Design and fabrication of two switched superconductivity microstrip hairpin filters using series micro-electro-mechanical systems (MEMS) switches

Series Micro-Electro-Mechanical System (MEMS) switches based on superconductor are utilized to switch between two bandpass hairpin filters with bandwidths of 365 MHz and nominal center frequencies of 2.1 GHz and 2.6 GHz. This was accomplished with 4 switches actuated in pairs, one pair at a time. When one pair was actuated the first bandpass filter was coupled to the input and output ports. When the other pair was actuated the second bandpass filter was coupled to the input and output ports. The device is made of a YBa2Cu 3O7 thin film deposited on a 20 mm x 20 mm LaAlO3 substrate by pulsed laser deposition. BaTiO3 deposited by RF magnetron sputtering in utilized as the insulation layer at the switching points of contact. These results obtained assured great performance showing a switchable device at 68 V with temperature of 40 K for the 2.1 GHz filter and 75 V with temperature of 30 K for the 2.6 GHz hairpin filter. ^

A generalized adaptive mathematical morphological filter for LIDAR data

Airborne Light Detection and Ranging (LIDAR) technology has become the primary method to derive high-resolution Digital Terrain Models (DTMs), which are essential for studying Earth's surface processes, such as flooding and landslides. The critical step in generating a DTM is to separate ground and non-ground measurements in a voluminous point LIDAR dataset, using a filter, because the DTM is created by interpolating ground points. As one of widely used filtering methods, the progressive morphological (PM) filter has the advantages of classifying the LIDAR data at the point level, a linear computational complexity, and preserving the geometric shapes of terrain features. The filter works well in an urban setting with a gentle slope and a mixture of vegetation and buildings. However, the PM filter often removes ground measurements incorrectly at the topographic high area, along with large sizes of non-ground objects, because it uses a constant threshold slope, resulting in "cut-off" errors. A novel cluster analysis method was developed in this study and incorporated into the PM filter to prevent the removal of the ground measurements at topographic highs. Furthermore, to obtain the optimal filtering results for an area with undulating terrain, a trend analysis method was developed to adaptively estimate the slope-related thresholds of the PM filter based on changes of topographic slopes and the characteristics of non-terrain objects. The comparison of the PM and generalized adaptive PM (GAPM) filters for selected study areas indicates that the GAPM filter preserves the most "cut-off" points removed incorrectly by the PM filter. The application of the GAPM filter to seven ISPRS benchmark datasets shows that the GAPM filter reduces the filtering error by 20% on average, compared with the method used by the popular commercial software TerraScan. The combination of the cluster method, adaptive trend analysis, and the PM filter allows users without much experience in processing LIDAR data to effectively and efficiently identify ground measurements for the complex terrains in a large LIDAR data set. The GAPM filter is highly automatic and requires little human input. Therefore, it can significantly reduce the effort of manually processing voluminous LIDAR measurements.