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International audience; This paper introduces a model of environmental acoustic scenes which adopts a morphological approach by ab-stracting temporal structures of acoustic scenes. To demonstrate its potential, this model is employed to evaluate the performance of a large set of acoustic events detection systems. This model allows us to explicitly control key morphological aspects of the acoustic scene and isolate their impact on the performance of the system under evaluation. Thus, more information can be gained on the behavior of evaluated systems, providing guidance for further improvements. The proposed model is validated using submitted systems from the IEEE DCASE Challenge; results indicate that the proposed scheme is able to successfully build datasets useful for evaluating some aspects the performance of event detection systems, more particularly their robustness to new listening conditions and the increasing level of background sounds.

Organizations undertake more and more software modernization projects every day, mostly owing to rapid changes in the technological landscape pushing them to evolve their systems before those systems become obsolete. Such projects are sometimes taken too lightly and start more because of passing fads than because they're motivated by real technological limitations or system problems. In our experience, many managers have only a partial view of these projects' complexity and consequences.

International audience; This study investigated human-machine cooperation when driving with different degrees of a shared control system. By means of a direct intervention on the steering wheel, shared control systems partially correct the vehicle's trajectory and, at the same time, provide continuous haptic guidance to the driver. A crucial point is to determine the optimal level of steering assistance for effective cooperation between the two agents. Five system settings were compared with a condition in which no assistance was present. In addition, road visibility was manipulated by means of additional fog or self-controlled visual occlusions. Several performance indicators and subjective assessments were analyzed. The results show that the best repartition of control in terms of cooperation between human and machine can be identified through an analysis of the steering wheel reversal rate, the steering effort and the mean lateral position of the vehicle. The best cooperation was achieved with systems of relatively low-level haptic authority, although more intervention may be preferable in poor visibility conditions. Increasing haptic authority did not yield higher benefits in terms of steering behavior, visual demand or subjective feeling.

International audience; In this paper, we consider the setting of large scale distributed systems, in which each node needs to quickly process a huge amount of data received in the form of a stream that may have been tampered with by an adversary. In this situation, a fundamental problem is how to detect and quantify the amount of work performed by the adversary. To address this issue, we propose a novel algorithm AnKLe for estimating the Kullback-Leibler divergence of an observed stream compared with the expected one. AnKLe combines sampling techniques and information-theoretic methods. It is very efficient, both in terms of space and time complexities, and requires only a single pass over the data stream. We show that AnKLe is an (ε, δ)-approximation algorithm with a space complexity Õ(1/ε + 1/ε^2) bits in "most" cases, and Õ(1/ε + (n−ε−1)/ε^2) otherwise, where n is the number of distinct data items in a stream. Moreover, we propose a distributed version of AnKLe that requires at most O (rl (log n + 1)) bits of communication between the l participating nodes, where r is number of rounds of the algorithm. Experimental results show that the estimation provided by AnKLe remains accurate even for different adversarial settings for which the quality of other methods dramatically decreases.

International audience; Hyperspectral data unmixing aims at identifying the components (endmembers) of an observed surface and at determining their fractional abundances inside each pixel area. Assuming that the spectral signatures of the surface components have been previously determined by an endmember extraction algorithm, or to be part of an available spectral library, the main problem is reduced to the estimation of the fractional abundances. For large hyperspectral image data sets, the estimation of the abundance maps requires the resolution of a large-scale optimization problem subject to linear constraints such as non-negativity and sum less or equal to one. This paper proposes a primal-dual interior-point optimization algorithm allowing a constrained least squares estimation approach. In comparison with existing methods, the proposed algorithm is more flexible since it can handle any linear equality and/or inequality constraint and has the advantage of a reduced computational cost. It also presents an algorithmic structure suitable for a parallel implementation on modern intensive computing devices such as Graphics Processing Units (GPU). The implementation issues are discussed and the applicability of the proposed approach is illustrated with the help of examples on synthetic and real hyperspectral data.

