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Articles 751 - 780 of 823
Full-Text Articles in Computer Sciences
Interpolating Implicit Surfaces From Scattered Surface Data Using Compactly Supported Radial Basis Functions, Bryan S. Morse, David T. Chen, Penny Rheingans, Kalpathi Subramanian, Terry S. Yoo
Interpolating Implicit Surfaces From Scattered Surface Data Using Compactly Supported Radial Basis Functions, Bryan S. Morse, David T. Chen, Penny Rheingans, Kalpathi Subramanian, Terry S. Yoo
Faculty Publications
We describe algebraic methods for creating implicit surfaces using linear combinations of radial basis interpolants to form complex models from scattered surface points. Shapes with arbitrary topology are easily represented without the usual interpolation or aliasing errors arising from discrete sampling. These methods were first applied to implicit surfaces by Savchenko, et al. and later developed independently by Turk and O'Brien as a means of performing shape interpolation. Earlier approaches were limited as a modeling mechanism because of the order of the computational complexity involved. We explore and extend these implicit interpolating methods to make them suitable for systems of …
Parallel Phylogenetic Inference, Mark J. Clement, David Mclaughlin, Quinn O. Snell, Michael Whiting
Parallel Phylogenetic Inference, Mark J. Clement, David Mclaughlin, Quinn O. Snell, Michael Whiting
Faculty Publications
Recent advances in DNA sequencing technology have created large data sets upon which phylogenetic inference can be performed. However, current research is limited by the prohibitive time necessary to perform tree search on even a reasonably sized data set. Some parallel algorithms have been developed but the biological research community does not use them because they don’t trust the results from newly developed parallel software. This paper presents a new phylogenetic algorithm that allows existing, trusted phylogenetic software packages to be executed in parallel using the DOGMA parallel processing system. The results presented here indicate that data sets that currently …
Toward Automated Abstraction For Protocols On Branching Networks, Michael D. Jones, Ganesh Gopalakrishnan
Toward Automated Abstraction For Protocols On Branching Networks, Michael D. Jones, Ganesh Gopalakrishnan
Faculty Publications
We have used various manual abstraction techniques to formally verify a transaction ordering property for an IO protocol over bus/bridge networks. In the context of network protocol verification, an abstraction is needed to reduce the unbounded number of network configurations to a small number of representative networks that can be checked using algorithmic methods. The manually derived abstraction was both brittle and difficult to validate. In this report, we discuss the need for abstraction techniques in the formal verification of protocols over networks and present our recent efforts to create an automatic abstraction technique for network protocols using predicate abstraction …
Optically Simulating A Quantum Associative Memory, Dan A. Ventura, John C. Howell, John A. Yeazell
Optically Simulating A Quantum Associative Memory, Dan A. Ventura, John C. Howell, John A. Yeazell
Faculty Publications
This paper discusses the realization of a quantum associative memory using linear integrated optics. An associative memory produces a full pattern of bits when presented with only a partial pattern. Quantum computers have the potential to store large numbers of patterns and hence have the ability to far surpass any classical neural network realization of an associative memory. In this work two 3-qubit associative memories will be discussed using linear integrated optics. In addition, corrupted, invented and degenerate memories are discussed.
Rescaling The Energy Function In Hopfield Networks, Tony R. Martinez, Xinchuan Zeng
Rescaling The Energy Function In Hopfield Networks, Tony R. Martinez, Xinchuan Zeng
Faculty Publications
In this paper we propose an approach that rescales the distance matrix of the energy function in the Hopfield network for solving optimization problems. We rescale the distance matrix by normalizing each row in the matrix and then adjusting the parameter for the distance term. This scheme has the capability of reducing the effects of clustering in data distributions, which is one of main reasons for the formation of invalid solutions. We evaluate this approach through a large number (20,000) simulations based on 200 randomly generated city distributions of the 10-city traveling salesman problem. The result shows that, compared to …
The Inefficiency Of Batch Training For Large Training Sets, Tony R. Martinez, D. Randall Wilson
The Inefficiency Of Batch Training For Large Training Sets, Tony R. Martinez, D. Randall Wilson
Faculty Publications
Multilayer perceptrons are often trained using error backpropagation (BP). BP training can be done in either a batch or continuous manner. Claims have frequently been made that batch training is faster and/or more "correct" than continuous training because it uses a better approximation of the true gradient for its weight updates. These claims are often supported by empirical evidence on very small data sets. These claims are untrue, however, for large training sets. This paper explains why batch training is much slower than continuous training for large training sets. Various levels of semi-batch training used on a 20,000-instance speech recognition …
Intelligent Selection Tools, William A. Barrett, Eric N. Mortensen, L. Jack Reese
Intelligent Selection Tools, William A. Barrett, Eric N. Mortensen, L. Jack Reese
Faculty Publications
Intelligent Scissors and Intelligent Paint are complementary interactive image segmentation tools that allow a user to quickly and accurately select objects of interest. This demonstration provides a means for participants to experience the dynamic nature of these tools.
