In this letter, a circular microstrip patch antenna with a conical cup is proposed. The results of a simulation and experiment show that the conical cup has a beneficial effect on the antenna's gain and principal plane beamwidths. The maximum gain of this antenna was 12.6 dBi, which is about 3 dB higher than one with a cylindrical cup. The 3-dB beamwidths of the E-and H-planes were 34and 44, respectively.

Particle swarm optimization (PSO), inspired by social psychology principles and evolutionary computations, has been successfully applied to a wide range of continuous optimization problems. However, research on discrete problems has been done not much even though discrete binary version of PSO (BPSO) was introduced by Kennedy and Eberhart in 1997. In this paper, we propose a modified BPSO algorithm, which escapes from a local optimum by employing a bit change mutation. The proposed algorithm was tested on De jong's suite and its results show that BPSO with the proposed mutation outperforms the original BPSO.

R-trees have been traditionally optimized for I/O performance with disk pages as tree nodes. Recently, researchers have proposed cache-conscious variations of R-trees optimized for CPU cache performance in main memory environments, where the node size is several cache lines wide and more entries are packed in a node by compressing MBR keys. However, because there is a big difference between the node sizes of two types of R-trees, disk-optimized R-trees show poor cache performance while cache-optimized R-trees exhibit poor disk performance. In this paper, we propose a cache and disk optimized R-tree, called PR-tree (Prefetching R-tree). For cache performance, the node size of the PR-tree is wider than a cache line, and the prefetch instruction is used to reduce the number of cache misses. For I/O performance, the nodes of the PR-tree are fitted into one disk page. We represent the detailed analysis of cache misses for range queries, and enumerate all the reasonable in-page leaf and nonleaf node sizes, and heights of in-page trees to figure out tree parameters for the best cache and I/O performance. The PR-tree that we propose achieves better cache performance than the disk-optimized R-tree: a factor of 3.5-15.1 improvement for one-by-one insertions, 6.5-15.1 improvement for deletions, 1.3-1.9 improvement for range queries, and 2.7-9.7 improvement for k-nearest neighbor queries. All experimental results do not show notable declines of I/O performance.

Most document clustering methods are a challenging issue for improving clustering performance. Document clustering based on semantic features is highly efficient. However, the method sometimes did not successfully cluster some documents, such as highly articulated documents. In order to improve the clustering success of complex documents using semantic features, this paper proposes a document clustering method that uses terms of the condensing document clusters and fuzzy association to efficiently cluster specific documents into meaningful topics based on the document set. The proposed method improves the quality of document clustering because it can extract documents from the perspective of the terms of the cluster topics using semantic features and synonyms, which can also better represent the inherent structure of the document in connection with the document cluster topics. The experimental results demonstrate that the proposed method can achieve better document clustering performance than other methods.