Scalar fields arise in every scientific application. Existing scalar visualization techniques require that the user infers the global scalar structure from what is frequently an insufficient display of information. We...
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Scalar fields arise in every scientific application. Existing scalar visualization techniques require that the user infers the global scalar structure from what is frequently an insufficient display of information. We present a visualization technique which numerically detects the structure at all scales, removing from the user the responsibility of extracting information implicit in the data, and presenting the structure explicitly for analysis. We further demonstrate how scalar topology detection proves useful for correct visualization and image processing applications such as image co-registration, isocontouring, and mesh compression.
Exact inference in densely connected Bayes an networks is computationally intractable, and so there is considerable interest in developing effective approximation schemes. One approach which has been adopted is to bou...
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ISBN:
(纸本)0262100762
Exact inference in densely connected Bayes an networks is computationally intractable, and so there is considerable interest in developing effective approximation schemes. One approach which has been adopted is to bound the log likelihood using a mean-field approximating distribution. While this leads to a tractable algorithm, the mean field distribution is assumed to be factorial and hence unimodal. In this paper we demonstrate the feasibility of using a richer class of approximating distributions based on mixtures of mean field distributions. We derive an efficient algorithm for updating the mixture parameters and apply it to the problem of learning in sigmoid belief networks. Our results demonstrate a systematic improvement over simple mean field theory as the number of mixture components is increased.
An optoelectronic model of discrete time cellular neural networks (DTCNN) is presented. Connections between cells and parallel input-output are realized using optoelectronic devices. As an emitter-receiver device an o...
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An optoelectronic model of discrete time cellular neural networks (DTCNN) is presented. Connections between cells and parallel input-output are realized using optoelectronic devices. As an emitter-receiver device an optical thyristor is applied. Connections between cells are realized by diffractive Damman gratings. We propose a dual rail system for early processing of binary images. The CNN system performs mathematical morphology and space logic operations.
Hypervolume visualization is designed to provide simple and fully explanatory images that give comprehensive in-sights into the global structure of scalar fields of any dimension. The basic idea is to have a dimension...
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Hypervolume visualization is designed to provide simple and fully explanatory images that give comprehensive in-sights into the global structure of scalar fields of any dimension. The basic idea is to have a dimension independent viewing system that scales nicely with the geometric dimension of the dataset and that can be combined with classical approaches like isocontouring and animation of slices of nD data. One completely abandons (for core simplicity) rendering techniques, such as hidden surface removal or lighting or radiosity, that enhance three dimensional realism and concentrate on the real-time display of images that highlight structural (topological) features of the no dataset (holes, tunnels, cavities, depressions, extrema, etc.). Hypervolume visualization on the one hand is a generalization of direct parallel projection methods in volume rendering. To achieve efficiency (and real-time performance on a graphics workstation) the authors combine the advantages of (i) a hierarchical representations of the hypervolume data for multiresolution display and (ii) generalized object space splatting combined with texture-mapped graphics hardware acceleration. The main results of the paper are thus both a multiresolution direct rendering algorithm and scalable graphical user interface that provides global views of scalar fields in any dimension, while maintaining the fundamental characteristics of ease of use, and quick exploratory user interaction.
It is common engineering practice to use response surface approximations as surrogates for an expensive objective function in engineering design. The rationale is to reduce the number of detailed, costly analyses requ...
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In this work a 3.97 /spl mu/m/sup 2/ 6T CMOS SRAM bitcell technology has been developed using a logic based platform incorporating a self-aligned local interconnect and copper metallization. This 0.20 /spl mu/m proces...
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In this work a 3.97 /spl mu/m/sup 2/ 6T CMOS SRAM bitcell technology has been developed using a logic based platform incorporating a self-aligned local interconnect and copper metallization. This 0.20 /spl mu/m process technology is suitable for stand-alone SRAM applications as well as embedded applications such as digital signal processors. A stable bitcell operation has been demonstrated for power supply (Vdd) of 1.8 V. In this technology, the minimum transistor is (0.27 /spl mu/m/spl times/0.15 /spl mu/m) with a gate pitch of 0.54 /spl mu/m and minimum metal pitch of 0.65 /spl mu/m.
Interactive procedures are very effective for exploring sets of alternatives with a view to finding the best compromise alternative. In this paper we consider the interactive exploration of implicitly or explicitly gi...
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The purpose of this paper is to examine the problem of controlling a linear discrete-time system subject to input saturation in order to have its output track (or reject) a family of reference (or disturbance) signals...
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End user computing (EUC) is an activity that is attracting increasing interest from information systems (IS) researchers and business organisations. The vast increase over recent years of the use of IT as part of ever...
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End user computing (EUC) is an activity that is attracting increasing interest from information systems (IS) researchers and business organisations. The vast increase over recent years of the use of IT as part of everyday business activities, and the growing direct involvement of business users in application development, clearly has implications for modern organisations. We discuss how an organisation might best approach the task of optimising the effectiveness of end user developed applications, and also of maximising the contribution that can be made by IT specialists. As part of this discussion we outline a modelling approach which is designed to enable the prediction of the impact of changes to identified success factors on the effectiveness of end user developed applications. We discuss the results of a questionnaire survey of 69 business users. We show how business users can be categorised by their levels of IT/business/IS knowledge and expertise, and how this can be used to identify which users are best suited to taking part in end user centred development projects.
In the construction of a Bayesian network from observed data, the fundamental assumption that the variables starting from the same parent are conditionally independent can be met by introduction of hidden node (C.K. K...
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In the construction of a Bayesian network from observed data, the fundamental assumption that the variables starting from the same parent are conditionally independent can be met by introduction of hidden node (C.K. Kwoh and D.F. Gillies, 1994). We show that the conditional probability matrices for the hidden node for a triplet, linking three observed nodes, can be determined by the gradient descent method. As in all operational research problems, the quality of the result depends on the ability to locate a feasible solution for the conditional probabilities. C.K. Kwoh and D.F. Gillies (1995) presented a paper in which they detailed the methodologies for estimating the initial values of unobservable variables in Bayesian networks. We present the concept of determining the best conditional matrices as an estimation problem. The discrepancies between the observed and predicted values are mapped into a monotonic function where its gradients are used for adjusting the parameters to be estimated. We present our investigation of choosing among various popular error cost functions for training the networks with hidden nodes and determined that both cross entropy and sum of squared error cost functions work equally well for our implementation.
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