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6th Symposium on Novel Optoelectronic Detection Technology and Applications

作者： Wang, Shaofei Du, Baolin Guo, Shiyong Zhang, Peng Luoyang Electroopt Equipment Res Inst AVIC Luoyang Henan Peoples R China Sci & Technol Electroopt Control Lab Luoyang Henan Peoples R China

ISBN: (纸本)9781510637054

In order to improve the readability of infrared image, this paper proposes an image fusion algorithm based on the characteristics of the target region. The algorithm uses wavelet transform to decompose the multi-source image, and uses image segmentation algorithm to extract the target features of the multi-source image, and then uses different fusion strategies to fuse the infrared image and the visible image according to the features of the infrared image extraction and the low-frequency and high-frequency components of the visible image in different regions. The simulation results show that the algorithm can achieve better image fusion results.

关键词： fusion algorithm characteristic target image

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Phenology-Based Rice Paddy Mapping Using Multi-Source Satellite Imagery and a fusion algorithm Applied to the Poyang Lake Plain, Southern China

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REMOTE SENSING 2020年第6期12卷 1022-1022页

作者： Ding, Mingjun Guan, Qihui Li, Lanhui Zhang, Huamin Liu, Chong Zhang, Le Jiangxi Normal Univ Minist Educ Key Lab Poyang Lake Wetland & Watershed Res Nanchang 330028 Jiangxi Peoples R China Jiangxi Normal Univ Sch Geog & Environm Nanchang 330028 Jiangxi Peoples R China Xiamen Univ Technol Fujian Key Lab Pattern Recognit & Image Understan Xiamen 361024 Peoples R China Xiamen Univ Technol Dept Spatial Informat Sci & Engn Xiamen 361024 Peoples R China Sun Yat Sen Univ Sch Geospatial Engn & Sci Guangzhou 510275 Peoples R China

Accurate information about the spatiotemporal patterns of rice paddies is essential for the assessment of food security, management of agricultural resources, and sustainability of ecosystems. However, accurate spatial datasets of rice paddy fields and multi-cropping at fine resolution are still lacking. Landsat observation is the primary source of remote sensing data that has continuously mapped regional rice paddy fields at a 30-m spatial resolution since the 1980s. However, Landsat data used for rice paddy studies reveals some challenges, especially data quality issues (e.g., cloud cover). Here, we present an algorithm that integrates time-series Landsat and MODIS (Moderate-resolution Imaging Spectroradiometer) images with a phenology-based approach (ILMP) to map rice paddy planting fields and multi-cropping patterns. First, a fusion of MODIS and Landsat data was used to reduce the cloud contamination, which added more information to the Landsat time series data. Second, the unique biophysical features of rice paddies during the flooding and open-canopy periods (which can be captured by the dynamics of the vegetation indices) were used to identify rice paddy regions as well as those of multi-cropping. This algorithm was tested for 2015 in Nanchang County, which is located on the Poyang Lake plain in southern China. We evaluated the resultant map of the rice paddy and multi-cropping systems using ground-truth data and Google Earth images. The overall accuracy and kappa coefficient of the rice paddy planting areas were 93.66% and 0.85, respectively. The overall accuracy and kappa coefficient of the multi-cropping regions were 92.95% and 0.89, respectively. In addition, our algorithm was more capable of capturing detailed information about areas with fragmented cropland than that of the National Land Cover Dataset (NLCD) from 2015. These results demonstrated the great potential of our algorithm for mapping rice paddy fields and using the multi-cropping index in comp

关键词： rice paddies Landsat MODIS fusion algorithm phenology southern China

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A Feasible RSU Deployment Planner Using fusion algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2021年第3期116卷 1849-1866页

作者： Sankaranarayanan, Manipriya Chelliah, Mala Mathew, Samson Natl Inst Technol Dept Comp Sci & Engn Tiruchirappalli 620015 India Natl Inst Technol Dept Civil Engn Tiruchirappalli 620015 India

