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Abstract:The Clouds and the Earth's Radiant Energy System (CERES) project provides observations of Earth's radiation budget using measurements from CERES instruments onboard the Terra, Aqua and Suomi National Polar-orbiting Partnership (S-NPP) satellites. As the objective is to create a long-term climate data record, it is necessary to periodically reprocess the data in order to incorporate the latest calibration changes and algorithm improvements. Here, we focus on the improvements and validation of CERES Terra and Aqua radiances in Edition 4, which are used to generate higher-level climate data products. Onboard sources indicate that the total (TOT) channel response to longwave (LW) radiation has increased relative to the start of the missions by 0.4% to 1%. In the shortwave (SW), the sensor response change ranges from´0.4% to 0.6%. To account for in-orbit changes in SW spectral response function (SRF), direct nadir radiance comparisons between instrument pairs on the same satellite are made and an improved wavelength dependent degradation model is used to adjust the SRF of the instrument operating in a rotating azimuth plane scan mode. After applying SRF corrections independently to CERES Terra and Aqua, monthly variations amongst these instruments are highly correlated and the standard deviation in the difference of monthly anomalies is 0.2 Wm´2 for ocean and 0.3 Wm´2 for land/desert. Additionally, trends in CERES Terra and Aqua monthly anomalies are consistent to 0.21 Wm´2 per decade for ocean and 0.31 Wm´2 per decade for land/desert. In the LW, adjustments to the TOT channel SRF are made to ensure that removal of the contribution from the SW portion of the TOT channel with SW channel radiance measurements during daytime is consistent throughout the mission. Accordingly, anomalies in day-night LW difference in Edition 4 are more consistent compared to Edition 3, particularly for the Aqua land/desert case.

We design and develop a low-cost pyroelectric detectorbased IR motion-tracking system. We study the characteristics of the detector and the Fresnel lenses that are used to modulate the visibility of the detectors. We build sensor clusters in different configurations and demonstrate their use for human motion tracking.

The size of infrared camera systems can be reduced by collecting low-resolution images in parallel with multiple narrow-aperture lenses rather than collecting a single high-resolution image with one wide-aperture lens. We describe an infrared imaging system that uses a three-by-three lenslet array with an optical system length of 2.3 mm and achieves Rayleigh criteria resolution comparable with a conventional single-lens system with an optical system length of 26 mm. The high-resolution final image generated by this system is reconstructed from the low-resolution images gathered by each lenslet. This is accomplished using superresolution reconstruction algorithms based on linear and nonlinear interpolation algorithms. Two implementations of the ultrathin camera are demonstrated and their performances are compared with that of a conventional infrared camera.

This paper presents a design and development of a low power consumption, and low cost, human identification system using a pyroelectric infrared (PIR) sensor whose visibility is modulated by a Fresnel lens array. The optimal element number of the lens array for the identification system was investigated and the experimental results suggest that the lens array with more elements can yield a better performance in terms of identification and false alarm rates. The other parameters of the system configuration such as the height of sensor location and sensor-to-object distance were also studied to improve spectral distinctions among sensory data of human objects. The identification process consists of two parts: training and testing. For the data training, we employed a principal components regression (PCR) method to cluster data with respect to different registered objects at different speed levels. The feature data of different objects walking along the same path in training yet at random speeds are then tested against the pre-trained clusters to decide whether the target is registered, and which member of the registered group it is.