In this paper, we propose a ping-pong avoidance algorithm capable of effectively alleviating the unnecessary vertical handovers between 3G cellular and WLAN hotspots. The proposed algorithm determine the appropriate time at which a terminal should initiate a handover. It can be classified into two major schemes: One is designed to separate the single vertical handover threshold; the other to estimate the transition of the beacon signal strengths received by the terminal from WLAN hotspots on the basis of the least square approach. The aim of this study is to prevent such performance degradations as the increase of the service interruption time which follows unnecessary vertical handovers.

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In this paper, we design and implement SPA (Switched Parasitic Antenna) antenna which can control its beampattern using multiple parasitic elements. By applying SPA antenna to wireless communication system and implementing beamforming scheme, we show that SPA antenna can be used to improve the performance of wireless communication systems. SPA antenna consists of a single active antenna and multiple parasitic elements around the active one, and can control its beampattern by switching the parasitic elements. Using this characteristic of the SPA antenna, it is possible to impelemtent beamforming technique with single RF chain, which enables to design low cost, low complexity and low power wireless communication systems. In order to verify the beamforming gain, we measure and analyze the system level performance, such as SNR, PER, and throughput.

Despite many advances, the problem of determining the proper size of a neural network is important, especially for its practical implications in such issues as learning and generalization. Unfortunately, it is not usually obvious which size is best; a system that is too small will not be able to learn the data, while one that is just big enough may learn very slowly and be very sensitive to initial conditions and learning parameters. There are two types of approach to determining the network size: pruning and growing. Pruning consists of training a network which is larger than necessary, and then removing unnecessary weights/nodes. Here, a new pruning method is developed, based on the penalty-term method. This method makes the neural networks good for generalization, and reduces the retraining time needed after pruning weights/nodes.