1712871
Quasi-interpolants Based Multilevel B-Spline Surface Reconstruction from Scattered Data
This paper presents a new fast and local method of 3D surface reconstruction for scattered data. The algorithm makes use of quasi-interpolants to compute the control points from a coarse to fine hierarchy to generate a sequence of bicubic B-spline functions whose sum approaches to the desired interpolation function. Quasi-interpolants gives a procedure for deriving local spline approximation methods where a B-spline coefficient only depends on data points taken from the neighborhood of the support corresponding B-spline. Experimental results demonstrate that high-fidelity reconstruction is possible from a selected set of irregular samples.
13264531
An efficient scattered data approximation using multilevel B-splines based on quasi-interpolants
In this paper, we propose an efficient approximation algorithm using multilevel B-splines based on quasi-interpolants. Multilevel technique uses a coarse to fine hierarchy to generate a sequence of bicubic B-spline functions whose sum approaches the desired interpolation function. To compute a set of control points, quasi-interpolants gives a procedure for deriving local spline approximation methods where a B-spline coefficient only depends on data points taken from the neighborhood of the support corresponding the B-spline. Experimental results show that the smooth surface reconstruction with high accuracy can be obtained from a selected set of scattered or dense irregular samples.
2235500
Nonverbal Behavior Generator for Embodied Conversational Agents
Believable nonverbal behaviors for embodied conversational agents (ECA) can create a more immersive experience for users and improve the effectiveness of communication. This paper describes a nonverbal behavior generator that analyzes the syntactic and semantic structure of the surface text as well as the affective state of the ECA and annotates the surface text with appropriate nonverbal behaviors. A number of video clips of people conversing were analyzed to extract the nonverbal behavior generation rules. The system works in real-time and is user-extensible so that users can easily modify or extend the current behavior generation rules.
40603353
3D GIS for Geo-coding Human Activity in Micro-scale Urban Environments
The study of human activity and movement in space and time has been an important research area in the social sciences. However, the difficulty in collecting and analyzing the space-time activity (STA) data using current 3D location positioning techniques has limits in applying the time-geographic and activity theory to transportation and urban research and analyses. This paper develops a “3D Indoor Geo-Coding” technique to identify 3D indoor locational data for analyzing human activities. For implementing 3D positioning methods, this paper presents (1) to model micro-scale urban environments to be a frame of reference (reference data) to identify distance and direction, in order to obtain locational data, and (2) to develop a 3D indoor geo-coding method to identify locational data on individual activities based on the reference data. Finally, this paper describes the output from implementing the 3D indoor geo-coding technique to demonstrate the potential benefits of the 3D indoor geo-coding technique for improving the speed of emergency response using GIS data of the study area at Minnesota State University-Mankato, MN (USA).
26557344
A Knowledge-Based Approach to Rank Documents for Boolean Queries
13362734
FINDIT: a fast and intelligent subspace clustering algorithm using dimension voting
Abstract The aim of this paper is to present a novel subspace clustering method named FINDIT. Clustering is the process of finding interesting patterns residing in the dataset by grouping similar data objects from dissimilar ones based on their dimensional values. Subspace clustering is a new area of clustering which achieves the clustering goal in high dimension by allowing clusters to be formed with their own correlated dimensions. In subspace clustering, selecting correct dimensions is very important because the distance between points is easily changed according to the selected dimensions. However, to select dimensions correctly is difficult, because data grouping and dimension selecting should be performed simultaneously. FINDIT determines the correlated dimensions for each cluster based on two key ideas: dimension-oriented distance measure which fully utilizes dimensional difference information, and dimension voting policy which determines important dimensions in a probabilistic way based on V nearest neighbors' information. Through various experiments on synthetic data, FINDIT is shown to be very successful in the high dimensional clustering problem. FINDIT satisfies most requirements for good clustering methods such as accuracy of results, robustness to the noise and the cluster density, and scalability to the dataset size and the dimensionality. Moreover, it is gracefully scalable to full dimension without any modification to algorithm.
275005
Using a user-level memory thread for correlation prefetching
This paper introduces the idea of using a User-Level Memory Thread (ULMT) for correlation prefetching. In this approach, a user thread runs on a general-purpose processor in main memory, either in the memory controller chip or in a DRAM chip. The thread performs correlation prefetching in software, sending the prefetched data into the L2 cache of the main processor. This approach requires minimal hardware beyond the memory processor: the correlation table is a software data structure that resides in main memory, while the main processor only needs a few modifications to its L2 cache so that it can accept incoming prefetches. In addition, the approach has wide usability, as it can effectively prefetch even for irregular applications. Finally, it is very flexible, as the prefetching algorithm can be customized by the user on an application basis. Our simulation results show that, through a new design of the correlation table and prefetching algorithm, our scheme delivers good results. Specifically, nine mostly-irregular applications show an average speedup of 1.32. Furthermore, our scheme works well in combination with a conventional processor-side sequential prefetcher, in which case the average speedup increases to 1.46. Finally, by exploiting the customization of the prefetching algorithm, we increase the average speedup to 1.53.