This paper presents field experiments on open-loop transmit diversity in downlink OFDM based radio access conducted in a measurement course in Yokosuka city near Tokyo. The experimental results obtained under actual propagation channel conditions show that Space Frequency Block Code (SFBC) and the combination of SFBC and Frequency Switched Transmit Diversity (FSTD) (or Cyclic Delay Diversity (CDD)) are the most promising open-loop transmit diversity schemes for two- and four-antenna transmission, respectively, from the viewpoint of the required average received signal-to-noise power ratio (SNR).

In this paper, we describe a two-phase method for biomedical named entity recognition consisting of term boundary detection and biomedical category labeling. The term boundary detection can be defined as a task to assign label sequences to a given sentence, and biomedical category labeling can be viewed as a local classification problem which does not need knowledge of the labels of other named entities in a sentence. The advantage of dividing the recognition process into two phases is that we can measure the effectiveness of models at each phase and select separately the appropriate model for each subtask. In order to obtain a better performance in biomedical named entity recognition, we conducted comparative experiments using several learning methods at each phase. Moreover, results by these machine learning based models are refined by rule-based postprocessing. We tested our methods on the JNLPBA 2004 shared task and the GENIA corpus.