International audience; This paper shows how a sensor inspired by an electric fish could be used to help navigate in confined environments. Exploiting the morphology of the sensor, the physics of electric interactions, as well as taking inspiration from passive electrolocation in real fish, a set of reactive control laws encoding simple behaviors, such as avoiding any electrically contrasted object, or seeking a set of objects while avoiding others according to their electric properties, is proposed. These reflex behaviors are illustrated on simulations and experiments carried out on a setup dedicated to the study of electric sense. The approach does not require a model of the environment and is quite cheap to implement.

International audience; This paper presents an advanced driver assistance system (ADAS) for lane keeping, together with an analysis of its performance and stability with respect to variations in driver behavior. The automotive ADAS proposed is designed so as to share control of the steering wheel with the driver in the best possible way. Its development was derived from a H2-preview optimization control problem, which is based on the global driver-vehicle-road (DVR) system. The DVR model makes use of a cybernetic driver model so as to take into account any driver-vehicle interactions. Such a formulation allows to: i) consider driver-assistance cooperation criteria in the control synthesis, ii) improve the performance of the assistance as a cooperative copilot, and iii) analyze the stability of the whole system in the presence of driver model uncertainty. The results have been validated experimentally with one participant using a fixed-base driving simulator. The developed assistance system improved lane-keeping performance and reduced the risk of a lane departure accident. Good results were obtained using several criteria for human-machine cooperation. Poor stability situations were successfully avoided thanks to the robustness of the whole system in spite of a large range of driver model uncertainty.

International audience; Fixation density maps (FDM) created from eye tracking experiments are widely used in image processing applications. The FDM are assumed to be reliable ground truths of human visual attention and as such one expects high similarity between FDM created in different laboratories. So far, no studies have analysed the degree of similarity between FDM from independent laboratories and the related impact on the applications. In this paper, we perform a thorough comparison of FDM from three independently conducted eye tracking experiments. We focus on the effect of presentation time and image content and evaluate the impact of the FDM differences on three applications: visual saliency modelling, image quality assessment, and image retargeting. It is shown that the FDM are very similar and that their impact on the applications is low. The individual experiment comparisons, however, are found to be significantly different, showing that inter-laboratory differences strongly depend on the experimental conditions of the laboratories. The FDM are publicly available to the research community.

International audience; Three-dimensional video content has attracted much attention in both the cinema and television industries, because 3D is considered to be the next key feature that can significantly enhance the visual experience of viewers. However, one of the major challenges is the difficulty in providing high quality images that are comfortable to view and that also meet signal transmission requirements over a limited bandwidth for display on television screens. The different processing steps that are necessary in a 3D-TV delivery chain can all introduce artifacts that may create problems in terms of human visual perception. In this paper, we highlight the importance of considering 3D visual attention when addressing 3D human factors issues. We provide a review of the field of 3D visual attention, discuss the challenges in both the understanding and modeling of 3D visual attention, and provide guidance to researchers in this field. Finally, we identify perceptual issues generated during the various steps in a typical 3D-TV broadcasting delivery chain, review them and explain how consideration of 3D visual attention modeling can help improve the overall 3D viewing experience.

This paper is related to the inverse-dynamic modeling of mobile multibody systems articulated with joints and wheels. An easily-implementable algorithm, which is based on Newton-Euler (NE) recursive dynamics, is proposed. From imposed joint and/or actuated-wheel motions, the algorithm performs fast calculations of the control torques, as well as the overall rigid motions involved in locomotion tasks. The engineering applications include tree-like mobile manipulators, satellite-reorientation systems, and modular robots, such as snake-like robots, eel-like robots, and a snakeboard.

Aspect-oriented software development techniques provide a means to modularize crosscutting concerns in software systems. A survey of industrial projects reveals the benefits and potential pitfalls of aspectoriented technologies.The past decade has seen the increased use of aspect-oriented software development (AOSD) technique as a means to modularize crosscutting concerns in software systems, thereby improving a development organization's working practices and return on investment (ROI). Numerous industrial-strength aspect-oriented (AO) programming frameworks exist, including AspectJ, JBoss, and Spring, as do various aspect-oriented analysis and design techniques.The software systems using AOSD that we have studied are medium to large-scale and span a wide range of domains including enterprise systems, e-health, e-transport, telecommunications, Web based information systems, multimedia applications, and workflow systems. Our analysis highlights typical usage patterns of AO techniques-for instance, they are mainly used for modularizing well-known crosscutting problems and incrementally introduced, addressing developmental concerns and other noncore product features first.

