Design of an X-band tray-type spatial power combiner using uniplanar quasi-Yagi antennas

The design of an X-band tray-type spatial power combiner, which employs uniplanar quasi-Yagi antennas (QYAs) for receiving and transmitting signals by individual amplifiers, is presented. Passive and active varieties of a seven-tray power-combining structure that includes two hard horns for uniform signal launching and combining across the tray stack are developed and measured. In order to compensate for nonuniform phase across the stack, which is caused by the nonplanar wave front of the horn antennas, Schiffman phase shifters are implemented in individual trays. The experimental-results show an improved performance of the investigated tray-type power combiner when the proposed phase-error compensation is implemented. (C) 2004 Wiley Periodicals, Inc.

On aggregation issues in spatial data management

Large amounts of information can be overwhelming and costly to process, especially when transmitting data over a network. A typical modern Geographical Information System (GIS) brings all types of data together based on the geographic component of the data and provides simple point-and-click query capabilities as well as complex analysis tools. Querying a Geographical Information System, however, can be prohibitively expensive due to the large amounts of data which may need to be processed. Since the use of GIS technology has grown dramatically in the past few years, there is now a need more than ever, to provide users with the fastest and least expensive query capabilities, especially since an approximated 80 % of data stored in corporate databases has a geographical component. However, not every application requires the same, high quality data for its processing. In this paper we address the issues of reducing the cost and response time of GIS queries by preaggregating data by compromising the data accuracy and precision. We present computational issues in generation of multi-level resolutions of spatial data and show that the problem of finding the best approximation for the given region and a real value function on this region, under a predictable error, in general is "NP-complete.