Algorithme de Viterbi

Résumé

L'algorithme de Viterbi, d'Andrew Viterbi, permet de corriger, dans une certaine mesure, les erreurs survenues lors d'une transmission à travers un canal bruité. Son utilisation s'appuie sur la connaissance du canal bruité, c'est-à-dire la probabilité qu'une information ait été modifiée en une autre, et permet de simplifier radicalement la complexité de la recherche du message d'origine le plus probable. D'exponentielle, cette complexité devient linéaire. Cet algorithme a pour but de trouver la séquence d'états la plus probable ayant produit la séquence mesurée. Principe Soit un message m diffusé à travers un canal bruité connu (par exemple, qui supprime tous les accents d'un texte) et le message o observé en sortie du canal. Pour retrouver le message d'origine, on cherche, à partir de o le message le plus probable. La méthode brute consiste à tester, pour chaque lettre de o la lettre la plus probable dans m en émettant l'hypothèse que le canal bruité n'a pas ajouté

Source officielle

https://fr.wikipedia.org/wiki/Algorithme_de_Viterbi

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Gaussian channel transmission of images and audio files using cryptcoding

Vladimir Ilievski

Random codes based on quasigroups (RCBQ) are cryptcodes, i.e. they are error-correcting codes, which provide information security. Cut-Decoding and 4-Sets-Cut-Decoding algorithms for these codes are defined elsewhere. Also, the performance of these codes for the transmission of text messages is investigated elsewhere. In this study, the authors investigate the RCBQ's performance with Cut-Decoding and 4-Sets-Cut-Decoding algorithms for transmission of images and audio files through a Gaussian channel. They compare experimental results for both coding/decoding algorithms and for different values of signal-to-noise ratio. In all experiments, the differences between the transmitted and decoded image or audio file are considered. Experimentally obtained values for bit-error rate and packet error rate and the decoding speed of both algorithms are compared. Also, two filters for enhancing the quality of the images decoded using RCBQ are proposed.

2019

Chargement

The invention of Fountain codes is a major advance in the field of error correcting codes. The goal of this work is to study and develop algorithms for source and channel coding using a family of Fountain codes known as Raptor codes. From an asymptotic point of view, the best currently known sum-product decoding algorithm for non binary alphabets has a high complexity that limits its use in practice. For binary channels, sum-product decoding algorithms have been extensively studied and are known to perform well. In the first part of this work, we develop a decoding algorithm for binary codes on non-binary channels based on a combination of sum-product and maximum-likelihood decoding. We apply this algorithm to Raptor codes on both symmetric and non-symmetric channels. Our algorithm shows the best performance in terms of complexity and error rate per symbol for blocks of finite length for symmetric channels. Then, we examine the performance of Raptor codes under sum-product decoding when the transmission is taking place on piecewise stationary memoryless channels and on channels with memory corrupted by noise. We develop algorithms for joint estimation and detection while simultaneously employing expectation maximization to estimate the noise, and sum-product algorithm to correct errors. We also develop a hard decision algorithm for Raptor codes on piecewise stationary memoryless channels. Finally, we generalize our joint LT estimation-decoding algorithms for Markov-modulated channels. In the third part of this work, we develop compression algorithms using Raptor codes. More specifically we introduce a lossless text compression algorithm, obtaining in this way competitive results compared to the existing classical approaches. Moreover, we propose distributed source coding algorithms based on the paradigm proposed by Slepian and Wolf.

EPFL2009

Chargement

Fixed-wing drones for communication networks

Maja Varga

In the last decade, drones became frequently used to provide eye-in-the-sky overview in the outdoor environment. Their main advantage compared to the other types of robots is that they can fly above obstacles and rough terrains and they can quickly cover large areas. These properties also open a new application; drones could provide a multi-hop, line of sight communication for groups of ground users. The aim of this thesis is to develop a drone team that will establish wireless ad-hoc network between users on the ground and distributively adapt links and spatial arrangement to the requirements and motion of the ground users. For this application, we use fixed wing drones. Such platforms can be easily and quickly deployed. Fixed wing drones have higher forward speed and higher battery life than hovering platforms. On the other hand, fixed wing drones have unicycle dynamics with constrained forward speed which makes them unable to hover or perform sharp turns. The first challenge consists in bridging unicycle dynamics of the fixed wing drones. Some control strategies have been proposed and validated in simulations using the average distance between the target and the drone as a performance metric. However, besides the distance metric, energy expenditure of the flight also plays an important role in assessing the overall performance of the flight. We propose a new methodology that introduces a new metric (energy expenditure), we compare existing methods on a large set of target motion patterns and present a comparison between the simulation and field experiments on proposed target motion patterns. The second challenge consists in developing a formation control algorithm that will allow fixed wing robots to provide a wide area coverage and to relay data in a wireless ad-hoc network. In such applications fixed wing drones have to be able to regulate an inter-drone distance. Their reduced maneuverability presents the main challenge to design a formation algorithm that will regulate an inter-drone distance. To address this challenge, we present a distributed control strategy that relies only on local information. Each drone has its own virtual agent, it follows the virtual agent by performing previously evaluated and selected target tracking strategy, and flocking interaction rules are implemented between virtual agents. It is shown in simulation and in field experiments with a team of fixed wing drones that using this distributed formation algorithm, drones can cover an area by creating an equilateral triangular lattice and regulate communication link quality between neighboring drones. The third challenge consists in allowing connectivity between independently moving ground users using fixed wing drone team. We design two distributed control algorithms that change drones' spatial arrangement and interaction topology to maintain the connectivity. We propose a potential field based strategy which adapts distance between drones to shrink and expand the fixed wing drones' formation. In second approach, market-based adaptation, drones distributively delete interaction links to expand the formation graph to a tree graph. In simulations and field experiments we show that our proposed strategies successfully maintain independently moving ground users connected. Overall, this thesis presents synthesis of distributed algorithms for fixed wing drones to establish and maintain wireless ad-hoc communication networks.

EPFL2016

Chargement

Fixed-wing drones for communication networks

Maja Varga

EPFL2016