With the aim of contribution to the study of atmospheric ozone layer, a new sensitive radiometer for atmospheric minor
constituents has been installed in the Observatorio Atmosférico de la Patagonia Austral, División LIDAR, CEILAP
(CITEDEF-CONICET), in October 2010. This observatory is established in the city of Rio Gallegos (51° 36' S, 69° 19'
W), Argentina, close to the spring ozone hole. The millimeter wave radiometer was developed in STEL (Solar
Terrestrial Environment Laboratory), Nagoya University, Japan. This passive remote sensing instrument is able to
measure the ozone (O3) amount in the high stratosphere and mesosphere continuously and automatically with a high time
resolution. The millimeter wave radiometer ozone profiles will be supplemented with the ozone profiles obtained from
the DIAL system existent in the observatory.
The millimeter wave radiometer is based on the spectral signal detection from the atmosphere due to the molecular
rotational transition of molecules under study. The operation is based on a superheterodyne system which uses a
Superconductor-Insulator-Superconductor (SIS) mixer receiver operating at 203.6GHz. The SIS mixer junction consists
of a sandwich structure of Nb/AlOx/Nb, and is cooled to 4.2K with a closed cycle He-gas refrigerator. Two additional
heterodyne-mixed stages are realized with the aim to shift the measured spectral line until a frequency around of 500
MHz. A FFT (Fast Fourier Transform) spectrometer system is used as a back end.
The aims of this work are to show the potential of the millimeter wave radiometer installed in the subpolar latitudes close
to the polar ozone hole and to present the preliminary result of the first measurements.
In a multiple camera system that consists of a large number of cameras, each camera has to be calibrated in order to use the image information obtained from them effectively. When a target scene is large, the conventional calibration methods using 3D or 2D object are difficult to apply because setting these objects is an elaborate task. Although another approach called self-calibration using only image point correspondences seems to be suitable in such a situation, this method is often susceptible to noise. In this paper, we propose a new camera calibration method for such systems using 1D object, which has three points on a line with known distances of each other. The main reason for using 1D object as a calibration object is because it is more flexible than 3D or 2D object in a large scene. By using the free-moving 1D object without knowledge about its position and only one calibrated camera, we can calibrate multiple cameras simultaneously, so the proposed method presents an easy and practical solution. Experimental results of computer-simulated data are shown in this paper. In the presentation, experimental results of real image data will be presented.