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2005 IEEE International Workshop on Intelligent Signal Processing

作者： Mota, Hilton De O. Vasconcelos, Fláio H. Da Silva, Rogério M. Post-graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil Electrical Engineering Department UFMG Belo Horizonte Brazil

ISBN: (纸本)078039030X

Two new algorithms to compute the direct and the inverse discrete wavelet transform of continuous streams of data are discussed in this paper. The algorithms are optimized to be used in uniprocessor systems, presenting as one of their main features the ability to compute over the borders of the data segments without relying on any techniques that are traditionally used for this task, like zero padding. A modified version of the Recursive Pyramid Algorithm was used to compute the direct transform, keeping just the features to minimize data storage but eliminating its dependency on extension techniques. The inverse transform is computed by an algorithm inspired in the RPA, the quadrature mirror filter bank and the overlap-save method for filter convolutions. To evaluate the performance the algorithms were implemented on a DSP coupled to a digitizer through its external memory bus, what allowed deterministic behavior. After a stage of optimization an analysis of data storage and computation load were made. These results and the potential applications are discussed at the end of the paper. ©2005 IEEE.

关键词： Data processing

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Real-time optimization using gradient adaptive selection and classification from infrared sensors measurement for esterification oleic acid with glycerol

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International Journal of Intelligent Computing and Cybernetics 2017年第2期10卷 130-144页

作者： Iwan Aang Soenandi Taufik Djatna Ani Suryani Irzaman Post Graduate Program of Agro-industrial Technology Bogor Agricultural UniversityBogorIndonesia and Faculty of Engineering and Computer ScienceKrida Wacana Christian UniversityJakartaIndonesia Post Graduate Program of Agro-industrial Technology Bogor Agricultural UniversityBogorIndonesia Department of Physics Faculty of Mathematics and Natural SciencesBogor Agricultural UniversityBogorIndonesia

Purpose-The production of glycerol derivatives by the esterification process is subject to many constraints related to the yield of the production target and the lack of process *** accurate monitoring and controlling of the process can improve production yield and *** purpose of this paper is to propose a real-time optimization(RTO)using gradient adaptive selection and classification from infrared sensor measurement to cover various disturbances and uncertainties in the ***/methodology/approach-The integration of the esterification process optimization using self-optimization(SO)was developed with classification process was combined with necessary condition optimum(NCO)as gradient adaptive selection,supported with laboratory scaled medium wavelength infrared(mid-IR)sensors,and measured the proposed optimization system indicator in the batch *** Process Modeling and Notation(BPMN 2.0)was built to describe the tasks of SO workflow in collaboration with NCO as an abstraction for the conceptual ***,Stateflow modeling was deployed to simulate the three states of gradient-based adaptive control combined with support vector machine(SVM)classification and Arduino microcontroller for ***-This new method shows that the real-time optimization responsiveness of control increased product yield up to 13 percent,lower error measurement with percentage error 1.11 percent,reduced the process duration up to 22 minutes,with an effective range of stirrer rotation set between 300 and 400 rpm and final temperature between 200 and 210℃ which was more efficient,as it consumed less *** limitations/implications-In this research the authors just have an experiment for the esterification process using glycerol,but as a development concept of RTO,it would be possible to apply for another chemical reaction or *** implications-This research introduces new development of an RTO approach to optimal con

关键词： Gradient technique Infrared sensor Real-time optimization Simulation and modelling Support vector machine

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Fast saliency detection using sparse random color samples and joint upsampling 29

Fast saliency detection using sparse random color samples an...

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29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016

作者： Lie, Maiko Min Ian Borba, Gustavo Benvenutti Neto, Hugo Vieira Gamba, Humberto Remigio Graduate Program in Electrical and Computer Engineering Federal University of Technology - Paraná Curitiba Brazil Graduate Program in Biomedical Engineering Federal University of Technology - Paraná Curitiba Brazil

ISBN: (纸本)9781509035687

The human visual system employs a mechanism of visual attention, which selects only part of the incoming information for further processing. Through this mechanism, the brain avoids overloading its limited cognitive capacities. In computer vision, this task is usually accomplished through saliency detection, which outputs the regions of an image that are distinctive with respect to its surroundings. This ability is desirable in many technological applications, such as image compression, video quality assessment and content-based image retrieval. In this paper, a saliency detection method based on color distance with sparse random samples and joint upsampling is presented. This approach computes full-resolution saliency maps with short runtime by leveraging both edge-preserving smoothing and joint upsampling capabilities of the Fast Global Smoother. The proposed method is assessed through precision-recall curves, F-measure and average runtime on the MSRA1K dataset. Results show that the method is competitive with state-of-the-art algorithms in both saliency detection accuracy and runtime. © 2016 IEEE.

