Ultraviolet Scattering Communication Using Subcarrier Intensity Modulation over Atmospheric Turbulence Channels

A closed-form non-line-of-sight (NLOS) turbulenceinduced fluctuation model is derived for ultraviolet scattering communication (USC), which models the received irradiance fluctuation by Meijer G function. Based on this model, we investigate the error rates of the USC system in NLOS case using different modulation techniques. Closed-form error rate results are derived by integration of Meijer G function. Inspired by the decomposition of different turbulence parameters, we use a series expansion of hypergeometric function and obtain the error rate expressions by the sum of four infinite series. The numerical results show that our error rate results are accurate in NLOS case. We also study the relationship between the turbulence influence and NLOS transceiver configurations. The numerical results show that when two-LOS link formulates the same distance, the turbulence influence is the strongest for long ranges and the weakest for short ranges.

An Unbiased Symmetric Matrix Estimator for Topology Inference under Partial Observability

Network topology inference is a fundamental problem in many applications of network science, such as locating the source of fake news, brain connectivity networks detection, etc. Many real-world situations suffer from a critical problem that only a limited part of observations are available. This letter considers the problem of network topology inference under the framework of partial observability. Based on the vector autoregressive model, we propose a novel unbiased estimator for the symmetric network topology with the Gaussian noise and the Laplacian combination rule. Theoretically, we prove that it converges to the network combination matrix in probability. Furthermore, by utilizing the Gaussian mixture model algorithm, an effective algorithm called network inference Gauss algorithm is developed to infer the network structure. Finally, compared with the state-of-the-art methods, numerical experiments demonstrate the proposed algorithm enjoys better performance in the case of small sample sizes.

Attention Cube Network for Image Restoration

Recently, deep convolutional neural network (CNN) have been widely used in image restoration and obtained great success. However, most of existing methods are limited to local receptive field and equal treatment of different types of information. Besides, existing methods always use a multi-supervised method to aggregate different feature maps, which can not effectively aggregate hierarchical feature information. To address these issues, we propose an attention cube network (A-CubeNet) for image restoration for more powerful feature expression and feature correlation learning. Specifically, we design a novel attention mechanism from three dimensions, namely spatial dimension, channel-wise dimension and hierarchical dimension. The adaptive spatial attention branch (ASAB) and the adaptive channel attention branch (ACAB) constitute the adaptive dual attention module (ADAM), which can capture the long-range spatial and channel-wise contextual information to expand the receptive field and distinguish different types of information for more effective feature representations. Furthermore, the adaptive hierarchical attention module (AHAM) can capture the long-range hierarchical contextual information to flexibly aggregate different feature maps by weights depending on the global context. The ADAM and AHAM cooperate to form an "attention in attention" structure, which means AHAM's inputs are enhanced by ASAB and ACAB. Experiments demonstrate the superiority of our method over state-of-the-art image restoration methods in both quantitative comparison and visual analysis.