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In this work, we consider a cooperative system in which UAVs perform long-distance missions with assistance of unmanned ground vehicles (UGVs) for battery charging. We propose an autonomous landing scheme for the UAV to land on a mobile UGV with high precision by leveraging multiple-scale Quick Response (QR)-codes for different altitudes. These QR-codes support the acquisition of the relative distance and direction between the UGV and UAV. As a result, the UGV is not required to report its accurate state to the UAV. In contrast to the conventional landing algorithms based on the global positioning system (GPS), the proposed vision-based autonomous landing algorithm is available for both outdoor and GPS-denied scenarios. To cope with the challenge on the moving platform landing, a quadratic programming (QP) problem is formulated to design the velocity controller by combining a control barrier function (CBF) and a control Lyapunov function (CLF). Finally, both extensive computer simulation and a prototype of the proposed system are shown to confirm the feasibility of our proposed system and landing scheme.
Guanchong Niu; Qingkai Yang; Yunfan Gao; Man-On Pun. Vision-based Autonomous Landing for Unmanned Aerial and Mobile Ground Vehicles Cooperative Systems. IEEE Robotics and Automation Letters 2021, PP, 1 -1.
AMA StyleGuanchong Niu, Qingkai Yang, Yunfan Gao, Man-On Pun. Vision-based Autonomous Landing for Unmanned Aerial and Mobile Ground Vehicles Cooperative Systems. IEEE Robotics and Automation Letters. 2021; PP (99):1-1.
Chicago/Turabian StyleGuanchong Niu; Qingkai Yang; Yunfan Gao; Man-On Pun. 2021. "Vision-based Autonomous Landing for Unmanned Aerial and Mobile Ground Vehicles Cooperative Systems." IEEE Robotics and Automation Letters PP, no. 99: 1-1.
Mobile edge computing (MEC) can provide computing and storage services to user equipments (UEs) by utilizing edge nodes known as the small base stations (SBS's) deployed at the edge of the network. The short-distance transmission nature between SBS's and UEs makes the millimeter-wave (mmWave) communication empowered with multiple-input multiple-output (MIMO) hybrid precoding techniques particularly attractive for MEC. In this work, we consider the UE-SBS association, precoding design and power allocation for MEC networks endowed with mmWave MIMO. More specifically, the user association problem is first formulated as a max-k-cut (MkC) problem and then, solved by a distributed local-search algorithm. Next, the joint optimization of precoding and power allocation is cast into the difference of two convex functions (D.C.) programming framework before an iterative rank-constrained D.C. programming algorithm is developed to maximize the weighted sum-rate (WSR) of all UEs while taking into account the quality of service (QoS) requirement of each UE. Furthermore, the monotonic convergence of the proposed iterative algorithm is analytically proven. Finally, extensive computer simulation is conducted to demonstrate the effectiveness of the proposed iterative algorithm.
Guanchong Niu; Qi Cao; Man-On Pun. QoS-Aware Resource Allocation for Mobile Edge Networks: User Association, Precoding and Power Allocation. IEEE Transactions on Vehicular Technology 2021, PP, 1 -1.
AMA StyleGuanchong Niu, Qi Cao, Man-On Pun. QoS-Aware Resource Allocation for Mobile Edge Networks: User Association, Precoding and Power Allocation. IEEE Transactions on Vehicular Technology. 2021; PP (99):1-1.
Chicago/Turabian StyleGuanchong Niu; Qi Cao; Man-On Pun. 2021. "QoS-Aware Resource Allocation for Mobile Edge Networks: User Association, Precoding and Power Allocation." IEEE Transactions on Vehicular Technology PP, no. 99: 1-1.
Using remote sensing techniques to monitor landslides and their resultant land cover changes is fundamentally important for risk assessment and hazard prevention. Despite enormous efforts in developing intelligent landslide mapping (LM) approaches, LM remains challenging owing to high spectral heterogeneity of very-high-resolution (VHR) images and the daunting labeling efforts. To this end, a deep learning model based on semi-supervised multi-temporal deep representation fusion network, namely SMDRF-Net, is proposed for reliable and efficient LM. In comparison with previous methods, the SMDRF-Net possesses three distinct properties. 1) Unsupervised deep representation learning at the pixel- and object-level is performed by transfer learning using the Wasserstein generative adversarial network with gradient penalty to learn discriminative deep features and retain precise outlines of landslide objects in the high-level feature space. 2) Attention-based adaptive fusion of multi-temporal and multi-level deep representations is developed to exploit the spatio-temporal dependencies of deep representations and enhance the feature representation capability of the network. 3) The network is optimized using limited samples with pseudo-labels that are automatically generated based on a comprehensive uncertainty index. Experimental results from the analysis of VHR aerial orthophotos demonstrate the reliability and robustness of the proposed approach for LM in comparison with state-of-the-art methods.
Xiaokang Zhang; Man-On Pun; Ming Liu. Semi-supervised Multi-Temporal Deep Representation Fusion Network for Landslide Mapping from Aerial Orthophotos. Remote Sensing 2021, 13, 548 .
AMA StyleXiaokang Zhang, Man-On Pun, Ming Liu. Semi-supervised Multi-Temporal Deep Representation Fusion Network for Landslide Mapping from Aerial Orthophotos. Remote Sensing. 2021; 13 (4):548.
Chicago/Turabian StyleXiaokang Zhang; Man-On Pun; Ming Liu. 2021. "Semi-supervised Multi-Temporal Deep Representation Fusion Network for Landslide Mapping from Aerial Orthophotos." Remote Sensing 13, no. 4: 548.
Beam Division Multiple Access (BDMA) with hybrid precoding has recently been proposed for multi-user multiple-input multiple-output (MU-MIMO) systems by simultaneously transmitting multiple digitally precoded users’ data-streams via different beams. In contrast to most existing works that assume the number of radio frequency (RF) chains must be greater than or equal to that of data-streams, this work proposes a novel BDMA downlink system by first grouping transmitting data-streams before digitally precoding data group by group. To fully harvest the benefits of this new architecture, a greedy user grouping algorithm is devised to minimize the inter-group interference while two digital precoding approaches are developed to suppress the intra-group interference by maximizing the signal-to-interference-and-noise ratio (SINR) and the signal-to-leakage-and-noise ratio (SLNR), respectively. As a result, the proposed BDMA system requires less RF chains than the total number of transmit data-streams. Furthermore, we optimize the power allocation to satisfy each user’s quality of service (QoS) requirement using the D.C. (difference of convex functions) programming technique. Simulation results confirm the effectiveness of the proposed scheme.
Guanchong Niu; Qi Cao; Manon Pun. Block Diagonal Hybrid Precoding and Power Allocation for QoS-Aware BDMA Downlink Transmissions. Sensors 2020, 20, 4497 .
AMA StyleGuanchong Niu, Qi Cao, Manon Pun. Block Diagonal Hybrid Precoding and Power Allocation for QoS-Aware BDMA Downlink Transmissions. Sensors. 2020; 20 (16):4497.
Chicago/Turabian StyleGuanchong Niu; Qi Cao; Manon Pun. 2020. "Block Diagonal Hybrid Precoding and Power Allocation for QoS-Aware BDMA Downlink Transmissions." Sensors 20, no. 16: 4497.