascular Epidemiology Unit Department of Public Health & Primary Care University of Cambridge Cambridge UK School of Government University of Birmingham Birmingham UK Economic Analysis of Climate Impacts and Policy Division Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy Institute for Environmental Design and Engineering University College London London UK Natural Resources Institute University of Greenwich London UK Department of Environmental Health Sciences and Yale Center on Climate Change and Health Yale University New Haven CT USA Department of Civil and Environmental Engineering Northeastern University Boston MA USA Department of Fish and Wildlife Conservation Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Geography University College London London UK NU

With advancements in the science of detection and attribution studies, the influence of climate change over many events has now been quantified. Because of the rapidly increasing temperatures, vulnerable populations (adults older than 65 years, and children younger than one year of age) were exposed to 3·7 billion more heatwave days in 2021 than annually in 1986–2005 (indicator 1.1.2), and heat-related deaths increased by 68% between 2000–04 and 2017–21 (indicator 1.1.5), a death toll that was significantly exacerbated by the confluence of the COVID-19 pandemic. Despite the local cooling and overall health benefits of urban greenspaces, only 277 (27%) of 1038 global urban centres were at least moderately green in 2021 (indicator 2.2.3), and the number of households with air conditioning increased by 66% from 2000 to 2020, a maladaptive response that worsens the energy crisis and further increases urban heat, air pollution, and greenhouse gas emissions. [...]millions of people do not have access to the energy needed to keep their homes at healthy temperatures, preserve food and medication, and meet the seventh Sustainable Development Goal (to ensure access to affordable, reliable, sustainable, and modern energy for all). Aggravating this situation even further, governments continue to incentivise fossil fuel production and consumption: 69 (80%) of 86 countries reviewed had net-negative carbon prices (ie, provided a net subsidy to fossil fuels) for a net total of US$400 billion

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Inter-comparison of multiple Global Climate Model (GCM) data based on spatial pattern of rainfall over Indonesia

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IOP Conference Series: Earth and Environmental Science 2019年第1期284卷

作者： Elania Aflahah Arnida Lailatul Latifah Rahmat Hidayat Rini Hidayati Andi Ihwan Applied Climatology Study Program Graduate School Bogor Agricultural University Bogor 16680 Laboratorium of High-Performance Computing Research Center for Informatics Indonesian Institute of Sciences Bogor West Java 16916 Department of Geophysics and Meteorology Bogor Agricultural University Bogor 16680

The Coupled Model Inter-comparison Project Phase 5 (CMIP5) is the output of many coupled atmosphere-ocean of global climate models (GCMs) and widely used for climate research, especially for driving regional climate model. There are more than 40 CMIP5 GCMs data available, but no single model can be considered as the best for every region. The use of CMIP5 GCMs data for rainfall projection in Indonesia is important to improve the accuracy of the monthly and seasonal rainfall forecast. Then, this study evaluates the capability of the CMIP5 GCMs data for Indonesia region by quantitatively comparing the spatial pattern of the precipitation mean and standard deviation of the CMIP5 data against GPCP, GPCC, and CRU data in the period 1980-2005. Furthermore, the composite analysis is conducted to observe the model performance in reproducing the precipitation characteristic over some areas in Indonesia. In conclusion, the models NorESM1-M, NorESM1-ME, GFDL-ESM2M, CSIRO-MK3-6-0 perform the rainfall mean better than others, while the standard deviation of the rainfall show that the models NorESM1-M, BNU-ESM, CMCC-CMS are superior in which NorESM1-M gives the best performance. The annual precipitation pattern of the model NorESM1-M over various areas in Indonesia is also highly correlated with the observations. Thus, the most suitable model for Indonesia region is NorESM1-M.

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Deep Machine Learning Techniques for the Detection and Classification of Sperm Whale Bioacoustics (vol 9, 12588, 2019)

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SCIENTIFIC REPORTS 2019年第1期9卷 12588-12588页

作者： Bermant, Peter C. Bronstein, Michael M. Wood, Robert J. Gero, Shane Gruber, David F. Radcliffe Institute for Advanced Study Harvard University Cambridge MA USA Department of Computing Imperial College London UK Twitter 20 Air Street London W1B 5DL United Kingdom Wyss Institute for Biologically Inspired Engineering Harvard University Cambridge MA USA Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA USA Department of Zoophysiology Institute for Bioscience Aarhus University C.F. Møllers Allé 3 8000 Aarhus Denmark Department of Natural Sciences Baruch College and The Graduate Center PhD Program in Biology City University of New York New York NY USA

