65 research outputs found
Deep Learning-enabled Spatial Phase Unwrapping for 3D Measurement
In terms of 3D imaging speed and system cost, the single-camera system
projecting single-frequency patterns is the ideal option among all proposed
Fringe Projection Profilometry (FPP) systems. This system necessitates a robust
spatial phase unwrapping (SPU) algorithm. However, robust SPU remains a
challenge in complex scenes. Quality-guided SPU algorithms need more efficient
ways to identify the unreliable points in phase maps before unwrapping.
End-to-end deep learning SPU methods face generality and interpretability
problems. This paper proposes a hybrid method combining deep learning and
traditional path-following for robust SPU in FPP. This hybrid SPU scheme
demonstrates better robustness than traditional quality-guided SPU methods,
better interpretability than end-to-end deep learning scheme, and generality on
unseen data. Experiments on the real dataset of multiple illumination
conditions and multiple FPP systems differing in image resolution, the number
of fringes, fringe direction, and optics wavelength verify the effectiveness of
the proposed method.Comment: 26 page
Achieving Energy-Efficient Uplink URLLC with MIMO-Aided Grant-Free Access
The optimal design of the energy-efficient multiple-input multiple-output
(MIMO) aided uplink ultra-reliable low-latency communications (URLLC) system is
an important but unsolved problem. For such a system, we propose a novel
absorbing-Markov-chain-based analysis framework to shed light on the puzzling
relationship between the delay and reliability, as well as to quantify the
system energy efficiency. We derive the transition probabilities of the
absorbing Markov chain considering the Rayleigh fading, the channel estimation
error, the zero-forcing multi-user-detection (ZF-MUD), the grant-free access,
the ACK-enabled retransmissions within the delay bound and the interactions
among these technical ingredients. Then, the delay-constrained reliability and
the system energy efficiency are derived based on the absorbing Markov chain
formulated. Finally, we study the optimal number of user equipments (UEs) and
the optimal number of receiving antennas that maximize the system energy
efficiency, while satisfying the reliability and latency requirements of URLLC
simultaneously. Simulation results demonstrate the accuracy of our theoretical
analysis and the effectiveness of massive MIMO in supporting large-scale URLLC
systems.Comment: 14 pages, 9 figures, accepted to appear on IEEE Transactions on
Wireless Communications, Aug. 202
An integrated decision-making framework for highway autonomous driving using combined learning and rule-based algorithm
In order to solve the manual labelling, long-tail effect and driving conservatism of the existing decision-making algorithm. This paper proposed an integrated decision-making framework (IDF) for highway autonomous vehicles. Firstly, states of the highway traffic are extracted by the velocity, time headway (TH) and the probabilistic lane distribution of the surrounding vehicles. With the extracted traffic state, the reinforcement learning (RL) is adopted to learn the optimal state-action pair for specific scenario. Analogously, by mapping millions of traffic scenarios, huge amounts of state-action pairs can be stored in the experience pool. Then the imitation learning (IL) is further employed to memorize the experience pool by deep neural networks. The learning result shows that the accuracy of the decision network can reach 94.17%. Besides, for some imperfect decisions of the network, the rule-based method is taken to rectify by judging the long-term reward. Finally, the IDF is simulated in G25 highway and has promising results, which can always drive the vehicle to the state with high efficiency while ensuring safety.This work was supported in part by the National Nature Science Foundation of China under Grants 52072175 and 51775007 and in part by the China Scholarship Council under Grant 202006830050
Advanced Sensing and Control for Connected and Automated Vehicles
In recent years, connected and automated vehicles (CAV) have been a transformative technology that is expected to reduce emissions and change and improve the safety and efficiency of the mobilities [...]PolyU (UGC): A004025
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