Cinéma expérimental

Le cinéma expérimental est une pratique artistique relevant à la fois des arts plastiques et du cinéma d'art et d'essai. Plus précisément, le cinéma expérimental s'est développé à partir de deux corpus : celui des avant-gardes littéraires et plastiques du XX ème Siècle (cubisme, Dada, surréalisme) et celui de l’histoire du cinéma. C'est un art en soi, qui se développe en marge de l'industrie et du système commercial. Souvent esthétiquement hors-normes, il n'obéit pas à des règles prédéfinies, mais promeut ses propres codes expressifs, son esthétique et, souvent, ses modes de diffusion. Il s'est forgé, depuis les années 1920, une histoire spécifique. Cette histoire appartient néanmoins à celle du cinéma . Définition Il semble difficile de définir exactement le cinéma expérimental. La notion d'expérimentation, si elle est importante, s'avère insuffisante à sa définition. De nombreux termes ont servi à le désigner, à partir de ses

Mechanistic Modeling of Genetic Circuits for ArsR Arsenic Regulation

Yves Berset, Vassily Hatzimanikatis

Bioreporters are living cells that generate an easily measurable signal in the presence of a chemical compound. They acquire their functionality from synthetic gene circuits, the configuration of which defines the response signal and signal-to-noise ratio. Bioreporters based on the Escherichia coli ArsR system have raised significant interest for quantifying arsenic pollution, but they need to be carefully optimized to accurately work in the required low concentration range (1-10 mu g arsenite L-1). To better understand the general functioning of ArsR-based genetic circuits, we developed a comprehensive mechanistic model that was empirically tested and validated in E. coli carrying different circuit configurations. The model accounts for the different elements in the circuits (proteins, DNA, chemical species), and their detailed affinities and interactions, and predicts the (fluorescent) output from the bioreporter cell as a function of arsenite concentration. The model was parametrized using existing ArsR biochemical data, and then complemented by parameter estimations from the accompanying experimental data using a scatter search algorithm. Model predictions and experimental data were largely coherent for feedback and uncoupled circuit configurations, different ArsR alleles, promoter strengths, and presence or absence of arsenic efflux in the bioreporters. Interestingly, the model predicted a particular useful circuit variant having steeper response at low arsenite concentrations, which was experimentally confirmed and may be useful as arsenic bioreporter in the field. From the extensive validation we expect the mechanistic model to further be a useful framework for detailed modeling of other synthetic circuits.

Amer Chemical Soc2017

Vision-Based Sense and Avoid Algorithms for Unmanned Aerial Vehicles

Steven Adriaan Roelofsen

The field of Unmanned Aerial Vehicles (UAVs), also known as drones, is rapidly growing, both in terms of size and of number of applications. Civil applications range from mapping, inspection, search and rescue, taking aerial footage, to art show, entertainment and more. Currently, most applications have a human pilot supervising or controlling the vehicles, but UAVs are expected to gain more autonomy with time. To fly in general airspace, used by both general and commercial aviation, a high level of autonomy is required from UAVs. A core functionality required to fly in general airspace is the UAVs' ability to detect and avoid collisions with other aircraft or objects. This functionality is handled by a so called Sense And Avoid (SAA) system. From among several sensors investigated to be used for a SAA system, a vision-based sensor is seen as a good candidate for a SAA system due to its ability to detect and identify a large variety of objects, as well as being close to the human's main mean to detect aircraft and other objects. To be as general as possible, this work focuses on non-cooperative algorithms that do not take assumptions on the motion of other aircraft. This thesis presents algorithms for a vision-based SAA system. It focuses on the relationship between sensing and avoidance, and how the limitations of one constrain the second. In particular, this thesis studies the consequences of the limited Field Of View (FOV) of a camera sensor on the collision avoidance algorithms. Given the assumptions above, the sensing and tracking of other UAVs is performed using cameras with fish-eye lenses that have a large enough FOV for the collision avoidance algorithms to guarantee to be collision-free. The detection of other UAVs is performed using two methods: a marker-based or a marker-less computer vision algorithms. Using the measurements from the computer vision algorithm, the positions and velocities of neighboring UAVs are tracked using a Gaussian mixture probability hypothesis density filter. This tracking algorithm is able to track multiple UAVs while requiring little computational resources, therefore representing a suitable candidate for on-board deployment. In this work, it is mathematically proven that the motion of an UAV has to be constrained according to the FOV of its sensor. Following that result, several collision avoidance algorithms are adapted to ensure collision-free navigation when used with a sensor with a limited FOV. Sensory limitations such as noise, lag, limited range and FOV, and their effects on the performance of collision avoidance algorithms are studied. Experimental work using high-fidelity simulation and real robots shows that algorithms that only use position information from the sensors are overall more reliable, although less efficient (in terms of distance traveled or trajectory smoothness) than algorithms that also use velocity estimates from the sensing system.

EPFL2017

Vision-Based Sense and Avoid Algorithms for Unmanned Aerial Vehicles

Steven Adriaan Roelofsen

EPFL2017

Experimental Program at the Lotus Facility

Mechanistic Modeling of Genetic Circuits for ArsR Arsenic Regulation

Vision-Based Sense and Avoid Algorithms for Unmanned Aerial Vehicles

Vision-Based Sense and Avoid Algorithms for Unmanned Aerial Vehicles

Experimental Program at the Lotus Facility

Mechanistic Modeling of Genetic Circuits for ArsR Arsenic Regulation