In recent years, unmanned aerial vehicles (UAVs) have been widely used in various industries (e.g., search and rescue, express delivery, etc.) due to their high flexibility. In addition, the deployment of UAVs equipped with mobile-edge computing (MEC) servers to provide computing services at the edges of networks has become an emerging method. Under complex and limited resource constraints, increasing the total number of computation bits in the system becomes a challenging problem. Motivated by this, in this article, we propose an optimization framework to maximize the computation bits of the whole system by jointly optimizing the bandwidth allocation, the task offloading time allocation, and the trajectory of the UAV under the energy constraints of ground devices (GDs) and the maximal battery energy of the UAV. The formulated problem is a nonconvex and nonconcave problem that is very difficult to solve. To this end, we decompose the objective function into three suboptimization problems and adopt successive convex optimization techniques to solve them. Then, we utilize the block coordinate descent (BCD) algorithm to address the overall optimization problem. By doing so, the bandwidth allocation of GDs, task offloading time and local computing time allocation in each time slot, and the trajectory of the UAV are optimized alternately during each iteration. We conduct extensive simulations, and the results verify that the proposed solution achieves a better performance than those of other benchmark schemes.

Video moment localization has gradually become a hot research problem in video understanding. Despite much remarkable progress, there are still two limitations in the following aspects. Firstly, previous methods usually directly regarded the moment candidate with the highest confidence score as the final localization result; they overlooked the inevitable deviations between moment candidates and the ground truth. Secondly, past models have not considered the problem that moment candidates with various qualities have different impacts on model training. Therefore, this paper proposes a novel Temporal Map-based Boundary Refinement Network to solve the above problems. Specifically, besides the conventional confidence scores’ prediction network, we introduce a boundary refinement network based on the 2D temporal map, which can fine-tune the temporal boundaries of generated moment candidates to obtain more precise results. Additionally, to discriminately treat diverse moment candidates with different qualities and further boost the localization performance, we devise an innovative weighted refinement loss to guide the model to focus more on refining processes of those moment candidates closer to the ground truth. Finally, we evaluate our model on two publicly available datasets, and the results of extensive experiments show that our technique outperforms the state-of-the-art methods (e.g., for the ActivityNet Captions dataset, a relative improvement of 4.63% on R1@0.7).