In this paper, the secrecy performance of single-input-multiple-output systems over correlated κ-µ shadowed fading channels is investigated. In particular, based on the classic Wyner's wiretap model, we derive analytical expressions for secure outage probability (SOP) and the probability of strictly positive secrecy capacity (SPSC) over correlated κ-µ shadowed fading channels. In order to further study the impact of channel parameters on the secrecy performance, novel SOP and the probability of SPSC over independent and identically distributed κ-µ shadowed fading channels are also obtained. In addition, we discuss the asymptotic expressions of the SOP and the SPSC. The match between the analytical results and simulations is excellent for all parameters under considerations. It is interesting to find that the results show that when the signal-to-noise ratio of the main channel is lower than that of the eavesdropping channel, the larger value of correlation coefficient is helpful to improve the secrecy performance and vice versa.INDEX TERMS Single-input multiple-output, κ-µ shadowed fading, the probability of strictly positive secrecy capacity (SPSC), secure outage probability (SOP).HONGXIA BIE received the Ph.D. degree from Jilin University, China, in 2000. She is currently a Professor with the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications. Her main research interests include physical layer security, cooperative communications, multimedia information processing, and wireless data transmission.XINGWANG LI (S'14-M'15) received the B.Sc. degree in communication engineering from

The resolution in optical coherence tomography imaging is an important parameter which determines the size of the smallest features that can be visualized. Sparse sampling approaches have shown considerable promise in producing high resolution OCT images with fewer camera pixels, reducing both the cost and the complexity of an imaging system. In this paper, we propose a non-local approach to the reconstruction of high resolution OCT images from sparsely sampled measurements. An iterative strategy is introduced for minimizing a homotopic, non-local regularized functional in the spatial domain, subject to data fidelity constraints in the k-space domain. The novel algorithm was tested on human retinal, corneal, and limbus images, acquired in-vivo, demonstrating the effectiveness of the proposed approach in generating high resolution reconstructions from a limited number of camera pixels.

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Super dense and distributed wireless sensor networks have become very popular with the development of small cell technology, Internet of Things (IoT), Machine-to-Machine (M2M) communications, Vehicular-to-Vehicular (V2V) communications and public safety networks. While densely deployed wireless networks provide one of the most important and sustainable solutions to improve the accuracy of sensing and spectral efficiency, a new channel access scheme needs to be designed to solve the channel congestion problem introduced by the high dynamics of competing nodes accessing the channel simultaneously. In this paper, we firstly analyzed the channel contention problem using a novel normalized channel contention analysis model which provides information on how to tune the contention window according to the state of channel contention. We then proposed an adaptive channel contention window tuning algorithm in which the contention window tuning rate is set dynamically based on the estimated channel contention level. Simulation results show that our proposed adaptive channel access algorithm based on fast contention window tuning can achieve more than 95% of the theoretical optimal throughput and 0.97 of fairness index especially in dynamic and dense networks.