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Reconstruction, which is the core of compressive sensing (CS), can be implemented by l0 norm minimization. In practice, l0 norm minimization is a NP-hard problem that requires exhaustively listing all possibilities of the original signal and is difficult to achieve by traditional algorithms. This paper proposes a signal reconstruction algorithm combining genetic algorithm with simulated annealing...
The theory of compressive sensing (CS) mainly includes three aspects, i.e., sparse representation, uncorrelated sampling, and signal reconstruction, in which signal reconstruction serve as the core of CS. The constraint of signal sparsity can be implemented by l0 norm minimization, which is an NP-hard problem that requires exhaustively listing all possibilities of the original signal and is difficult...
This paper proposes a feature relation network (FRN) to model the underlying feature relation structures of a set of observations. A pattern classification system is then constructed based on the feature relation network, namely PCS-FRN. During training process, PCS-FRN will form an attractor for each group of samples in order to lower the overall energy states. The attractor, or a feature relation...
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