A comparison of performance indexes in DC-DC converters under different stabilizing state-dependent switching laws

This paper deals with the problem of establishing stabilizing state-dependent switching laws in DC-DC converters operating at continuous conduction mode (CCM) and comparing their performance indexes. Firstly, the nature of the problem is defined, that is, the study of switched affine systems, which may not share a common equilibrium point. The concept of stability is, therefore, broadened. Then, the central theorem is proposed, from which a family of switching laws can be derived, namely the minimum law and the hold state law. Some of these are proved to stabilize the basic DC-DC converters and then, their performances are compared to another law, from a previous work, by simulation, where a great reduction in overshoot is obtained. © 2011 IEEE.

A malware detection system inspired on the human immune system

Malicious programs (malware) can cause severe damage on computer systems and data. The mechanism that the human immune system uses to detect and protect from organisms that threaten the human body is efficient and can be adapted to detect malware attacks. In this paper we propose a system to perform malware distributed collection, analysis and detection, this last inspired by the human immune system. After collecting malware samples from Internet, they are dynamically analyzed so as to provide execution traces at the operating system level and network flows that are used to create a behavioral model and to generate a detection signature. Those signatures serve as input to a malware detector, acting as the antibodies in the antigen detection process. This allows us to understand the malware attack and aids in the infection removal procedures. © 2012 Springer-Verlag.

Monitoramento de hosts em redes tcp/ip sobre base georreferenciada com métricas de qualidade

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Sistema de coleta, análise e detecção de código malicioso baseado no sistema imunológico humano

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Usabilidade de software: estudo de recomendações básicas para verificação do nível de conhecimento dos alunos dos cursos de Design Gráfico e Sistemas de Informação da UNESP/Bauru

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Camada de gerenciamento para comunicação entre computadores baseada em redes sem fio (WSE-OS)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Detecção de intrusão em redes de computadores utilizando Floresta de Caminhos Ótimos

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Análise de riscos: um modelo de assinatura de risco voltada para segurança da informação

Pós-graduação em Ciência da Computação - IBILCE

Implementação em FPGA de um sistema para processamento de imagens digitais para aplicações diversificadas

Pós-graduação em Ciência da Computação - IBILCE

Ambiente data cleaning: suporte extensível, semântico e automático para análise e transformação de dados

Pós-graduação em Ciência da Computação - IBILCE

Arquitetura para publicação e universalização do acesso a transdutores inteligentes

Pós-graduação em Engenharia Elétrica - FEIS

Redes neurais aplicadas em processos de usinagem da madeira

Pós-graduação em Engenharia Mecânica - FEG

Uma arquitetura multiagente para sistemas de supervisão e controle de processos

Pós-graduação em Ciência da Computação - IBILCE

Modelos de recuperação de informação e web semântica: a questão da relevância

Introduction: In the Web environment, there is a need for greater care with regard to the processing of descriptive and thematic information. The concern with the recovery of information in computer systems precedes the development of the first personal computers. Models of information retrieval have been and are today widely used in databases specific to a field whose scope is known. Objectives: Verify how the issue of relevance is treated in the main computer models of information retrieval and, especially, as the issue is addressed in the future of the Web, the called Semantic Web. Methodology: Bibliographical research. Results: In the classical models studied here, it was realized that the main concern is retrieving documents whose description is closest to the search expression used by the user, which does not necessarily imply that this really needs. In semantic retrieval is the use of ontologies, feature that extends the user's search for a wider range of possible relevant options. Conclusions: The relevance is a subjective judgment and inherent to the user, it will depend on the interaction with the system and especially the fact that he expects to recover in your search. Systems that are based on a model of relevance are not popular, because it requires greater interaction and depend on the user's disposal. The Semantic Web is so far the initiative more efficient in the case of information retrieval in the digital environment.

Image Re-Ranking Acceleration on GPUs

Huge image collections are becoming available lately. In this scenario, the use of Content-Based Image Retrieval (CBIR) systems has emerged as a promising approach to support image searches. The objective of CBIR systems is to retrieve the most similar images in a collection, given a query image, by taking into account image visual properties such as texture, color, and shape. In these systems, the effectiveness of the retrieval process depends heavily on the accuracy of ranking approaches. Recently, re-ranking approaches have been proposed to improve the effectiveness of CBIR systems by taking into account the relationships among images. The re-ranking approaches consider the relationships among all images in a given dataset. These approaches typically demands a huge amount of computational power, which hampers its use in practical situations. On the other hand, these methods can be massively parallelized. In this paper, we propose to speedup the computation of the RL-Sim algorithm, a recently proposed image re-ranking approach, by using the computational power of Graphics Processing Units (GPU). GPUs are emerging as relatively inexpensive parallel processors that are becoming available on a wide range of computer systems. We address the image re-ranking performance challenges by proposing a parallel solution designed to fit the computational model of GPUs. We conducted an experimental evaluation considering different implementations and devices. Experimental results demonstrate that significant performance gains can be obtained. Our approach achieves speedups of 7x from serial implementation considering the overall algorithm and up to 36x on its core steps.