Feng Lin

Shiyu Zhao, Feng Lin, Kemao Peng et al.

This paper studies distributed formation control of multiple agents in the plane using bearing-only measurements. It is assumed that each agent only measures the local bearings of their neighbor agents. The target formation considered in this paper is a circular formation, where each agent has exactly two neighbors. In the target formation, the angle subtended at each agent by their two neighbors is specified. We propose a distributed control law that stabilizes angle-constrained target formations merely using local bearing measurements. The stability of the target formation is analyzed based on Lyapunov approaches. We present a unified proof to show that our control law not only can ensure local exponential stability but also can give local finite-time stability. The exponential or finite-time stability can be easily switched by tuning a parameter in the control law.

Shiyu Zhao, Feng Lin, Kemao Peng et al.

This paper studies decentralized formation control of multiple vehicles when each vehicle can only measure the local bearings of their neighbors by using bearing-only sensors. Since the inter-vehicle distance cannot be measured, the target formation involves no distance constraints. More specifically, the target formation considered in this paper is an angle-constrained circular formation, where each vehicle has exactly two neighbors and the angle at each vehicle subtended by its two neighbors is pre-specified. To stabilize the target formation, we propose a discontinuous control law that only requires the sign information of the angle errors. Due to the discontinuity of the proposed control law, the stability of the closed-loop system is analyzed by employing a locally Lipschitz Lyapunov function and nonsmooth analysis tools. We prove that the target formation is locally finite-time stable with collision avoidance guaranteed. The evolution of the vehicle positions in the plane is also characterized.

Mo Shan, Fei Wang, Feng Lin et al.

We propose a framework for Google Map aided UAV navigation in GPS-denied environment. Geo-referenced navigation provides drift-free localization and does not require loop closures. The UAV position is initialized via correlation, which is simple and efficient. We then use optical flow to predict its position in subsequent frames. During pose tracking, we obtain inter-frame translation either by motion field or homography decomposition, and we use HOG features for registration on Google Map. We employ particle filter to conduct a coarse to fine search to localize the UAV. Offline test using aerial images collected by our quadrotor platform shows promising results as our approach eliminates the drift in dead-reckoning, and the small localization error indicates the superiority of our approach as a supplement to GPS.

Hu Chen, Yi Zhang, Mannudeep K. Kalra et al.

Given the potential X-ray radiation risk to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. The current main stream low-dose CT methods include vendor-specific sinogram domain filtration and iterative reconstruction, but they need to access original 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, the 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.