We implemented Machine Learning (ML) techniques to advance the study of sperm whale (Physeter macrocephalus) bioacoustics. This entailed employing Convolutional Neural Networks (CNNs) to construct an echolocation click detector designed to classify spectrograms generated from sperm whale acoustic data according to the presence or absence of a click. The click detector achieved 99.5% accuracy in classifying 650 spectrograms. The successful application of CNNs to clicks reveals the potential of future studies to train CNN-based architectures to extract finer-scale details from cetacean spectrograms. Long short-term memory and gated recurrent unit recurrent neural networks were trained to perform classification tasks, including (1) 'coda type classification' where we obtained 97.5% accuracy in categorizing 23 coda types from a Dominica dataset containing 8,719 codas and 93.6% accuracy in categorizing 43 coda types from an Eastern Tropical Pacific (ETP) dataset with 16,995 codas; (2) 'vocal clan classification' where we obtained 95.3% accuracy for two clan classes from Dominica and 93.1% for four ETP clan types; and (3) 'individual whale identification' where we obtained 99.4% accuracy using two Dominica sperm whales. These results demonstrate the feasibility of applying ML to sperm whale bioacoustics and establish the validity of constructing neural networks to learn meaningful representations of whale vocalizations. [ABSTRACT FROM AUTHOR]

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Identification and quantification of kaolinite in mixtures with goethite using short-wave infrared (SWIR) reflectance spectroscopy 37

Identification and quantification of kaolinite in mixtures w...

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

作者： De Souza, Marcelo K. Veronez, Mauricio R. Tognoli, Francisco M. W. Gonzaga, Luiz De Souza, Lais V. Kochhann, Marcus V. L. Da Silva, Nadine G. Marson, Fernando P. Cagliari, Joice Graduate Program in Geology Vale Do Rio Dos Sinos University São Leopoldo RS Brazil Vale Do Rio Dos Sinos University São Leopoldo RS Brazil Graduate Program in Applied Computing Vale Do Rio Dos Sinos University São Leopoldo RS Brazil

ISBN: (纸本)9781509049516

We investigate here the potential of the spectroscopy in the identification and quantification of mixtures of kaolinite and goethite from the Continuum Removal (CR) of the spectra in the short-wave infrared. For this purpose, spectral measurements of the kaolinite, goethite, and controlled mixtures of these minerals were systematically performed. The continuum of the results were removed, the depth of the kaolinite diagnostic absorption was calculated and compared with a spectral library. It was possible to identify the kaolinite with high determination coefficient (R2>0.95) when its proportion reaches at least 60% in the mixture. For quantification purposes, it was possible to quantify kaolinite using the diagnostic absorption feature depth in the CR with a coefficient of determination of 0.99. © 2017 IEEE.

关键词： Absorption Minerals Reflectivity Spectroscopy Libraries Geologic measurements Remote sensing

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CEMCO: A Novel Competitive Evolutionary Multi-Colony Optimization Algorithm for Constrained Problems

CEMCO: A Novel Competitive Evolutionary Multi-Colony Optimiz...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Mário Tasso Ribeiro Serra Neto Marco Antônio Florenzano Mollinetti Demison Rolins de Souza Alves Rodrigo Lisbôa Pereira Otávio Noura Teixeira University Centre of the State of Pará (CESUPA) Computational Intelligence Laboratory (LINCOMP) Belém Pará Brazil Systems Optimization Laboratory Tsukuba University Tsukuba Ibaraki Japan Federal Rural University of Amazon (UFRA) Electrical Engineering Post Graduation Program of the Federal University of Par (UFPA) Paragominas Pará Brazil Applied Computing Post Graduation Program (PPCA) Federal University of Pará Tucuruí (UFPA-TUC) Tucuruí Pará Brazil

ISBN: (纸本)9781538680988;9781538680971

The following paper presents a hybrid Swarm Intelligence algorithm, named Competitive Evolutionary Multi-Colony Optimization (CEMCO). CEMCO is based on the well-known bee inspired algorithm Artificial Bee Colony (ABC), introducing Evolutionary Strategies (ES)under a parallel master-slave architecture utilizing the General Purpose computing on Graphics Processing Units (GPGPU)paradigm. The goal of this approach is to enhance the overall performance of the master algorithm, producing high quality solutions, and significant solutions under a reduced processing time. Performance evaluation of CEMCO is performed by two large scale constrained optimization problems: Design of Pressure Vessel (DPV)and Welded Beam Design (WBD). Results obtained are compared against prominent techniques found in the literature.

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