International audience; Ontologies offer shared vocabularies that are key to agent cooperation and knowledge systems integration as well as fundamental to the Semantic Web. As the number of ontologies increases, so does the need for new tools and techniques to establish agreement between different knowledge representatuions. Six essays based on papers accepted for the First Workshop on Ontology Alignment and Visualization (OnAV 2008) exemplify some of the ways researchers are extending the state of the art in algorithms that can establish correspondences between different but related onologies.

International audience; Separation of concerns or aspects is a way to deal with the increasing complexity of systems. The separate design of models for different aspects also promotes a better reusability level. However, an important issue is then to define means to integrate them into a global model. We present a formal and tool-equipped approach for the integration of dynamic models (behaviors expressed using state diagrams) and static models (formal data types) with the benefit to share advantages of both: graphical user-friendly models for behaviors, formal and abstract models for data types. Integration is achieved in a generic way so that it can deal with both different static specification languages (algebraic specifications, Z, B) and different dynamic specification semantics

The Arachne aspect-oriented programming system developers modularize changes to networking software with little perceptible performance overhead. Writing good software is often a challenge; writing adaptable software can be even more difficult. In legacy Web caches such as Squid, such adaptation interfaces are typically needed for functionalities that are applied across the legacy code, functionalities whose code is scattered and tangled in the Web cache code's files and functions. An AOP system's join point model defines the relevant basic execution events of a given base application. Pointcuts allow programmers to refer to all such events, at which a functionality of interest is applied across, or crosscuts, the base application. At the execution events matched by pointcuts, advice can be used to modify the base application's execution. Arachne features two basic kinds of join points: C function calls and read/write accesses to global variables and their local aliases

In this paper, we propose a novel universal framework for salient object detection, which aims to enhance the performance of any existing saliency detection method. First, rough salient regions are extracted from any existing saliency detection model with distance weighting, adaptive binarization, and morphological closing. With the superpixel segmentation, a Bayesian decision model is adopted to refine the rough saliency map to obtain a more accurate saliency map. An iterative optimization method is designed to obtain better saliency results by exploiting the characteristics of the output saliency map each time. Through the iterative optimization process, the rough saliency map is updated step by step with better and better performance until an optimal saliency map is obtained. Experimental results on the public salient object detection datasets with ground truth demonstrate the promising performance of the proposed universal framework subjectively and objectively.

International audience; Previous works on parallel robots have shown that their visual servoing using the observation of their leg directions was possible. There were however found two main results for which no answer was given. These results were that (i) the observed robot which is composed of n legs could be controlled in most cases using the observation of only m leg directions (m < n), and that (ii) in some cases, the robot did not converge to the desired end-effector pose, even if the observed leg directions did (i.e. there was not a global diffeomorphism between the observation space and the robot space). Recently, it was shown that the visual servoing of the leg directions of the Gough-Stewart platform and the Adept Quattro was equivalent to controlling other virtual robots that have assembly modes and singular configurations different from those of the real ones. These hidden robot models are tangible visualizations of the mapping between the observation space and the real robots Cartesian space. Thanks to this concept, all the aforementioned points pertaining to the studied robots were answered. In this paper, the concept of the hidden robot model is generalized for any type of parallel robots controlled using visual servos based on the observation of elements other than the end-effector, such as the robot legs into motion. It is shown that the concept of the hidden robot model is a powerful tool that gives useful insights about the visual servoing of robots and that it helps define the necessary features to observe in order to ensure the controllability of the robot in its whole workspace. All theoretical concepts are validated through simulations with an Adams mockup linked to Simulink.

