Myeong Soo Choi

Ship to shore maritime communication for e-Navigation using IEEE 802.16e

Maritime wireless communication is different from terrestrial wireless communication, due to difference of environments. Nevertheless technologies based on IEEE 802.16e for maritime communication promised to deliver high data rates over large distances and deliver multimedia services and are expected to use e-Navigation system. This paper focuses to simulate on IEEE 802.16e for ship-to-shore maritime communication under various marine environments. We describe BER (Bit Error Rate) level according to sea states under Rician fading channel model. BER patterns are designed using MATLAB.

Energy Based Interference Avoidance Scheme in Ubiquitous Medical Environments

WLAN and WPAN technologies will play an important role in ubiquitous healthcare application. In this paper, we propose an energy based interference avoidance scheme in ubiquitous medical environment using 2.4 GHz unlicensed industrial, scientific and medical (ISM) bands. This paper focuses on the coexistence of WLAN (IEEE 802.11b) and WPAN (IEEE 802.15.4) in the 2.4 GHz band. In the proposed scheme, we propose a new energy detection (ED) estimation scheme to detect interference from another network. The experiment and simulation results show that the proposed system improves coexistence performance required for ubiquitous medical environments.

Personalized text summarization using NMF and cluster refinement

As accessing text information on the Internet has become popular, the needs for automatic personalized document summarization have increased. In this paper, a personalized document summarization method that uses Non-negative Matrix Factorization (NMF) and cluster refinement is proposed. The proposed method uses NMF with cluster refinement to summarize generic summary so that it can extract sentences covering the major topics of the document. In addition, the method can improve the quality of personalized summaries because the inherent semantics of the documents are well reflected with respect to user interest. The experimental results demonstrate that the proposed method achieves better performance the than other methods.

A Hybrid ML Decoding Scheme for Multiple Input Multiple Output Signals on Partitioned Tree

In this paper, we propose a novel ML decoding scheme based on the combination of depth- and breadth-first search methods on a partitioned tree for multiple input multiple output (MIMO) systems. The proposed scheme first partitions the searching tree into several stages, each of which is then searched by a depth- or breadth-first search method, maximally exploiting the advantages of both the depth- and breadth-first search methods. Numerical results indicate that, when the depth- and breadth-first search algorithms are adopted appropriately, the proposed scheme exhibits substantially lower computational complexity than conventional ML decoders while maintaining the ML bit error performance.