Hyperspectral sensors provides informative data in many fields related to Earth observation. On the coastal zone, the inversion of radiative transfer models of the light has shown the ability to estimate parameters characterizing the water column. Particularly the water column depth, its concentration in non-algal particles, in phytoplankton, the bottom reflectance can be retrieved. Nevertheless the reliability of these estimations are difficult to clearly assess. In this paper a methodology based on interval arithmetic is presented to estimate the likelihood function. This allows to describe the behavior of this function around its maximum and therefore to evaluate the reliability of the solution. This methodology is applied to describe the influence of the noise level.

The inversion of the semi-analytical radiative tranfer model proposed by Lee allows to estimate the water column paramters and depth. The solution generally used to deal with the sea bottom reflectance at the inversion stage is to use a spectral library containing relevant reflectance spectra of the studied area. In this paper, we will evaluate the influence of that spectral library. For this purpose, the simplest possible spectral library will be used: a spectral library including only one flat spectrum. The observations made using that spectral library will show results that re-evaluate the purpose of the spectral library itself at the inversion stage, leading us to propose a method to estimate the sea bottom spectral reflectance.

We present in this letter a blind frame synchronization method based on a maximum a posteriori probability (MAP) approach. Applied to coded communication systems, this method is based on the calculation of the log-likelihood ratios (LLR) of the syndrome obtained according to the parity check matrix of the code. After presenting the proposed method, we compare it to an existing blind synchronization method previously introduced. Simulation results show that the proposed method outperforms the existing one and presents good performance when applied to codes having a sparse parity check matrix such as low density parity check (LDPC) codes and convolutional codes.

The genetic code is a key concept in genetics. This code establishes the link between the DNA (desoxyribonucleic acid), composed of nucleic acids, and proteins, composed of amino acids. The genetic code is known to be universal, indeed almost all organisms use this code, and it is degenerated, this means that several sequences of nucleic acids can be translated into the same amino acid. This feature is also present in multiplexed codes which is a new concept recently introduced for robust source coding. In this paper we present a comparison between multiplexed codes and the genetic code. We show why this comparison is relevant, and we point out the fact that the genetic code is almost optimal in the context of multiplexed codes theory.

In decentralized communication schemes such as ad-hoc networks, a major challenge concerns the synchronization and detection of users sending information simultaneously. Classical synchronisation techniques based on training sequences decrease dramatically the spectral efficiency and are rarely applicable in non-cooperative settings. In this contribution, we propose an efficient technique for blind detection in the context of block coded interleaved division multiple access (IDMA). Interestingly, without any additional dimension (space, time or frequency), the users can be fully separated when using low rate LDPC codes with the same interleavers (which is a new feature compared to classical IDMA schemes). Results, in the context of additive white Gaussian noise (AWGN) and Rayleigh flat fading channels, demonstrate the potential of the method in the multi-user setting.

Interleaving is a key component of many digital communication systems involving error correction schemes. It provides a form of time diversity to guard against bursts of errors. Recently, interleavers have become an even more integral part of the code design itself, if we consider for example turbo and turbo-like codes. In a non-cooperative context, such as passive listening, it is a challenging problem to estimate the interleaver parameters. In this paper we propose an algorithm that allows us to estimate the parameters of the interleaver at the output of a binary symmetric channel and to locate the codewords in the interleaved block. This gives us some clues about the interleaving function used.

We propose a technique, based on a fuzzy hidden Markov chain (HMC) model, for the unsupervised segmentation of images. Our main contribution is to use Dirac and Lebesgue measures simultaneously at the class chain level. This model allows the coexistence of hard and fuzzy pixels in the same picture. The fuzzy approach enriches the classical model by adding a fuzzy class, which has several interpretations in signal processing. One such interpretation in image segmentation is the simultaneous appearance of several thematic classes on the same pixel (mixture). Model parameter estimation is performed through an extension of the iterative conditional estimation (ICE) algorithm to take into account the fuzzy part. The fuzzy segmentation of a real image of clouds is studied and compared to the classification obtained with a "classical" hard HMC model.