International audience; This brief presents a set of experimental results concerning the sliding mode control of an electropneumatic system. Two discrete-time control strategies are considered: an explicit and an implicit (that is very easy to implement with a projection on the interval [−1, 1]) Euler discretizations. While the explicit implementation is known to generate numerical chattering , the implicit one is expected to significantly reduce chattering while keeping the accuracy. The experimental results reported in this brief remarkably confirm that the implicit discrete-time sliding mode supersedes the explicit ones, with several important features: chattering in the control input is almost eliminated (while the explicit and saturated controllers behave like high-frequency bang–bang inputs), the input magnitude depends only on the perturbation size and is independent of the controller gain and sampling time.

International audience; In this paper, multifractal analysis is adapted to reduced-reference image quality assessment (RR-IQA). A novel RR-QA approach is proposed, which measures the difference of spatial arrangement between the reference image and the distorted image in terms of spatial regularity measured by fractal dimension. An image is first expressed in Log-Gabor domain. Then, fractal dimensions are computed on each Log-Gabor subband and concatenated as a feature vector. Finally, the extracted features are pooled as the quality score of the distorted image using ℓ1 distance. Compared with existing approaches, the proposed method measures image quality from the perspective of the spatial distribution of image patterns. The proposed method was evaluated on seven public benchmark data sets. Experimental results have demonstrated the excellent performance of the proposed method in comparison with state-of-the-art approaches.

International audience; Linearization by output injection has played a key role in the observer design for nonlinear control systems for almost three decades. In this paper, following some recent works, ge- ometric necessary and sufficient conditions are derived for the existence of a dynamic compensator solving the problem under regular output transformation. An algorithm which computes a compensator of minimal order is given.

12 pages; International audience; In this paper, we study a scheduling problem, in which every job is associated with a release time, deadline, required computation time and in addition required energy. We focus on an important special case where the jobs execute on a uniprocessor system that is supplied by a renewable energy source and uses a rechargeable storage unit with limited capacity. Earliest Deadline First (EDF) is a class one online algorithm in the classical real-time scheduling theory where energy constraints are not considered. We propose a semi-online EDF-based scheduling algorithm theoretically optimal (i.e. processing and energy costs neglected). This algorithm relies on the notions of energy demand and slack energy which are different from the well known notions of processor demand and slack time. We provide an exact feasibility test. There are no restrictions on this new scheduler: each job can be one instance of a periodic, aperiodic or sporadic task with deadline.

A popular clustering algorithm for biological networks which was proposed by Hartuv and Shamir identifies nonoverlapping highly connected components. We extend the approach taken by this algorithm by introducing the combinatorial optimization problem Highly Connected Deletion, which asks for removing as few edges as possible from a graph such that the resulting graph consists of highly connected components. We show that Highly Connected Deletion is NP-hard and provide a fixed-parameter algorithm and a kernelization. We propose exact and heuristic solution strategies, based on polynomial-time data reduction rules and integer linear programming with column generation. The data reduction typically identifies 75 percent of the edges that are deleted for an optimal solution; the column generation method can then optimally solve protein interaction networks with up to 6,000 vertices and 13,500 edges within five hours. Additionally, we present a new heuristic that finds more clusters than the method by Hartuv and Shamir.

International audience; Source separation is one of the most relevant estimation problems found in chemistry. Indeed, dealing with mixtures is paramount in different kinds of chemical analysis. For instance, there are some cases where the analyte is a chemical mixture of different components, e.g., in the analysis of rocks and heterogeneous materials through spectroscopy. Moreover, a mixing process can also take place even when the components are not chemically mixed. For instance, in ionic analysis of liquid samples, the ions are not chemically connected, but, due to the lack of selectivity of the chemical sensors, the acquired responses may be influenced by ions that are not the desired ones. Finally, there are some situations where the pure components cannot be isolated chemically since they appear only in the presence of other components. In this case, BSS may provide these components that cannot be retrieved otherwise. In this paper, our aim is to shed some light on the use of BSS in chemical analysis. In this context, we firstly provide a brief overview on source separation (Section II), with particular attention to the classes of linear and nonlinear mixing models (Sections III and IV, respectively). Then, (in Section V), we will give some conclusions and focus on challenging aspects that are found in chemical analysis. Although dealing with a relatively new field of applications, this article is not an exhaustive survey of source separation methods and algorithms, since there are solutions originated in closely related domains (e.g. remote sensing and hyperspectral imaging) that suit well several problems found in chemical analysis. Moreover, we do not discuss the supervised source separation methods, which are basically multivariate regression techniques, that one can find in chemometrics.

