On the universality of the global slope -- anisotropy inequality

Recently, some intriguing results have lead to speculations whether the central density slope -- velocity dispersion anisotropy inequality (An & Evans) actually holds at all radii for spherical dynamical systems. We extend these studies by providing a complete analysis of the global slope -- anisotropy inequality for all spherical systems in which the augmented density is a separable function of radius and potential. We prove that these systems indeed satisfy the global inequality if their central anisotropy is $\beta_0\leq 1/2$. Furthermore, we present several systems with $\beta_0 > 1/2$ for which the inequality does not hold, thus demonstrating that the global density slope -- anisotropy inequality is not a universal property. This analysis is a significant step towards an understanding of the relation for general spherical systems.Comment: 8 pages, 1 figure, accepted for publication in Ap

Activity monitoring of people in buildings using distributed smart cameras

Systems for monitoring the activity of people inside buildings (e.g., how many people are there, where are they, what are they doing, etc.) have numerous (potential) applications including domotics (control of lighting, heating, etc.), elderly-care (gathering statistics on the daily live) and video teleconferencing. We will discuss the key challenges and present the preliminary results of our ongoing research on the use of distributed smart cameras for activity monitoring of people in buildings. The emphasis of our research is on: - the use of smart cameras (embedded devices): video is processed locally (distributed algorithms), and only meta-data is send over the network (minimal data exchange) - camera collaboration: cameras with overlapping views work together in a network in order to increase the overall system performance - robustness: system should work in real conditions (e.g., robust to lighting changes) Our research setup consists of cameras connected to PCs (to simulate smart cameras), each connected to one central PC. The system builds in real-time an occupancy map of a room (indicating the positions of the people in the room) by fusing the information from the different cameras in a Dempster-Shafer framework

A new method for detection and source analysis of EEG spikes

In the past our research group has developed a method for the detection of focal epileptic EEG (electroencephalogram) spikes that is based on the dipole source localization technique and provides a source localization for each detected spike. In this paper we revisit this method and propose a more accurate explanation of its behavior. Based on this we (i) propose a new method for the detection of epileptic EEG spikes in which the eccentricity of the fitted dipole serves as a new decision variable (ii) conclude that for EEG spike detection one has to make a distinction between EEGs acquired during sleep and during wake

Rigorous analysis of internal resonances in 3-D hybrid FE-BIE formulations by means of the Poincaré-Steklov operator

3-D hybrid finite-element (FE) boundary integral equation (BIE) formulations are widely used because of their ability to simulate large inhomogeneous structures in both open and bounded simulation domains by applying each method where it is the most efficient. However, some formulations suffer from breakdown frequencies at which the solution is not uniquely defined and errors are introduced due to internal resonances. In this paper, we investigate the occurrence of spurious solutions resulting from these resonances by using the concept of the Poincare-Steklov or Dirichlet-to-Neumann operator, which provides a relation between the tangential electric field and the electric current on the boundary of a domain. By identifying this operator in both the FE and BIE method, several new properties of internal resonances in 3-D hybrid FE-BIE formulations are easily derived. Several conformal and nonconformal formulations are studied and the theory is then applied to a scattering problem

Self-learning voxel-based multi-camera occlusion maps for 3D reconstruction

The quality of a shape-from-silhouettes 3D reconstruction technique strongly depends on the completeness of the silhouettes from each of the cameras. Static occlusion, due to e.g. furniture, makes reconstruction difficult, as we assume no prior knowledge concerning shape and size of occluding objects in the scene. In this paper we present a self-learning algorithm that is able to build an occlusion map for each camera from a voxel perspective. This information is then used to determine which cameras need to be evaluated when reconstructing the 3D model at every voxel in the scene. We show promising results in a multi-camera setup with seven cameras where the object is significantly better reconstructed compared to the state of the art methods, despite the occluding object in the center of the room

Demo: real-time indoors people tracking in scalable camera networks

In this demo we present a people tracker in indoor environments. The tracker executes in a network of smart cameras with overlapping views. Special attention is given to real-time processing by distribution of tasks between the cameras and the fusion server. Each camera performs tasks of processing the images and tracking of people in the image plane. Instead of camera images, only metadata (a bounding box per person) are sent from each camera to the fusion server. The metadata are used on the server side to estimate the position of each person in real-world coordinates. Although the tracker is designed to suit any indoor environment, in this demo the tracker's performance is presented in a meeting scenario, where occlusions of people by other people and/or furniture are significant and occur frequently. Multiple cameras insure views from multiple angles, which keeps tracking accurate even in cases of severe occlusions in some of the views