6507492
Induction of Classification Rules by Granular Computing
A granular computing model is used for learning classification rules by considering the two basic issues: concept formation and concept relationships identification. A classification rule induction method is proposed. Instead of focusing on the selection of a suitable partition, i.e., a family of granules defined by values of an attribute, in each step, we concentrate on the selection of a single granule. This leads to finding a covering of the universe, which is more general than partition based methods. For the design of granule selection heuristics, several measures on granules are suggested.
37683026
Web Intelligence (WI)
The 21st century is the age of the Internet and the World Wide Web. The Web revolutionizes the way we gather, process, and use information. At the same time, it also redefines the meanings and processes of business, commerce, marketing, finance, publishing, education, research, development, as well as other aspects of our daily life. The revolution is just beginning. Although individual Web based information systems are constantly being deployed, advanced issues and techniques for developing and for benefiting from Web intelligence still remain to be systematically studied. The article defines a new research field, namely Web Intelligence (WI) by giving a complete picture of WI related topics for systematic study on advanced Web technology and developing Web based intelligent information systems. Roughly speaking, WI exploits AI and advanced information technology on the Web and Internet. It is the key and the most urgent research field of IT for business intelligence.
2932585
A Granular Computing Approach to Machine Learning
The key to granular computing is to make use of granules in problem solving. Classification is one of the well studied problems in machine learning and data mining as it involves of discovery knowledge from large databases. We presents a granular computing view to classification problems and propose a granular computing approach to classification in this paper. The ID3 [8] and PRISM [2] algorithma are studied and extended to granular computing algorithm.
1875630
A Comparative Study of Fuzzy Sets and Rough Sets
This paper reviews and compares theories of fuzzy sets and rough sets. Two approaches for the formulation of fuzzy sets are reviewed, one is based on many-valued logic and the other is based on modal logic. Two views of rough sets are presented, set-oriented view and operator-oriented view. Rough sets under set-oriented view are closely related to fuzzy sets, which leads to non-truth-functional fuzzy set operators. Both of them may be considered as deviations of classical set algebra. In contrast, rough sets under operator-oriented view are different from fuzzy sets, and may be regarded as an extension of classical set algebra.
17927523
An energy-efficient and access latency optimized indexing scheme for wireless data broadcast
Data broadcast is an attractive data dissemination method in mobile environments. To improve energy efficiency, existing air indexing schemes for data broadcast have focused on reducing tuning time only, i.e., the duration that a mobile client stays active in data accesses. On the other hand, existing broadcast scheduling schemes have aimed at reducing access latency through nonflat data broadcast to improve responsiveness only. Not much work has addressed the energy efficiency and responsiveness issues concurrently. This paper proposes an energy-efficient indexing scheme called MHash that optimizes tuning time and access latency in an integrated fashion. MHash reduces tuning time by means of hash-based indexing and enables nonflat data broadcast to reduce access latency. The design of hash function and the optimization of bandwidth allocation are investigated in depth to refine MHash. Experimental results show that, under skewed access distribution, MHash outperforms state-of-the-art air indexing schemes and achieves access latency close to optimal broadcast scheduling
2273900
Channel estimation and interference suppression for space-time coded systems in frequency-selective fading channels
Summary It is of great interest to provide high data rate services in wireless communication systems. In order to support such services, it is desirable to extend space-time (ST) coding, originally proposed for known, frequency-nonselective fading channels, to unknown, multipath channels. In this paper, we consider the problem of interference suppression for wireless TDMA (time division multiple access) systems equipped with multiple transmit antennas and receive antennas in frequency-selective fading channels. A novel scheme with space-time block coding based transmit diversity (STTD) is presented to estimate the multipath channel, coherently demodulate information symbols, and meanwhile suppress radio interference. The proposed scheme is simple to implement and able to mitigate interference of various origins, including intersymbol interference (ISI), cochannel interference (CCI), and others. Numerical examples are presented to illustrate the performance of the proposed estimator and detector in multipath Rayleigh-fading channels. Copyright  2002 John Wiley & Sons, Ltd.
18103228
In-Network Processing of Nearest Neighbor Queries for Wireless Sensor Networks
Wireless sensor networks have been widely used for civilian and military applications, such as environmental monitoring and vehicle tracking. The sensor nodes in the network have the abilities to sense, store, compute and communicate. To enable object tracking applications, spatial queries such as nearest neighbor queries are to be supported in these networks. The queries can be injected by the user at any sensor node. Due to the limited power supply for sensor nodes, energy efficiency is the major concern in query processing. Centralized data storage and query processing schemes do not favor energy efficiency. In this paper, we propose a distributed scheme called DNN for in-network processing of nearest neighbor queries. A cost model is built to analyze the performance of DNN. Experimental results show that DNN outperforms the centralized scheme significantly in terms of energy consumption and network lifetime.
32948109
Electronic image stabilization using multiple visual cues
Image stabilization is a key preprocessing step in dynamic image analysis and deals with the removal of unwanted image motion in a video sequence. This paper presents an integrated algorithm for the problem of image stabilization. The algorithm combines various visual cues such as points and horizon lines, and relies on an extended Kalman filter for the estimation of parameters of interest. We study both calibrated and uncalibrated stabilization cases, and consider the problem of the selection of model dynamics for the estimation of warping parameters. Experimental results from video sequences generated from off-road vehicle platforms show good performance of stabilization algorithm.