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The detection algorithms of small objects in remote sensing images is often challenging due to the complex background and limited pixels. This can lead to reduced accuracy in detection and an increased number of missed small objects. So this paper introduces YOLOFNC, an enhanced network based on YOLOv7. To improve the model's ability to capture features of small objects, an enhanced C3-Faster module based on the C3 module is designed and integrated into the YOLOv7 network. This module helps extract more features related to small objects. Additionally, we employ Normalized Wasserstein Distance (NWD) fusion GIoU as a novel loss function to refine the accuracy of network optimization weights and the small object regression framework. Furthermore, a coordinated attention (CA) mechanism is incorporated at strategic locations in the model to reduce redundant information in the feature layer and prevent the loss of important small object features. we conduct comparison experiments between YOLO-FNC and other commonly used object detection algorithms on DIOR, AITOD, and VisDrone datasets. The experimental results show that YOLO-FNC achieves 84.4% mAP on the DIOR dataset, 35.9% mAP on the AI-TOD dataset, and 52.6% mAP on the VisDrone dataset. Compared to YOLOv7 and other remote sensing object detection models, YOLO-FNC demonstrates better performance in object detection. [ABSTRACT FROM AUTHOR]

The production of remote sensing products is very time-consuming, because it contains a large amount of remote sensing data to be processed. To increase the production efficiency of remote sensing products, a remote sensing processing job is usually divided into multiple tasks that are executed in parallel in the cluster system. So efficient task scheduling is the core problem of remote sensing products production system. This paper issued this problem and proposed a hybrid particle swarm optimization algorithm to improve the production efficiency of the remote sensing products. First, the task scheduling model of remote sensing products was established to minimize the maximum task execution time. Then, in the framework of particle swarm optimization, an inertia weight parameters adjustment strategy based on linear decreasing was adapted. For the solution obtained in each iteration, a local search procedure of task movement between the production nodes was employed to improve the quality of the solution. In addition, a probability acceptance rule was also introduced to allow the acceptance of poor solutions avoid trapping into local optimum prematurely. Finally, we carried out several experiments in the CloudSim simulation platform. The experimental results demonstrate that the proposed algorithm is effective and more competition when compared with the existing scheduling algorithms, such as ant colony optimization, tabu search and other classical scheduling algorithms. [ABSTRACT FROM AUTHOR]

Studies have shown that Wushu training can induce the slow wave activity rhythm of cerebral with high amplitude, which is beneficial to people's physical and mental health and stable nerves. In order to further explore the influence of Wushu training on the human brain electrical nerve, according to the theoretical design experiment for the influence of the head down tilt on the brain electrical activity, the EEG data of the shooting athletes after handstand training of Wushu and the Taijiquan training are obtained. The experimental data show that there are significant differences in the power value of α and β frequency band of the cerebral nerve, the index of EEG coherence function, the comprehensive index of EEG after the handstand training and the posture of sitting. The indexes of the athletes' EEG are significantly higher than those before the handstand training, indicating that the handstand training of Wushu has a positive effect on the human brain electricity. After the Taijiquan training, the power value of the slow wave of athletes' brain nerve (δ, θ wave) decreased, the power value of the fast wave (β wave) increased, indicating that Taijiquan training helps the coordination operation of the brain on both sides of the body. [ABSTRACT FROM AUTHOR]