Given the potential risk of X-ray radiation to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. Currently, the main stream low-dose CT methods include vendor-specific sinogram domain filtration and iterative reconstruction algorithms, but they need to access raw data, whose formats are not transparent to most users. Due to the difficulty of modeling the statistical characteristics in the image domain, the existing methods for directly processing reconstructed images cannot eliminate image noise very well while keeping structural details. Inspired by the idea of deep learning, here we combine the autoencoder, deconvolution network, and shortcut connections into the residual encoder-decoder convolutional neural network (RED-CNN) for low-dose CT imaging. After patch-based training, the proposed RED-CNN achieves a competitive performance relative to the-state-of-art methods in both simulated and clinical cases. Especially, our method has been favorably evaluated in terms of noise suppression, structural preservation, and lesion detection.

This paper presents a sophisticated vision-aided flocking system for unmanned aerial vehicles (UAVs), which is able to operate in GPS-denied unknown environments to realize exploring and searching missions, and also able to adopt two types of vision sensors: day and thermal cameras, to measure relative motion between UAVs in different illumination conditions without using wireless communication. In order to realize robust vision-aided flocking, an integrated framework of tracking-learning-detection on the basis of multi-feature coded correlation filter has been developed. To achieve the long term tracking, a re-detector is trained on-line to adaptively re-initialize target for global sensing. An advanced flocking strategy is developed to address the autonomous multi-UAVs cooperative flight. LiDAR-based navigation modules are developed for autonomous localization, mapping and obstacle avoidance. Flight experiments of a team of UAVs have been conducted to verify the performance of this flocking system in GPS-denied environment. The extensive experiments validate the robustness of proposed vision algorithms in challenging scenarios.

In this paper, we present a comprehensive design and implementation for a micro aerial vehicle (MAV) that is able to perform 3D autonomous navigation and obstacle avoidance in cluttered and realistic unknown environments without the aid of GPS and other external sensors or markers. To achieve these autonomous missions, modularized components are developed for the MAV, including visual inertial odometry (VIO), 3D occupancy mapping, and motion planning. The proposed system is implemented to run on a small embedded computer in real-time. It is demonstrated to be robust in both simulation and real flight experiments. The demonstration video is available at: \url{https://youtu.be/KUKzsnORm-4}.

In wireless rechargeable sensor networks (WRSNs), researchers address the energy problem in wireless sensor networks by introducing fixed chargers or mobile chargers to recharge energy-starving nodes. The majority of existing studies on WRSNs investigate either the deployment of fixed chargers or the scheduling of mobile chargers to charge sensor nodes. In this work, we investigate the hybrid charging problem, i.e., the problem of recharging nodes by fixed chargers and mobile chargers cooperatively, in scenarios with arbitrary node distribution, with the goal of minimizing the number of energy-exhausted nodes. We deduce the problem into two sub-problems, namely, the Fixed Chargers Deployment Problem (FCDP) and the Mobile Charger Path Planning problem (MCPP), respectively. We present the definitions of these two sub-problems and prove both of them are NP-hard. Then, we propose a hybrid charging scheme which comprises of two algorithms to address these two sub-problems one by one. Extensive simulation experiments confirm the scheme’s effectiveness and the results show that the proposed scheme outperforms the existing methods(i.e., TCA-MCPPA, FCDA-JERDC, MSC) in minimizing the number of energy-exhausted nodes. In the uniform node distribution, they are reduced by 79.62%, 88.2%, and 95.01%, respectively. In the triple Gaussian node distribution, they are reduced by 99.90%, 99.71%, and 99.98%, respectively.