This paper presents the modelling of new WLAN models for different indoor environments. This work was carried out in the frame of the FIL project which is funded by the French research agency ANR, in collaboration with Thales Alenia Space France. Based on the standard Opnet models for WLAN nodes, the propagation loss estimation for these types of environment has been improved. We derive an empirical model for spatial registration patterns of mobile users as they move within a TeSA Labs wireless local area network (WLAN) environment and register signal power from different access points. Such a model can be very useful in a variety of simulation studies of the performance of mobile wireless systems, to address issues such as resource management and mobility management protocols. We base the model on extensive experimental data from a TeSA Labs 2.4 GHz WiFi LAN installation. We divide the empirical data available to us into training and test data sets, develop the model based on the training set, and evaluate it against the test set. The new scenarios used to simulate these new propagation models are shown. Finally, results, conclusions and further work are given.

Hyperspectral images present some specific characteristics that should be used by an efficient compression system. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity. Some wavelet-based compression algorithms have been successfully used for some hyperspectral space missions. This paper focuses on the optimization of a full wavelet compression system for hyperspectral images. Each step of the compression algorithm is studied and optimized. First, an algorithm to find the optimal 3-D wavelet decomposition in a rate-distortion sense is defined. Then, it is shown that a specific fixed decomposition has almost the same performance, while being more useful in terms of complexity issues. It is shown that this decomposition significantly improves the classical isotropic decomposition. One of the most useful properties of this fixed decomposition is that it allows the use of zero tree algorithms. Various tree structures, creating a relationship between coefficients, are compared. Two efficient compression methods based on zerotree coding (EZW and SPIHT) are adapted on this near-optimal decomposition with the best tree structure found. Performances are compared with the adaptation of JPEG 2000 for hyperspectral images on six different areas presenting different statistical properties.

The French Space Agency, CNES, is interested in the transforms derived from the wavelets in order to increase the image compression efficiency on-board of Earth observation satellites. In this thesis, the post-transforms are studied. They are employed after the wavelet transform. Each block of wavelet coefficients is further transformed in a basis selected among a dictionary by minimization of a rate-distortion criterion. First, we emphasize dependencies between wavelet coefficients limiting the compression efficiency. Then, we study the bandelet transform by blocks, from which the post-transforms derive, and we optimize its parameter for the compression of satellite images. Particularly, we adapt Shoham and Gersho optimization method to the problem of the selection of the best bandelet basis. We deduce from these results an expression of the optimal Lagrangian multiplier used in the rate-distortion criterion. Next, we analyze dependencies between wavelet coefficient which are not exploited by the bandelet transform and we define new post-transform bases. Bases build by PCA minimize the correlations between post-transformed coefficients and compact the energy of each block on a small number of coefficients. This feature is exploited during the entropy coding process. Last, we modify the bases selection criterion to adapt the post-transform to progressive compression schemes. We then employ the Hadamard post-transform with the CCSDS image encoder to obtain a low computational complexity yet efficient compression scheme.

Satellite networks have been built by the DVB group and dedicated to digital television service. However, in the current service convergence trend, a future satellite network architecture has to be built in a less dedicated way to fit heterogeneous services. This work begins with a description of DVB satellite networks. Then, network convergence solutions are studied for the satellite context. IP and MPLS have then been chosen to build a satellite convergent architecture. Several scenarios are examined so as to evaluate this architecture. A first one deals with the historical television service in a unidirectional, transparent satellite context. We show the feasibility of such a scenario with similar performances and better protocol organisation which simplifies satellite evolution. The next scenarios concern an interactive television service with a return link and a voice over IP service. The ability of deploying new services in a simple manner is thus highlighted. The last scenario applies our convergent approach to the ULISS industrial project of a regenerative hybrid satellite. It shows the flexibility of our architecture and expand ULISS service capabilities.

