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Data fusion is an important technology in WSN, But with the expansion of the scope object sensor and complexity increase, the fusion function is more and more complex, Energy consumption of the fusion operation is required more and more. Therefore, the energy consumption of the data fusion and the data transmission has been can't ignored already. Using the compressive sensing theory in the WSN data...
We present a belief propagation (BP)-based sparse Bayesian learning (SBL) algorithm, referred to as the BP-SBL, to recover sparse transform coefficients in large scale compressed sensing problems. BP-SBL is based on a widely used hierarchical Bayesian model, which is turned into a factor graph so that BP can be applied to achieve computational efficiency. We prove that the messages in BP are Gaussian...
Sparse Bayesian Learning (SBL) has been used as a sparse signal recovery algorithm for compressed sensing. It has been shown that SBL is easy to use and can recover sparse signals more accurately than the l1 based optimization approaches, which require a delicate choice of user parameters. We propose herein a modified Expectation Maximization (EM) based SBL algorithm referred to as SBL-alpha and...
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