Digital beamforming (DBF) with scan-on-receive (SCORE) technique in elevation is a powerful technique that enables a spaceborne synthetic aperture radar (SAR) to achieve high-resolution wide swath (HRWS) imaging. In the spaceborne DBF-SAR system, sampling signals from each channel are weighted by weights generated by a digital signal processing system in real-time. However, the contradiction between the shortage of spaceborne hardware resources and resource demand of the multichannel real-time signal processing increases the difficulty of system design. In order to solve this problem, a novel weight generator and an improved intermediate frequency (IF) DBF real-time processing architecture are proposed in this article. By taking advantage of the special properties of the SCORE algorithm, the proposed weight generator calculates weights using a linear polynomial algorithm. The simulation result shows that a low-order approximation can achieve high performance. The proposed generator can correct multichannel amplitude and phase error at a low cost on hardware resources. The effectiveness of the proposed method is verified by experiments with a raw data processing instance of an X-band 16 channels DBF-SAR.

With the increasing amount of web services on the Internet, personalized web service selection and recommendation are becoming more and more important. In this paper, we present a new similarity measure for web service similarity computation and propose a novel collaborative filtering approach, called normal recovery collaborative filtering, for personalized web service recommendation. To evaluate the web service recommendation performance of our approach, we conduct large-scale real-world experiments, involving 5,825 real-world web services in 73 countries and 339 service users in 30 countries. To the best of our knowledge, our experiment is the largest scale experiment in the field of service computing, improving over the previous record by a factor of 100. The experimental results show that our approach achieves better accuracy than other competing approaches.

MapReduce is an important programming model for large-scale data-intensive applications such as web indexing, scientific simulation, and data mining. Hadoop is an open-source implementation of MapReduce enjoying wide adoption. Partition function is an important component of Hadoop which split outputs of maps into bulks that place the input data of reduces. Based on the assumptions that cluster nodes are homogeneous and perform work at roughly the same rate, its default partition function splits intermediate keys into reduces. However, in practice the homogeneity assumptions seldom hold and cluster nodes usually perform work at different rate. In this paper, we design a heterogeneity-load-aware partition function named proportional partition function (PPF). Besides the dynamic loading of cluster nodes, PPF considers the capacity diversity of cluster nodes such as CPU processing speed and disk writing speed.

Since there are many Web services on the Internet, personalized Web service selection and recommendation is very important. In this paper, we present a new similarity measure for Web service similarity computation and propose a normal recovery collaborative filtering (NRCF) method for personalized Web service recommendation.