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G. Lefranc

Pontificia Universidad Catolica de Valparaiso, Escuela de Ingeniería Eléctrica, Department Member

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Kansas State University

Rice University

Mehmet Akif KILIC

Sağlık Bilimleri Üniversitesi

Universidade de São Paulo

Universidad Central de Venezuela

shivani R pandya

Parul university

Balasubramanian Thiagarajan

Dr MGR Educational and Research Institute University

Mikdat Kadıoğlu

Istanbul Technical University

Eskisehir Osmangazi University, Turkey

University of Southern California

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Papers by G. Lefranc

Subclass distribution of antigen-specific IgA antibodies in normal donors and individuals with homozygous C alpha 1 or C alpha 2 gene deletions

The Journal of Immunology

To analyze the subclass restriction of Ag-specific IgA, sera and saliva from healthy blood donors... more To analyze the subclass restriction of Ag-specific IgA, sera and saliva from healthy blood donors and from IgA class or subclass deficient individuals were studied. The latter included donors with or without C alpha 1 or C alpha 2 gene deletions. Monoclonal human IgA1 and a genetically engineered IgA2 antibody, normal human serum and colostrum IgA were used as standards to estimate serum and saliva levels of Ag-specific antibodies. In normal individuals, there was a strong IgA1 preference of naturally acquired antibodies in serum against both polysaccharide Ag (PPS 6A, PPS 23, pneumococcal C-polysaccharide, and LPS from Escherichia coli) and protein Ag (Staphylococcus aureus alpha-toxin and HSV). Specific IgA2 in serum against the tested Ag were frequently not measurable. In contrast, most of the individuals with homozygous C alpha 1 gene deletions displayed substantial amounts of specific IgA2 against protein as well as polysaccharide Ag. The median levels of specific IgA in serum ...

Comparison of two neural network methods applied to a tracking control system

Proceedings of 1994 IEEE International Symposium on Industrial Electronics (ISIE'94)

COMPARISON OF TWO NEURAL NETWORK METHODS APPLIED TO A TRACKING CONTROL SYSTEM ... Cliston Lcfr;ir... more

A new colour image segmentation

2016 6th International Conference on Computers Communications and Control (ICCCC), 2016

In this paper an unsupervised colour image segmentation algorithm is presented. This method combi... more In this paper an unsupervised colour image segmentation algorithm is presented. This method combines the advantages of the approaches based on split&merge and region growing, and the use of the RGB and HSV colour representation models. The effectiveness of the proposed method has been verified by the implementation of the algorithm using three different testing images with homogeneous regions, spatially compact and continuous. It was observed that the proposed algorithm outperforms the other analysed techniques requiring shorter processing time when compared with the other analysed methods.

Mobile Robot Navigation Assisted by GPS

IEEE Latin America Transactions, 2015

El Filtro De Bosso y Aplicaciones a Imágenes Digitales

PHAROS, 1998

EL FILTRO DE BOSSO ... ♦ 27 EL FILTRO DE BOSSO Y APLICACIONES A IMÁGENES DIGITALES* The Bosso Fil... more

Mobile robot navigation using potential fields and LMA

2016 IEEE International Conference on Automatica (ICA-ACCA), 2016

A common objective in mobile robotics is to find an optimal trajectory to reach a final destinati... more A common objective in mobile robotics is to find an optimal trajectory to reach a final destination from a starting point location. Obstacles are likely to be encountered in the robot's trajectory that need to be avoided. In this article, a path planning approach for mobile robots that uses the algorithms of potential field in conjunction with Local Minimal Avoidance (LMA) is presented in order to avoid local minima and to achieve an optimal robot's navigation.

Object recognition using App Android

2015 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), 2015

In this article, the implementation of a machine vision algorithm into a mobile navigation robot ... more In this article, the implementation of a machine vision algorithm into a mobile navigation robot for object recognition is presented. By using its color and machine vision algorithms, such as Canny and the Hough transform as a complement in order, improves the recognition of the selected object. These algorithms is implemented in a device with Android Operating System as an application (Apps).