Bounding Interval Rational Bézier Curves With Interval Polynomial Bézier Curves, Thomas W. Sederberg, Falai Chen, Wenping Lou
Bounding Interval Rational Bézier Curves With Interval Polynomial Bézier Curves, Thomas W. Sederberg, Falai Chen, Wenping Lou
Faculty Publications
In this paper, we put forward and study the problem of bounding an interval rational Bézier curve with an interval polynomial Bézier curve. We propose three different methods—Hybrid Method, Perturbation Method and Linear Programming Method to solve this problem. Examples are illustrated to compare the three different methods. The empirical results show that the Perturbation Method and the Linear Programming Method produce much tighter bounds than the Hybrid Method, though they are computationally several times more expensive.
Designing Human-Centered Automation: Tradeoffs In Collision Avoidance System Design, Michael A. Goodrich, Erwin R. Boer
Designing Human-Centered Automation: Tradeoffs In Collision Avoidance System Design, Michael A. Goodrich, Erwin R. Boer
Faculty Publications
Technological advances have made plausible the design of automated systems that share responsibility with a human operator. The decision to use automation to assist or replace a human operator in safety-critical tasks must account for not only the technological capabilities of the sensor and control subsystems, but also the autonomy, capabilities, and preferences of the human operator. By their nature, such human-centered automation problems have multiple attributes: an attribute reflecting human goals and capabilities, and an attribute reflecting automation goals and capabilities. Although good theories exist that describe portions of human behavior generation, in the absence of a general theory …
Alternate Path Routing For Multicast, Daniel Zappala
Alternate Path Routing For Multicast, Daniel Zappala
Faculty Publications
Alternate path routing has been well-explored in telecommunication networks as a means of decreasing the call blocking rate and increasing network utility. However, aside from some work applying these concepts to unicast flows, alternate path routing has received little attention in the Internet community. We describe and evaluate an architecture for alternate path routing for multicast flows. For path installation, we design a receiver-oriented alternate path protocol and prove that it reconfigures multicast trees without introducing loops. For path computation, we propose a scalable local search heuristic that allows receivers to find alternate paths using only partial network information. We …
Learning Quantum Operators, Dan A. Ventura
Learning Quantum Operators, Dan A. Ventura
Faculty Publications
Consider the system Fx = w where F is unknown. We examine the possibility of learning the operator F inductively, drawing analogies with ideas from classical computational learning.
