The sub-Nyquist sampling technique offers the possibility of wideband spectrum sensing using portable devices. Therefore, it becomes more urgent and important to improve the hardware and computational efficiency of existing sparse recovery algorithms. In this paper, we propose an improved non-iterative joint support set recovery algorithm based on the MUSIC criterion for Modulated Wideband Converter (MWC) sub-Nyquist sampling front-ends. Through subspace analysis, we find that the MUSIC-based algorithm has the potential to reduce hardware requirements. Moreover, simulations show that the proposed improved MUSIC algorithm has comparable performance to the iterative SOMP in coarse spectrum sensing applications.

Wideband spectrum sensing based on sub-Nyquist sampling is an attractive approach to advance dynamic spectrum sharing (DSS), which can improve frequency resource utilization while overcoming sampling bottlenecks. Under the Compressive Sensing (CS) framework, finding occupied subbands can be equivalent to computing the support set for the Multiple Measurement Vectors (MMV) problem. To guarantee the performance of joint support recovery in noisy environments, different kinds of prior information are required, one of which is sparsity, a time-varying parameter. To address the dependence of recovery performance on signal sparsity, this paper proposed a two-step scheme for blind wideband spectrum sensing using a Modulated Wideband Converter (MWC) sub-Nyquist sampling front-end. The scheme first adopts the model order selection (MOS) method to estimate sparsity from the compressed covariance matrix, and then uses the estimates to dynamically adjust joint support recovery. The complete theoretical derivation innovatively applies MOS to sub-Nyquist sampling and presents a design method for MOS penalty constant. Extensive simulation results show that the proposed scheme can not only achieve blind sensing under the spectrum occupancy up to 40%, reduce the overall computational complexity of iterative SOMP, but also significantly improve the false alarm performance, meeting the requirements of the IEEE 802.22 standard.

Wideband spectrum sensing based on sub-Nyquist sampling is an attractive approach to propel Cognitive Radios (CRs), which aims to alleviate the burden of sampling and improve the utilization of frequency resources. In the context of compressive sensing (CS), finding idle frequency bands can be equivalent to calculating the complement of support set in a constructed multiple measurement vectors (MMV) problem. To ensure the performance of support set recovery in noisy environments, most existing algorithms require the prior knowledge of signal sparsity order, which is an unknown time-varying variable. To address the dependence of recovery performance on signal sparsity order, a two-step solution for wideband spectrum sensing in the Modulated Wideband Converter (MWC) sub-Nyquist sampling architecture is proposed in this paper. The proposed solution will first estimate the signal sparsity order from the compressed covariance matrix through state-of-the-art Model Order Selection (MOS) techniques, and then use the estimated sparsity order to adjust the support set recovery algorithms. Moreover, the relationship between the rank of the compressed covariance matrix and the signal sparsity order is provided through theoretical and numerical analysis. Simulations verify that the proposed solution can effectively reduce the probability of false alarms to meet the stringent sensing requirements of IEEE 802.22 in the medium sparse CR network.

The Bayesian network (BN) is a powerful model for probabilistic knowledge representation and inference and is increasingly used in the field of reliability evaluation. This paper presents a bibliographic review of BNs that have been proposed for reliability evaluation in the last decades. Studies are classified from the perspective of the objects of reliability evaluation, i.e., hardware, structures, software, and humans. For each classification, the construction and validation of a BN-based reliability model are emphasized. The general procedural steps for BN-based reliability evaluation, including BN structure modeling, BN parameter modeling, BN inference, and model verification and validation, are investigated. Current gaps and challenges in reliability evaluation with BNs are explored, and a few upcoming research directions that are of interest to reliability researchers are identified.

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 Cepstral Coefficients (LSFCC) which evolves from Mel frequency cepstral coefficients (MFCC) for ground target classification by means of geophone. With the LSFCCs, the average classification accuracy of tracked and wheeled vehicle is more than 89% in three different geographical environments by only one classifier which is trained in one of the three environments.