With day to day exponential increase in the number of vehicles, it is essential to communicate travel information for road travellers to enhance their travelling experience. One of the recent technologies that aid in real time and faster communication of information is Vehicular Ad-hoc Network (VANET). To ensure significant connectivity and continuous data flow, Road Side Units (RSU) are installed in VANET. RSUs receive the traffic information from vehicles and communicate it to other RSUs, Traffic Management Centre (TMC) or other adjacent vehicles. However, identifying the appropriate locations to deploy RSUs to maintain network connectivity and reduce cost of installation in a given area are still challenging and cannot be decided based on a single parameter. This paper proposes an Optimal RSU Distribution Planner (ORDP) using a fusion algorithm (FA) comprising of Evolutionary Genetic algorithm (EGA) and D-Trimming. Further it also provides the feasibility to choose the appropriate parameter configuration based on user requirement which makes the model feasible and efficient. The scalability and efficiency of the planner are tested against simulated and realistic datasets and it is seen that ORDP has proved to deliver improved results compared with other greedy approaches.

关键词： VANET Optimal RSU Distribution Evolutionary Genetic algorithm Trimming algorithm fusion algorithm

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A New fusion algorithm for Depth Images Based on Virtual Views

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2020年第2期58卷 1171-1181页

作者： Liu, Jianchen Zhang, Linjing Wang, Zhen Wang, Renli Shandong Univ Sci & Technol Coll Geomat Qingdao 266590 Peoples R China

Common depth image fusion methods use each original image as a reference plane and fuse the depth images using mutual projection. These methods can eliminate inconsistency between the depth images, but they cannot alleviate the point cloud redundancy and computational complexity. This article proposes a virtual view method for depth image fusion, defines a limited number of virtual views by means of view clustering, reduces the redundant calculations, and covers all scenes as much as possible. The depth image is merged ray by ray, and a reliable depth value is obtained via the F-test. Compared with the modified semiglobal matching (TSGM) stereo dense matching algorithm, the accuracy is improved by approximately 50 & x0025;and the roughness is improved by approximately 50 & x0025;. Compared with the classic surface reconstruction (SURE) fusion algorithm, there is more fusion depth value in each ray, and the accuracy and roughness are slightly improved. In addition, the algorithm of this article greatly reduces the number of reference planes.

关键词： Three-dimensional displays Image fusion Optical imaging Cameras Surface reconstruction Fuses Redundancy Depth image fusion algorithm oblique image virtual view

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Medical data fusion algorithm based on Internet of things

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PERSONAL AND UBIQUITOUS COMPUTING 2018年第5-6期22卷 895-902页

作者： Zhang, Weiping Yang, Jingzhi Su, Hang Kumar, Mohit Mao, Yihua Nanchang Univ Dept Elect Informat Engn Nanchang Jiangxi Peoples R China Zhejiang Univ Binhai Ind Technol Res Inst Tianjin 300301 Peoples R China Univ Rostock Fac Comp Sci & Elect Engn D-18055 Rostock Germany Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310027 Zhejiang Peoples R China

In order to explore the data fusion algorithm in medical Internet of things, the monitoring of medical data in the Internet of things is discussed and studied focusing on data fusion and related routing technology. According to the particularity of the data in the medical Internet of things, a data fusion cluster-tree construction algorithm based on event-driven (DFCTA) is proposed. The fusion delay problem in the network is analyzed, and the minimum fusion delay method is proposed by calculation of the fusion waiting time of the nodes. Finally, the intelligent health management data fusion system in the medical Internet of things is designed. Aiming at the characteristics of multilevel integration of multisource heterogeneous data fusion for intelligent health management, the data fusion architecture of fusion tree composed of fusion nodes is proposed. The experiment shows that the DFCTA algorithm has good fusion performance. Based on the above findings, it is concluded that the algorithm is a fast and reliable method, which has important practical significance.