Estimating Tessellation Parameter Intervals For Rational Curves And Surfaces, Thomas W. Sederberg, Jianmin Zheng
Estimating Tessellation Parameter Intervals For Rational Curves And Surfaces, Thomas W. Sederberg, Jianmin Zheng
Faculty Publications
This paper presents a method for determining a priori a constant parameter interval with which a rational curve or surface can be tessellated such that the deviation of the curve or surface from its piecewise linear approximation is within a specified tolerance. The parameter interval is estimated based on information about the second order derivatives in the homogeneous coordinates, instead of using affine coordinates directly. This new step size can be found with roughly the same amount of computation as the step size presented in [Cheng 1992], though it can be proven to always be larger than Cheng's step size. …
Cross Validation And Mlp Architecture Selection, Timothy L. Andersen, Tony R. Martinez
Cross Validation And Mlp Architecture Selection, Timothy L. Andersen, Tony R. Martinez
Faculty Publications
The performance of cross validation (CV) based MLP architecture selection is examined using 14 real world problem domains. When testing many different network architectures the results show that CV is only slightly more likely than random to select the optimal network architecture, and that the strategy of using the simplest available network architecture performs better than CV in this case. Experimental evidence suggests several reasons for the poor performance of CV. In addition, three general strategies which lead to significant increase in the performance of CV are proposed. While this paper focuses on using CV to select the optimal MLP …
Extending The Power And Capacity Of Constraint Satisfaction Networks, Tony R. Martinez, Xinchuan Zeng
Extending The Power And Capacity Of Constraint Satisfaction Networks, Tony R. Martinez, Xinchuan Zeng
Faculty Publications
This work focuses on improving the Hopfield network for solving optimization problems. Although much work has been done in this area, the performance of the Hopfield network is still not satisfactory in terms of valid convergence and quality of solutions. We address this issue in this work by combing a new activation function (EBA) and a new relaxation procedure (CR) in order to improve the performance of the Hopfield network. Each of EBA and CR has been individually demonstrated capable of substantially improving the performance. The combined approach has been evaluated through 20,000 simulations based on 200 randomly generated city …
The Little Neuron That Could, Timothy L. Andersen, Tony R. Martinez
The Little Neuron That Could, Timothy L. Andersen, Tony R. Martinez
Faculty Publications
SLPs (single layer perceptrons) oflen exhibit reasonable generalization performance on many problems of interest. However, due to the well known limitations of SLPs very little effort has been made to improve their performance. This paper proposes a method for improving the performance of SLPs called "wagging" (weight averaging). This method involves training several different SLPs on the same training data, and then averaging their weights to obtain a single SLP. The performance of the wagged SLP is compared with other more complex learning algorithms (bp, c4.5, ibl, MML, etc) on 15 data sets from real world problem domains. Surprisingly, the …
A Neural Model Of Centered Tri-Gram Speech Recognition, Tony R. Martinez, Dan A. Ventura, D. Randall Wilson, Brian Moncur
A Neural Model Of Centered Tri-Gram Speech Recognition, Tony R. Martinez, Dan A. Ventura, D. Randall Wilson, Brian Moncur
Faculty Publications
A relaxation network model that includes higher order weight connections is introduced. To demonstrate its utility, the model is applied to the speech recognition domain. Traditional speech recognition systems typically consider only that context preceding the word to be recognized. However, intuition suggests that considering both preceding context as well as following context should improve recognition accuracy. The work described here tests this hypothesis by applying the higher order relaxation network to consider both precedes and follows context in speech recognition. The results demonstrate both the general utility of the higher order relaxation network as well as its improvement over …
The Robustness Of Relaxation Rates In Constraint Satisfaction Networks, Tony R. Martinez, Dan A. Ventura, D. Randall Wilson, Brian Moncur
The Robustness Of Relaxation Rates In Constraint Satisfaction Networks, Tony R. Martinez, Dan A. Ventura, D. Randall Wilson, Brian Moncur
Faculty Publications
Constraint satisfaction networks contain nodes that receive weighted evidence from external sources and/or other nodes. A relaxation process allows the activation of nodes to affect neighboring nodes, which in turn can affect their neighbors, allowing information to travel through a network. When doing discrete updates (as in a software implementation of a relaxation network), a goal net or goal activation can be computed in response to the net input into a node, and a relaxation rate can then be used to determine how fast the node moves from its current value to its goal value. An open question was whether …
Combining Cross-Validation And Confidence To Measure Fitness, Tony R. Martinez, D. Randall Wilson
Combining Cross-Validation And Confidence To Measure Fitness, Tony R. Martinez, D. Randall Wilson
Faculty Publications
Neural network and machine learning algorithms often have parameters that must be tuned for good performance on a particular task. Leave-one-out cross-validation (LCV) accuracy is often used to measure the fitness of a set of parameter values. However, small changes in parameters often have no effect on LCV accuracy. Many learning algorithms can measure the confidence of a classification decision, but often confidence alone is an inappropriate measure of fitness. This paper proposes a combined measure of Cross- Validation and Confidence (CVC) for obtaining a continuous measure of fitness for sets of parameters in learning algorithms. This paper also proposes …
A Comparison Of Eclectic Learning And Stagger, J. Cory Barker, Jargalsaihan Batsaihan
A Comparison Of Eclectic Learning And Stagger, J. Cory Barker, Jargalsaihan Batsaihan
Faculty Publications
This project compares two machine-learning methods, Stagger and Eclectic on their classification correctness. Both systems were tested with real-world data sets previously used and tested in other machine learning and statistical literature. The Eclectic System performed better than Stagger on every data set.
