A novel two-section tunable discrete mode Fabry-PÉrot laser exhibiting nanosecond wavelength switching

A novel widely tunable laser diode is proposed and demonstrated. Mode selection occurs by etching perturbing slots into the laser ridge. A two-section device is realized with different slot patterns in each section allowing Vernier tuning. The laser operates at 1.3 mum and achieves a maximum output power of 10 mW. A discontinuous tuning range of 30 nm was achieved with a side mode suppression greater than 30 dB. Wavelength switching times of approximately 1.5 ns between a number of wavelength channels separated by 7 nm have been demonstrated

Generic Drone Control Platform for Autonomous Capture of Cinema Scenes

The movie industry has been using Unmanned Aerial Vehicles as a new tool to produce more and more complex and aesthetic camera shots. However, the shooting process currently rely on manual control of the drones which makes it difficult and sometimes inconvenient to work with. In this paper we address the lack of autonomous system to operate generic rotary-wing drones for shooting purposes. We propose a global control architecture based on a high-level generic API used by many UAV. Our solution integrates a compound and coupled model of a generic rotary-wing drone and a Full State Feedback strategy. To address the specific task of capturing cinema scenes, we combine the control architecture with an automatic camera path planning approach that encompasses cinematographic techniques. The possibilities offered by our system are demonstrated through a series of experiments

Nonlinear Geometric Optics Based Multiscale Stochastic Galerkin Methods for Highly Oscillatory Transport Equations with Random Inputs

We develop generalized polynomial chaos (gPC) based stochastic Galerkin (SG) methods for a class of highly oscillatory transport equations that arise in semiclassical modeling of non-adiabatic quantum dynamics. These models contain uncertainties, particularly in coefficients that correspond to the potentials of the molecular system. We first focus on a highly oscillatory scalar model with random uncertainty. Our method is built upon the nonlinear geometrical optics (NGO) based method, developed in \cite{NGO} for numerical approximations of deterministic equations, which can obtain accurate pointwise solution even without numerically resolving spatially and temporally the oscillations. With the random uncertainty, we show that such a method has oscillatory higher order derivatives in the random space, thus requires a frequency dependent discretization in the random space. We modify this method by introducing a new "time" variable based on the phase, which is shown to be non-oscillatory in the random space, based on which we develop a gPC-SG method that can capture oscillations with the frequency-independent time step, mesh size as well as the degree of polynomial chaos. A similar approach is then extended to a semiclassical surface hopping model system with a similar numerical conclusion. Various numerical examples attest that these methods indeed capture accurately the solution statistics {\em pointwisely} even though none of the numerical parameters resolve the high frequencies of the solution.Comment: 35 page

Characterization of wavelength tunable lasers for future optical communication systems

The use of tunable lasers (TL) in dense wavelength division multiplexed (DWDM) networks for optical switching, routing and networking has gained a lot of interest in recent years. Employment of such TLs as tunable transmitters in wavelength packet switched (WPS) networks is one of the possible applications of these devices. In such systems, the information to be transmitted could be encoded onto a destination dependent wavelength and the routing of traffic could be performed on a packet-by-packet basis. The authors investigate the possibility of using TLs in DWDM WPS networks by focusing on the characterisation of the instantaneous frequency drift of a TL due to wavelength tuning and direct modulation. Characterization of the linewidth of the TLs is also presented to verify the feasibility of using TLs in systems employing advanced modulation formats

Network Flow Algorithms for Structured Sparsity

We consider a class of learning problems that involve a structured sparsity-inducing norm defined as the sum of $\ell_\infty$-norms over groups of variables. Whereas a lot of effort has been put in developing fast optimization methods when the groups are disjoint or embedded in a specific hierarchical structure, we address here the case of general overlapping groups. To this end, we show that the corresponding optimization problem is related to network flow optimization. More precisely, the proximal problem associated with the norm we consider is dual to a quadratic min-cost flow problem. We propose an efficient procedure which computes its solution exactly in polynomial time. Our algorithm scales up to millions of variables, and opens up a whole new range of applications for structured sparse models. We present several experiments on image and video data, demonstrating the applicability and scalability of our approach for various problems.Comment: accepted for publication in Adv. Neural Information Processing Systems, 201

A faster pseudo-primality test

We propose a pseudo-primality test using cyclic extensions of $\mathbb Z/n \mathbb Z$. For every positive integer $k \leq \log n$, this test achieves the security of $k$ Miller-Rabin tests at the cost of $k^{1/2+o(1)}$ Miller-Rabin tests.Comment: Published in Rendiconti del Circolo Matematico di Palermo Journal, Springe

A Hierarchical Bayesian Model for Frame Representation

In many signal processing problems, it may be fruitful to represent the signal under study in a frame. If a probabilistic approach is adopted, it becomes then necessary to estimate the hyper-parameters characterizing the probability distribution of the frame coefficients. This problem is difficult since in general the frame synthesis operator is not bijective. Consequently, the frame coefficients are not directly observable. This paper introduces a hierarchical Bayesian model for frame representation. The posterior distribution of the frame coefficients and model hyper-parameters is derived. Hybrid Markov Chain Monte Carlo algorithms are subsequently proposed to sample from this posterior distribution. The generated samples are then exploited to estimate the hyper-parameters and the frame coefficients of the target signal. Validation experiments show that the proposed algorithms provide an accurate estimation of the frame coefficients and hyper-parameters. Application to practical problems of image denoising show the impact of the resulting Bayesian estimation on the recovered signal quality

Patient-specific numerical simulation of stent-graft deployment: Validation on three clinical cases.

International audienceEndovascular repair of abdominal aortic aneurysms faces some adverse outcomes, such as kinks or endoleaks related to incomplete stent apposition, which are difficult to predict and which restrain its use although it is less invasive than open surgery. Finite element simulations could help to predict and anticipate possible complications biomechanically induced, thus enhancing practitioners' stent-graft sizing and surgery planning, and giving indications on patient eligibility to endovascular repair. The purpose of this work is therefore to develop a new numerical methodology to predict stent-graft final deployed shapes after surgery. The simulation process was applied on three clinical cases, using preoperative scans to generate patient-specific vessel models. The marketed devices deployed during the surgery, consisting of a main body and one or more iliac limbs or extensions, were modeled and their deployment inside the corresponding patient aneurysm was simulated. The numerical results were compared to the actual deployed geometry of the stent-grafts after surgery that was extracted from postoperative scans. We observed relevant matching between simulated and actual deployed stent-graft geometries, especially for proximal and distal stents outside the aneurysm sac which are particularly important for practitioners. Stent locations along the vessel centerlines in the three simulations were always within a few millimeters to actual stents locations. This good agreement between numerical results and clinical cases makes finite element simulation very promising for preoperative planning of endovascular repair