Movement and Color Detection of a Dynamic Object: An application to a Mobile Robot

Studies in Informatics and Control, 2012

The paper describes the integration of several image processing algorithms necessary to recognize... more The paper describes the integration of several image processing algorithms necessary to recognize a particular color and the movement of an object. The main objective is to detect the object by its color and track it by a mobile robot. Mean filter is applied to soften and sharpen the input image. Then, RGB filter is applied to calculate the center of mass and area of the object and to locate its position in a real environment to develop the robot motion. These algorithms are applied to a mobile robot, in a tested scenario, tracking an object.

A Simple Model for the Generation of LRD Self-similar Traffic Using Piecewise Affine Chaotic One-dimensional Maps

Studies in Informatics and Control, 2010

A qualitative and quantitative extension of the chaotic models used to generate self-similar traf... more A qualitative and quantitative extension of the chaotic models used to generate self-similar traffic with long range dependence LRD is presented by means of the formulation of a model that considers the use of piecewise affine one-dimensional maps. Based on the disaggregation of the temporal series generated, a valid explanation of the behavior of the values of Hurst's exponents is proposed and the feasibility of their control from the parameters of the proposed model is shown.

Time series analysis of computer network traffic in a dedicated link aggregation

Fractal behavior and long-range dependence are widely observed in measurements and characterizati... more Fractal behavior and long-range dependence are widely observed in measurements and characterization of traffic flow in high-speed computer networks of different technologies and coverage levels. This paper presents the results obtained when applying fractal analysis techniques on a time series obtained from traffic captures coming from an application server connected to the Internet through a high-speed link. The results obtained show that traffic flow in the dedicated high-speed network link have fractal behavior when the Hurst exponent is in the range of 0.5, 1, the fractal dimension between 1, 1.5, and the correlation coefficient between –0.5, 0. Based on these results, it is ideal to characterize both the singularities of the traffic and its impulsiveness during a fractal analysis of temporal scales. Finally, based on the results of the time series analyses, the fact that the traffic flows of current computer networks exhibit fractal behavior with a long-range dependency is reaf...

Presentation of an Estimator for the Hurst Parameter for a Self-Similar Process Representing the Traffic in IEEE 802.3 Networks

International Journal of Computers Communications & Control, 2009

The hypothesis for the existence of a process with long term memory structure, that represents th... more The hypothesis for the existence of a process with long term memory structure, that represents the independence between the degree of randomness of the traffic generated by the sources and the pattern of traffic stream exhibited by the network is presented, discussed and developed. This methodology is offered as a new and alternative way of approaching the estimation of performance and the design of computer networks ruled by the standard IEEE 802.3-2005.

$Research paper thumbnail of A Fast Multifractal Model for Self-Similar Traffic Flows in High-Speed Computer Networks$

A Fast Multifractal Model for Self-Similar Traffic Flows in High-Speed Computer Networks

Procedia Computer Science, 2013

This paper proposes a new multifractal model, with the aim of providing a possible explanation fo... more This paper proposes a new multifractal model, with the aim of providing a possible explanation for the locality phenomenon that appears in the estimation of the Hurst exponent in stationary second order temporal series representing self-similar traffic flows in current high-speed computer networks. It is shown analytically that this phenomenon occurs if the network flow consists of several components with different Hurst exponents.

A Novel Ann Training Approach for Supervised and Constructive Learning Applied to Fault Classification & Short-Circuit Zoning in Rural Primary Distribution Systems

In this paper, artificial neural networks (ANN) are proposed to classify and establish zoning fau... more In this paper, artificial neural networks (ANN) are proposed to classify and establish zoning faults (single-phase, bi-phase and tri-phase short circuits) in primary distribution systems. The proposed classification and zoning models are oriented toward the stochastic tracking of failures via emergency brigades. This work, instead of training a big ANN model, proposes to decompose the problem into two smaller ones in order to take advantage of the available previous knowledge and improved training. The classification model is based on an ANN with supervised learning (SL). The zoning representation is based on a simple binary perceptron with constructive learning (CL). In this article, the theoretical elements underpinning the proposal are detailed and justified. The performance of the proposed models is verified through various real primary network applications.

