Dongfeng Xie

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Seismic signal is widely used in ground target classification due to its inherent characteristics. However, its propagation is highly dependent on local underlying geology. It means that nearly every one geographical environment requires a unique classifier. To resolve the problem, this paper presents a robust feature extraction method Log-Sigmoid Frequency(More)
In this paper, a new sensor array geometry, called a compressed symmetric nested array (CSNA), is designed to increase the degrees of freedom in the near field. As its name suggests, a CSNA is constructed by getting rid of some elements from two identical nested arrays. The closed form expressions are also presented for the sensor locations and the largest(More)
Seismic signal is widely used in ground vehicle classification due to its inherent characteristics. But the generalization accuracy of classifier is heavily degraded due to different underlying geologies. To overcome the weakness of the seismic signal, a feature extraction method is proposed in this paper. The extracted feature is the cepstrum of the(More)
Clustering problem is one of the significant issues for wireless sensor networks concerned with energy consumption and large-scale deployment. Several energy-efficient clustering algorithms have been proposed to improve the energy utilization efficiency and prolong the network lifetime. In this paper, we propose a new clustering scheme after a comprehensive(More)
This paper proposes an efficient data gathering protocol for large scale wireless sensor networks by using the compressive sensing technology. All sensor nodes construct a chain by a greedy algorithm. Data packets are transmitted through this chain from one node to another and till the end node of the chain. This end node, namely chain leader, then relays(More)
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