14780024
Fuzzy causal networks: general model, inference, and convergence
In this paper, we first propose a general framework for fuzzy causal networks (FCNs). Then, we study the dynamics and convergence of such general FCNs. We prove that any general FCN with constant weight matrix converges to a limit cycle or a static state, or the trajectory of the FCN is not repetitive. We also prove that under certain conditions a discrete state general FCN converges to its limit cycle or static state in O(n) steps, where n is the number of vertices of the FCN. This is in striking contrast with the exponential running time 2/sup n/, which is accepted widely for classic FCNs.
7502395
Dynamic image sequence analysis using fuzzy measures
In this paper, we present an image understanding system using fuzzy sets and fuzzy measures. This system is based on a symbolic object-oriented image interpretation system. We apply a simple, powerful three-dimensional (3-D) recursive filter to tracking moving objects in a dynamic image sequence. This filter has a time-varying 3-D frequency-planar passband that is adapted in a feedback system to automatically track moving objects. However, as objects in the image sequence are not well-defined and are engaged in dynamic activities, their shapes and trajectories in most cases can be described only vaguely. In order to handle these uncertainties, we use fuzzy measures to capture subtle variations and manage the uncertainties involved. This enables us to develop an image understanding system that produces a very natural output. We demonstrate the effectiveness of our system with complex real traffic scenes.
207320245
Hidden Markov models with factored Gaussian mixtures densities
We present a factorial representation of Gaussian mixture models for observation densities in hidden Markov models (HMMs), which uses the factorial learning in the HMM framework. We derive the reestimation formulas for estimating the factorized parameters by the Expectation Maximization (EM) algorithm and propose a novel method for initializing them. To compare the performances of the proposed models with that of the factorial hidden Markov models and HMMs, we have carried out extensive experiments which show that this modelling approach is effective and robust.
3758350
An Analytical Approach to the Performance Evaluation of Master-Slave Computational Models
Abstract Many stochastic models and analysis techniques have been proposed in the literature during the last two decades for the performance evaluation of parallel and distributed systems. However, few of them are directly applicable to practical systems which are generally too complex. In this paper, we analyze performances of the master–slave computational model, one of the most commonly used models in parallel and distributed computations. We propose a hybrid analytical approach by using techniques from the theories of both stochastic task graphs and queueing networks. We apply this method to the analysis of a computational chemistry application running on a Transputer based system. The proposed method turns out to be not only very efficient in time for large systems but also very accurate compared to measuring.
2458786
High Performance Embedded Route Lookup Coprocessor for Network Processors
Embedded Route Lookup Coprocessors (RLCs) are attractive for their potential in building high-performance Network Processors. But compared with conventional lookup schemes, it always imposes more severe restrictions on table size and power consumption, which poses challenge in the state of art. In this paper, we propose a novel lookup mechanism, Compounded CAM with Optimized Bitmap Compression (CCAM-OBC), which employs different lookup methods for prefixes of different length ranges, so as to combine the benefits of CAMs and bitmap compressed tries. With this scheme, table size, power consumption and update complexity are all well optimized while very high lookup throughput is achieved, which makes it a perfect solution to embedded RLC. For a real-life 130K-prefix route table, the implemented prototype performs more than 100 Million Packets Per Second (MPPS) with only 24KB TCAM, 48KB BCAM and 251KB SRAM. Furthermore, each update needs only 2 memory accesses averagely.
8336028
Evaluation and optimization of clustering in gene expression data analysis
MOTIVATION
A measurement of cluster quality is needed to choose potential clusters of genes that contain biologically relevant patterns of gene expression. This is strongly desirable when a large number of gene expression profiles have to be analyzed and proper clusters of genes need to be identified for further analysis, such as the search for meaningful patterns, identification of gene functions or gene response analysis.


RESULTS
We propose a new cluster quality method, called stability, by which unsupervised learning of gene expression data can be performed efficiently. The method takes into account a cluster's stability on partition. We evaluate this method and demonstrate its performance using four independent, real gene expression and three simulated datasets. We demonstrate that our method outperforms other techniques listed in the literature. The method has applications in evaluating clustering validity as well as identifying stable clusters.


AVAILABILITY
Please contact the first author.
7545798
Verification of fault tolerance and real time
A transformational method is given for specifying and verifying fault-tolerant, real-time programs. Such a program needs to be provably correct according to both its functional and real-time requirements, despite the possible occurrence of system failures. The paper demonstrates that a suitably expressive logic for real-time systems makes it possible to naturally model the state changes caused by system failures and determine their effect on the functional and real-time properties of executions.
17293889
Complexity of Task Graph Scheduling with Fixed Communication Capacity
Consider a scheduling problem of parallel computations in multiprocessor systems. Let a parallel program be modeled by a task graph, where vertices represent tasks and arcs the communications between tasks. An interprocessor communication time incurs when two tasks assigned to two different processors have to communicate. Such a scheduling problem has recently been studied in the literature, mostly for the case where interprocessor communication times are fully determined. In this paper, we consider the scheduling problem with communication resource constraints. More specifically, we consider the case where all interprocessor communications take place on a network of bounded capacity. We consider two variants of the problem: communications with independent-data semantics and common-data semantics. We show that even for very specific subproblems, viz. scheduling of general graphs on two processors and scheduling of binary trees on an infinite number of processors, the minimization of the makespan of parallel programs in such a multiprocessor system is strongly -hard. We first establish the results for the case of capacity 1, referred to as the single-bus system. We then extend the results to the more general case of fixed communication capacities. As a consequence, the general scheduling problem of parallel programs with communication resource constraints is strongly -hard. These results are to be contrasted with the corresponding scheduling problems without contraint on the communication capacity, where the two-processor case has unknown time complexity and the infinite-processor case is polynomial. Our results are also extended to the case of broadcasting communications, and can be applied to multiprocessor systems with shared memory.