This paper deals with the important topic of industrial robot identification. The usual identification method is based on the inverse dynamic identification model and the least squares technique. This method has been successfully applied on several industrial robots. Good results can be obtained, provided a well tuned derivative band-pass filtering of joint positions is used to calculate the joint velocities and accelerations. However, one cannot be sure whether or not the band-pass filtering is well tuned. An alternative is the instrumental variable (IV) method, which is robust to data filtering and is statistically optimal. In this paper, a generic IV approach suitable for robot identification is proposed. The instrument set is the inverse dynamic model built from simulated data calculated from simulation of the direct dynamic model. The simulation is based on previous estimates and assumes the same reference trajectories and the same control structure for both actual and simulated robots. Finally, gains of the simulated controller are updated according to IV estimates to obtain a valid instrument set at each step of the algorithm. The proposed approach validates the inverse and direct dynamic models simultaneously, is not sensitive to initial conditions, and converges rapidly. Experimental results obtained on a six-degrees-of-freedom industrial robot show the effectiveness of this approach: 60 dynamic parameters are identified in three itérations.

Given two genomes possibly with duplicate genes, the exemplar distance problem is that of removing all but one copy of each gene in each genome, so as to minimize the distance between the two reduced genomes according to some measure. Let $((s,t))$-exemplar distance denote the exemplar distance problem on two genomes $(G_1)$ and $(G_2)$, where each gene occurs at most $(s)$ times in $(G_1)$ and at most $(t)$ times in $(G_2)$. We show that the simplest nontrivial variant of the exemplar distance problem, $((1,2))$-Exemplar Distance, is already hard to approximate for a wide variety of distance measures, including both popular genome rearrangement measures such as adjacency disruptions, signed reversals, and signed double-cut-and-joins, and classic string edit distance measures such as Levenshtein and Hamming distances.

The Discrete Fourier Transform (DFT) underpins the solution to many inverse problems commonly possessing missing or un-measured frequency information. This incomplete coverage of Fourier space always produces systematic artefacts called Ghosts. In this paper, a fast and exact method for de-convolving cyclic artefacts caused by missing slices of the DFT is presented. The slices discussed here originate from the exact partitioning of DFT space, under the projective Discrete Radon Transform, called the Discrete Fourier Slice Theorem. The method has a computational complexity of O(n log2 n) (where n = N^2) and is constructed from a new Finite Ghost theory. This theory is also shown to unify several aspects of work done on Ghosts over the past three decades. The paper concludes with a significant application to fast, exact, non-iterative image reconstruction from sets of discrete slices obtained for a limited range of projection angles., 10 pages, 18 figures (submitted to IEEE Image Proc.)

International audience; New paradigms for brain computer interfacing (BCI), such as based on imagination of task characteristics, require long training periods, have limited accuracy, and lack adaptation to the changes in the users' conditions. Error poten- tials generated in response to an error made by the translation algorithm can be used to improve the performance of a BCI, as a feedback extracted from the user and fed into the BCI system. The present study addresses the inclusion of error potentials in a BCI system based on the decoding of movement-related cortical potentials (MRCPs) associated to the speed of a task. First, we theoretically quantified the improvement in accuracy of a BCI system when using error potentials for correcting the output decision, in the general case of multiclass BCI. The derived theoretical expressions can be used during the design phase of any BCI system. They were applied to experimentally estimated accuracies in decoding MRCPs and error potentials. Second we studied in simulation the performance of the closed-loop system in order to evaluate its ability to adapt to the changes in the mental states of the user. By setting the parameters of the simulator to experimentally determined values, we showed that updating the learning set with the examples estimated as correct based on the decoding of error potentials leads to convergence to the optimal solution.

