Expressing and enforcing user-defined constraints of AADL models

The Architecture Analysis and Design Language AADL allows one to model complete systems, but also to define specific extensions through property sets and library of models. Yet, it does not define an explicit mechanism to enforce some semantics or consistency checks to ensure property sets are correctly used. In this paper, we present REAL (Requirements and Enforcements Analysis Language) as an integrated solution to this issue. REAL is defined as an AADL annex language. It adds the possibility to express constraints as theorems based on set theory to enforce implicit semantics of property sets or AADL models. We illustrate the use of the language on case studies we developed with industrial partners

Characterizing Self-Developing Biological Neural Networks: A First Step Towards their Application To Computing Systems

Carbon nanotubes are often seen as the only alternative technology to silicon transistors. While they are the most likely short-term one, other longer-term alternatives should be studied as well. While contemplating biological neurons as an alternative component may seem preposterous at first sight, significant recent progress in CMOS-neuron interface suggests this direction may not be unrealistic; moreover, biological neurons are known to self-assemble into very large networks capable of complex information processing tasks, something that has yet to be achieved with other emerging technologies. The first step to designing computing systems on top of biological neurons is to build an abstract model of self-assembled biological neural networks, much like computer architects manipulate abstract models of transistors and circuits. In this article, we propose a first model of the structure of biological neural networks. We provide empirical evidence that this model matches the biological neural networks found in living organisms, and exhibits the small-world graph structure properties commonly found in many large and self-organized systems, including biological neural networks. More importantly, we extract the simple local rules and characteristics governing the growth of such networks, enabling the development of potentially large but realistic biological neural networks, as would be needed for complex information processing/computing tasks. Based on this model, future work will be targeted to understanding the evolution and learning properties of such networks, and how they can be used to build computing systems

A MDE-based optimisation process for Real-Time systems

The design and implementation of Real-Time Embedded Systems is now heavily relying on Model-Driven Engineering (MDE) as a central place to define and then analyze or implement a system. MDE toolchains are taking a key role as to gather most of functional and not functional properties in a central framework, and then exploit this information. Such toolchain is based on both 1) a modeling notation, and 2) companion tools to transform or analyse models. In this paper, we present a MDE-based process for system optimisation based on an architectural description. We first define a generic evaluation pipeline, define a library of elementary transformations and then shows how to use it through Domain-Specific Language to evaluate and then transform models. We illustrate this process on an AADL case study modeling a Generic Avionics Platform

Collective Motion of Vibrated Polar Disks

We experimentally study a monolayer of vibrated disks with a built-in polar asymmetry which enables them to move quasi-balistically on a large persistence length. Alignment occurs during collisions as a result of self-propulsion and hard core repulsion. Varying the amplitude of the vibration, we observe the onset of large-scale collective motion and the existence of giant number fluctuations with a scaling exponent in agreement with the predicted theoretical value.Comment: 4 pages, 4 figure

Deep and optically resolved imaging through scattering media by space-reversed propagation

We propose a novel technique of microscopy to overcome the effects of both scattering and limitation of the accessible depth due to the objective working distance. By combining Laser Optical Feedback Imaging (LOFI) with Acoustic Photon Taging (APT) and Synthetic Aperture (SA) refocusing we demonstrate an ultimate shot noise sensitivity at low power (required to preserve the tissues) and a high resolution beyond the microscope working distance. More precisely, with a laser power of 10mW, we obtain images with a micrometric resolution over ~8 transport mean free paths, corresponding to 1.3 times the microscope working distance. Various applications such as biomedical diagnosis, research and development of new drugs and therapies can benefit from our imaging setup

Coherent microscopy by laser optical feedback imaging (LOFI) technique

The application of the non conventional imaging technique LOFI (Laser Optical Feedback Imaging) to coherent microscopy is presented. This simple and efficient technique using frequency-shifted optical feedback needs the sample to be scanned in order to obtain an image. The effects on magnitude and phase signals such as vignetting and field curvature occasioned by the scanning with galvanometric mirrors are discussed. A simple monitoring method based on phase images is proposed to find the optimal position of the scanner. Finally, some experimental results illustrating this technique are presented

Proposition d'une Taxonomie Fonctionnelle des Environnements de Réalité Augmentée

8 pagesL'objectif de cet article est double. Nous proposons premiè- rement une définition des environnements de réalité aug- mentée (RA). Ensuite nous proposons, basée sur notre dé- finition, une nouvelle taxonomie permettant de classer ces environnements. Après une brève revue des classifications existantes, nous définissons la RA par sa finalité qui est de permettre à une personne de réaliser des activités sensori- motrices et cognitives dans un nouvel espace en associant l'environnement réel et un environnement virtuel. Nous pré- sentons ensuite notre taxonomie fonctionnelle des environ- nements de RA. Nous divisons ces environnements en deux groupes distincts. Le premier concerne les différentes fonc- tionnalités permettant de s'informer et de comprendre notre environnement, une perception augmentée de la réalité. Le deuxième correspond aux applications ayant pour finalité de créer un environnement imaginaire. Enfin, plus qu'une dif- férence fonctionnelle, nous démontrons qu'il est possible de considérer que les deux types de RA ont une finalité prag- matique. La différence semble donc tenir à la capacité de ces deux types de RA à s'affranchir ou pas de la localisation spatio-temporelle