OURSES project, French acronym for Offer of Services Rural Use using Satellite, proposes three telemedicine applications linked to services for elderly people. It focuses particularly on the use of satellites as a complement to terrestrial technologies to ensure the deployment of tele-services in areas where telecommunication infrastructure is lacking. This paper describes the three telemedicine applications which can be viewed as tele-monitoring systems for elderly people.

This paper presents the recent results of SCA project (Communication Systems for Avionics), on the evaluation of the means of communication able to ensure airlines companies needs on airport platforms. Exchange of AOC/AAC (Aeronautical Operational Control/ Aeronautical Administrative Communication) messages between aircrafts and their airline companies is required on the ground. The concept of technology readiness level is presented to lead to the best choice of wireless technology. Evaluation of each technology is described for various scenarios of aircraft location within the airport.

This thesis studies classifcation of digital linear and nonlinear modulations using Bayesian methods. Modulation recognition consists of identifying, at the receiver, the type of modulation signals used by the transmitter. It is important in many communication scenarios, for example, to secure transmissions by detecting unauthorized users, or to determine which transmitter interferes the others. The received signal is generally affected by a number of impairments. We propose several classifcation methods that can mitigate the effects related to imperfections in transmission channels. More specifcally, we study three techniques to estimate the posterior probabilities of the received signals conditionally to each modulation. The first technique estimates the unknown parameters associated with various imperfections using a Bayesian approach coupled with Markov Chain Monte Carlo (MCMC) methods. A second technique uses the Baum Welch (BW) algorithm to estimate recursively the posterior probabilities and determine the most likely modulation type from a catalogue. The last method studied in this thesis corrects synchronization errors (phase and frequency offsets) with a phase-locked loop (PLL). The classifcation algorithms considered in this thesis can recognize a number of linear modulations such as Quadrature Amplitude Modulation (QAM), Phase Shift Keying (PSK), and nonlinear modulations such as Gaussian Minimum Shift Keying (GMSK).

The pseudo maximum likelihood estimator allows one to estimate the unknown parameters of Brown’s model for altimeter wave- forms. However, the optimality of this estimator, for instance in terms of minimizing the mean square errors of the unknown para- meters is not guarantied. Thus it is not clear whether there is some space for developing new estimators for the unknown parameters of altimetric signals. This paper derives the Crame ́r-Rao lower bounds of the parameters associated to Brown’s model. These bounds provide the minimum variances of any unbiased estima- tor of these parameters, i.e. a reference in terms of estimation er- ror. A comparison between the mean square errors of the standard estimators and Crame ́r-Rao bounds allows one to evaluate the po- tential gain (in terms of estimation variance) that could be achieved with new estimation strategies.

Several telemedicine applications are proposed within the frame of OURSES project, French acronym for Offer of Rural Use of Services by Satellite, providing services for elderly people. The main objective of this project is to show the interest of using satellites as a complement to terrestrial technologies, in areas where telecommunication infrastructure is lacking or incomplete. This paper describes one of these applications: an ECG monitoring system. This telemonitoring system allows, thanks to a wireless wearable sensor, to detect possible cardiac problems of elderly people. ECG signals are analyzed through signal processing algorithms and if some abnormal condition is detected, an alarm is raised and sent via satellite to the physician's office. The corresponding physician is able to access at any time the recorded ECG signals, whenever he is willing to, in the presence of an alarm or not. This allows a constant monitoring of the elderly people. Tests realized in a real environment have demonstrated the feasibility and the interest of this application.

This paper addresses the problem of classifying altimetric waveforms backscattered from different kinds of surfaces including oceans, ices, deserts and forests. Appropriate features associated with altimetric radar waveforms are first introduced for this classification. These features are completed by radiometer temperatures and pre-processed using a linear discriminant analysis for dimensionality reduction. The classification of altimetric waveforms is finally achieved using the resulting pre-processed vector with reduced dimension. Different classification strategies are finally considered. These strategies are based on the nearest mean rule, the nearest neighbor method or on the multilayer perceptron. Various simulation results illustrate the performance of the proposed classifier.