We introduce a new class of problems called network information flow which is inspired by computer network applications. Consider a point-to-point communication network on which a number of information sources are to be mulitcast to certain sets of destinations. We assume that the information

-depth understanding of the RED algorithm. Using the tools developed in this paper, we present a critical analysis of the RED algorithm. We explain the role played by the RED configuration parameters on the behavior of the algorithm in a network. We point out a flaw in the RED averaging mechanism which we believe is a

conservation to completely describe fluid motion. Our starting point is an environment containing an arbitrary distribution of fluid, and submerged or semi-submerged obstacles. Velocity and pressure are defined everywhere within this environment, and updated using a set of finite difference expressions

, file transfer rates, and processing performance. The evaluation includes a computational fluid dynamics code and an N-body gravitational simulation program. It is shown that the Beowulf architecture provides a new operating point in performance to cost for high performance workstations, especially

, the particle-based approach makes mass conservation equations and convection terms dispensable which reduces the complexity of the simulation. In addition, the particles can directly be used to render the surface of the fluid. We propose methods to track and visualize the free surface using point splatting

Point-of-collection oral fluids drug-testing devices are being marketed for a variety of medico-legal purposes where they may complement existing technologies and be used to detect drugs following recent ingestion. To assess the utility of these devices for use in drugged-driving investigations, we

The electroencephalogram (EEG) is a non-invasive measure of brain electrical activity recorded as changes in potential difference between points on the human scalp. Because of volume conduction through cerebrospinal fluid, skull and scalp, EEG data collected from any point on the scalp includes

of the velocity field of the fluid. Using the linearity of this deformation we derive a convolution filter which we use in a scalespace framework. We also demonstrate that the 'demon'-based registration method of Thirion [13] can be seen as an approximation to the fluid registration method and point

systems, which, unlike previous techniques, can be used to split, join, or extend surfaces without the need for manual intervention. The particles we use have long-range attraction forces and short-range repulsion forces and follow Newtonian dynamics, much like recent computational models of fluids

mechanism, which provides integrated control of rotation and translation using only a single touch-point for input. We present an empirical evaluation comparing RNT to a common rotation mechanism that separates control of rotation and translation. Results of this study indicate RNT is more efficient than