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Due to the negative impact from spatial correlation, spatially correlated cognitive radio (CR) based devices participating in cooperative spectrum sensing may be harmful to the detection performance. In this paper, we propose an energy-efficient cooperative spectrum sensing scheme based on spatial correlation for cognitive Internet of Things (CIoT). To mitigate the communication overhead and ensure sufficient sensing accuracy, the CR-based devices (CRDs) can be grouped into several clusters. The member nodes undertake cooperative spectrum sensing tasks in turn by rotating, and send the local test statistic to their cluster head nearby. Then, by exploiting the spatial correlation of the members, the cluster head combines the sensing results and make use of likelihood ratio test to obtain the cluster decision. After receiving the decisions from all clusters, the fusion center employs hard scheme to make the final decision about spectrum occupancy. The simulation results show that our scheme not only provides the better sensing performance, but also improve the energy efficiency.

Mass spectrometry (MS) has played a vital role across a broad range of fields and applications in proteomics. The development of high-resolution MS has significantly advanced biology in areas such as protein structure, function, post-translational modification and global protein dynamics. The two most widely used MS ionization techniques in proteomics are electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI). ESI typically yields multiple charge values for each molecular mass and an isotopic cluster for each nominal mass-to-charge (m/z) value. Although MALDI mass spectra typically contain only singly charged ions, overlapping isotope patterns can be problematic for accurate mass measurement. To overcome these challenges of overlapping isotope patterns associated with complex samples in MS-based proteomics research, deconvolution strategies are being used. This manuscript describes a wide variety of deconvolution strategies, including de-isotoping and de-charging processes, deconvolution of co-eluting isomers or peptides with different sequences in data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes, and data analysis in intact protein mass determination, ion mobility MS, native MS, and hydrogen/deuterium exchange MS. It concludes with a discussion of future prospects in the development of bioinformatics and potential new applications in proteomics.

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