Unmanned Aerial Vehicle (UAV)-assisted mobile edge computing (MEC) technology has garnered extensive attention with the widespread adoption of 5G technology, owing to its flexibility, improved system capacity and computational capabilities, and ability to overcome complex geographical environments. This paper initially elucidates the advantages of UAV-assisted MEC systems in the current communication domain, outlining the definitions of various modules within the UAV-assisted MEC architecture. Subsequently, it summarizes the main application scenarios and key technologies, providing a concise description of the design approach for UAV MEC systems and highlighting the information security issues therein. Finally, the paper discusses the challenges and prospects for future research in this field.
Small target detection is a difficult point in target detection. Small target detection needs to identify the location and type of targets with few pixels in the picture and little resolution and feature information, and the algorithms used in the current application of mature medium and large target detection do not work well in detecting small targets. Therefore, improving the capability of small target detection is a current challenge in the field of target detection and an important research direction. In this paper, we will focus on deep learning small target detection technology, first introduce the definition of small targets and the reasons for the difficulty of small target detection, then comprehensively discuss the methods to improve the effectiveness of small target detection, and finally introduce the common small target datasets and the evaluation index of detection algorithms.
Classification, segmentation, and detection are the most important tasks in computer vision, and target detection as one of them is a hot research topic in the field of computer vision, which is widely used in medical, traffic, surveillance, etc. YOLOv4 and R-CNN have excellent target detection performance, and an improved YOLOv4 target detection algorithm is proposed to improve the real-time detection of small targets for target recognition. A priori frames are designed using the K-means clustering algorithm for adapting to different small and medium sizes; a feature layer is extracted according to the size of small and medium-sized labeled objects and four different feature layers are fused for detection; the Mish activation function is applied to the neck of the detection model to improve the detection performance. The experimental results show that the improved algorithm can effectively improve the detection accuracy.
KEYWORDS: Image compression, Image storage, Printing, Image transmission, Image processing, Data transmission, Data storage, Rhodium, Image information entropy, Data compression
A predictive coding algorithm for image lossless compression is introduced. In the prediction stage, the algorithm uses the local change rate of the pixel value to adjust the prediction model adaptively, and in the coding stage, the error feedback technology is used to further reduce the information entropy of the error image. The simulation test results on standard images show that the performance of the algorithm is significantly better than the standard lossless compression algorithm. The compression algorithm we proposed uses the local change rate of the image in the decorrelation phase to improve the prediction accuracy, and in the coding phase, the algorithm uses error feedback technology to further reduce the error.
The forming of the ink droplet is very important in the inkjet control system. It is related to the forming of the ink droplet on the substrate with good printing effect. In the process can use of inkjet ink observation system to observe the ink drops of spray forming process, at the same time, use the LED flash and CCD camera to film the formation of the ink droplets, through the pictures use edge detection algorithm to analyze drops forming condition and flight path, still can use the shape of the Hough transform to detect the drops (mainly used to detect circular drops), and finally by MATLAB/Simulink simulation tool to drops control system simulation analysis, The data of ink droplet forming is analyzed and tested in the ink feeding system.
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