Network State Estimation and Prediction for Real-Time Traffic Management

Advanced Traveler Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS) have the potential to contribute to the solution of the traffic congestion problem. DynaMIT is a real-time system that can be used to generate guidance for travelers. The main principle on which DynaMIT is based is that information should be consistent, and user optimal. Consistency implies that the traffic conditions experienced by the travelers are consistent with the condition assumed in generating the guidance. To generate consistent user optimal information, DynaMIT performs two main functions: state estimation and prediction. A demand simulator and a supply simulator interact to perform these tasks. A case study demonstrates the value of the system.     

Versatile Tuning of Humanoid Agent Activity

In this paper, we present an integration framework for heterogeneous motion generators. The objective is to outline issues that are currently easily solved in professional post-processing systems used in film and game production but which cannot be transposed as is to real-time systems with autonomous agents. We summarise our approach for articulated agent-modelling and their animation by combining heterogeneous motion generators, such as real-time motion capturing, key-framing, inverse kinematics, procedural walking. We propose an agent/action-oriented framework. Activity properties such as action simultaneity and motion blending, spatial coherence, motion-flow update schemes, agent attachments, and location corrections, are the main topics handled by our generic animation framework. Numerous examples throughout the paper illustrate our approach and outline encountered problems and solutions or open research directions.     

Distributed boundary coverage with a team of networked miniature robots using a robust market-based algorithm

We study distributed boundary coverage of known environments using a team of miniature robots. Distributed boundary coverage is an instance of the multi-robot task-allocation problem and has applications in inspection, cleaning, and painting among others. The proposed algorithm is robust to sensor and actuator noise, failure of individual robots, and communication loss. We use a market-based algorithm with known lower bounds on the performance to allocate the environmental objects of interest among the team of robots. The coverage time for systems subject to sensor and actuator noise is significantly shortended by on-line task re-allocation. The complexity and convergence properties of the algorithm are formally analyzed. The system performance is systematically analyzed at two different microscopic modeling levels, using agent-based, discrete-event and module-based, realistic simulators. Finally, results obtained in simulation are validated using a team of Alice miniature robots involved in a distributed inspection case study.     

Smoothed aggregation multigrid for a Stokes problem

We discuss advantages of using algebraic multigrid based on smoothed aggregation for solving indefinite linear problems. The ingredients of smoothed aggregation are used to construct a black-box monolithic multigrid method with indefinite coarse problems. Several techniques enforcing inf–sup stability conditions on coarse levels are presented. Numerical experiments are designed to support recent stability results for coupled algebraic multigrid. Comparison of the proposed multigrid preconditioner with other methods shows its robust behaviour even for very elongated geometries, where the pressure mass matrix is no longer a good preconditioner for the pressure Schur complement. Supported by the Swiss CTI grant no. 6437.1 IWS-IW, in collaboration with Alcan-Péchiney.     

Force-clamp spectroscopy with a small dithering of AFM tip, and its application to explore the energy landscape of single avidin-biotin complex

We have recently developed a new method for directly measuring the spring constant of single molecules and molecular complexes on a real-time basis [L.A. Chtcheglova, G.T. Shubeita, S.K. Sekatskii, G. Dietler, Biophys. J. 86 (2004) 1177]. The technique combines standard force spectroscopy with a small dithering of tip. Changes in the amplitude of the oscillations are measured as a function of the pulling-off force to yield the spring constant of the complex. In this report, we present the first results of combination of this approach with the force-clamp spectroscopy. The standard atomic-force microscope has been supplemented with an electronic unit, which is capable of realizing an arbitrary force function, and permits the force-loading regime to be interrupted at any time. Using this method, the time needed to rupture a single bond can be measured as a function of the force that is required to maintain the complex in a stretched condition. The energy landscape of the avidin-biotin complex is explored and discussed.     

Vehicle Teleoperation Interfaces

Despite advances in autonomy, there will always be a need for human involvement in vehicle teleoperation. In particular, tasks such as exploration, reconnaissance and surveillance will continue to require human supervision, if not guidance and direct control. Thus, it is critical that the operator interface be as efficient and as capable as possible. In this paper, we provide an overview of vehicle teleoperation and present a summary of interfaces currently in use.     

Dense deformation field estimation for atlas-based segmentation of pathological MR brain images

Atlas registration is a recognized paradigm for the automatic segmentation of normal MR brain images. Unfortunately, atlas-based segmentation has been of limited use in presence of large space-occupying lesions. In fact, brain deformations induced by such lesions are added to normal anatomical variability and they may dramatically shift and deform anatomically or functionally important brain structures. In this work, we chose to focus on the problem of inter-subject registration of MR images with large tumors, inducing a significant shift of surrounding anatomical structures. First, a brief survey of the existing methods that have been proposed to deal with this problem is presented. This introduces the discussion about the requirements and desirable properties that we consider necessary to be fulfilled by a registration method in this context: To have a dense and smooth deformation field and a model of lesion growth, to model different deformability for some structures, to introduce more prior knowledge, and to use voxel-based features with a similarity measure robust to intensity differences. In a second part of this work, we propose a new approach that overcomes some of the main limitations of the existing techniques while complying with most of the desired requirements above. Our algorithm combines the mathematical framework for computing a variational flow proposed by Hermosillo et al. [G. Hermosillo, C. Chefd'Hotel, O. Faugeras, A variational approach to multi-modal image matching, Tech. Rep., INRIA (February 2001).] with the radial lesion growth pattern presented by Bach et al. [M. Bach Cuadra, C. Pollo, A. Bardera, O. Cuisenaire, J.-G. Villemure, J.-Ph. Thiran, Atlas-based segmentation of pathological MR brain images using a model of lesion growth, IEEE Trans. Med. Imag. 23 (10) (2004) 1301-1314.]. Results on patients with a meningioma are visually assessed and compared to those obtained with the most similar method from the state-of-the-art.     

How causal knowledge simplifies decision-making

Making decisions can be hard, but it can also be facilitated. Simple heuristics are fast and frugal but nevertheless fairly accurate decision rules that people can use to compensate for their limitations in computational capacity, time, and knowledge when they make decisions [Gigerenzer, G., Todd, P. M., & the ABC Research Group (1999). Simple Heuristics That Make Us Smart. New York: Oxford University Press.]. These heuristics are effective to the extent that they can exploit the structure of information in the environment in which they operate. Specifically, they require knowledge about the predictive value of probabilistic cues. However, it is often difficult to keep track of all the available cues in the environment and how they relate to any relevant criterion. This problem becomes even more critical if compound cues are considered. We submit that knowledge about the causal structure of the environment helps decision makers focus on a manageable subset of cues, thus effectively reducing the potential computational complexity inherent in even relatively simple decision-making tasks. We review experimental evidence that tested this hypothesis and report the results of a simulation study. We conclude that causal knowledge can act as a meta-cue for identifying highly valid cues, either individual or compound, and helps in the estimation of their validities.