Preliminaries on the accurate estimation of the Hurst exponent using time series (Draft 1)

This article explores the required amount of time series points from a high-speed computer networ... more This article explores the required amount of time series points from a high-speed computer network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series addresses resulting from the capture of high-speed network traffic, followed by addressing the minimum amount of point required to obtain in accurate estimates of the Hurst exponent. The methodology addresses the exhaustive analysis of the Hurst exponent considering bias behaviour, standard deviation, and Mean Squared Error using fractional Gaussian noise signals with stationary increases.

SVM and ANN Application to Multivariate Pattern Recognition Using Scatter Data

IEEE Latin America Transactions, 2015

ABSTRACT several methods of Statistical Process Control (SPC) are used to analyze process measure... more ABSTRACT several methods of Statistical Process Control (SPC) are used to analyze process measurements with the purpose to detect faults that affect the process stability. SPC has a major drawback because it indicates the presence of faults without explaining which ones and where are the faults. In practical applications, SPC just analyses univariate signals limiting the study of multiple measures. Nowadays, novel methods have been developed for fault analysis based on pattern recognition in control charts. However, the majority of these studies follow a univariate approach. This article proposes a multivariate pattern recognition approach using machine learning algorithms in conjunction with a scatter diagram as the proposed method. In particular the aim of this approach is to monitor quality characteristics of a product in a multivariate environment considering states in control and out of control without the constraints of statistical conditions with the possibility of its application in real time.Results using Support Vector Machines (SVM) and the FuzzyARTMAP neural network showed that multivariate patterns can be recognized successfully in 81% of the cases.

Integration of Algorithms for Maps Construction and Simultaneous Localization in a Mobile Robot

by [email protected] Passold and G. Lefranc

6th Int. Conf. on Management and Control of Production and Logistics (MCPL'2013)

In this paper it is presented an integration of algorithms that permits maps construction and nav... more In this paper it is presented an integration of algorithms that permits maps construction and navigation of mobile robots. Simultaneous Localization and Mapping (SLAM) algorithm is used based on FastSLAM method. Navigation system is based on VHF to avoid obstacle and a spiral way trajectory method. To three different complex simulation maps are used to evaluate the system.

Ontology Model of a Robotics Agents Community

by Renato Salinas, Karina Harispe, and G. Lefranc

International Journal of …, 2011

This paper presents an Ontology Model of necessities and decisions for a cooperative community of... more This paper presents an Ontology Model of necessities and decisions for a cooperative community of heterogeneous robotic agents. Based on an ant community, it defines the characteristics of a collaborative and cooperative community of robots, using multi agent theory where each robot shares information with others robots in the community, to accomplish a common objective.

Subclass distribution of antigen-specific IgA antibodies in normal donors and individuals with homozygous C alpha 1 or C alpha 2 gene deletions

The Journal of Immunology

To analyze the subclass restriction of Ag-specific IgA, sera and saliva from healthy blood donors... more To analyze the subclass restriction of Ag-specific IgA, sera and saliva from healthy blood donors and from IgA class or subclass deficient individuals were studied. The latter included donors with or without C alpha 1 or C alpha 2 gene deletions. Monoclonal human IgA1 and a genetically engineered IgA2 antibody, normal human serum and colostrum IgA were used as standards to estimate serum and saliva levels of Ag-specific antibodies. In normal individuals, there was a strong IgA1 preference of naturally acquired antibodies in serum against both polysaccharide Ag (PPS 6A, PPS 23, pneumococcal C-polysaccharide, and LPS from Escherichia coli) and protein Ag (Staphylococcus aureus alpha-toxin and HSV). Specific IgA2 in serum against the tested Ag were frequently not measurable. In contrast, most of the individuals with homozygous C alpha 1 gene deletions displayed substantial amounts of specific IgA2 against protein as well as polysaccharide Ag. The median levels of specific IgA in serum ...

Comparison of two neural network methods applied to a tracking control system

Proceedings of 1994 IEEE International Symposium on Industrial Electronics (ISIE'94)

COMPARISON OF TWO NEURAL NETWORK METHODS APPLIED TO A TRACKING CONTROL SYSTEM ... Cliston Lcfr;ir... more

A new colour image segmentation

2016 6th International Conference on Computers Communications and Control (ICCCC), 2016

In this paper an unsupervised colour image segmentation algorithm is presented. This method combi... more In this paper an unsupervised colour image segmentation algorithm is presented. This method combines the advantages of the approaches based on split&merge and region growing, and the use of the RGB and HSV colour representation models. The effectiveness of the proposed method has been verified by the implementation of the algorithm using three different testing images with homogeneous regions, spatially compact and continuous. It was observed that the proposed algorithm outperforms the other analysed techniques requiring shorter processing time when compared with the other analysed methods.

