920610
Interactive visualization of three-dimensional vector fields with flexible appearance control
We present an interactive texture-based algorithm for visualizing three-dimensional steady and unsteady vector fields. The goal of the algorithm is to provide a general volume rendering framework allowing the user to compute three-dimensional flow textures interactively and to modify the appearance of the visualization on the fly. To achieve our goal, we decouple the visualization pipeline into two disjoint stages. First, flow lines are generated from the 3D vector data. Various geometric properties of the flow paths are extracted and converted into a volumetric form using a hardware-assisted slice sweeping algorithm. In the second phase of the algorithm, the attributes stored in the volume are used as texture coordinates to look up an appearance texture to generate both informative and aesthetic representations of the vector field. Our algorithm allows the user to interactively navigate through different regions of interest in the underlying field and experiment with various appearance textures. With our algorithm, visualizations with enhanced structural perception using various visual cues can be rendered in real time. A myriad of existing geometry-based and texture-based visualization techniques can also be emulated.
10816332
Statistically quantitative volume visualization
Visualization users are increasingly in need of techniques for assessing quantitative uncertainty and error in the images produced. Statistical segmentation algorithms compute these quantitative results, yet volume rendering tools typically produce only qualitative imagery via transfer function-based classification. This paper presents a visualization technique that allows users to interactively explore the uncertainty, risk, and probabilistic decision of surface boundaries. Our approach makes it possible to directly visualize the combined "fuzzy" classification results from multiple segmentations by combining these data into a unified probabilistic data space. We represent this unified space, the combination of scalar volumes from numerous segmentations, using a novel graph-based dimensionality reduction scheme. The scheme both dramatically reduces the dataset size and is suitable for efficient, high quality, quantitative visualization. Lastly, we show that the statistical risk arising from overlapping segmentations is a robust measure for visualizing features and assigning optical properties.
537628
Chameleon: an interactive texture-based rendering framework for visualizing three-dimensional vector fields
In this paper we present an interactive texture-based technique for visualizing three-dimensional vector fields. The goal of the algorithm is to provide a general volume rendering framework allowing the user to compute three-dimensional flow textures interactively, and to modify the appearance of the visualization on the fly. To achieve our goal, we decouple the visualization pipeline into two disjoint stages. First, streamlines are generated from the 3D vector data. Various geometric properties of the streamlines are extracted and converted into a volumetric form using a hardware-assisted slice sweeping algorithm. In the second phase of the algorithm, the attributes stored in the volume are used as texture coordinates to look up an appearance texture to generate both informative and aesthetic representations of the underlying vector field. Users can change the input textures and instantaneously visualize the rendering results. With our algorithm, visualizations with enhanced structural perception using various visual cues can be rendered in real time. A myriad of existing geometry-based and texture-based visualization techniques can also be emulated.
5907828
Error correcting least-squares Subspace algorithm for blind identification and equalization
Abstract A subspace based blind channel identification algorithm using only the fact that the received signal can be oversampled is proposed. No direct use is made in this algorithm of either the statistics of the input sequence or even of the fact that the symbols are from a finite set and therefore this algorithm can be used to identify even channels in which arbitrary symbols are sent. Using this algorithm as a base and using the extra information which becomes available when the transmitted symbols are from a known finite set, the EC-LS-Subspace algorithm is derived. The EC-LS-Subspace algorithm operates directly on the data domain and therefore avoids the problems associated with other algorithms which use the statistical information contained in the received signal directly. In the noiseless case, if some conditions are met, it is possible for the proposed Basic Subspace algorithm to identify the channel exactly using an observation interval of just ( J +2) T , if the length of the impulse response of a channel is JT , T being the symbol interval. In the noisy case, simulations have shown that the channel can be identified accurately by using a very small observation interval (comparable to ( J +2) T ).
1032115
Incremental Mining of Frequent Query Patterns from XML Queries for Caching
Existing studies for mining frequent XML query patterns mainly introduce a straightforward candidate generate-and-test strategy and compute frequencies of candidate query patterns from scratch periodically by checking the entire transaction database, which consists of XML query patterns transformed from user queries. However, it is nontrivial to maintain such discovered frequent patterns in real XML databases because there may incur frequent updates that may not only invalidate some existing frequent query patterns but also generate some new frequent ones. Accordingly, existing proposals are inefficient for the evolution of the transaction database. To address these problems, this paper presents an efficient algorithm IPS-FXQPMiner for mining frequent XML query patterns without candidate maintenance and costly tree-containment checking. We transform XML queries into sequences through a one- to-one mapping and then mine the frequent sequences to generate frequent XML query patterns. More importantly, based on IPS-FXQPMiner, an efficient incremental algorithm, Incre-FXQPMiner is proposed to incrementally mine frequent XML query patterns, which can minimize the I/O and computation requirements for handling incremental updates. Our experimental study on various real-life datasets demonstrates the efficiency and scalability of our algorithms over previous known alternatives.
33634319
A genetic algorithm for the multiple destination routing problems
The multiple destination routing (MDR) problem can be formulated as finding a minimal cost tree which contains designated source and multiple destination nodes so that certain constraints in a given communication network are satisfied. This is a typical NP-hard problem, and therefore only heuristic algorithms are of practical value. As a first step, a new genetic algorithm is developed to solve the MDR problems without constraints. It is based on the transformation of the underlying network of an MDR problem into its distance complete form, a natural chromosome representation of a minimal spanning tree (an individual), and a completely new computation of the fitness of individual. Compared with the known genetic algorithms and heuristic algorithms for the same problem, the proposed algorithm has several advantages. First, it guarantees convergence to an optimal solution with probability one. Second, not only are the resultant solutions all feasible, the solution quality is also much higher than that obtained by the other methods (indeed, in almost every case in our simulations, the algorithm can find the optimal solution of the problem). Third, the algorithm is of low computational complexity, and this can be decreased dramatically as the number of destination nodes in the problem increases. The simulation studies for the sparse and dense networks all demonstrate that the proposed algorithm is highly robust and very efficient in the sense of yielding high-quality solutions.
664043
Evolutionary structure learning algorithm for Bayesian network and Penalized Mutual Information metric
The paper formulates the problem of learning Bayesian network structures from data as determining the structure that best approximates the probability distribution indicated by the data. A new metric, Penalized Mutual Information metric, is proposed, and an evolutionary algorithm is designed to search for the best structure among alternatives. The experimental results show that this approach is reliable and promising.
17312614
Secrecy of Signals by Typing in Signal Transduction
We discuss secrecy of signals in signal transduction. As we have developed a basic concurrent language with interferencial coefficients, Iπ-calculus, to describe aberrance in biological models, a typing system for Iπ-calculus is proposed for achieving secrecy of signals in signal transduction. We show that this typing system guarantees that, if signal transduction typechecks, then it does not leak aberrance of signals.