Deraining and Desnowing Algorithm on Adaptive Tolerance and Dual-tree Complex Wavelet Fusion

Severe weather conditions such as rain and snow often reduce the visual perception quality of the video image system, the traditional methods of deraining and desnowing usually rarely consider adaptive parameters. In order to enhance the effect of video deraining and desnowing, this paper proposes a video deraining and desnowing algorithm based on adaptive tolerance and dual-tree complex wavelet. This algorithm can be widely used in security surveillance, military defense, biological monitoring, remote sensing and other fields. First, this paper introduces the main work of the adaptive tolerance method for the video of dynamic scenes. Second, the algorithm of dual-tree complex wavelet fusion is analyzed and introduced. Using principal component analysis fusion rules to process low-frequency sub-bands, the fusion rule of local energy matching is used to process the high-frequency sub-bands. Finally, this paper used various rain and snow videos to verify the validity and superiority of image reconstruction. Experimental results show that the algorithm has achieved good results in improving the image clarity and restoring the image details obscured by raindrops and snows

Research on Image Transmission System Based on 3G Communication Platform

A wireless image real-time transmission system is designed by using 3G wireless communication platform and ARM + DSP embedded system. In the environment of 3G networks, the embedded equipment has realized the functions of coding, acquisition, network transmission, decoding and playing. It is realized for real-time video of intelligent control and video compression, storage and playback in the 3G embedded image transmission system. It is especially suitable for remote location or irregular cable network transmission conditions applications. It is shown that in the 3G network video files are transferred quickly. The real-time transmission of H.264 video is broadcasted smoothly, and color distortion is less. The server can control client by remote intelligent units

Temperature dependence of speed of actin filaments (Vf) propelled by different skeletal myosin isoforms

Red body disease occurred on a large scale in the shrimp farm in Liaoning Province in July, 2001. The characteristics of red body disease were observed by the authors. Infected animals displayed red bodies, sluggish swimming, disgusted feeding, hard shell and high mortality. Three bacterial strains were isolated from the muscles of the diseased Litopenaeus vannamei. In artificial infection test, one of them was proved to be the pathogen, numbered 0107. According to the traditional biochemical identification and 16S rRNA gene homology analysis, the pathogenic bacteria were Vibrio parahaemolyticus. Drug sensitivity test showed that the pathogenic bacteria are highly sensitive to cefoperazone, ceftriaxone, etc., while not sensitive to ampicillin and benzylpenicillin

Considering critical building materials for embodied carbon emissions in buildings: A machine learning-based prediction model and tool

Construction activities discharge considerable carbon emissions, causing serious environmental problems and gaining increasing attention. For the large-scale construction area, high emission intensity, and significant carbon reduction potential, embodied carbon emissions of buildings worth special studying. However, previous studies are usually post-evaluation and ignore the influences of project, construction and management. This paper focuses on critical building materials and adopts machine learning methods to realize carbon prediction at design stage. The activity data, including critical building materials, water, and energy consumption, is analyzed and 30 influencing factors at the project, construction, and management levels are identified. Three algorithms (artificial neural network, support vector regression and extreme gradient boosting) are used to develop machine learning models. The proposed methodology is applied to 70 projects in the Yangtze River Delta region of China. Results show that the established models achieved high interpretability (R2 >0.7) and small average error (5.33%), well proving theirs feasibility. Furthermore, an automated tool is developed to assist practitioners to predict the critical materials consumption and embodied carbon emissions conveniently. The proposed operable model and practical tool can efficiently support effective adjustments and improvement to reduce carbon in construction

MOESM1 of Wenyang Huazhuo Tongluo formula inhibits fibrosis via suppressing Wnt/β-catenin signaling pathway in a Bleomycin-induced systemic sclerosis mouse model

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