• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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• pp.2845-2862
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• 1999

As the technical advances in portable computers and wireless communication technologies, mobile computing environment has been rapidly expanded. The mobile users on mobile host can access information via wireless communication from the distributed heterogeneous multidatabase system in which pre-existing independent local information systems are integrated into one logical system to support mobile applications. Hence, mobile transaction model should include not only the features for heterogeneous multidatabase systems but also the ones for mobile computing environment. In this paper, we proposed a mobile flex transaction model which extends the flexible transaction model that previously proposed for heterogeneous multidatabase systems is extended to support the requirements of mobile heterogeneous multidatabase systems. We also presented the execution control mechanism of the mobile flex transaction model. The proposed mobile flex transaction model allows the definition of location-dependent subtransactions, the effective support of hand-over, and the flexibility of transaction executions. Hence, the proposed mobile flex transaction model can be suit to mobile heterogeneous multidatabase systems that have low power capability, low bandwidth, and high communication failure possibility.

• Journal of Navigation and Port Research
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• v.29 no.5 s.101
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• pp.403-408
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• 2005

The techniques of estimating angle of arrival(AOA) have played a key role for enhancement of wireless communications using array antennas. Among those techniques, the superresolution algorithms, such as MUSIC and ESPRIT, calculate the covariance matrix of the array output vectors which are observed at the array antennas, and then by using eigen-decomposition of the covariance matrix, they estimate AOAs of the received signals with high accuracy. However, superresolution algorithms based eigenvalue decomposition fails to estimate AOAs under multipath environments. Under multipath environments, it is difficult to estimate AOAs of the received signals due to coherency and high-correlation. To resolve coherent signals, the covariance matrix is calculated by using the conventional spatial smoothing technique, and then the techniques based on eigen-descomposition is applied. The result of the conventional spatial smoothing technique, however, is obtained at the cost of losing effective spatial aperture. Moreover, the conventional technique ignores any information in the cross-correlations of the array outputs the subarrays. As the result, the performance for AOA estimation is degraded. In this paper, we propose a new spatial smoothing technique, which consider the cross-correlation for subarrays. By computer simulation, the AOA estimation performance of the proposed method is compared with the conventional method and evaluated.

• The KIPS Transactions:PartD
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• v.12D no.2 s.98
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• pp.233-246
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• 2005

To enhance the user response time of content-based retrieval service for multimedia information, several multi-dimensional index schemes have been proposed. M-tree, a well-known multidimensional index scheme is of metric space access method, and is based on the distance between objects in the metric space. However, since the overlap between index spaces of nodes might enlarge the number of nodes of M-tree accessed for query processing, the user response time for content-based multimedia information retrieval grows longer. In this paper, we propose a node split algorithm which is able to reduce the sire of overlap between index spaces of nodes in M-tree. In the proposed scheme, we choose a virtual center point as the routing object and entry redistribution as the postprocessing after node split in order to reduce the radius of index space of a node, and finally in order to reduce the overlap between the index spaces of routing nodes. From the experimental results, we can see the proposed split algorithm reduce the overlap between index space of nodes and finally enhance the user response time for similarity-based query processing.