Paraxial approximation defines the electric field of an optical beam at each point as a two-dimensional vector orthogonal to the direction of propagation. The Stokes decomposition theorem asserts that “any light beam is equivalent to the sum of two lights, one of which is polarized and the other unpolarized”. In a modern framework of random stationary processes, the theorem needs more accurate statements. In this paper, we study three-dimensional fields, and we prove that the decomposition problem has at most two solutions (except for an undetermined argument) which are characterized by well determined circuits of LIF (Linear Invariant Filters).

Transmission count, the number of transmissions required for delivering a data packet over a link, is part of almost all state-of-the-art routing metrics for wireless networks. In traditional networks, peer-to-peer interference and channel errors are what define its value for the most part. In cognitive radio networks, however, there is a third culprit that can impact the transmission count: primary user interference. It may be tempting to think of primary user interference as no different than interference caused by other peers. However, unlike peers, primary users do not follow the same protocol and have strict channel access priority over the secondary users. Motivated by this observation, we carry out an empirical study on a USRP testbed for analyzing the impact of primary users. Our measurements show that a primary user has a distinct impact on the transmission count, which the de facto standard approach, ETX, designed for traditional networks, fails to capture. To resolve this, we present COExiST (for COgnitive radio EXpected transmISsion counT): a link metric that accurately captures the expected transmission count over a wireless link subject to primary user interference. Extensive experiments on a five-node USRP testbed demonstrate that COExiST accurately captures the actual transmission count in the presence of primary users – the 80th percentile of the error is less than 20%.

We propose a novel DTN routing algorithm, called DQN, specifically designed for quasi-deterministic networks with an application to satellite constellations. We demonstrate that our proposal efficiently forwards the information over a satellite network derived from the Orbcomm topology while keeping a low replication overhead. We compare our algorithm against other well-known DTN routing schemes and show that we obtain the lowest replication ratio with a delivery ratio of the same order of magnitude than a reference theoretical optimal routing. We also analyse the impact of terrestrial gateways density and analyse DQN performances in heterogeneous cases.

This paper proposes a fast multi-band image fusion algorithm, which combines a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image. The well admitted forward model is explored to form the likelihoods of the observations. Maximizing the likelihoods leads to solving a Sylvester equation. By exploiting the properties of the circulant and downsampling matrices associated with the fusion problem, a closed-form solution for the corresponding Sylvester equation is obtained explicitly, getting rid of any iterative update step. Coupled with the alternating direction method of multipliers and the block coordinate descent method, the proposed algorithm can be easily generalized to incorporate prior information for the fusion problem, allowing a Bayesian estimator. Simulation results show that the proposed algorithm achieves the same performance as the existing algorithms with the advantage of significantly decreasing the computational complexity of these algorithms.

The installation of a condition monitoring system aims to reduce the operating costs of the monitored system by applying a predictive maintenance strategy. However, a system-driven configuration of the condition monitoring system requires the knowledge of the system kinematics and could induce lots a false alarms because of predefined thresholds. The purpose of this paper is to propose a complete data-driven method to automatically generate system health indicators without any a priori on the monitored system or the acquired signals. This method is composed of two steps. First, every acquired signal is analysed: the spectral peaks are detected and then grouped in more complex structure as harmonic series or modulation sidebands. Then, a time-frequency tracking operation is applied on all available signals: the spectral peaks and the spectral structures are tracked over time and grouped in trajectories, which will be used to generate the system health indicators. The proposed method is tested on real-world signals coming from a wind turbine test rig. The detection of a harmonic series and a modulation sideband reports the birth of a fault on the main bearing inner ring. The evolution of the fault severity is characterised by three automatically generated health indicators and is confirmed by experts.

We present a new delay-based transport protocol named FLOWER, that aims at providing a Lower-than-Best-Effort (LBE) service. The objective is to propose an alternative to the Low Extra Delay Background Transport (LEDBAT) widely deployed within the official BitTorrent client. Indeed, besides its intra-fairness problem, known as latecomer unfairness, LEDBAT can be too aggressive against TCP, making it ill suited for providing LBE services over certain networks such as constrained wireless networks. By using a fuzzy controller to modulate the sending rate, FLOWER aims to solve LEDBAT issues while fulfilling the role of a LBE protocol. Our simulation results show that FLOWER can carry LBE traffic in network scenarios where LEDBAT cannot while solving the latecomer unfairness problem. Finally, the presented algorithm is simple to implement and does not require complex computation that would prevent its deployment.

The use of optical communication to transfer data between LEO satellite and optical ground station is being studied. It creates the opportunities to highly increase a transmitted data rate across a free space. The optical propagation channel has specificities that imply the potential use of error correcting code (ECC) and interleaving at physical and higher layer. The study aims to assess the performance of a combination of ECC and interleaving in presence of various channel scenarios and receiver architectures. As a result of these studies, a functional physical layer simulator is provided. The simulator emulates a signal generation and applies time series representing the propagation channel with an effect of receiver front-ends. It also features various detection methods and computes mutual information (MI) in order to approximate ECC performances. A number of receiver architectures and channel scenarios were studied. The channel scenarios combine a direct coupling of the received signal into the photo-detector (PD) and among other assume the use of pre-amplified receiver implying the signal coupling into a standard single mode fiber (SSMF) prior to the detection. Time series were generated and represent the power received at PD input depending on the chosen scenarios (without adaptive optics (AO), with tip-tilt correction, with no dynamical coupling losses or with higher order AO correction). Two modulations of OOK and DBPSK along with various detection methods were examined. The tuning of ECC parameters was studied through the computation of mutual information. Additionally two cases of physical and higher layer interleaving were implemented providing an excellent diversity to the channel seen by the codeword of ECC. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

This Ph.D. thesis aims at evaluating the interest of using multivariate distributions for the analysis of heterogeneous images. The considered heterogeneous data are composed of images acquired by different sensors (including optical, radar and hyperspectral sensors) and possibly of an object database (containing roads, building, etc.). The applications considered in this thesis are mainly image registration, change detection and database updating. All these applications require to define a similarity measure between the different images or between features estimated from these images (called modalities).

Remote sensing images are images of the Earth surface acquired from satellites or airborne equipment. These images are beco-ming widely available nowadays, with many commercial and non-commercial services pro-viding them. The technology of the sensors required to capture this kind of images is evol-ving fast. Not only classical sensors are impro-ving in terms of resolution and noise level, but also new kinds of sensors are proving to be useful. Multispectral image sensors are stan-dard nowadays, synthetic aperture radar (SAR) images are very popular, and hyperspectral sen-sors are receiving more and more attention in many applications. One of the main applications of remote sensing images is the detection of changes in multitem-poral datasets, i.e., detecting changes in images of the same area acquired at different times. Change detection for images acquired by ho-mogeneous sensors has been of interest for a long time. However the wide range of different sensors found in remote sensing makes the de-tection of changes in images acquired by hete-rogeneous sensors an interesting challenge. The main interest of this thesis is to study statistical approaches to detect changes in images acquired by heterogeneous sensors.