Meaningful representation and effective retrieval of video shots in a large-scale database has been a profound challenge for the image/video processing and computer vision communities. A great deal of effort has been devoted to the extraction of low-level visual features, such as color, shape, texture, and motion for characterizing and retrieving video shots. However, the accuracy of these feature descriptors is still far from satisfaction due to the well-known semantic gap. In order to alleviate the problem, this paper investigates a novel methodology of representing and retrieving video shots using human-centric high-level features derived in brain imaging space (BIS) where brain responses to natural stimulus of video watching can be explored and interpreted. At first, our recently developed dense individualized and common connectivity-based cortical landmarks (DICCCOL) system is employed to locate large-scale functional brain networks and their regions of interests (ROIs) that are involved in the comprehension of video stimulus. Then, functional connectivities between various functional ROI pairs are utilized as BIS features to characterize the brain's comprehension of video semantics. Then an effective feature selection procedure is applied to learn the most relevant features while removing redundancy, which results in the formation of the final BIS features. Afterwards, a mapping from low-level visual features to high-level semantic features in the BIS is built via the Gaussian process regression (GPR) algorithm, and a manifold structure is then inferred, in which video key frames are represented by the mapped feature vectors in the BIS. Finally, the manifold-ranking algorithm concerning the relationship among all data is applied to measure the similarity between key frames of video shots. Experimental results on the TRECVID 2005 dataset demonstrate the superiority of the proposed work in comparison with traditional methods.

Microwave radiometer on board the FY-4 microwave satellite, which is China's second generation geostationary orbit meteorological satellite, is able to acquire temperature profiles and humidity profiles at 54, 118, 183, 380 and 425GHz. The FY-4 microwave radiometer receives the radiation emitted by the earth surface and atmospheric molecules at the five-frequency bands. A multiplexer is an important part of the radiometer, which separates the radiation beam into five-frequency bands signal and transmits the signal to respective receiver of each frequency band. In this paper, a five-frequency bands quasi-optical multiplexer is designed for the low-loss frequency band separation. Meanwhile, the quasi-optical multiplexer seen as a feed network of the radiometer has to provide the proper antenna illumination in amplitude and phase for the antenna reflector. Quasi-optical techniques are used to design the overall five-frequency bands multiplexer.