Image sequence segmentation using mixture-models with uniqueness and spatio-temporal consistency constraints

The use of mixture-model techniques for motion estimation and image sequence segmentation was discussed. The issues such as modeling of occlusion and uncovering, determining the relative depth of the objects in a scene, and estimating the number of objects in a scene were also investigated. The segmentation algorithm was found to be computationally demanding, but the computational requirements were reduced as the motion parameters and segmentation of the frame were initialized. The method provided a stable description, in whichthe addition and removal of objects from the description corresponded to the entry and exit of objects from the scene.

Optimal control with stochastic PDE constraints and uncertain controls

The optimal control of problems that are constrained by partial differential equations with uncertainties and with uncertain controls is addressed. The Lagrangian that defines the problem is postulated in terms of stochastic functions, with the control function possibly decomposed into an unknown deterministic component and a known zero-mean stochastic component. The extra freedom provided by the stochastic dimension in defining cost functionals is explored, demonstrating the scope for controlling statistical aspects of the system response. One-shot stochastic finite element methods are used to find approximate solutions to control problems. It is shown that applying the stochastic collocation finite element method to the formulated problem leads to a coupling between stochastic collocation points when a deterministic optimal control is considered or when moments are included in the cost functional, thereby forgoing the primary advantage of the collocation method over the stochastic Galerkin method for the considered problem. The application of the presented methods is demonstrated through a number of numerical examples. The presented framework is sufficiently general to also consider a class of inverse problems, and numerical examples of this type are also presented. © 2011 Elsevier B.V.

Emerging modes of collective cell migration induced by geometrical constraints.

The role of geometrical confinement on collective cell migration has been recognized but has not been elucidated yet. Here, we show that the geometrical properties of the environment regulate the formation of collective cell migration patterns through cell-cell interactions. Using microfabrication techniques to allow epithelial cell sheets to migrate into strips whose width was varied from one up to several cell diameters, we identified the modes of collective migration in response to geometrical constraints. We observed that a decrease in the width of the strips is accompanied by an overall increase in the speed of the migrating cell sheet. Moreover, large-scale vortices over tens of cell lengths appeared in the wide strips whereas a contraction-elongation type of motion is observed in the narrow strips. Velocity fields and traction force signatures within the cellular population revealed migration modes with alternative pulling and/or pushing mechanisms that depend on extrinsic constraints. Force transmission through intercellular contacts plays a key role in this process because the disruption of cell-cell junctions abolishes directed collective migration and passive cell-cell adhesions tend to move the cells uniformly together independent of the geometry. Altogether, these findings not only demonstrate the existence of patterns of collective cell migration depending on external constraints but also provide a mechanical explanation for how large-scale interactions through cell-cell junctions can feed back to regulate the organization of migrating tissues.

Generating the Sparse Point Cloud of a Civil Infrastructure Scene Using a Single Video Camera under Practical Constraints

Automating the model generation process of infrastructure can substantially reduce the modeling time and cost. This paper presents a method to generate a sparse point cloud of an infrastructure scene using a single video camera under practical constraints. It is the first step towards establishing an automatic framework for object-oriented as-built modeling. Motion blur and key frame selection criteria are considered. Structure from motion and bundle adjustment are explored. The method is demonstrated in a case study where the scene of a reinforced concrete bridge is videotaped, reconstructed, and metrically validated. The result indicates the applicability, efficiency, and accuracy of the proposed method.

The energy required to produce materials: constraints on energy-intensity improvements, parameters of demand.

In this paper, we review the energy requirements to make materials on a global scale by focusing on the five construction materials that dominate energy used in material production: steel, cement, paper, plastics and aluminium. We then estimate the possibility of reducing absolute material production energy by half, while doubling production from the present to 2050. The goal therefore is a 75 per cent reduction in energy intensity. Four technology-based strategies are investigated, regardless of cost: (i) widespread application of best available technology (BAT), (ii) BAT to cutting-edge technologies, (iii) aggressive recycling and finally, and (iv) significant improvements in recycling technologies. Taken together, these aggressive strategies could produce impressive gains, of the order of a 50-56 per cent reduction in energy intensity, but this is still short of our goal of a 75 per cent reduction. Ultimately, we face fundamental thermodynamic as well as practical constraints on our ability to improve the energy intensity of material production. A strategy to reduce demand by providing material services with less material (called 'material efficiency') is outlined as an approach to solving this dilemma.

Linear regression under fixed-rank constraints: A Riemannian approach

In this paper, we tackle the problem of learning a linear regression model whose parameter is a fixed-rank matrix. We study the Riemannian manifold geometry of the set of fixed-rank matrices and develop efficient line-search algorithms. The proposed algorithms have many applications, scale to high-dimensional problems, enjoy local convergence properties and confer a geometric basis to recent contributions on learning fixed-rank matrices. Numerical experiments on benchmarks suggest that the proposed algorithms compete with the state-of-the-art, and that manifold optimization offers a versatile framework for the design of rank-constrained machine learning algorithms. Copyright 2011 by the author(s)/owner(s).

Multispacecraft refueling optimization considering the J2 perturbation and window constraints

The optimization of a near-circular low-Earth-orbit multispacecraft refueling problem is studied. The refueling sequence, service time, and orbital transfer time are used as design variables, whereas the mean mission completion time and mean propellant consumed by orbital maneuvers are used as design objectives. The J2 term of the Earth's nonspherical gravity perturbation and the constraints of rendezvous time windows are taken into account. A hybridencoding genetic algorithm, which uses normal fitness assignment to find the minimum mean propellant-cost solution and fitness assignment based on the concept of Pareto-optimality to find multi-objective optimal solutions, is presented. The proposed approach is demonstrated for a typical multispacecraft refueling problem. The results show that the proposed approach is effective, and that the J2 perturbation and the time-window constraints have considerable influences on the optimization results. For the problems in which the J2 perturbation is not accounted for, the optimal refueling order can be simply determined as a sequential order or as the order only based on orbitalplane differences. In contrast, for the problems that do consider the J2 perturbation, the optimal solutions obtained have a variety of refueling orders and use the drift of nodes effectively to reduce the propellant cost for eliminating orbital-plane differences. © 2013 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

Reconfigurable predictive control for redundantly actuated systems with parameterised input constraints

A method is proposed for on-line reconfiguration of the terminal constraint used to provide theoretical nominal stability guarantees in linear model predictive control (MPC). By parameterising the terminal constraint, its complete reconstruction is avoided when input constraints are modified to accommodate faults. To enlarge the region of feasibility of the terminal control law for a certain class of input faults with redundantly actuated plants, the linear terminal controller is defined in terms of virtual commands. A suitable terminal cost weighting for the reconfigurable MPC is obtained by means of an upper bound on the cost for all feasible realisations of the virtual commands from the terminal controller. Conditions are proposed that guarantee feasibility recovery for a defined subset of faults. The proposed method is demonstrated by means of a numerical example. © 2013 Elsevier B.V. All rights reserved.