39794072
Three-Dimensional Effects Created by Macromedia Flash MX
15418886
Progressive Principal Component Analysis
Principal Component Analysis (PCA) is a feature extraction approach directly based on a whole vector pattern and acquires a set of projections that can realize the best reconstruction for an original data in the mean squared error sense. In this paper, the progressive PCA (PrPCA) is proposed, which could progressively extract features from a set of given data with large dimensionality and the extracted features are subsequently applied to pattern recognition. Experiments on the FERET database show its face recognition performance is better than those based on both E(PC)2A and FLDA.
14597486
Modeling agent-based task handling in a peer-to-peer grid
In a peer-to-peer grid, tasks are distributed to grid nodes in a decentralized fashion. We present an agent-based task handling mechanism in a peer-to-peer grid and then provide a macroscopic model to characterize the process of task handling. Our model consists of functional differential equations. Through case studies: (1) we show that our model is effective in characterizing the process of task handling; (2) we examine the effects of time delay, service time, etc. on the global performance of a peer-to-peer grid. Based on our model, we further simulate a complete process of task handling in a grid and show the main characteristics of a real grid.
2727114
Modified Rate-Monotonic Algorithm for Scheduling Periodic Jobs with Deferred Deadlines
The deadline of a request is the time instant at which its execution must complete. The deadline of the request in any period of a job with deferred deadline is some time instant after the end of the period. The authors describe a semi-static priority-driven algorithm for scheduling periodic jobs with deferred deadlines: each job is assigned two priorities, the higher one for old requests and the lower one for the current request. This algorithm is called the modified rate-monotonic algorithm and is based on the well-known rate-monotonic algorithm. It is shown that the modified rate-monotonic algorithm is optimal when the deadline of every job is deferred by max (1, gamma -1) periods or more, where gamma is the ratio between the longest period and the shortest period. When the deadline of each job is deferred by one period of the job, any set of n independent jobs whose total utilization is equal to or less than (1+n(2/sup 1/n/-1))/2 can be feasibly scheduled by this algorithm. This bound approaches 0.845 when n approaches infinity. >
30478531
Hamiltonian decompositions of Cayley graphs on Abelian groups
Abstract Alspach has conjectured that any 2k-regular connected Cayley graph cay(A,S) on a finite abelian group A can be decomposed into k hamiltonian cycles. In this paper, the conjecture is shown to be true if S={s1,s2,s3} is a minimal generating set of A with |A| odd, or S={s1,s2,…,sk} is a generating set of A such that gcd(ord(si),ord(sj))= 1 for i≠j.
15520988
Multiscale Modeling and Simulation of Worm Effects on the Internet Routing Infrastructure
An unexpected consequence of recent worm attacks on the Internet was that the routing infrastructure showed evidence of increased BGP announcement churn. As worm propagation dynamics are a function of the topology of a very large-scale network, a faithful simulation model must capture salient features at a variety of resolution scales. This paper describes our efforts to model worm propagation and its affect on routers and application traffic. Using our implementations of the Scalable Simulation Framework (SSF) API, we model worm propagation, its affect on the routing infrastructure and its affect on application traffic using multiscale traffic models.
8891616
Distributed state representation for tracking problems in sensor networks
This paper investigates the problem of designing decentralized representations to support monitoring and inferences in sensor networks. State-space models of physical phenomena such as those arising from tracking multiple interacting targets, while commonly used in signal processing and control, suffer from the curse of dimensionality as the number of phenomena of interest increases. Furthermore, mapping an inference algorithm onto a distributed sensor network must appropriately allocate scarce sensing and communication resources. We address the state-space explosion problem by developing a distributed state-space model that switches between factored and joint state spaces as appropriate. We develop a collaborative group abstraction as a mechanism to effectively support the information ow within and across subspaces of the state-space model, which can be efficiently supported in a communication-constrained network. The approach has been implemented and demonstrated in a simulation of tracking multiple interacting targets.