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At 525 km in mean diameter, Vesta is the second-most massive object in the main asteroid belt of our Solar System. At all scales, pyroxene absorptions are the most prominent spectral features on Vesta and overall, Vesta mineralogy indicates a complex magmatic evolution that led to a differentiated crust and mantle [1]. The thermal behavior of areas of unusual albedo seen on the surface at the local scale can be related to physical properties that can provide information about the origin of those materials. Dawn's Visible and Infrared Mapping Spectrometer (VIR) [2] hyperspectral images are routinely used, by means of temperature-retrieval algorithms, to compute surface temperatures along with spectral emissivities. Here we present temperature maps of several local-scale features of Vesta that were observed by Dawn under different illumination conditions and different local solar times.

Personalized recommendation involves a process of gathering and storing information about website visitors, from which user's characteristic knowledge is exploited to satisfy the personalized needs. Facing the difficulty of timely identifying new data computing in updating real-time user behaviors, we propose a case-intelligence system framework along with a feature-based multi-layer feed-forward neural networks (MFNN) approach to personalized recommendation that is capable of handling the massive with dynamic data effectively. Our experimental results indicate that better performance in our recommender comes from the both sides: the one is that our MFNN has understandable, constructive and reliable process, unlike the black box of the other ANN networks; the other is our covering algorithm can decrease the complexity of ANN algorithm effectively.

Radio Frequency Interference (RFI) suppression is an important technique in the ultra-wideband synthetic aperture radar (UWB SAR). In this paper, we mainly analyze the performance of a notch filter for RFI suppression. The theoretical output from notch filter is presented based on the properties of narrowband RFI. The results indicate that the notch filter has little effect on the resolution, but it has significant effect on sidelobes, which can be considered to be false targets. This theoretical result is verified by simulation and experimental UWB SAR imaging.

Similar pattern matching of sequence is an important field in time series data mining. Since time series may be very long, which results in query performance decreasing sharply when the database is large, therefore, dimension reduction is required before pattern matching. Fourier transform can be used for dimension reduction, but it cannot provide any feature of signals in local interval. According to this situation, a new similar pattern matching method is proposed in this paper. Firstly, trends of time series are extracted by empirical mode decomposition, and the trends are translated into vectors to realize dimension reduction. Secondly, the vectors are clustered by a forward propagation learning algorithm. Finally, all the series that are similar with the query are found by calculating Euclidean distance between the query and the series that belong to the same category with it. Experimental results show that it is an effective pattern-matching algorithm.

Case-based reasoning systems running in interactive domains like e-commerce, can easily reach thousands of cases which are stored in the irreducible case library, and the deletion of any single case means that the uniquity is lost. Aiming on the efficiency of retrieval in this kind of environment, this paper proposes the methods to achieve case-base maintenance (CBM) from the both sides: One is employing Alternative-Covering Algorithm to partition the case library to many Covering Domains and thus realizing the selective filtering; the other is using Multi-layer Feedforward Neural Networks to deal with case retrieval within the large-scale case library. Our experimental results indicate that the integrated method, which is especially feasible for the processing of vast and high dimensional data, can effectively guarantee the system's usability and enhance its capability.