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International Conference on Ultra Modern Telecommunications and Workshops (ICUMT)

作者： Márcio Gomes Rodrigo da Rosa Righi Cristiano André da Costa Applied Computing Graduate Program Universidade do Vale do Rio dos Sinos Brazil

Internet of Things, or IoT, is a new revolution of the Internet, supporting a connection between objects and humans yet more strong. This area is known by the huge quantity of data that can be generated from the RFID sensors at realtime. On managing this data, the IoT middleware introduces some time constrains regarding both accessing applications and filtering and storing data actions. Thus, middleware scalability is a key requirement for supporting the current growth of IoT. In this context, this article firstly presents μIB - Micro Internet of Things Benchmark - that aims at evaluating the scalability of EPC-compliant middlewares. μIB combines data from tags, RFID readers, sequential requests and number of threads to compose an evaluation setup to assess EPCIS and ALE components. We used this benchmark to evaluate Fosstrak, which is one of the most used IoT middlewares, in a single server. The tests emphasized that the exhaustion in one component influences the performance of the others directly. Following this research line, this article also describes the first ideas on redesigning the IoT middleware using techniques such as cloud virtualization and elasticity, service decomposition and multithreading programming.

关键词： Middleware Scalability Time factors Instruction sets Servers RFID tags

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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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Comparative experimental analysis of Docker container networking drivers

Comparative experimental analysis of Docker container networ...

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IEEE International Conference on Cloud Networking (CLOUDNET)

作者： Lucas Litter Mentz Wilton Jaciel Loch Guilherme Piêgas Koslovski Graduate Program in Applied Computing (PPGCA) Santa Catarina State University (UDESC) Joinville Brazil

ISBN: (数字)9781728194868

ISBN: (纸本)9781728194875

Virtualization allows for more efficient hardware usage by allowing several instances of Operating Systems (OSs) or Virtual Machines (VMs) to run on a physical server. Containers are a subset of lightweight virtualization and reduce the overhead of virtualizing an entire OS by sharing the server's OS with the virtualized instances. Moreover, containers work closer to hardware than VMs and are similar to Linux processes, however, this limits connectivity freedom since processes do not have access to network addressing. To resemble container's communication to that of conventional networks we use network drivers. Studies show that in processing- or memory-bound scenarios, containers perform better than VMs, but in network-bound scenarios they achieve less performance. This work analyzes performance of networking implementations for Docker in different container allocations and workload scenarios.

关键词： Cloud computing Linux Bandwidth Containers Hardware Servers Virtualization

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A Multi-objective Approach to the Protein Structure Prediction Problem using the Biased Random-Key Genetic Algorithm

A Multi-objective Approach to the Protein Structure Predicti...

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Congress on Evolutionary Computation

作者： Felipe Marchi Rafael Stubs Parpinelli Graduate Program in Applied Computing Santa Catarina State University Joinville Santa Catarina Brazil

Proteins are base molecules present in live organisms. The study of their structures and functions is of considerable importance for many application fields, particularly for the pharmaceutical area. This paper presents a multi-objective model to the Protein Structure Prediction problem, using three objectives: energy score, secondary structure information, and contact maps information. A BRKGA method is used as a global optimizer and adapted to work with multiple objective problems. Also, the MUFOLD-CL clustering method is applied to select a single predicted structure. Experiments were carried out to analyze the proposed model performance using state-of-the-art ab initio algorithms for comparison. Results obtained indicate that the proposed approach is competitive in terms of RMSD and GDT metrics.

关键词： Proteins Measurement Adaptation models Evolutionary computation Predictive models Prediction algorithms Organisms

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

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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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A GPU-Based Hybrid jDE Algorithm applied to the 2D-AB Protein Structure Prediction Problem

A GPU-Based Hybrid jDE Algorithm Applied to the 2D-AB Protei...

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Brazilian Conference on Intelligent Systems (BRACIS)

作者： Mateus Boiani Gabriel Dominico Rafael Stubs Parpinelli Graduate Program in Applied Computing Santa Catarina State University Joinville Santa Catarina Brazil

Protein Structure Prediction (PSP) problem is an open problem in bioinformatics and, as the problem scales, complexity and processing time increases. In this way, robust methods and massively parallel architectures are required. This paper presents a GPU-based hybrid algorithm, named cuHjDE, to handle the 2D-AB off-lattice PSP problem. The cuHjDE is composed of the jDE algorithm and the Hooke-Jeeves local search algorithm. An important feature present in the cuHjDE algorithm is the use of a crowding mechanism to avoid premature convergence promoting diversification in the search space. The proposed algorithm is compared with four state-of-the-art algorithms in both artificial and real proteins. Also, the impact of using GPU is analyzed. Experimental results point out that the proposed approach is competitive in artificial sequences and achieved new best results for all real sequence proteins.

关键词： Kernel Graphics processing units Proteins Instruction sets Optimization Amino acids Biological system modeling

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Mineralogical Classification of Sedimentary Rocks by Spectral Matching Techniques Based on Library and Field-Work References 14

Mineralogical Classification of Sedimentary Rocks by Spectra...

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14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2024

作者： Zanotta, Daniel C. De Souza, Lais V. De Souza, Marcelo K. Ayres, Alysson S. Sales, Vinicius F. Racolte, Graciela R. Ibanez, Delano M. Silveira, Luiz G. Veronez, Mauricio R. UNISINOS Vizlab X-Reality and GeoInformatics Lab RS São Leopoldo Brazil Graduate Program in Applied Computing Vale do Rio dos Sinos University RS São Leopoldo Brazil RJ Brazil

ISBN: (纸本)9798331513139

Sedimentary rocks are of great importance for the oil and gas industry. Spectral matching using known references made on hyperspectral images is one of the most rapid and cost effective alternatives for the detailed analysis in these areas, since they can adequately catch the specific absorption features of the multiple minerals available. In many initiatives, scientists run the methods with standard spectral references, usually from spectral libraries or samples from other places. However, for the success of their application, they must be used along with spectral references that accurately describe the actual constituents of the surface. In this paper we scrutinize three different ways for mineralogical mapping performed by spectral matching techniques. The results showed that using reference spectra from samples collected directly on the surveyed environment can considerably improve the results, reaching high scores, pointing out that many places share common substances, but each specific material has particular differences that cannot be generalized. © 2024 IEEE.

关键词： Gas industry

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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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Optimization of feedforward neural network by Multiple Particle Collision Algorithm

Optimization of feedforward neural network by Multiple Parti...

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IEEE Symposium on Foundations of Computational Intelligence, FOCI

作者： Juliana A Anochi Haroldo F. de Campos Velho Applied Computing Graduate Program National Institute for Space Research SP Brazil Laboratory Computing and Applied Mathematics National Institute for Space Research SP Brazil

Optimization of neural network topology, weights and neuron activation functions for given data set and problem is not an easy task. In this article, a technique for automatic configuration of parameters topology for feedforward artificial neural networks (ANN) is presented. The determination of optimal parameters is formulated as an optimization problem, solved with the use of meta-heuristic Multiple Particle Collision Algorithm (MPCA). The self-configuring networks are applied to predict the mesoscale climate for the precipitation field. The results obtained from the neural network using the method of data reduction by the Theory of Rough Sets and the self-configuring network by MPCA were compared.

关键词： Artificial neural networks Topology Meteorology Network topology Training Atmospheric modeling Biological neural networks

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