The emergence of mobile edge computing (MEC) supports large-scale computing demand in the Internet of Things era by moving computing pressure from cloud center to the edge. However, computing resources of edge servers are scattered and restricted. A carefully designed computation offloading mechanism is the key to MEC. Besides, it is necessary for cloud centers and edge servers to participate in computation together. Most of the existing researches focus only on a single computation architecture to offload computing tasks and neglect the impact of load balancing on offloading efficiency. In this paper, we consider a cloud-edge-device collaborative computing scenario, leveraging the advantages of different computing levels. In order to direct computing task to service node with sufficient computing resources and avoid edge servers overloading or being idle all the time, we introduce an occupancy table to dynamically track the edge load status. Then we propose a deep Q-network (DQN) based collaborative computation offloading (DQNCCO) method for edge load balancing, jointly minimizing delay, energy consumption and edge load balancing. Experimental results show that the proposed method can balance the use of the whole edge computing resources while realizing suitable fine-grained offloading decisions.
Photosensor which converts the optical signal into electrical signal is an essential element in optoelectronic integrated circuits. Lead halide perovskites have been widely investigated for photosensor applications due to their excellent photoelectronic properties and superior stability. Moreover, single crystalline perovskite films have fewer defects comparing with polycrystalline films, which can further improve the material properties. In this study, vapour phase epitaxy was used to grow large scale single crystalline CsPbBr3 thin films with lateral sizes up to 2.25 cm 2 , Microstructure characterization of the films reveals that they are of high quality, with smooth surfaces. Furthermore, photo sensors constructed on these single crystalline films exhibits excellent on/off current ratio of 2.4×10 5 . Our work not only presents a facile method for producing large area CsPbBr3 single crystalline films but also provide a platform for developing high-quality halide per-ovskite photo sensors and their large-scale integration.
Significant progress has been made in the supervised semantic segmentation of remote sensing images (RSI), mainly attributed to the design of models and the large amount of high-quality pixel-by-pixel annotated data. However, pixel-level annotated data is extremely costly and requires specific knowledge. In addition, different application scenarios usually require different target classes of segmentation, which makes the annotated data and the trained models lack generalization. In this paper, we devise a semantic-aware self-supervised learning method termed SASS which can learn potential patterns from massive unlabeled remote sensing images and obtain a dense feature representation with se-mantic information. Besides, an efficient contrastive-based loss function is designed to optimize the proposed SASS. After training, the features extracted by SASS can be borrowed to implement unsupervised semantic segmentation (USSS) of remote sensing images. Experiments show our proposed method surpasses recent works by a large margin, which achieves 55.7% mIoU and 71.89% overall accuracy in Potsdam-3 dataset with the improvement of 5.12% and 6%.
Space-Air-Ground Integrated Network (SAGIN) is a promising architecture for next-generation wireless networks. It combines satellite networks, aerial networks, and terrestrial networks, offering ubiquitous global network services to ground users and enhancing connectivity for a wide range of wireless communications. Additionally, free-space optical (FSO) communication has gained more attention recently because of its energy efficiency and exceptional high-speed data delivery capabilities. However, the data transmission efficiency in SAGIN remains limited due to the dynamic and time- varying network topology and long-distance transmission link. Moreover, efficiently utilizing the limited power resources in satellites and aircraft to achieve effective data routing is still a crucial challenge. To address these issues, we construct an FSO/radio frequency (RF) space-air-ground integrated network that aims to enable large-scale data trans-mission as well as degrading the burden on terrestrial networks. Furthermore, a decentralized deep-Q network-based reinforcement learning with two hops node information is proposed to execute dynamic energy-efficient hybrid routing while ensuring system load balancing. The experimental results demonstrate that the proposed method reaches significant network performance improvement compared with baseline methods.
In this paper we propose and implements a standing long jump movements analysis algorithm based on artificial intelligence to help students improve their performance. We select the BlazePose, a lightweight neural network under the Mediapipe framework for human pose estimation. We extracte the coordinate information of body joints with Mediapipe, and use the extracted coordinate information to design the algorithm of standing long jump action segmentation and key frame selection for quantitative analysis movements. To evaluate the effectiveness of the project, we recruit 24 students to attend the Experiments with two rounds of tests, Experiment results show that the proposed algorithm can automatically detect and identify problems and deficiencies in key movements, and give suggestions for improvement. By analyzing the data of jumpers' take-off skills, posture control and landing methods, we can provide suggestions to help them improve their skills and performance.
