X-ray package security check systems are widely used in public places, but they face difficulties in accurately detecting prohibited items due to the stacking and diversity of shapes of the objects inside the luggage, posing a threat to personal safety in public places. The existing methods for X-ray image object detection suffer from low accuracy and poor generalization, mainly due to the lack of large-scale and high-quality datasets. To address this gap, a novel large-scale X-ray image dataset for object detection, LSIray, is provided, consisting of high-quality X-ray images of luggage and objects of 21 types and sizes. LSIray covers some common categories that were neglected in previous research. The dataset provides more realistic and rich data resources for X-ray image object detection. To address the problem of poor security inspection, an improved model based on YOLOv8 is proposed, named SC- YOLOv8, consisting of two new modules: CSPnet Deformable Convolution Network Module (C2F_DCN) and Spatial Pyramid Multi-Head Attention Module (SPMA). C2F_DCN uses deformable convolution, which can adaptively adjust the position and shape of the receptive field to accommodate the diversity of targets. SPMA adopts the spatial pyramid head attention mechanism, which can utilize feature information from different scales and perspectives to enhance the representation ability of targets. The proposed method is evaluated through extensive experiments using the LSIray dataset and comparisons with the existing methods. The results show that the method surpasses the state-of-the-art methods on various indicators. Experimenting using the LSIray dataset and the OPIXray dataset, our SC-YOlOv8 model achieves 82.7% and 89.2% detection accuracies, compared to the YOLOv8 model, which is an improvement of 1.4% and 1.2%, respectively. The work not only provides valuable data resources, but also offers a novel and effective solution for the X-ray image security check problem.

The widespread use of mobile devices and compute-intensive applications has increased the connection of smart devices to networks, generating significant data. Real-time execution faces challenges due to limited resources and demanding applications in edge computing environments. To address these challenges, an enhanced whale optimization algorithm (EWOA) was proposed for task scheduling. A multi-objective model based on CPU, memory, time, and resource utilization was developed. The model was transformed into a whale optimization problem, incorporating chaotic mapping to initialize populations and prevent premature convergence. A nonlinear convergence factor was introduced to balance local and global search. The algorithm's performance was evaluated in an experimental edge computing environment and compared with ODTS, WOA, HWACO, and CATSA algorithms. Experimental results demonstrated that EWOA reduced costs by 29.22%, decreased completion time by 17.04%, and improved node resource utilization by 9.5%. While EWOA offers significant advantages, limitations include the lack of consideration for potential network delays and user mobility. Future research will focus on fault-tolerant scheduling techniques to address dynamic user needs and improve service robustness and quality.

Efficient and rapid analysis of X-ray images is crucial for detecting contraband, including weapons and explosives, at transportation hubs. While existing models like YOLOv8 offer high performance, they often fall short in real-time scenarios due to slow processing speeds. This study proposes SC-Lite, an improved model based on YOLOv8, designed to improve real-time detection of prohibited items in environments with limited computational power, such as embedded systems. We propose a novel acceleration module, the CSPNet Faster Convolution Network Module (C2F_FM), which reduces computational redundancy and optimizes memory usage by focusing standard convolutions on key channels, thereby enhancing efficiency. Furthermore, the Adaptation-BiFPN module improves multi-scale feature fusion with dynamic, content-aware weighting, increasing both the sensitivity and accuracy of detections. Additionally, the LAMP pruning strategy is utilized to optimize the model for deployment in resource-limited environments, significantly reducing the complexity of its architecture. After extensive experimental validation, SC-Lite outperforms all mainstream models in terms of real-time performance and detection accuracy, and the model outperforms existing methods in all metrics. The model achieved reductions of 72%, 62%, and 70% in parameters, computation, and model size, respectively. On the LSIray dataset, SC-Lite boosted the mean average precision (mAP) by 0.3% to 94.3% and enhanced the frame rate from 87 FPS to 136 FPS, an improvement of 49 FPS. For the OPIXray dataset, SC-Lite increased the mAP by 1.5% to 94.8%, with the frame rate rising from 344 FPS to 400 FPS, an improvement of 56 FPS. This study provides an innovative solution to improve the real-time performance and accuracy of security inspection systems.

With the surge in tasks for in-vehicle terminals, the resulting network congestion and time delay cannot meet the service needs of users. Offloading algorithms are introduced to handle vehicular tasks, which will greatly improve the above problems. In this paper, the dependencies of vehicular tasks are represented as directed acyclic graphs, and network slices are integrated within the edge server. The Dynamic Selection Slicing-based Offloading Algorithm for in-vehicle tasks in MEC (DSSO) is proposed. First, a computational offloading model for vehicular tasks is established based on available resources, wireless channel state, and vehicle loading level. Second, the solution of the model is transformed into a Markov decision process, and the combination of the DQN algorithm and Dueling Network from deep reinforcement learning is used to select the appropriate slices and dynamically update the optimal offloading strategy for in-vehicle tasks in the effective interval. Finally, an experimental environment is set up to compare the DSSO algorithm with LOCAL, MINCO, and DJROM, the results show that the system energy consumption of DSSO algorithm resources is reduced by 10.31%, the time latency is decreased by 22.75%, and the ratio of dropped tasks is decreased by 28.71%.

In this thesis, transesophageal multiplanar ultrasound probes are used to detect epicardial echocardiography during cardiac valve replacement surgery. We observed the heart structure and valve function, left ventricular systolic function, residual shunt, and other information. There were three patients in the ultrasound examination before cardiopulmonary bypass. This has new findings and supplements for preoperative diagnosis. So, we changed the surgical approach and surgical plan and increased the surgical content. Epicardial echocardiography is easy to operate and has a wide range of indications. It has particular clinical application value in cardiac surgery.