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A miniaturized quad-band bandpass filter (BPF) using compact quad-mode stepped impedance resonator (IQMSIR) is proposed. Based on the symmetric presented by the resonator, four modes are deduced using even-and odd-mode technique. Design formulas are also derived and they are used to guide the filter design. Multiple coupling circuit technique is employed to realize the four passbands. Independent design for four passbands are achieved due to the physical dimensions and additional cross slots. Upper stopband is performed by adding open loop SIR at I/O ports. Transmission zeros among each passbands are generated, resulting in high isolation and selectivity. A quad-band filter is designed, fabricated and measured. The operating central frequencies of the fabricated filter are at 2.54/3.36/5.24/6.6 GHz. The measured 3-dB fractional bandwidths of the four passbands are about 3.9/6.4/4/4.6% and the minimum insertion losses are about 2.3/0.89/3.2/2.3 dB. Measurements correlate well with the simulation results. [ABSTRACT FROM AUTHOR]

A novel dual-band bandpass filter (DB-BPF) based on SIW dual-mode resonant cavity is proposed in this study. This resonant cavity works in the degenerate mode TE102 mode and TE201 mode. The two modes of operation are coupled by opening a window between the resonant cavities, and the passband frequency of the DB-BPF is adjusted by controlling two degenerate modes. This filter works at 9 GHz and 9.5 GHz and has a 3-dB fractional bandwidth of 2.2% and 3.5% GHz, respectively, and has three transmission zeros (TZs) which improve out-of-band rejection and isolation between high-passband and low-passband. The return loss in the high-passband and low-passband is below 13 dB; the insertion loss of the high-passband and low-passband is 2.32 dB and 3.16 dB, respectively. The measured results agree well with the simulation results, which proves the feasibility of the design method. [ABSTRACT FROM AUTHOR]

It has been found that the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is systematically higher than the actual land surface in vegetated areas. This study developed a new globally corrected SRTM DEM through reducing its systematic bias in vegetated areas. Over 150, 000 km2airborne light detection and ranging (LiDAR) data along with spaceborne LiDAR, global canopy height data, global canopy cover data and global land cover data were collected to correct the SRTM DEM. A linear regression based method was used to estimate the original SRTM DEM error and therefore correct the SRTM DEM data. The results show that the original SRTM DEM data is around 6 m higher than the actual land surfaces on average across all vegetation types. Our corrected SRTM DEM data can significantly reduce the significant bias to near zero, and can also reduce the root-mean-square error by 1 m. [ABSTRACT FROM PUBLISHER]