International audience; 3DTV technology has brought out new challenges such as the question of synthesized view evaluation. Synthesized views are generated through a depth image-based rendering (DIBR) process. This process induces new types of artifacts whose impact on visual quality has to be identified considering various contexts of use. While visual quality assessment has been the subject of many studies in the last twenty years, there are still some unanswered questions regarding new technological improvement. DIBR brings new challenges mainly because it deals with geometric distortions. This paper considers the DIBR-based synthesized view evaluation problem. Different experiments have been carried out. They question the protocols of subjective assessment and the reliability of the objective quality metrics in the context of 3DTV, in these specific conditions (DIBR-based synthesized views). They consist in assessing seven different view synthesis algorithms through subjective and objective measurements. Results show that usual metrics are not sufficient for assessing 3-D synthesized views, since they do not correctly render human judgment. Synthesized views contain specific artifacts located around the disoccluded areas, but usual metrics seem to be unable to express the degree of annoyance perceived in the whole image. The study provides hints for a new objective measure. Two approaches are proposed: the first one is based on the analysis of the shifts of the contours of the synthesized view; the second one is based on the computation of a mean SSIM score of the disoccluded areas.

International audience; Formulated as a least square problem under an $\ell_0$ constraint, sparse signal restoration is a discrete optimization problem, known to be NP complete. Classical algorithms include, by increasing cost and efficiency, Matching Pursuit (MP), Orthogonal Matching Pursuit (OMP), Orthogonal Least Squares (OLS), stepwise regression algorithms and the exhaustive search. We revisit the Single Most Likely Replacement (SMLR) algorithm, developed in the mid-80's for Bernoulli-Gaussian signal restoration. We show that the formulation of sparse signal restoration as a limit case of Bernoulli-Gaussian signal restoration leads to an $\ell_0$-penalized least square minimization problem, to which SMLR can be straightforwardly adapted. The resulting algorithm, called Single Best Replacement (SBR), can be interpreted as a forward-backward extension of OLS sharing similarities with stepwise regression algorithms. Some structural properties of SBR are put forward. A fast and stable implementation is proposed. The approach is illustrated on two inverse problems involving highly correlated dictionaries. We show that SBR is very competitive with popular sparse algorithms in terms of trade-off between accuracy and computation time.

International audience; We address the problem of joint signal restoration and parameter estimation in the context of the forthcoming MUSE instrument, which will provide spectroscopic measurements of light emitted by very distant galaxies. Restoration of spectra is formulated as a linear inverse problem, accounting for the instrument response and the noise spectral variability. Estimation is considered in the setting of sparse approximation, where restoration is performed jointly with the detection of relevant patterns in the spectra. To this aim, a dictionary of elementary spectral features is designed according to astrophysical spectroscopy. Sparse estimation is considered through the minimization of a quadratic data misfit criterion with an ℓ1-norm penalization, where nonzero components are associated to the detected features. An efficient optimization strategy is proposed, based on the Iterative Coordinate Descent (ICD) principle, with accelerations that dramatically reduce the computational cost. The algorithm does not rely on fast transforms and can be applied to a wide variety of criteria if the sparsity constraint is separable. Results on simulated MUSE-like data reveal satisfactory performance in terms of denoising and detection of physically relevant spectral features. On such data, the proposed algorithm is shown to outperform both state-of-the-art gradient-based and homotopy continuation methods. Simulations with a compressed sensing-like random matrix also reveal better performance compared with usual algorithms, showing that ICD can be a powerful strategy for sparse optimization.