Model Predictive Satisficing Fuzzy Logic Control, Richard L. Frost, Michael A. Goodrich, Wynn C. Stirling
Model Predictive Satisficing Fuzzy Logic Control, Richard L. Frost, Michael A. Goodrich, Wynn C. Stirling
Faculty Publications
Model-predictive control, which is an alternative to conventional optimal control, provides controller solutions to many constrained and nonlinear control problems. However, even when a good model is available, it may be necessary for an expert to specify the relationship between local model predictions and global system performance. We present a satisficing fuzzy logic controller that is based on a receding control horizon, but which employs a fuzzy description of system consequences via model predictions. This controller considers the gains and losses associated with each control action, is compatible with robust design objectives, and permits flexible defuzzifier design. We demonstrate the …
Multiscale Image Registration Using Scale Trace Correlation, Bruce B. Hansen, Bryan S. Morse
Multiscale Image Registration Using Scale Trace Correlation, Bruce B. Hansen, Bryan S. Morse
Faculty Publications
This paper presents a method for registering images at different magnifications (scales) by treating the problem not only as one of scaling the image coordinates but also as one inherently involving multiresolution information. While some existing methods for multiresolution registration do consider the way the resolution (scale) affects the image, they often consider the image one scale at a time, using geometric properties within that scale. Others use multiscale information, usually to produce more robust results, but only to register same-magnification images (e.g., stereo). A scale trace is the set of values that a single pixel takes on as magnification …
Toboggan-Based Intelligent Scissors With A Four-Parameter Edge Model, William A. Barrett, Eric N. Mortensen
Toboggan-Based Intelligent Scissors With A Four-Parameter Edge Model, William A. Barrett, Eric N. Mortensen
Faculty Publications
Intelligent Scissors is an interactive image segmentation tool that allows a user to select piece-wise globally optimal contour segments that correspond to a desired object boundary. We present a new and faster method of computing the optimal path by over-segmenting the image using tobogganing and then imposing a weighted planar graph on top of the resulting region boundaries. The resulting region-based graph is many times smaller than the previous pixel-based graph, thus providing faster graph searches and immediate user interaction. Further, tobogganing provides an new systematic and predictable framework for computing edge model parameters, allowing subpixel localization as well as …
Performance Surface Prediction For Wan-Based Clusters, Mark J. Clement, J. Kelly Flanagan, Glenn M. Judd, Bryan S. Morse, Joy L. Peterson
Performance Surface Prediction For Wan-Based Clusters, Mark J. Clement, J. Kelly Flanagan, Glenn M. Judd, Bryan S. Morse, Joy L. Peterson
Faculty Publications
The last five years have been a period of exponential growth in the number of machines connected to the Internet and the speed at which these machines communicate. The infrastructure is now in place to consider a nationwide cluster of workstations as a viable parallel processing platform. In order to achieve acceptable performance on this kind of a machine, performance prediction tools must provide information on where to place computational objects. Incorrect object placement can result in poor performance and congestion in the network. This research develops a new paradigm for predicting performance in the Wide Area Network (WAN) based …
A Work Minimization Approach To Image Morphing, Peisheng Gao, Thomas W. Sederberg
A Work Minimization Approach To Image Morphing, Peisheng Gao, Thomas W. Sederberg
Faculty Publications
An algorithm is presented for morphing two images, often with little or no user interaction. For two similar images (such as different faces against a neutral background) the algorithm generally can create a pleasing morph completely automatically. The algorithm seeks the minimum work to deform one image into the other, where work is a function of the amount of warping and recoloration. A hierarchical method for finding a minimal work solution is invoked. Anchor point constraints are satisfied by imposing penalties on deformations that disobey these constraints. Good results can be obtained in less than ten seconds for 256 x …
A Theory Of Satisficing Decisions And Control, Richard L. Frost, Michael A. Goodrich, Wynn C. Stirling
A Theory Of Satisficing Decisions And Control, Richard L. Frost, Michael A. Goodrich, Wynn C. Stirling
Faculty Publications
