In this paper, by establishing a liquid crystal model of beam steering, a rapid iterative algorithm called Rosenbrock
algorithm is proposed for obtaining wave-control data of liquid crystal phased array (LCPA). The method doesn’t need
wavefront reconstruction, the error is reduced, and the searching direction is adjusted automatically to right space of
solution, thus the algorithm converge rapidly, and the performance of beam steering is optimized at the same time. In this
paper, firstly, a liquid crystal model of beam steering is established. Then, the effectiveness and superiority of this
algorithm is verified compared with Pattern Search algorithm. Finally, we evaluate our algorithm by experiment. The
results show that Rosenbrok algorithm can optimize the efficiency of beam steering rapidly and significantly.
Data skew is one of most important reasons to deteriorate the performance of parallel spatial database. This paper studies
the issues of handling data skew in shared nothing parallel spatial database system architecture. A novel data skew
handling method is proposed, which fulfill spatial data distribution balancing based on the spatial proximity of data
fragments. The minimum spatial proximity is used to be the principle of moving data fragments among different network
parallel nodes. Our experimental results show that the proposed data skew handling method can achieve dynamic data
load balancing and offer significant improvement for reducing response time of parallel spatial queries.
KEYWORDS: Data storage, Geographic information systems, Data processing, Remote sensing, Data acquisition, Logic, Associative arrays, Roads, Databases, Structural design
Spatial data partitioning strategy plays an important role in GIS spatial data distributed storage and processing, its key
problem is how to partition spatial data to distributed nodes in network environment. Existing main spatial data
partitioning methods doesn't consider spatial locality and unstructured variable length characteristics of spatial data,
these methods simply partition spatial data based on one or more attributes value that could result in storage capacity
imbalance between distributed processing nodes. Aiming at these, we point out the two basic principles that spatial data
partitioning should meet to in this paper. We propose a new spatial data partitioning method based on hierarchical
decomposition method of low order Hilbert space-filling curve, which could avoid excessively intensive space
partitioning by hierarchically decomposing subspaces. The proposed method uses Hilbert curve to impose a linear
ordering on the multidimensional spatial objects, and partition the spatial objects according to this ordering.
Experimental results show the proposed spatial data partitioning method not only achieves better storage load balance
between distributed nodes, but also keeps well spatial locality of data objects after partitioning.
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