International audience; Fast humanwalking includes a phase where the stance heel rises from the ground and the stance foot rotates about the stance toe. This phase where the biped becomes underactuated is not present during the walk of current humanoid robots. The objective of this study is to determine whether the introduction of this phase for a 3-D bipedal robot is useful to reduce the energy consumed in the walking. In order to study the efficiency of this new gait, two cyclic gaits are presented. The first cyclic motion is composed of successive single-support phases with a flat stance foot on the ground, and the stance foot does not rotate. The second cyclic motion is composed of single-support phases that include a subphase of rotation of the supporting foot about the toe. The single-support phases are separated by a double-support phase. For simplicity, this double-support phase is considered as instantaneous (passive impact). For these two gaits, optimal motions are designed by minimizing the torques cost. The given performances of actuators are taken into account. It is shown that, for a fast motion, a foot-rotation subphase is useful to reduce the cost criterion. These gaits are illustrated with simulation results.

International audience; The question of characterizing those time-delay systems which are equivalent (up to bicausal transformations) to a delay-free system is a natural and fundamental problem which has been raised since the early 1980s. Surprisingly this problem remains largely open despite that some partial answers have been provided. This technical note tackles this problem for linear systems with constant time delays, and gives necessary and sufficient conditions to eliminate, whenever is possible, or to reduce the number of time delays and a constructive way to get the corresponding bicausal change of coordinates.

An alternative formulation of the microwave tomography problem is proposed, in order to reduce the computation time of the inversion procedure. The well-known multiplicative regularized contrast source inversion method is used as reference algorithm. Our formulation is based on a truncated singular value decomposition of the matrix involved in the most computationally intensive operations of the inversion algorithm. After theoretical and practical investigation of the behavior of the singular values, it is shown that the corresponding approximation does not yield any significant degradation of the quality of the solutions even for large complex permittivity contrasts, while the computation times are reduced by a factor ranging between 1.1 and 6 with respect to the original method.

This paper deals with H infin delay-scheduled control of linear systems with time-varying delays. First, a new model transformation is given which allows to provide a unified approach to stability analysis and state-feedback control synthesis for time-delay systems represented in "linear fractional transformation (LFT)" form. A new type of controller is then synthesized, where the state-feedback is scheduled by the value of the delay (delay-scheduled controllers). The results are provided in terms of linear matrix inequalities (LMIs) which are known to be efficiently solvable.

International audience; The present paper deals with pattern recognition in a distributed computing context of the peer-to-peer type, that should be more and more interesting for multimedia data indexing and retrieval. Our goal is estimating of classconditional probability densities, that take the form of Gaussian mixture models (GMM). Originally, we propagate GMMs in a decentralized fashion (gossip) in a network, and aggregate GMMs from various sources, through a technique that only involves little computation and that makes parcimonious usage of the network resource, as model parameters rather than data are transmitted. The aggregation is based on iterative optimization of an approximation of a KL divergence allowing closed-form computation between mixture models. Experimental results demonstrate the scheme to the case of speaker recognition.

International audience; Consider a biped evolving in the sagittal plane. The unexpected rotation of the supporting foot can be avoided by controlling the zero moment point or ZMP. The objective of this study is to propose and analyze a control strategy for simultaneously regulating the position of the ZMP and the joints of the robot. If the tracking requirements were posed in the time domain, the problem would be underactuated in the sense that the number of inputs would be less than the number of outputs. To get around this issue, the proposed controller is based on a pathfollowing control strategy previously developed for dealing with the underactuation present in planar robots without actuated ankles. In particular, the control law is defined in such a way that only the kinematic evolution of the robot's state is regulated, but not its temporal evolution. The asymptotic temporal evolution of the robot is completely defined through a one degree of freedom subsystem of the closed-loop model. Since the ZMP is controlled, bipedal walking that includes a prescribed rotation of the foot about the toe can also be considered. Simple analytical conditions are deduced which guarantee the existence of a periodic motion and the convergence towards this motion.

