Yacine Oussar

Training wavelet networks for nonlinear dynamic input–output modeling

Abstract In the framework of nonlinear process modeling, we propose training algorithms for feedback wavelet networks used as nonlinear dynamic models. An original initialization procedure is presented that takes the locality of the wavelet functions into account. Results obtained for the modeling of several processes are presented; a comparison with networks of neurons with sigmoidal functions is performed.

Initialization by Selection for Wavelet Network Training

Abstract We present an original initialization procedure for the parameters of feedforward wavelet networks, prior to training by gradient-based techniques. It takes advantage of wavelet frames stemming from the discrete wavelet transform, and uses a selection method to determine a set of best wavelets whose centers and dilation parameters are used as initial values for subsequent training. Results obtained for the modeling of two simulated processes are compared to those obtained with a heuristic initialization procedure, and the effectiveness of the proposed method is demonstrated.

How to be a gray box: dynamic semi-physical modeling

A general methodology for gray-box, or semi-physical, modeling is presented. This technique is intended to combine the best of two worlds: knowledge-based modeling, whereby mathematical equations are derived in order to describe a process, based on a physical (or chemical, biological, etc.) analysis, and black-box modeling, whereby a parameterized model is designed, whose parameters are estimated solely from measurements made on the process. The gray-box modeling technique is very valuable whenever a knowledge-based model exists, but is not fully satisfactory and cannot be improved by further analysis (or can only be improved at a very large computational cost). We describe the design methodology of a gray-box model, and illustrate it on a didactic example. We emphasize the importance of the choice of the discretization scheme used for transforming the differential equations of the knowledge-based model into a set of discrete-time recurrent equations. Finally, an application to a real, complex industrial process is presented.

High-Performance Indoor Localization with Full-Band GSM Fingerprints

GSM trace mobile measurements are used to study indoor handset localization in an urban apartment setting. Nearest-neighbor, Support Vector Machine (SVM), and Gaussian Process classifiers are compared. A linear SVM is found to provide mean room-level classification efficiency near 100%, but only when the full set of GSM carriers is used. To our knowledge, this is the first study to use fingerprints containing all GSM carriers, and the first to suggest that GSM could be useful for very high-performance indoor localization.

Prospects for a Silent Speech Interface using Ultrasound Imaging

The feasibility of a silent speech interface using ultrasound (US) imaging and lip profile video is investigated by examining the quality of line spectral frequencies (LSF) derived from the image sequences. It is found that the data do not at present allow reliable identification of silences and fricatives, but that LSFs recovered from vocalized passages are compatible with the synthesis of intelligible speech

Indoor localization based on cellular telephony RSSI fingerprints containing very large numbers of carriers

A new approach to indoor localization is presented, based upon the use of Received Signal Strength (RSS) fingerprints containing data from very large numbers of cellular base stations--up to the entire GSM band of over 500 channels. Machine learning techniques are employed to extract good quality location information from these high-dimensionality input vectors. Experimental results in a domestic and an office setting are presented, in which data were accumulated over a 1-month period in order to assure time robustness. Room-level classification efficiencies approaching 100% were obtained, using Support Vector Machines in one-versus-one and one-versus-all configurations. Promising results using semi-supervised learning techniques, in which only a fraction of the training data is required to have a room label, are also presented. While indoor RSS localization using WiFi, as well as some rather mediocre results with low-carrier count GSM fingerprints, have been discussed elsewhere, this is to our knowledge the first study to demonstrate that good quality indoor localization information can be obtained, in diverse settings, by applying a machine learning strategy to RSS vectors that contain the entire GSM band.

Towards the Optimal Design of Numerical Experiments

This paper addresses the problem of the optimal design of numerical experiments for the construction of nonlinear surrogate models. We describe a new method, called learner disagreement from experiment resampling (LDR), which borrows ideas from active learning and from resampling methods: the analysis of the divergence of the predictions provided by a population of models, constructed by resampling, allows an iterative determination of the point of input space, where a numerical experiment should be performed in order to improve the accuracy of the predictor. The LDR method is illustrated on neural network models with bootstrap resampling, and on orthogonal polynomials with leave-one-out resampling. Other methods of experimental design such as random selection and D-optimal selection are investigated on the same benchmark problems.

Regularized Recurrent Least Squares Support Vector Machines

Support vector machines are widely used for classification and regression tasks. They provide reliable static models, but their extension to the training of dynamic models is still an open problem. In the present paper, we describe Regularized Recurrent Support Vector Machines, which, in contrast to previous Recurrent Support Vector Machine, models, allow the design of dynamical models while retaining the built-in regularization mechanism present in Support Vector Machines. The principle is validated on academic examples, it is shown that the results compare favorably to those obtained by unregularized Recurrent Support Vector Machines and to regularized, partially recurrent Support Vector Machines.

Full-Band GSM Fingerprints for Indoor Localization Using a Machine Learning Approach

Indoor handset localization in an urban apartment setting is studied using GSM trace mobile measurements. Nearest-neighbor, Support Vector Machine, Multilayer Perceptron, and Gaussian Process classifiers are compared. The linear Support Vector Machine provides mean room classification accuracy of almost 98% when all GSM carriers are used. To our knowledge, ours is the first study to use fingerprints containing all GSM carriers, as well as the first to suggest that GSM can be useful for localization of very high performance.

Carrier relevance study for indoor localization using GSM

A study is made of subsets of relevant GSM carriers for an indoor localization problem. A database was created containing power measurement scans of all available GSM carriers in 5 of 8 rooms of a second storey laboratory in central Paris, France, and a statistical learning algorithm developed to discriminate between rooms based on these carrier strengths. To optimize the system, carrier relevance was ranked using either Orthogonal Forward Regression or Support Vector Machine - Recursive Feature Elimination procedures, and a subset of relevant variables obtained with cross-validation. Results show that the 60 most relevant carriers are sufficient to correctly localize 97% of scans in an independent test set.