721 research outputs found

    An Efficient Feature Extraction Scheme for Mobile Anti-Shake in Augmented Reality

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    In recent years, augmented reality on mobile devices has become popular. Mobile shakes are the most typical type of interference in mobile augmented reality. To negate such interference, anti-shake is an urgent requirement. To enhance anti-shake efficiency, we propose an efficient feature extraction scheme for mobile anti-shake in augmented reality. The scheme directly detects corners to avoid the non-extreme constraint such that the efficiency of feature extraction is improved. Meanwhile, the scheme only updates the added corners during mobile shakes, which improves the accuracy of feature extraction. In the experiments, the memory consumption of existing methods is almost double compared to that in our scheme. Further, the runtime of our scheme is only half of the runtime of the existing methods. The experimental results demonstrate that our scheme performs better than the existing classic methods on mobile anti-shake in terms of memory consumption, efficiency, and accuracy

    Necessary Optimality Conditions for a Class of Impulsive and Switching Systems

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    An optimal control problem for a class of hybrid impulsive and switching systems is considered. By defining switching times as part of extended state, we get the necessary optimality conditions for this problem. It is shown that the adjoint variables satisfy certain jump conditions and the Hamiltonian are continuous at switching instants. In addition, necessary optimality conditions of Fréchet subdifferential form are presented in this paper

    Physical Layer Security of Cooperative NOMA for IoT Networks under I/Q Imbalance

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    In this paper, we investigate the reliability and security of cooperative dual-hop non-orthogonal multiple access (NOMA) for internet-of-thing (IoT) networks, in which the transceivers consider a detrimental factor of in-phase and quadrature-phase imbalance (IQI). The communication between the source and destination is accomplished through a decode-and-forward (DF) relay in the presence of an eavesdropper. In order to characterize the performance of the considered system, exact analytical expressions for the outage probability (OP) and intercept probability (IP) are derived in closed-form. Furthermore, to better understanding the performance of the considered system, we further derive the asymptotic expressions of OP in the high signal-to-noise ratio (SNR) regime and IP at the high main eavesdropping ratio (MER) region. A large number of analysis and Monte Carlo simulation results show that the existence of IQI usually increases the corresponding OP and reduces the IP, which means that reduces the reliability of the system and improves the security. In addition, the provided results provide useful insights into the trade-off between reliability and security of secure cooperative communication systems

    SyreaNet: A Physically Guided Underwater Image Enhancement Framework Integrating Synthetic and Real Images

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    Underwater image enhancement (UIE) is vital for high-level vision-related underwater tasks. Although learning-based UIE methods have made remarkable achievements in recent years, it's still challenging for them to consistently deal with various underwater conditions, which could be caused by: 1) the use of the simplified atmospheric image formation model in UIE may result in severe errors; 2) the network trained solely with synthetic images might have difficulty in generalizing well to real underwater images. In this work, we, for the first time, propose a framework \textit{SyreaNet} for UIE that integrates both synthetic and real data under the guidance of the revised underwater image formation model and novel domain adaptation (DA) strategies. First, an underwater image synthesis module based on the revised model is proposed. Then, a physically guided disentangled network is designed to predict the clear images by combining both synthetic and real underwater images. The intra- and inter-domain gaps are abridged by fully exchanging the domain knowledge. Extensive experiments demonstrate the superiority of our framework over other state-of-the-art (SOTA) learning-based UIE methods qualitatively and quantitatively. The code and dataset are publicly available at https://github.com/RockWenJJ/SyreaNet.git.Comment: 7 pages; 10 figure
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