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An introduction to multichannel NMF for audio source separation
No full textInternational audienceThis chapter introduces multichannel nonnegative matrix factorization (NMF) methods for audio source separation. All the methods and some of their extensions are introduced within a more general local Gaussian modeling (LGM) framework. These methods are very attractive since allow combining spatial and spectral cues in a joint and principal way, but also are natural extensions and generalizations of many single-channel NMF-based methods to the multichannel case. The chapter introduces the spectral (NMF-based) and spatial models, as well as the way to combine them within the LGM framework. Model estimation criteria and algorithms are described as well, while going deeper into details of some of them
Sequential LMI approach for design of a BMI-based robust observer state feedback controller with nonlinear uncertainties
No full textInternational audienceThis paper aims at developing a robust observer–based estimated state feedback control design method for an uncertain dynamical system that can be represented as a linear time-invariant system connected with an integral quadratic constraint–type nonlinear uncertainty. Traditionally, in existing design methodologies, a convex semidefinite constraint is obtained at the cost of conservatism and unrealistic assumptions. This paper avoids such assumptions and formulates, the design of the robust observer state feedback controller as the feasibility problem of a bilinear matrix inequality (BMI) constraint. Unfortunately, the search for a feasible solution of a BMI constraint is an NP-hard problem in general. The applicability of a linearization method, such as the variable change method and the congruence transformation, depends on the specific structure of the problem at hand and cannot be generalized. This paper transforms the feasibility analysis of the BMI constraint into an eigenvalue problem and applies the convex-concave–based sequential linear matrix inequality optimization method to search for a feasible solution. Furthermore, an augmentation of the sequential linear matrix inequality algorithm to improve its numerical stability is presented. In the application part, a vehicle lateral control problem is presented to demonstrate the applicability of the proposed algorithm to a real-world estimated state feedback control design problem and the necessity of the augmentation for numerical stability
ChaLearn Looking at People: A Review of Events and Resources
No full textPaper to appear in proceedings of IJCNN 2017 - IEEE - Associated website: http://chalearnlap.cvc.uab.esThis paper reviews the historic of ChaLearn Looking at People (LAP) events. We started in 2011 (with the release of the first Kinect device) to run challenges related to human action/activity and gesture recognition. Since then we have regularly organized events in a series of competitions covering all aspects of visual analysis of humans. So far we have organized more than 10 international challenges and events in this field. This paper reviews associated events, and introduces the ChaLearn LAP platform where public resources (including code, data and preprints of papers) related to the organized events are available. We also provide a discussion on perspectives of ChaLearn LAP activities
Comparative study in the identification of liquid to solid transition phase with DSC, Raman spectra analysis and chemiometrics methods applied to phase change materials used for icing-delay in civil engineering infrastructures
No full textInternational audienceIn a costs reduction and comfort requirements context, the use of phase change materials (PCM) is a sustainable and economical answer. For transportation infrastructures and winter maintenance, they avoid ice occurrence or snow accumulation. Their characteristics, and more specifically, the solid to liquid phase transition temperature and enthalpy, are usually obtained through DSC. Raman spectroscopy can bring answers and information on their microstructures. The liquid to solid phase change was investigated on three PCM, a paraffin, formic acid and diluted formic acid. A comparison made on freezing temperature obtained through DSC, Raman spectroscopy associated with chemiometrics indicated a consistency between the methods. Raman spectroscopy coupled with multivariate data analysis allowed the identification of an additional specificity in the freezing process of the paraffin. All methods provided results consistent between each other, although some differences between literature and experimental freezing temperatures of the considered PCM were observed in all cases
MLweb: A toolkit for machine learning on the web
No full textInternational audienceThis paper describes MLweb, an open source software toolkit for machine learning on the web. The specificity of MLweb is that all computations are performed on the client side without the need to send data to a third-party server. MLweb includes three main components: a JavaScript API for scientific computing (LALOLib), an extension of this library with machine learning tools (ML.js) and an online development environment (LALOLab) with many examples
Global optimization for low-dimensional switching linear regression and bounded-error estimation
No full textInternational audienceThe paper provides global optimization algorithms for two particularly difficult nonconvex problems raised by hybrid system identification: switching linear regression and bounded-error estimation. While most works focus on local optimization heuristics without global optimality guarantees or with guarantees valid only under restrictive conditions, the proposed approach always yields a solution with a certificate of global optimality. This approach relies on a branch-and-bound strategy for which we devise lower bounds that can be efficiently computed. In order to obtain scalable algorithms with respect to the number of data, we directly optimize the model parameters in a continuous optimization setting without involving integer variables. Numerical experiments show that the proposed algorithms offer a higher accuracy than convex relaxations with a reasonable computational burden for hybrid system identification. In addition, we discuss how bounded-error estimation is related to robust estimation in the presence of outliers and exact recovery under sparse noise, for which we also obtain promising numerical results
Special Issue on Logics for Resources, Processes, and Programs of Journal of Logic and Computation
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Online Leader Selection for Improved Collective Tracking and Formation Maintenance
No full textInternational audienceThe goal of this work is to propose an extension of the popular leader-follower framework for multi-agent collective tracking and formation maintenance in presence of a time-varying leader. In particular, the leader is persistently selected online so as to optimize the tracking performance of an exogenous collective velocity command while also maintaining a desired formation via a (possibly time-varying) communication-graph topology. The effects of a change in the leader identity are theoretically analyzed and exploited for defining a suitable error metric able to capture the tracking performance of the multi-agent group. Both the group performance and the metric design are found to depend upon the spectral properties of a special directed graph induced by the identity of the chosen leader. By exploiting these results, as well as distributed estimation techniques, we are then able to detail a fully-decentralized adaptive strategy able to periodically select online the best leader among the neighbors of the current leader. Numerical simulations show that the application of the proposed technique results in an improvement of the overall performance of the group behavior w.r.t. other possible strategies
Distributed Faulty Node Detection in Delay Tolerant Networks: Design and Analysis
No full textInternational audiencePropagation of faulty data is a critical issue. In case of Delay Tolerant Networks (DTN) in particular, the rare meeting events require that nodes are efficient in propagating only correct information. For that purpose, mechanisms to rapidly identify possible faulty nodes should be developed. Distributed faulty node detection has been addressed in the literature in the context of sensor and vehicular networks, but already proposed solutions suffer from long delays in identifying and isolating nodes producing faulty data. This is unsuitable to DTNs where nodes meet only rarely. This paper proposes a fully distributed and easily implementable approach to allow each DTN node to rapidly identify whether its sensors are producing faulty data. The dynamical behavior of the proposed algorithm is approximated by some continuous-time state equations, whose equilibrium is characterized. The presence of misbehaving nodes, trying to perturb the faulty node detection process, is also taken into account. Detection and false alarm rates are estimated by comparing both theoretical and simulation results. Numerical results assess the effectiveness of the proposed solution and can be used to give guidelines for the algorithm design
A symbolic approach to multiple zeta values at negative integers
No full textInternational audienceA symbolic computation technique is used to derive closed-form expressions for an analytic continuation of the Euler–Zagier zeta function evaluated at negative integers. This continuation was recently proposed by Sadaoui. The approach presented here yields explicit contiguity identities, recurrences on the depth of the zeta values and their generating functions. Moreover, it allows to prove that the resulting multiple zeta values computed at negative integers coincide with those obtained by another analytic continuation technique that uses the Euler–MacLaurin summation formula