Mobile Robot Navigation Assisted by GPS

IEEE Latin America Transactions, 2015

El Filtro De Bosso y Aplicaciones a Imágenes Digitales

PHAROS, 1998

EL FILTRO DE BOSSO ... ♦ 27 EL FILTRO DE BOSSO Y APLICACIONES A IMÁGENES DIGITALES* The Bosso Fil... more

Mobile robot navigation using potential fields and LMA

2016 IEEE International Conference on Automatica (ICA-ACCA), 2016

A common objective in mobile robotics is to find an optimal trajectory to reach a final destinati... more A common objective in mobile robotics is to find an optimal trajectory to reach a final destination from a starting point location. Obstacles are likely to be encountered in the robot's trajectory that need to be avoided. In this article, a path planning approach for mobile robots that uses the algorithms of potential field in conjunction with Local Minimal Avoidance (LMA) is presented in order to avoid local minima and to achieve an optimal robot's navigation.

Object recognition using App Android

2015 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), 2015

In this article, the implementation of a machine vision algorithm into a mobile navigation robot ... more In this article, the implementation of a machine vision algorithm into a mobile navigation robot for object recognition is presented. By using its color and machine vision algorithms, such as Canny and the Hough transform as a complement in order, improves the recognition of the selected object. These algorithms is implemented in a device with Android Operating System as an application (Apps).

Movement and Color Detection of a Dynamic Object: An application to a Mobile Robot

Studies in Informatics and Control, 2012

The paper describes the integration of several image processing algorithms necessary to recognize... more The paper describes the integration of several image processing algorithms necessary to recognize a particular color and the movement of an object. The main objective is to detect the object by its color and track it by a mobile robot. Mean filter is applied to soften and sharpen the input image. Then, RGB filter is applied to calculate the center of mass and area of the object and to locate its position in a real environment to develop the robot motion. These algorithms are applied to a mobile robot, in a tested scenario, tracking an object.

A Simple Model for the Generation of LRD Self-similar Traffic Using Piecewise Affine Chaotic One-dimensional Maps

Studies in Informatics and Control, 2010

A qualitative and quantitative extension of the chaotic models used to generate self-similar traf... more A qualitative and quantitative extension of the chaotic models used to generate self-similar traffic with long range dependence LRD is presented by means of the formulation of a model that considers the use of piecewise affine one-dimensional maps. Based on the disaggregation of the temporal series generated, a valid explanation of the behavior of the values of Hurst's exponents is proposed and the feasibility of their control from the parameters of the proposed model is shown.

Time series analysis of computer network traffic in a dedicated link aggregation

Fractal behavior and long-range dependence are widely observed in measurements and characterizati... more Fractal behavior and long-range dependence are widely observed in measurements and characterization of traffic flow in high-speed computer networks of different technologies and coverage levels. This paper presents the results obtained when applying fractal analysis techniques on a time series obtained from traffic captures coming from an application server connected to the Internet through a high-speed link. The results obtained show that traffic flow in the dedicated high-speed network link have fractal behavior when the Hurst exponent is in the range of 0.5, 1, the fractal dimension between 1, 1.5, and the correlation coefficient between –0.5, 0. Based on these results, it is ideal to characterize both the singularities of the traffic and its impulsiveness during a fractal analysis of temporal scales. Finally, based on the results of the time series analyses, the fact that the traffic flows of current computer networks exhibit fractal behavior with a long-range dependency is reaf...

Presentation of an Estimator for the Hurst Parameter for a Self-Similar Process Representing the Traffic in IEEE 802.3 Networks

International Journal of Computers Communications & Control, 2009

The hypothesis for the existence of a process with long term memory structure, that represents th... more The hypothesis for the existence of a process with long term memory structure, that represents the independence between the degree of randomness of the traffic generated by the sources and the pattern of traffic stream exhibited by the network is presented, discussed and developed. This methodology is offered as a new and alternative way of approaching the estimation of performance and the design of computer networks ruled by the standard IEEE 802.3-2005.

