Intelligent reflecting surface (IRS) is an emerging paradigm to enhance terahertz (THz) communications with low hardware costs and reduced power consumptions. In this work, we study an IRS‐aided THz multi‐input‐single‐output secure communication system, where a base station (BS) transmits confidential signals to a single‐antenna user with the presence of a single‐antenna eavesdropper. We seek to maximize the secrecy rate by designing the discrete phase‐shifts at the IRS and the precoder at the BS, which is an intractable nonconvex optimization problem. To solve it, we propose to iteratively design the above two matrix variables. Specifically, with a given phase‐shift matrix, we can obtain the optimal precoder by the Rayleigh‐Ritz theorem. And with a given precoder matrix, we propose a cross‐entropy‐based algorithm to obtain the optimal phase‐shift matrix. Numerical results demonstrate that our proposed solution is able to improve the secrecy rate than that of the case without IRS.
The highly efficient and robust stitching of aerial video captured by unmanned aerial vehicles (UAVs) is a challenging problem in the field of robot vision. Existing commercial image stitching systems have seen success with offline stitching tasks, but they cannot guarantee high-speed performance when dealing with online aerial video sequences. In this paper, we present a novel system which has an unique ability to stitch high-frame rate aerial video at a speed of 150 frames per second (FPS). In addition, rather than using a high-speed vision platform such as FPGA or CUDA, our system is running on a normal personal computer. To achieve this, after the careful comparison of the existing invariant features, we choose the FAST corner and binary descriptor for efficient feature extraction and representation, and present a spatial and temporal coherent filter to fuse the UAV motion information into the feature matching. The proposed filter can remove the majority of feature correspondence outliers and significantly increase the speed of robust feature matching by up to 20 times. To achieve a balance between robustness and efficiency, a dynamic key frame-based stitching framework is used to reduce the accumulation errors. Extensive experiments on challenging UAV datasets demonstrate that our approach can break through the speed limitation and generate an accurate stitching image for aerial video stitching tasks.
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