We address the problem of estimating spectral lines from irregularly sampled data within the framework of sparse representations. Spectral analysis is formulated as a linear inverse problem, which is solved by minimizing an l1-norm penalized cost function. This approach can be viewed as a basis pursuit de-noising (BPDN) problem using a dictionary of cisoids with high frequency resolution. In the studied case, however, usual BPDN characterizations of uniqueness and sparsity do not apply. This paper deals with the l1-norm penalization of complex-valued variables, that brings satisfactory prior modeling for the estimation of spectral lines. An analytical characterization of the minimizer of the criterion is given and geometrical properties are derived about the uniqueness and the sparsity of the solution. An efficient optimization strategy is proposed. Convergence properties of the iterative coordinate descent (ICD) and iterative reweighted least-squares (IRLS) algorithms are first examined. Then, both strategies are merged in a convergent procedure, that takes advantage of the specificities of ICD and IRLS, considerably improving the convergence speed. The computation of the resulting spectrum estimator can be implemented efficiently for any sampling scheme. Algorithm performance and estimation quality are illustrated throughout the paper using an artificial data set, typical of some astrophysical problems, where sampling irregularities are caused by day/night alternation. We show that accurate frequency location is achieved with high resolution. In particular, compared with sequential Matching Pursuit methods, the proposed approach is shown to achieve more robustness regarding sampling artifacts.

This paper presents the dynamic modeling of a 3-D-serial underwater ee1-like robot using recursive algorithms based on the Newton-Euler equations. Both direct and inverse models are treated in the paper. The inverse dynamic model algorithm gives the head acceleration and the joint torques as a function of the joint positions, velocities, and accelerations. The direct dynamic model gives the head and joint accelerations as a function of the joint positions, velocities, and input torques. The proposed algorithms can be considered as a generalization of the recursive Newton-Euler dynamic algorithms of serial manipulators with fixed base. The algorithms are easy to implement and to simulate whatever the number of degrees of freedom of the robot. An example with 12 spherical joints is presented. The fluid forces have been taken into account using a simple model based on Morison's model.

International audience; The assemble-to-order (ATO) production strategy considers a tradeoff between the size of a product portfolio and the assembly lead time. The concept of modular design is often used in support of the ATO strategy. Modular design impacts the assembly of products and the supply chain, in particular, the storage, transport, and production are affected by the selected modular structure. The demand for products in a product family impacts the cost of the supply chain. Based on the demand patterns, a mix of modules and their stock are determined by solving an integer programming model. This model cannot be optimally solved due to its high computational complexity and, therefore, two heuristic algorithms are proposed. A simulated annealing algorithm improves on the previously generated solutions. The computational results reported in this paper show that significant savings could be realized by optimizing the composition of modules. The best performance is obtained by a simulated annealing combined with a heuristic approach.

In this paper, we validate the feasibility of an onboard velocity sensor using the self-mixing effect. Roughness of the target surface, wet target surface, noncontrolled changes of incident angle, and speed vector vertical components have been considered during this development. A first prototype has been designed with an automotive application so to illustrate this feasibility. This low-cost and low-clutter prototype presents an interesting basic performance (/spl sigma//spl ap/0.22%). In order to improve the accuracy as well as the robustness of the system, a double-laser diode sensor has then been tested successfully (/spl sigma//spl ap/0.038%) by removing the influence of the pitching and the pumping effects.

Presents a power-divider architecture whose characteristics are adjustable independently at two frequencies. The circuits uses a single semiloop stepped-impedance resonator filter coupled to the input and output lines. A specific methodology has been developed to design such a circuit.

Various control schemes for a single flexible robot arm are considered in this paper. They require a limited amount of on-line computations. All trials are performed on the physical process and embody a conclusive, although partial, comparison for this kind of single axis robot. They are representative of some major applied research directions which captured the attention of many researchers throughout the 1980's. Some general conclusions are derived. >

The paper presented by Gautier and Khalil (see ibid., vol.6, no.3, p.368-73, 1990) gives a direct and efficient method of calculating most of the minimum inertial parameters of serial robots. Some parameters concerning the translational links between the first two rotational joints that are not parallel need particular calculation; partial results concerning the case where these links are either perpendicular or parallel are given in the above paper. This correspondence presents a direct solution of this particular case, such that all the minimum inertial parameters can be obtained directly without calculating the energy or the dynamic model of any link. >