We consider the classical optical ow algorithm due to Horn and Schunck for estimating the motion of brightness patterns between image pairs. We use a modi-ed smoothness condition based on the divergence and curl of the velocity eld. In previous work, we have developed well-posed stochastic state-space models for these optical ow methods in two dimensions. This paper extends our results to 3-D. ...

We use stochastic /uid models (SFM) to capture the operation of threshold-based production control policies in manufacturing systems without resorting to detailed discrete event models. By applying in4nitesimal perturbation analysis (IPA) to a SFM of a workcenter, we derive gradient estimators of throughput and bu7er over/ow metrics with respect to production control parameters. It is shown tha...

In this paper we present new model-based controllers for active queue management (AQM) supporting TCP ows. Our control design is based on linearization of a previously developed TCP model using uid ow and stochastic di erential equations. An AQM using a simple PI controller is shown to signi cantly outperform RED AQM. However, we show that both AQMs are unable to maintain performance as the num...

The problem of computing performability probabilities in stochastic PERT and ow networks is studied when the network is \minimally designed" to withstand any two component failures. Polynomial-time algorithms to compute performability when the network is planar| the nonplanar versions being NP-hard | solve related \two-path subset" problems. Given an acyclic graph with weights on the arcs, the ...

Timed (or Stochastic) Data Flow Diagrams (TDFD's or SDFD's) introduced in [SB96b] are an extension of the Formalized Data Flow Diagrams, de ned in [LWBL96]. This extension allows us to assess the quantitative behavior (e. g., performance, throughput, average load of a bubble, etc.) as well as the qualitative behavior (e. g., deadlock, reachability, termination, niteness, liveness, etc.), eventu...

We study Bayesian models and methods for analysing network tra c counts in problems of inference about the tra c intensity between directed pairs of origins and destinations in networks. This is a class of problems very recently discussed by Vardi in a 1996 JASA article, and of interest in both communication and transportation network studies. The current paper develops the theoretical framewor...

A discrete stochastic uncoupling process for nite spaces is introduced, called the Markov Cluster Process. The process takes a stochastic matrix as input, and then alternates ow expansion and ow innation, each step deening a stochastic matrix in terms of the previous one. Flow expansion corresponds with taking the k th power of a stochastic matrix, where k 2 IN. Flow innation corresponds with a...

We study Bayesian models and methods for analysing network tra c counts in problems of inference about the tra c intensity between directed pairs of origins and destinations in networks. This is a class of problems very recently discussed by Vardi in a 1996 JASA article, and of interest in both communication and transportation network studies. The current paper develops the theoretical framewor...

We analyze the performance of a generic feedback ow control mechanism which captures the properties of several such mechanisms recently proposed in the literature. These mechanisms dynamically regulate the rate of data ow into a network based on feedback information about the network state. They are used in a variety of networks and they have been advocated for upcoming high-speed networks. How...