关键词： Image compression

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Granular approach for evolving system modeling

Granular approach for evolving system modeling

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13th International Conference on Information Processing and Management of Uncertainty, IPMU 2010

作者： Leite, Daniel Costa Jr., Pyramo Gomide, Fernando School of Electrical and Computer Engineering University of Campinas Brazil Graduate Program in Electrical Engineering Pontifical Catholic University of Minas Gerais Brazil

ISBN: (纸本)3642140483

In this paper we introduce a class of granular evolving system modeling approach within the framework of interval analysis. Our aim is to present an interval-based learning algorithm which develops both, granular and singular approximations of nonlinear nonstationary functions using singular data. The algorithm is capable of incrementally creating/adapting both model parameters and structure. These are key features in nonlinear systems modeling. In addition, interval analysis provides rigorous bounds on approximation errors, rounding errors, and on uncertainties in data propagated during computations. The learning algorithm is simple and particularly suited to process stream of data in real time. In this paper we focus on the foundations of the approach and on the details of the learning algorithm. An application concerning economic time series forecasting illustrates the usefulness and efficiency of the approach. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Learning algorithms

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Evolving granular neural network for semi-supervised data stream classification

Evolving granular neural network for semi-supervised data st...

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

作者： Leite, Daniel Costa, Pyramo Gomide, Fernando Department of Computer Engineering and Automation Faculty of Electrical and Computer Engineering University of Campinas Sao Paulo Brazil Graduate Program in Electrical Engineering Catholic University of Minas Gerais Brazil

ISBN: (纸本)9781424469178

In this paper we introduce an adaptive fuzzy neural network framework for classification of data stream using a partially supervised learning algorithm. The framework consists of an evolving granular neural network capable of processing nonstationary data streams using a one-pass incremental algorithm. The granular neural network evolves fuzzy hyperboxes and uses nullnorm based neurons to classify data. The learning algorithm performs structural and parametric adaptation whenever environment changes are reflected in input data. It needs no prior statistical knowledge about data and classes. Computational experiments show that the fuzzy granular neural network is robust against different types of concept drift, and is able to handle unlabeled examples efficiently. © 2010 IEEE.

关键词： Learning algorithms

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Automatic image thumbnailing based on fast visual saliency detection 16

Automatic image thumbnailing based on fast visual saliency d...

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22nd Brazilian Symposium on Multimedia and the Web, WebMedia 2016

作者： Lie, Maiko M. I. Neto, Hugo Vieira Borba, Gustavo B. Gamba, Humberto R. Graduate Program in Electrical and Computer Engineering Federal University of Technology - Paraná Curitiba Brazil Graduate Program in Biomedical Engineering Federal University of Technology - Paraná Curitiba Brazil

ISBN: (纸本)9781450345125

Image retargeting has seen many applications in areas such as content adaptation for small displays and thumbnailing for image database browsing. Most retargeting methods, however, are too expensive computationally to achieve fast performance on common desktop systems. This work addresses the problem of fast automatic thumbnailing for image browsing. A simple approach of automatic thresholding saliency maps and cropping using bounding box extraction is presented. Eight of the fastest saliency detectors in the literature and three automatic thresholding methods are assessed using precision, recall, F-score and execution time on the MSRA1K dataset. The results show that the approach is computationally efficient and adequate for fast automatic image thumbnailing. In particular, saliency detection with difference to random color samples (RS) thresholded by Rosin's method achieved the best trade-off between execution time and F-score. © 2016 ACM.

关键词： Economic and social effects

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Multi-agent Architecture for Passive Rootkit Detection with Data Enrichment

Multi-agent Architecture for Passive Rootkit Detection with...

