The rapid increase of information and accessibility in recent years has activated a paradigm shift in algorithm design for artificial intelligence. Recently, deep learning (a surrogate of Machine Learning) have won several contests in pattern recognition and machine learning. This review comprehensively summarises relevant studies, much of it from prior state-of-the-art techniques. This paper also discusses the motivations and principles regarding learning algorithms for deep architectures.
This paper we selected 5 typical texture class samples from Quick Bird RGB fused data with 0.61m resolution. We used GLCMs to quantitatively calculate texture features, which parameter values are suitable for the specific texture classifications. Six statistical features for every class sample in four orientations and 1 pixel of pair-wise distance were obtained, including: energy, entropy, contrast, homogeneity, correlation, and dissimilarity respectively. The average values in four directions were computed and compared. The results show that dissimilarity and entropy have biggest value differences among six samples. They are the most important features for classification or recognition of class samples. The statistics of dissimilarity, entropy, homogeneity, contrast have been demonstrated a decrease in classification ability. The results of the research supplied important references for the quantitative interpretation of VHR Quick Bird imagery in the applications of land cover/use classification and mapping.
The efficiency of metadata indexing is important to the performance of distributed file system. Time and space spending of current metadata management algorithms are unstable. In this paper, we use B-tree to index the metadata of distributed file system. Lustre is an open source distributed file system in which Hash function is used to manage the metadata. We implement the prototype of metadata indexing sub-system on Lustre and use Iozone to test the I/O performance of Lustre with and without the metadata indexing sub-system respectively. The simulation results show that Lustre with the metadata indexing sub-system has higher adaptability than Lustre with Hash-based metadata management algorithm.
In view of the problems of limited energy of the nodes and threat to routing security in wireless sensor networks, Energy-efficient Secure Routing Protocol (ESRP), an energy-efficient secure routing protocol for wireless sensor networks, is proposed. According to the structured topology of hexagonal mesh, hops at different directions are calculated on the optimal route for transmitting data packets in ESRP. With the help of characters of hops, the nodes can rapidly find a route among multiple optimal routes by the policy of the twice probability routing selection. Data breach is prevented by data encryption and data security is realized by one-way hash key chain and symmetric key authentication. Analysis and simulation results show that ESRP has better performance on security and saving energy.
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