ESO Imaging Survey: Infrared Deep Public Survey

This paper presents new J and Ks data obtained from observations conducted at the ESO 3.5m New Technology Telescope using the SOFI camera. These data were taken as part of the ESO Imaging Survey Deep Public Survey (DPS) and significantly extend the earlier optical/infrared EIS-DEEP survey presented in a previous paper. The DPS-IR survey comprises two observing strategies: shallow Ks observations providing nearly full coverage of pointings with complementary multi-band optical data and deeper J and Ks observations of the central parts of these fields. The DPS-IR survey provides a coverage of roughly 2.1 square degrees in Ks with 0.63 square degrees to fainter magnitudes and also covered in J, over three independent regions of the sky. The goal of the present paper is to describe the observations, the data reduction procedures, and to present the final survey products. The astrometric solution with an estimated accuracy of <0.15" is based on the USNO catalog. The final stacked images presented here number 89 and 272, in J and Ks, respectively, the latter reflecting the larger surveyed area. The J and Ks images were taken with a median seeing of 0.77" and 0.8". The images reach a median 5sigma limiting magnitude of J_AB~23.06 in an aperture of 2", while the corresponding limiting magnitude in Ks_AB is ~21.41 and ~22.16 mag for the shallow and deep strategies. Overall, the observed limiting magnitudes are consistent with those originally proposed. The quality of the data has been assessed by comparing the measured magnitude of sources at the bright end directly with those reported by the 2MASS survey and at the faint end by comparing the counts of galaxies and stars with those of other surveys to comparable depth and to model predictions. The final science-grade catalogs and images are available at CDS.Comment: Accepted for publication in A&A, 14 pages, 8 figures, a full resolution version of the paper is available from http://www.astro.ku.dk/~lisbeth/eisdata/papers/5019.pd

Magneto-Hall Characterization of Delta-Doped Pseudomorphic High-Electron-Mobility Transistor Structures

Conventional Hall‐effect determination of the two‐dimensional electron gas (2DEG) concentration n2D in pseudomorphic high electron mobility transistor structures is invalid because of interference from the highly doped GaAs cap. Furthermore, the usual methods of dealing with this cap‐interference problem, namely, (1) etching off the cap totally, (2) etching the cap until the mobility reaches a maximum, or (3) growing a separate structure with a thin, depleted cap, in general, give n2D values that are too low. However, we show here that magnetic‐field‐dependent Hall (M‐Hall) measurements can separately determine the carrier concentrations and mobilities in the cap and 2DEG regions, as verified by comparison with a self‐consistent, four‐band, k⋅p calculation and also by electrochemical capacitance‐voltage measurements in structures with different cap and spacer thicknesses

Automatic remote-sensing images registration by matching close-regions

The original publication is available at www.springerlink.comRemote-sensing images registration is a fundamental task in image processing, which is concerned with establishment of correspondence between two or more pictures taken, for example, at different times, from different sensors, or from different viewpoints. Because of the different gray level characters in such remote-sensing images, it's difficult to match them automatically. We usually constrain the images to some particular categories, or do the job manually. In this paper, we develop a new algorithm for remote-sensing images registration, which takes full advantage of the shape information of the close-regions bounded by contours after detecting and linking the edges in images. Based on the shape-specific points of the close-regions, we match the close-regions by evaluating their matching degrees. Using the matched pairs of the close-regions, the geometric parameters for images registration are computed and this registration task can be performed automatically and accurately. This new algorithm works well for those images where the contour information is well preserved, such as the optical images from LANDSAT and SPOT satellites. Experiments verified our algorithm, and showed that the performance of executing it sequentially depends a lot on the size of the input images. The time complexity will increase exponentially as the size of images increases. So we extend the sequential algorithm to a distributed scheme and perform the registration task more efficiently.Gui Xie, Hong She