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Au cours de la dernière décennie, les communications optiques en espace libre (FSO) ont pris de l’ampleur dans les deux domaines académiques et industriels. L’importance de FSO s’appuie sur la possibilité de faire un système de transmission économique et écologique avec un débit élevé et sans licence à l’opposition des systèmes de transmission radiofréquences (RF). Dans la plupart des travaux antécédents sur les systèmes multi-émetteurs, seulement les canaux décorrélés ont été considérés. Un canal décorrélé nécessite un espace suffisant entre les émetteurs. Cette condition devient difficile et non-réalisable dans certaines applications. Pour cette raison, nos études se focalisent sur les performances des codes à répétition RC (Repitition Codes) et les codes OSTBC (Orthogonal Space-Time Block Codes) dans des canaux log-normaux corrélés en utilisant une modulation d’intensité et une détection directe (IM/DD). En addition, les effets des différentes conditions météorologiques sur le taux d’erreur moyen (ABER) sont étudiés. Les systèmes FSO à multi-entrées/ multi-sorties MIMO (Multiple-Input Multiple-Output) avec une modulation SSK (Space Shift Keying) ont été abordés. Les résultats obtenus montrent que la SSK est supérieure aux RC avec une modulation d’impulsion (Multiple Pulse Amplitude Modulation) pour toute efficacité spectrale égale ou supérieure à 4 bit/s/Hz. Nous avons aussi analysé les performances d’un système à sauts multiples (Multi-Hop) et des relais à transmission directe (forward relays). Nos simulations montrent que le système ainsi considéré est efficace pour atténuer les effets météorologiques et les pertes géométriques dans les systèmes de communication FSO. Nous avons montré qu’un tel système avec plusieurs entrées et une sortie (MISO, i.e. multiple-input single-output) à sauts multiples est supérieur à un système MISO avec un lien direct (direct link) avec une forte atténuation. Pour satisfaire la demande croissante des réseaux de communication à débits élevés, la communauté scientifique s'intéresse de plus en plus aux systèmes FSO avec des relais full-duplex (FD). Pour ces derniers systèmes, nous avons étudié la probabilité d'erreur moyenne (ABER) et nous avons analysé leurs performances. En considérant des différentes conditions de transmission, les performances de relais FD ont été comparées à celles d'un système avec un lien direct ou des relais half-duplex. Les résultats obtenus montrent que les relais FD ont le minimum ABER. En conséquence, les résultats obtenus dans cette thèse sont très prometteurs pour la prochaine génération de FSO. / Free-space optical (FSO) communication has been the subject of ongoing research activities and commercial attention in the past few years. Such attention is driven by the promise of high data rate, license-free operation, and cheap and ecological friendly means of communications alternative to congested radio frequency communications. In most previous work considering multiple transmitters, uncorrelated channel conditions have been considered. An uncorrelated channel requires sufficient spacing between transmitters. However, this can be difficult and may not be always feasible in some applications. Thereby, this thesis studies repetition codes (RCs) and orthogonal space-time block codes performance in correlated log-normal FSO channels using intensity modulation and direct detection (IM/DD). Furthermore, the effect of different weather conditions on the average bit error rate (ABER) performance of the FSO links is studied. Multiple-input multiple-output (MIMO) FSO communication systems using space shift keying (SSK) modulation have been also analyzed. Obtained results show that SSK is a potential technique for spectral efficiencies equal or greater than 4 bits/s/Hz as compared to RCs with multiple pulse amplitude modulations. The performance analysis of a multi-hop decode and forward relays for FSO communication system using IM/DD is also considered in this thesis. It is shown that multi-hop is an efficient technique to mitigate atmospheric turbulence and different weather attenuation effects and geometric losses in FSO communication systems. Our simulation results show that multiple-input single-output (MISO) multi-hop FSO systems are superior to direct link and MISO systems over links exhibiting high attenuation. Meeting the growing demand for higher data rates communication networks, a system with full-duplex (FD) relays is considered. For such a system, the outage probability and the ABER performance are analyzed under different turbulence conditions, misalignment error and path loss effects. FD relays are compared with the direct link and half-duplex relays. Obtained results show that FD relays have the lowest ABER and the outage probability as compared to the two other systems. Finally, the obtained results in this thesis are very promising towards the next generation of FSO systems.

This master’s thesis deals with the impact of atmospheric turbulence on phase changes of a free space optical signal. This problematic is investigated by the interferometric method. A part of the thesis is focused on the phenomenon of atmospheric turbulence. Then the physical effect of interference is discussed and optical interferometers are described. The experimental part of the thesis is focused on measurement of phase shift of optical signal by interferometric method. The result of the thesis is to determine how phase shift of an optical beam depends on the strength of turbulence.

The aim of this thesis is to study a free space optics and its application in communication technologies. It describes possible interrupting impacts on the beamed optical signal, which are signal noise, attenuation of the atmosphere and atmospheric turbulence. The basis of the thesis is to describe the impact of the atmospheric turbulences on the optical beam. Fluctuation of optical intensity in the optical beam has been measured and index of refraction structure parameter calculated. With its assistance, turbulence rate has been determined.