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International Conference on computer Science, Electronics, and Industrial engineering, CSEI 2022

作者： Trinks, Maickel Gondim, João Albuquerque, Robson Professional Post-Graduate Program in Electrical Engineering - PPEE - Department of Electrical Engineering University of Brasília DF Brasília Brazil Department of Computer Science Universidade de Brasília DF Brasília Brazil

ISBN: (纸本)9783031305917

The added value of the information transmitted in a cybernetic environment has resulted in a sophisticated malicious actions scenario aimed at data exfiltration. In situations with advanced actors, like APTs, such actions use obfuscation techniques of harmful activities as persistence assurance on strategic targets. The MADEX and NERD architectures proposed flow analysis solutions to detect rootkits that hide network traffic;however, it presents some operational cost, either in traffic volume or due to lack of aggregated information. In that regard, this work changes and improves user flow analysis techniques to eliminate impacts on network traffic, with data enrichment on local and remote bases, detection of domains consulted by rootkits and aggregation of information to generate threat intelligence, while maintaining high performance. The results show the possibility of aggregating information to data flows used by rootkits in order to have effective cyber defense actions against cybernetic threats without major impacts on the existing network infrastructure. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multi agent systems

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Measurement equipment for characterization of ferromagnetic materials

Measurement equipment for characterization of ferromagnetic ...

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2015 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2015

作者： Moraes, Tarcisio Oliveira De Freire, Raimundo Carlos Silverio Vieira, Debora Albuquerque Souza, Cleonilson Protasio De Post-Graduate Program in Electrical Engineering - PPGEE COPELE Federal University of Campina Grande UFCG Campina Grande Brazil Federal University of Campina Grande UFCG Campina Grande Brazil Post-Graduate Program in Electrical Engineering - PPGEE/UFPB Department of Electrical Engineering Federal University of Paraíba UFPB João Pessoa Brazil

ISBN: (纸本)9781479961139

Ferromagnetic materials have magnetic properties which determine its magnetic field conductivity characteristics. Such characteristics are extremely important for the proper selection and use of these materials in measurement processes, for example. Therefore, this paper presents an equipment for the characterization of ferromagnetic materials constructed in a laboratory. The experimental results of this equipment are acquired and compared with the experimental results obtained with an industrially manufactured apparatus called hysteresisgraph, designed to provide magnetic characterization of magnetic materials. The results of the comparison demonstrate that the equipment presented has low relative error and can be applied in academic projects which require magnetic characterization of ferromagnetic materials developed in the laboratory. © 2015 IEEE.

关键词： Ferromagnetic materials

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Realizing and Experimentally Testing a Reluctance-Based Magnetic Lead Screw 19

Realizing and Experimentally Testing a Reluctance-Based Magn...

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19th International Symposium on Electromagnetic Fields in Mechatronics, electrical and Electronic engineering, ISEF 2019

作者： Cirolini, Mateus Filho, Aly Ferreira Flores Eckert, Paulo Roberto Franchi, Lucas Dalla Corte Federal University of Rio Grande Do sul Post-Graduate Program in Electrical Engineering Porto Alegre90035-190 Brazil

ISBN: (纸本)9781728115603

This paper presents the realization and the experimental tests of a new type of reluctance-based magnetic lead screw (RBMLS) that is attractive for many potential applications. The proposed topology presents structure simplification with the implementation of discretized permanent magnets (PM) and ferromagnetic semi-rings. The proposed RBMLS has PMs only in one of its movable parts, making it suitable for long-stroke applications. The dynamic experimental results for the realized prototype are going to be compared to the 3-D finite-element method (FEM) outcomes. Experimental static results have already been obtained and show good agreement. © 2019 IEEE.

关键词： Screws

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Two-phase open-end winding induction motor drive using improved current source inverter 9

Two-phase open-end winding induction motor drive using impro...

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9th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2017

作者： De Costa, Louelson A.L.A.C. Vitorino, Montiê A. Braga-Filho, Edgar R. Corrêa, Maurício B.R. Fernandes, Darlan A. Post-Graduate Program in Electrical Engineering - PPgEE - COPELE Brazil Campina Grande - PB Brazil Joao Pessoa - PB Brazil

ISBN: (数字)9781509029983

ISBN: (纸本)9781509029983

In this work it is presented a two-phase open-end winding induction motor (IM) driven by an improved current source inverter (CSI). The proposed inverter has independent full quadrant operation, lower DC-link current value, high magnetics power density, 25% more efficient and high AC output voltage waveform quality compared to conventional CSI solutions. A back-to-back AC-DC-AC current source converter is used to drive the two-phase open-end winding IM, where in the AC-DC portion it is used a conventional three-phase three-leg rectifier, and the proposed two-phase inverter is used in the DC-AC portion. The two-phase IM model is also presented. Two motor drive methods for the improved CSI are used: Volts/Hertz and modified q-axis indirect flux oriented control. Simulation and experimental results are provided in order to validate the theoretical approach. © 2017 IEEE.

关键词： Induction motors

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