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When processing sensing signals under low signal-to-noise ratio environment, the sensing performance cannot be guaranteed in existing algorithms. To ensure sensing performance, we propose a novel spectrum sensing algorithm based on soft low-rank subspace clustering (SLRSC) in this brief. Firstly, the lowest rank coefficient matrix of signal vectors is calculated by low-rank representation, and the adjacency matrix is built to make coefficient matrix balance. Secondly, the eigenvalue of the adjacency covariance matrix is extracted as a feature. Finally, variable weighting $k$ -means method is used to cluster, which avoids complicated threshold derivation and improves cluster accuracy. Simulation results prove that the proposed SLRSC algorithm has excellent sensing performance under high noise case. [ABSTRACT FROM AUTHOR]

To improve the ability of the acoustic computed tomography (CT) temperature field reconstruction system for reconstructing various temperature distributions in the measured area, square and circular layouts of transducers were studied. In case of the square layout, the focus was on the placement of the transducers, and in case of the circular layout, the focus was on the division of grids. We proposed the coefficient matrix theory and indexes for studying and evaluating transducers layout. Four typical temperature field models were simulated and reconstructed. Simulation results showed that in a square layout, optimizing the position of transducers can result in better reconstruction quality. In a circular layout, a regular hexagonal grid division was better than an equal-area division, and better reconstruction quality was obtained by choosing the appropriate number of grids. For a small number of transducers, the circular layout of nine transducers with 27 acoustic rays and 91 regular hexagonal grids resulted in better reconstruction quality than the square and circular layout of eight transducers; in each reconstruction, the error was ≤ 5.07%. [ABSTRACT FROM AUTHOR]