Time filter

Source Type

Goncalves A.P.C.,University of Campinas | Fioravanti A.R.,French National Institute for Research in Computer Science and Control | Geromel J.C.,University of Campinas
Signal Processing | Year: 2010

This paper addresses how several models available for a measurement transmission network channel, like the generalized Gilbert-Elliot, Fritchman or McCullough ones, from a linear and time invariant plant to its filter can be incorporated into the more general framework of Markov jump linear systems filtering. Combined with transmission protocols that allow package failure recognition, the particular concept of cluster availability of the Markov modes becomes appealing. Numerical examples are explored to show applicability of the results. The design of the filter is done using linear matrix inequalities (LMIs). © 2010 Elsevier B.V. All rights reserved. Source

Gonaalves A.P.C.,University of Campinas | Fioravanti A.R.,French National Institute for Research in Computer Science and Control | Geromel J.C.,University of Campinas
International Journal of Robust and Nonlinear Control | Year: 2011

This article addresses the filtering design problem for discrete-time Markov jump linear systems (MJLS) under the assumption that the transition probabilities are not completely known. We present the methods to determine ℋ2- and ℋ∞-norm bounded filters for MJLS whose transition probability matrices have uncertainties in a convex polytope and establish an equivalence with the ones with partly unknown elements. The proposed design, based on linear matrix inequalities, allows different assumptions on Markov mode availability to the filter and on system parameter uncertainties to be taken into account. Under mode-dependent assumption and internal model knowledge, observer-based filters can be obtained and it is shown theoretically that our method outperforms some available ones in the literature to date. Numerical examples illustrate this claim. © 2010 John Wiley & Sons, Ltd. Copyright © 2010 John Wiley & Sons, Ltd. Source

Silveira G.,Center for Information Technology Renato Archer | Malis E.,French National Institute for Research in Computer Science and Control
IEEE Transactions on Robotics | Year: 2012

This paper addresses the problem of stabilizing a robot at a pose specified via a reference image. Specifically, this paper focuses on six degrees-of-freedom visual servoing techniques that require neither metric information of the observed object nor precise camera and/or robot calibration parameters. Not requiring them improves the flexibility and robustness of servoing tasks. However, existing techniques within the focused class need prior knowledge of the object shape and/or of the camera motion. We present a new visual servoing technique that requires none of the aforementioned information. The proposed technique directly exploits 1) the projective parameters that relate the current image with the reference one and 2) the pixel intensities to obtain these parameters. The level of versatility and accuracy of servoing tasks are, thus, further improved. We also show that the proposed nonmetric scheme allows for path planning. In this way, the domain of convergence is greatly enlarged as well. Theoretical proofs and experimental results demonstrate that visual servoing can, indeed, be highly accurate and robust, despite unknown objects and imaging conditions. This naturally encompasses the cases of color images and illumination changes. © 2004-2012 IEEE. Source

Csepinszky A.,Ertico ITS Europe | Giustiniani G.,IT Ingegneria dei Trasporti | Holguin C.,University of Rome La Sapienza | Parent M.,French National Institute for Research in Computer Science and Control | And 2 more authors.
Transportation Research Record | Year: 2015

Automated road transport systems (ARTS) are based on the use of fully automated road vehicles controlled by a centralized system for fleet and infrastructure management. ARTS are aimed (at least at the beginning) at supplementing mass transit in the last mile and are commercially available today. However, their deployment is limited at the moment to protected or special roads. In urban areas, where these systems can be most beneficial, they cannot be implemented because of the absence of an adapted legal framework. The CityMobil2 project, financed by the European Commission, aims to remove the legal barriers that prevent the deployment of ARTS in urban areas by developing a specific legal framework. Previous experience based on risk assessment and failure mode, effects, and criticality analysis has demonstrated acceptability to national authorities. On this basis, the CityMobil2 project has started developing a methodology for the certification of full ARTS, aimed at guaranteeing an adequate level of safety. The certification framework has been developed at the theoretical level, but during the next phases of the CityMobil2 project, it will be tested in real-world conditions during the ARTS demonstrations that will be organized in several cities of Europe to make this methodology a major reference for a future legal framework. Source

Yao A.,Intel Corporation | Yu S.,French National Institute for Research in Computer Science and Control
IEEE Transactions on Image Processing | Year: 2013

A key issue in face recognition is to seek an effective descriptor for representing face appearance. In the context of considering the face image as a set of small facial regions, this paper presents a new face representation approach coined spatial feature interdependence matrix (SFIM). Unlike classical face descriptors which usually use a hierarchically organized or a sequentially concatenated structure to describe the spatial layout features extracted from local regions, SFIM is attributed to the exploitation of the underlying feature interdependences regarding local region pairs inside a class specific face. According to SFIM, the face image is projected onto an undirected connected graph in a manner that explicitly encodes feature interdependence-based relationships between local regions. We calculate the pair-wise interdependence strength as the weighted discrepancy between two feature sets extracted in a hybrid feature space fusing histograms of intensity, local binary pattern and oriented gradients. To achieve the goal of face recognition, our SFIM-based face descriptor is embedded in three different recognition frameworks, namely nearest neighbor search, subspace-based classification, and linear optimization-based classification. Extensive experimental results on four well-known face databases and comprehensive comparisons with the state-of-the-art results are provided to demonstrate the efficacy of the proposed SFIM-based descriptor. © 1992-2012 IEEE. Source

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