The existence of an optimal control policy and the techniques for finding it are grounded fundamentally in a global perspective. These techniques can be of limited value when the global behavior of the system is difficult to characterize, as it may be when the system is nonlinear, when the input is constrained, or when only partial information is available regarding system dynamics or the environment. Satisficing control theory is an alternative approach that is compatible with the limited rationality associated with such systems. This theory is extended by the introduction of the notion of strong satisficing to provide a systematic …
Isophote-Based Interpolation, Bryan S. Morse, Duane Schwartzwald
Isophote-Based Interpolation, Bryan S. Morse, Duane Schwartzwald
Faculty Publications
Standard methods for image interpolation are based on smoothly fitting the image intensity surface. Recent edge-directed interpolation methods add limited geometric information (edge maps) to build more accurate and visually appealing interpolations at key contours in the image. This paper presents a method for geometry-based interpolation that smoothly fits the isophote (intensity level curve) contours at all points in the image rather than just at selected contours. By using level set methods for curve evolution, no explicit extraction or representation of these contours is required (unlike earlier edge-directed methods). The method uses existing interpolation techniques as an initial approximation and …
Analysis Of The Interpolation Error Between Multiresolution Images, Bryan S. Morse
Analysis Of The Interpolation Error Between Multiresolution Images, Bryan S. Morse
Faculty Publications
Many rendering or image-analysis systems require calculation of versions of an image at lesser resolutions than the original. Because the filtering required to perform such calculations accurately cannot typically be done in real time, many systems use interpolation between images at precalculated resolutions. This discrete sampling of the scale component of multiresolution image spaces is analogous to spatial sampling in discrete images. This paper quantifies and bounds the error that can be introduced during such interpolation as a function of the scale-space sampling rate used. A method is presented that uses the diffusion equation to relate spatial derivatives to scale …
Constructing High Order Perceptrons With Genetic Algorithms, Timothy L. Andersen, Tony R. Martinez
Constructing High Order Perceptrons With Genetic Algorithms, Timothy L. Andersen, Tony R. Martinez
Faculty Publications
Constructive induction, which is defined to be the process of constructing new and useful features from existing ones, has been extensively studied in the literature. Since the number of possible high order features for any given learning problem is exponential in the number of input attributes (where the order of a feature is defined to be the number of attributes of which it is composed), the main problem faced by constructive induction is in selecting which features to use out of this exponentially large set of potential features. For any feature set chosen the desirable characteristics are minimality and generalization …
Optimal Control Using A Neural/Evolutionary Hybrid System, Tony R. Martinez, Dan A. Ventura
Optimal Control Using A Neural/Evolutionary Hybrid System, Tony R. Martinez, Dan A. Ventura
Faculty Publications
One of the biggest hurdles to developing neurocontrollers is the difficulty in establishing good training data for the neural network. We propose a hybrid approach to the development of neurocontrollers that employs both evolutionary computation (EC) and neural networks (NN). EC is used to discover appropriate control actions for specific plant states. The survivors of the evolutionary process are used to construct a training set for the NN. The NN leams the training set, is able to generalize to new plant states, and is then used for neurocontrol. Thus the EC/NN approach combines the broad, parallel search of EC with …
Quantum Associative Memory With Exponential Capacity, Tony R. Martinez, Dan A. Ventura
Quantum Associative Memory With Exponential Capacity, Tony R. Martinez, Dan A. Ventura
Faculty Publications
Quantum computation uses microscopic quantum level effects to perrform computational tasks and has produced results that in some cases are exponentially faster than their classical counterparts by taking advantage of quantum parallelism. The unique characteristics of quantum theory may also be used to create a quantum associative memory with a capacity exponential in the number of neurons. This paper covers necessary high-level quantum mechanical ideas and introduces a simple quantum associative memory. Further, it provides discussion, empirical results and directions for future work.