$Research paper thumbnail of A Fast Multifractal Model for Self-Similar Traffic Flows in High-Speed Computer Networks$

A Fast Multifractal Model for Self-Similar Traffic Flows in High-Speed Computer Networks

Procedia Computer Science, 2013

This paper proposes a new multifractal model, with the aim of providing a possible explanation fo... more This paper proposes a new multifractal model, with the aim of providing a possible explanation for the locality phenomenon that appears in the estimation of the Hurst exponent in stationary second order temporal series representing self-similar traffic flows in current high-speed computer networks. It is shown analytically that this phenomenon occurs if the network flow consists of several components with different Hurst exponents.

A Novel Ann Training Approach for Supervised and Constructive Learning Applied to Fault Classification & Short-Circuit Zoning in Rural Primary Distribution Systems

In this paper, artificial neural networks (ANN) are proposed to classify and establish zoning fau... more In this paper, artificial neural networks (ANN) are proposed to classify and establish zoning faults (single-phase, bi-phase and tri-phase short circuits) in primary distribution systems. The proposed classification and zoning models are oriented toward the stochastic tracking of failures via emergency brigades. This work, instead of training a big ANN model, proposes to decompose the problem into two smaller ones in order to take advantage of the available previous knowledge and improved training. The classification model is based on an ANN with supervised learning (SL). The zoning representation is based on a simple binary perceptron with constructive learning (CL). In this article, the theoretical elements underpinning the proposal are detailed and justified. The performance of the proposed models is verified through various real primary network applications.

Preliminaries on the accurate estimation of the Hurst exponent using time series (Draft 1)

This article explores the required amount of time series points from a high-speed computer networ... more This article explores the required amount of time series points from a high-speed computer network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series addresses resulting from the capture of high-speed network traffic, followed by addressing the minimum amount of point required to obtain in accurate estimates of the Hurst exponent. The methodology addresses the exhaustive analysis of the Hurst exponent considering bias behaviour, standard deviation, and Mean Squared Error using fractional Gaussian noise signals with stationary increases.

SVM and ANN Application to Multivariate Pattern Recognition Using Scatter Data

IEEE Latin America Transactions, 2015

ABSTRACT several methods of Statistical Process Control (SPC) are used to analyze process measure... more ABSTRACT several methods of Statistical Process Control (SPC) are used to analyze process measurements with the purpose to detect faults that affect the process stability. SPC has a major drawback because it indicates the presence of faults without explaining which ones and where are the faults. In practical applications, SPC just analyses univariate signals limiting the study of multiple measures. Nowadays, novel methods have been developed for fault analysis based on pattern recognition in control charts. However, the majority of these studies follow a univariate approach. This article proposes a multivariate pattern recognition approach using machine learning algorithms in conjunction with a scatter diagram as the proposed method. In particular the aim of this approach is to monitor quality characteristics of a product in a multivariate environment considering states in control and out of control without the constraints of statistical conditions with the possibility of its application in real time.Results using Support Vector Machines (SVM) and the FuzzyARTMAP neural network showed that multivariate patterns can be recognized successfully in 81% of the cases.

Integration of Algorithms for Maps Construction and Simultaneous Localization in a Mobile Robot

by [email protected] Passold and G. Lefranc

6th Int. Conf. on Management and Control of Production and Logistics (MCPL'2013)

In this paper it is presented an integration of algorithms that permits maps construction and nav... more In this paper it is presented an integration of algorithms that permits maps construction and navigation of mobile robots. Simultaneous Localization and Mapping (SLAM) algorithm is used based on FastSLAM method. Navigation system is based on VHF to avoid obstacle and a spiral way trajectory method. To three different complex simulation maps are used to evaluate the system.

Ontology Model of a Robotics Agents Community

by Renato Salinas, Karina Harispe, and G. Lefranc

International Journal of …, 2011

This paper presents an Ontology Model of necessities and decisions for a cooperative community of... more This paper presents an Ontology Model of necessities and decisions for a cooperative community of heterogeneous robotic agents. Based on an ant community, it defines the characteristics of a collaborative and cooperative community of robots, using multi agent theory where each robot shares information with others robots in the community, to accomplish a common objective.