In this dissertation, we discuss the problem of robust linear subspace estimation using low-rank optimization and propose three formulations of it. We demonstrate how these formulations can be used to solve fundamental computer vision problems, and provide superior performance in terms of accuracy and running time. Consider a set of observations extracted from images (such as pixel gray values, local features, trajectories . . . etc). If the assumption that these observations are drawn from a liner subspace (or can be linearly approximated) is valid, then the goal is to represent each observation as a linear combination of a compact basis, while maintaining a minimal reconstruction error. One of the earliest, yet most popular, approaches to achieve that is Principal Component Analysis (PCA). However, PCA can only handle Gaussian noise, and thus suffers when the observations are contaminated with gross and sparse outliers. To this end, in this dissertation, we focus on estimating the subspace robustly using low-rank optimization, where the sparse outliers are detected and separated through the `1 norm. The robust estimation has a two-fold advantage: First, the obtained basis better represents the actual subspace because it does not include contributions from the outliers. Second, the detected outliers are often of a specific interest in many applications, as we will show throughout this thesis. We demonstrate four different formulations and applications for low-rank optimization. First, we consider the problem of reconstructing an underwater sequence by removing the iii turbulence caused by the water waves. The main drawback of most previous attempts to tackle this problem is that they heavily depend on modelling the waves, which in fact is ill-posed since the actual behavior of the waves along with the imaging process are complicated and include several noise components; therefore, their results are not satisfactory. In contrast, we propose a novel approach which outperforms the state-of-the-art. The intuition behind our method is that in a sequence where the water is static, the frames would be linearly correlated. Therefore, in the presence of water waves, we may consider the frames as noisy observations drawn from a the subspace of linearly correlated frames. However, the noise introduced by the water waves is not sparse, and thus cannot directly be detected using low-rank optimization. Therefore, we propose a data-driven two-stage approach, where the first stage “sparsifies” the noise, and the second stage detects it. The first stage leverages the temporal mean of the sequence to overcome the structured turbulence of the waves through an iterative registration algorithm. The result of the first stage is a high quality mean and a better structured sequence; however, the sequence still contains unstructured sparse noise. Thus, we employ a second stage at which we extract the sparse errors from the sequence through rank minimization. Our method converges faster, and drastically outperforms state of the art on all testing sequences. Secondly, we consider a closely related situation where an independently moving object is also present in the turbulent video. More precisely, we consider video sequences acquired in a desert battlefields, where atmospheric turbulence is typically present, in addition to independently moving targets. Typical approaches for turbulence mitigation follow averaging or de-warping techniques. Although these methods can reduce the turbulence, they distort the independently moving objects which can often be of great interest. Therefore, we address the iv problem of simultaneous turbulence mitigation and moving object detection. We propose a novel three-term low-rank matrix decomposition approach in which we decompose the turbulence sequence into three components: the background, the turbulence, and the object. We simplify this extremely difficult problem into a minimization of nuclear norm, Frobenius norm, and `1 norm. Our method is based on two observations: First, the turbulence causes dense and Gaussian noise, and therefore can be captured by Frobenius norm, while the moving objects are sparse and thus can be captured by `1 norm. Second, since the object’s motion is linear and intrinsically different than the Gaussian-like turbulence, a Gaussian-based turbulence model can be employed to enforce an additional constraint on the search space of the minimization. We demonstrate the robustness of our approach on challenging sequences which are significantly distorted with atmospheric turbulence and include extremely tiny moving objects. In addition to robustly detecting the subspace of the frames of a sequence, we consider using trajectories as observations in the low-rank optimization framework. In particular, in videos acquired by moving cameras, we track all the pixels in the video and use that to estimate the camera motion subspace. This is particularly useful in activity recognition, which typically requires standard preprocessing steps such as motion compensation, moving object detection, and object tracking. The errors from the motion compensation step propagate to the object detection stage, resulting in miss-detections, which further complicates the tracking stage, resulting in cluttered and incorrect tracks. In contrast, we propose a novel approach which does not follow the standard steps, and accordingly avoids the aforementioned diffi- culties. Our approach is based on Lagrangian particle trajectories which are a set of dense trajectories obtained by advecting optical flow over time, thus capturing the ensemble motions v of a scene. This is done in frames of unaligned video, and no object detection is required. In order to handle the moving camera, we decompose the trajectories into their camera-induced and object-induced components. Having obtained the relevant object motion trajectories, we compute a compact set of chaotic invariant features, which captures the characteristics of the trajectories. Consequently, a SVM is employed to learn and recognize the human actions using the computed motion features. We performed intensive experiments on multiple benchmark datasets, and obtained promising results. Finally, we consider a more challenging problem referred to as complex event recognition, where the activities of interest are complex and unconstrained. This problem typically pose significant challenges because it involves videos of highly variable content, noise, length, frame size . . . etc. In this extremely challenging task, high-level features have recently shown a promising direction as in [53, 129], where core low-level events referred to as concepts are annotated and modelled using a portion of the training data, then each event is described using its content of these concepts. However, because of the complex nature of the videos, both the concept models and the corresponding high-level features are significantly noisy. In order to address this problem, we propose a novel low-rank formulation, which combines the precisely annotated videos used to train the concepts, with the rich high-level features. Our approach finds a new representation for each event, which is not only low-rank, but also constrained to adhere to the concept annotation, thus suppressing the noise, and maintaining a consistent occurrence of the concepts in each event. Extensive experiments on large scale real world dataset TRECVID Multimedia Event Detection 2011 and 2012 demonstrate that our approach consistently improves the discriminativity of the high-level features by a significant margin.