Photo Sequencing

A group of people taking pictures of a dynamic event with their mobile phones is a popular sight. The set of still images obtained this way is rich in dynamic content but lacks accurate temporal information. We propose a method for photo-sequencing—temporally ordering a set of still images taken asynchronously by a set of uncalibrated cameras. Photo-sequencing is an essential tool in analyzing (or visualizing) a dynamic scene captured by still images. The first step of the method detects sets of corresponding static and dynamic feature points across images. The static features are used to determine the epipolar geometry between pairs of images, and each dynamic feature votes for the temporal order of the images in which it appears. The partial orders provided by the dynamic features are not necessarily consistent, and we use rank aggregation to combine them into a globally consistent temporal order of images. We demonstrate successful photo-sequencing on several challenging collections of images taken using a number of mobile phones.     

Tracking in a Dense Crowd Using Multiple Cameras

Tracking people in a dense crowd is a challenging problem for a single camera tracker due to occlusions and extensive motion that make human segmentation difficult. In this paper we suggest a method for simultaneously tracking all the people in a densely crowded scene using a set of cameras with overlapping fields of view. To overcome occlusions, the cameras are placed at a high elevation and only people’s heads are tracked. Head detection is still difficult since each foreground region may consist of multiple subjects. By combining data from several views, height information is extracted and used for head segmentation. The head tops, which are regarded as 2D patches at various heights, are detected by applying intensity correlation to aligned frames from the different cameras. The detected head tops are then tracked using common assumptions on motion direction and velocity. The method was tested on sequences in indoor and outdoor environments under challenging illumination conditions. It was successful in tracking up to 21 people walking in a small area (2.5 people per m²), in spite of severe and persistent occlusions.     

Optimal structural topologies with transmissible loads

In optimal topological design of structures one obtains the configuration of optimal structures when the design domain, the displacement boundary conditions and the applied loads are specified. In the optimal structure one often notices a marked difference between the main bearing structure and the load transfer zones. The latter are composed of relatively light elements the exact nature of which is not always very distinct. The main purpose of this paper is to allow the main bearing part of the structure to emerge. Moreover the actual location of the load along its line of action is not always a design requirement. In order to include this relaxed condition regarding the loading position the concept of transmissible or sliding forces is introduced in topological design of structures. A transmissible force is a force of given magnitude and direction which can be applied at any point along the line of action of the force. The optimization formulation is similar to standard topological design procedure in addition to the condition of transmissability of the forces. It is shown that this condition reduces to an equal displacement constraint along the line of action of the forces. The method is illustrated by typical structural examples. It is observed that this numerical method produces indeed crisp images of the main structural components, unblurred by the secondary load transfer elements. It is also indicated that many results are often replicas of Prager structures which were previously obtained by analytical methods. Received March 3, 1999     

Topological design of modular structures under arbitrary loading

A numerical method for the topological design of periodic continuous domains under general loading is presented. Both the analysis and the design are defined over a single cell. Confining the analysis to the repetitive unit is obtained by the representative cell method which by means of the discrete Fourier transform reduces the original problem to a boundary value problem defined over one module, the representative cell. The repeating module is then meshed into a dense grid of finite elements and solved by finite element analysis. The technique is combined with topology optimization of infinite spatially periodic structures under arbitrary static loading. Minimum compliance structures under a constant volume of material are obtained by using the densities of material as design variables and by satisfying a classical optimality criterion which is generalized to encompass periodic structures. The method is illustrated with the design of an infinite strip possessing 1D translational symmetry and a cyclic structure under a tangential point force. A parametric study presents the evolution of the solution as a function of the aspect ratio of the representative cell.     

Estimating leaf nitrogen concentration from similarities in fresh and dry leaf spectral bands using a model population analysis framework

Fresh leaf spectral reflectance is primarily influenced by leaf water content and structural aspects such as the inter-cellular spaces within the spongy mesophyll, which also interfere with the estimation of the leaf nitrogen content. It is therefore essential to identify spectral bands that are least affected by the above perturbing factors for improving leaf nitrogen estimation for fresh leaves across any landscape. Wavelengths selection plays a vital role in identifying the best spectral features for assessing leaf nitrogen concentration from hyperspectral data of dry and fresh leaves. The primary objective of this study was to determine typical optimal bands for leaf nitrogen estimation from spectra (400–2500 nm) of whole fresh and dry leaves for the same specimens of Eucalyptus grandis. This was achieved via the use of competitive adaptive re-weighted sampling (CARS), and Monte Carlo cross-validation-competitive adaptive re-weighted sampling (MCCV-CARS) band selection approaches. Bands selected (931 nm, 1003 nm, 1027 nm, 1036 nm, 1177 nm, and 1180 nm) via the MCCV-CARS approach yielded the highest estimation accuracy for both fresh predicted coefficient of determination (R²_cal) = 0.82 and predicted root mean square error (RMSE_P) = 0.14) and dry leaves (R²_P = 0.88 and RMSE_P = 0.13) when compared to CARS (2044 nm, 2107 nm, and 2188 nm) only. The identified spectral features could be relevant for assessing leaf nitrogen concentration for different seasons, for example, wet to dry season.     

An adaptive non-linear observer for the estimation of temperature distribution in the planar solid oxide fuel cell

Minimising the thermal gradients is extremely important in a planar solid oxide fuel cell (SOFC) for improving the cell life. The estimation of the temperature distribution in the cell is necessary to achieve this objective through suitable control, since they are not generally measurable. In this work, we have designed a non-linear adaptive observer for estimating the temperatures inside the hydrogen fed planar SOFC. The observer design is based on a lumped parameter model of the SOFC. The stability of the proposed observer is proven using the Lyapunov function method and is based on the concept of input-to-state stability for cascaded systems. The simulations show that the developed observer can track the temperature and species concentration profiles in the planar SOFC during step changes in the cell current. The adaptive observer presented is valid for a wide operating range, requires fewer variables to be measured, and is robust to fluctuations in the inlet flows.     

Effects of temperature on a.c. magnetostriction in grain-oriented silicon-iron

The a.c. magnetostriction of conventional grain-oriented 3 1/4% silicon-iron and more highly oriented silicon-iron has been measured from room temperature up to the Curie point using high temperature strain gauges. Curves of magnetostriction plotted against flux density showed that a definite change in the magnetization process occurred at about 300° C extending to 400° C for both types of material. This was considered to be due to the onset of magnetic annealing, considerably modified by the effect of the coating on the materials. From the experimental results the magnetostriction constant ₁₀₀ has been calculated using a predicted domain structure and was found to agree well with single crystal values apart from within the 300 to 400° C region.     

Thin-walled solenoid end effects

The magnetic field maximum for air-core solenoids commonly occurs at the inside turn on the mid-plane. For thin-walled solenoids, however, the magnetic field maximum occurs at the ends of the solenoid. To study this end effect, closed form expressions are derived for the axial and radial magnetic field components in the vicinity of the solenoid windings. Formulae for single and double layer solenoids are developed as perturbations on the field of a uniform current sheet solenoid. Formulae for the axial and radial load stresses are developed also.     

Due date assignment using feedback control with reinforcement learning

Good due date assignment for an order requires the calculation of a time buffer that will account for the uncertainties associated with the arrival of future orders in a dynamic environment. This paper presents a method that controls the size of this time buffer for a discrete manufacturing system. The applicability of the method to an unrestricted class of discrete manufacturing systems is preserved by the use of a feedback control paradigm, and control knowledge is acquired using reinforcement learning. The current trajectory of the state of the shop is considered so that due date performance is improved during transient conditions. Results of simulation experiments demonstrate the effectiveness of the approach.