Design and implementation of temperature, humidity and wind speed information acquisition system based on the internet of things

Bai-bing, 徐白冰 Xu; Jian-li, 王建立 Wang; Jing-tai, 曹景太 Cao

doi:10.3788/yjyxs20163110.0936

Cited by 1 publication

(1 citation statement)

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“…When extracting the key points, the difference operation of operator DOG will make the image edge effect. In order to solve this problem, a fitting quadratic function is introduced to eliminate the edge response and the low contrast key points (Xu et al 2016b). This not only optimizes the extracted features, but also improves the denoising ability and enhances the accuracy of subsequent feature matching.…”

Section: Features Optimizationmentioning

confidence: 99%

Design of remote acquisition system for ecological water demand information of an estuarine wetland

Cao,

Bi,

Yang

et al. 2024

Rev. Int. Contam. Ambie.

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In order to improve data coverage and data collection efficiency, the design method for a remote collection system of ecological water demand information of an estuarine wetland is proposed. The hardware design of the remote information acquisition system is realized through the overall structure design, the Unified Modeling Language (UML), system outline design, system logic design, and system module division. The information is acquired by polling, and the acquisition frequency is adjusted according to the actual situation and user needs. In the system software design, the Scale-invariant feature transform algorithm (SIFT) is used to extract the image features of the estuarine wetland, and a support vector machine is used to cluster the collected information according to the image features to realize the remote acquisition of water demand information of the estuarine wetland. The experimental results show that the maximum data coverage of the proposed method is 95%, and the data acquisition time is less than 0.2 h, indicating that the proposed method has high information acquisition efficiency and high data coverage.

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Section: Features Optimizationmentioning

confidence: 99%