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.
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.
8483401
Optimum spatial-temporal receiver for wireless systems with ISI and CCI
Spatial-temporal processing is an effective technique for wireless systems with antenna arrays to suppress co-channel interference. In this paper, spatial-temporal receive filters for multiple receiver systems with intersymbol interference (ISI), co-channel interference (CCI), and additive white Gaussian noise (AWGN) are investigated. An analogical spatial-temporal matched filter that is optimum for any following T-spaced processor with any reasonable criterion of goodness is found. Therefore, it allows co-channel interference suppression and equalization to be treated separately.
1247256
Differential Geometric Consistency Extends Stereo to Curved Surfaces
Traditional stereo algorithms implicitly use the frontal parallel plane assumption when exploiting contextual information, since the smoothness prior biases towards constant disparity (depth) over a neighborhood. For curved surfaces these algorithms introduce systematic errors to the matching process. These errors are non-negligible for detailed geometric modeling of natural objects (e.g. a human face). We propose to use contextual information geometrically. In particular, we perform a differential geometric study of smooth surfaces and argue that geometric contextual information should be encoded in Cartan's moving frame model over local quadratic approximations of the smooth surfaces. The result enforces geometric consistency for both depth and surface normal. We develop a simple stereo algorithm to illustrate the importance of using such geometric contextual information and demonstrate its power on images of the human face.
26295637
A Linear-Time Algorithm for Computing Translocation Distance between Signed Genomes
The study of evolution based on rearrangements leads to a rearrangement distance problem, i.e., computing the minimum number of rearrangement events required to transform one geonome to another. In this paper we study the translocation distance problem, modeling the evolution of genomes by translocations. We present a linear-time algorithm for computing the translocation distance between signed genomes in this paper, improving a previous O(n 3) bound by Hannenhalli in 1996.
43356203
Robust transforms for channel estimator in clustered OFDM for high rate wireless data
Clustered OFDM can provide in-band diversity gain for wideband wireless channels. It is a promising technique for high-rate wireless packet data access, such as mobile multimedia communications. Due to smaller size of each cluster for clustered OFDM than for classical(non-clustered) OFDM, edge effects can be very large. We present new transforms for channel estimators in clustered OFDM systems. The new transforms are independent of the channel delay profiles and can effectively mitigate the edge effects. Computer simulation shows that the performance of clustered OFDM with the estimator using the new transforms is very close to the performance with the optimum estimator that depends on the channel delay profile. For the typical-urban or hilly-terrain delay profiles, clustered OFDM using the new transform based estimator is almost as good as classical OFDM with transmitter diversity.
6029798
Efficient Holistic Twig Joins in Leaf-to-Root Combining with Root-to-Leaf Way
Finding all the occurrences of a twig pattern on multiple elements in an XML document is a core operation for efficient evaluation of XML queries. Holistic twig join algorithms, TwigStack and TSGeneric, have been recognized as optimal solutions when the twig pattern only involves A-D(ancestordescendant) relationships, while iTwigJoin can be optimal for partial twig patterns that contain A-D only or P-C (parent-child) only relationships. However, existing algorithms involve unnecessary computations and CPU cost of them can be further improved, and we in this paper mainly address this problem. We first propose three effective optimization rules to avoid those unnecessary computations, and then present two algorithms incorporated with these optimization rules to effectively answer twig patterns in leaf-to-root combining with root-to-leaf way. Experimental results on various datasets indicate that our algorithms perform significantly better than existing proposals.