Mobile edge computing is an emerging computing paradigm boosting resource-demanding and delay-sensitive applications through deploying computing infrastructures at the edge of the Internet nearby mobile requesters and users. In an Internet of Vehicles (IoV) environment, Vehicular Edge Computing (VEC) is capable of exploiting network edge devices, in terms of, e.g., Roadside Units (RSUs), for predictive content caching for optimizing quality-of-experience (QoE) of nearby content requesters based on content popularity analysis, it remains a great challenge to accurately predict content popularity of mobile requesters and appropriately cache required content with low miss rate accordingly in a VEC environment with high user mobility and dynamics. To address this challenge mentioned above, in this paper, we propose predictive content popularity-aware approach, i.e., KM_SVD++, to edge caching in an VEC environment. The proposed approach is capable of achieving high hit rate of mobile content requestors in VEC and low latency of content delivery by leveraging a Kalman filtering model for predicting locations of vehicles and a SVD++ one for yielding decisions for cache deployment and replacement. We conduct extensive simulations as well to prove its effectiveness.

In focusing tasks on moving targets, traditional methods that rely on maximizing contrast struggle to capture moving objects due to insufficient focusing speed. Deep learning-based methods have attempted to directly predict the optimal focal length for the target; however, due to low prediction accuracy, they often lead to out-of-focus situations when capturing moving objects. In recent years, some approaches have utilized reinforcement learning to automatically explore focal length adjustment patterns, thus achieving better results than traditional methods. However, these approaches have not considered the motion characteristics of the targets, leading to a need for further improvement in focusing performance. To overcome these limitations, we introduce a motion-based feature and deep reinforcement learning-driven autofocus algorithm named MF-DRLAF (Motion Features based Deep Reinforcement Learning Autofocus Model) for moving targets. This novel method tracks the object, predicts its motion state through feature extraction, and uses deep reinforcement learning to dynamically adjust the focus. We utilize a self-attention mechanism to adaptively learn various motion patterns and employ a feature pool structure to enhance processing efficiency. Experiments and real-world testing on a Google Pixel3 demonstrate that our approach significantly enhances autofocus performance on moving objects, highlighting its potential for broader imaging applications. This approach offers a promising direction for future development in autofocus technology.

Traditional autofocus methods search for the optimal focal distance (FD) by evaluating image quality from focal stacks, resulting in time-consuming focusing processes. Recently, deep learning has being adopted for single-shot autofocus methods, which can predict the optimal FD directly from a single input image. However, these methods often suffer from low prediction accuracy due to the lack of global features and structured global supervisory information, as they rely solely on the image's region of interest (ROI) as input and a single value for supervision. We propose a deep learning network named MPFS (Multi-Head Network with Per-Pixel Focal Distance Supervision), which takes a full-frame photograph as input and uses the optimal focal distance per pixel for supervision, this method effectively addresses the issues of missing global features and insufficient supervisory information by leveraging these enhancements. Additionally, the network integrates current camera focal distance information to mitigate the scale ambiguity caused by the lack of absolute scale information. To validate the effectiveness of the proposed method, we designed an experiment using a dataset annotated with optimal FD per pixel. Experimental results on this dataset indicate that our approach achieves a 0.22 decrease in the Mean Absolute Error (Mae) metric compared to the state-of-the-art models, with improvements of 0.02 and 0.004 in$\boldsymbol{d}_{\mathbf{1}}$and$\boldsymbol{d}_{\mathbf{2}}$metrics.