No abstract

Smart phone and its applications are used more and more extensively in our daily life. Short delay of arriving data is important to these applications, especially to some time-sensitive ones. To reduce transmission latency and improve user experience, a dynamic decentralized data replica placement and management strategy which works in edge nodes is proposed in this article. It studies the location, access frequency, latency improvement, and cost spent on placing replicas on edge nodes to seek a balance between cost spent for storage and reduced latency. Specifically, dynamic and decentralized replica placement strategy algorithm has load guarantee for edge nodes to avoid overload; it dynamically create or delete data replicas on edge nodes according to the request frequency. Dynamic and decentralized replica placement strategy is decentralized to relieve transmission cost. Experiment results show that dynamic and decentralized replica placement strategy algorithm in edge computing environments can greatly reduce transmission latency, balance edge nodes load, and improve system performance. Dynamic and decentralized replica placement strategy also considers the cost spent for storage, and it pursues a balance between many factors.

Computer vision technology and image processing technology are applied in the field of agriculture gradually. How to diagnose crop diseases quickly and effectively has become a research hotspot. In this paper, we combine edge detection and fuzzy clustering algorithm to get the new algorithm through the experiment of more than 1500 pictures. The different kinds of diseases and insect pests of the 5 different crop leaves are used as the research object. Through the gray processing of the images, the removal of the unrelated background, the image segmentation, and the filling of the pixels of the crop disease and insect pests, the final calculation is final. The degree of crop leaf diseases and insect pests is calculated. From the experimental data, the degree of crop damage can be accurately reflected, and the degree of crop leaf disease and insect damage can be calculated, and the automatic batch operation of image segmentation can be realized.

Real-time monitoring of key data of underwater target in navigation status is an important basis for mastering and intervening the navigation status of underwater target in the whole process and ensuring the navigation safety of underwater target. With the help of CAN bus communication technology, this paper designs a key data monitoring software for underwater target based on CAN bus, which can monitor and display the key data of underwater target such as their attitude, depth, motor voltage, motor speed, driving plate temperature, etc. in real time. On the premise of ensuring real-time performance, the software makes full use of CAN bus communication features, and has the advantages of good human-computer interaction, simple operation and high visibility.

The image processing and pattern recognition technology of identifying the crop disease are rapid and simple in order to provide the required information by taking measures in time. The paper will use the crop disease based on image segmentation processing centered to utilize the segmentation theory of theshold segmentation and edge detection represented, and the segmentation method by combination of mathematical morphology, fuzzy clustering and specific theory for research. The data shows that the proposed algorithm keeps fit within the large image retireval system about the disease of the crop.