关键词： Internet of things Medical data fusion algorithm Cluster tree Intelligent health management

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Research on collaborative recommendation of multidimensional sparse data based on personalised directional information fusion algorithm

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INTERNATIONAL JOURNAL OF AUTONOMOUS AND ADAPTIVE COMMUNICATIONS SYSTEMS 2020年第4期13卷 311-328页

作者： Huang, Yi Hunan Vocat Coll Sci & Technol Sch Software Changsha 410004 Hunan Peoples R China

In order to solve the problem of poor precision of recommendation results in the traditional collaborative recommendation method for multidimensional sparse data, a collaborative recommendation method for multidimensional sparse data based on personalised directional information fusion algorithm was proposed. The cosine similarity of the data vector was calculated and modified, and the score prediction set was established. On the basis of the predicted value and variable quantity value, the median, mean and model were used to populate and deconstruct the standard representative data, construct the multiple score matrix, solve the data location problem of sparse matrix, and realise the data collaborative recommendation. The experimental results show that the average error of the research method is about 0.03, lower than the traditional method, which proves that the method can effectively improve the accuracy of data recommendation.

关键词： directional information fusion algorithm data quantity recommendation multidimensional sparse data predicted value variable quantity value

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Intelligent design and optimization of exercise equipment based on fusion algorithm of YOLOv5-ResNet 50

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ALEXANDRIA ENGINEERING JOURNAL 2024年 104卷 710-722页

作者： Wang, Long Ji, Wendong Wang, Gang Feng, Yinqiu Du, Minghua Shandong Sport Univ China Natl Basketball Acad Rizhao 276827 Shandong Peoples R China Sangmyung Univ Grad Sch Dept Phys Educ Seoul 03016 South Korea Binzhou Polytech Coll Informat Engn Binzhou 256603 Shandong Peoples R China NingboTech Univ Sch Comp & Data Engn Ningbo 315100 Zhejiang Peoples R China Imperial Coll London Dept Bioengn London SW7 2AZ England VeoRide Seattle WA 98008 USA Chinese PLA Peoples Hosp Med Ctr 1 Dept Emergency Beijing 100853 Peoples R China

In the context of a low-carbon economy, it is crucial to integrate environmentally friendly and intelligent designs into fitness facilities to ensure sustainable economic, environmental, and energy development. However, traditional fitness facilities face challenges in effectively monitoring user movements, conserving energy, and optimizing efficiency. The need to conserve energy and reduce resource costs in a low-carbon economy calls for the development of efficient algorithms that can maintain accuracy while minimizing computation and energy consumption. This study proposes a method that combines machine learning and computer vision techniques to enhance monitoring accuracy while minimizing computation and energy consumption. The ResNet-50 model is utilized to extract image features associated with human movements, while real-time object detection and tracking are performed using the YOLOv5 model. Experimental evaluations are conducted on a dataset comprising multiple action categories, and the results demonstrate the excellent performance and model efficiency of the proposed method. Specifically, on the KU-HAR dataset, the proposed algorithm achieves a reduction of over 46.8% in inference time and more than 45.9% in FLOPs, while improving the MAPE by more than 42.8%. These advancements significantly enhance the accuracy and robustness of human motion recognition, highlighting the importance of this approach in the green transformation and intelligent design of fitness facilities.

关键词： Green fitness Sustainable development Moving target recognition Energy efficiency fusion algorithm

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Prediction and fusion algorithm for meat moisture content measurement based on loss-on-drying method

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International Journal of Agricultural and Biological Engineering 2020年第4期13卷 198-204页

作者： Jing Ling Jie Xu Haijun Lin Jinyuan Lin School of Physics and Electronic-Electrical Engineering Ningxia UniversityYinchuan 750021China Department of Food Science and Technology The Ohio State UniversityColumbusOH43210USA College of Polytechnic Hunan Normal UniversityChangsha 410081China

The loss-on-drying method has been widely used as a standard approach for measuring the moisture content of high-moisture materials such as solid and semi-solid ***-on-drying method provides reliable results,whilst usually labor-intensive and *** paper presents a novel algorithm for predicting the moisture content of meats based on the loss-on drying *** proposed approach developed a drying kinetics model of meats based on Fick’s Second Law and designed a prediction algorithm for meat moisture content using the least-squares *** predicted results were compared with the official method recommended by the Association of Official Analytical Chemists(AOAC).When the moisture content of meat samples(beef and pork)was varied from 69.46%to 74.21%,the relative error of the meat moisture content(MMC)calculated by the proposed algorithm was 0.0017-0.0117,the absolute errors were less than 1%.The testing time was about 40.18%-56.87%less than the standard detection procedure.

关键词： meat moisture content loss-on-drying method Fick’s Second Law fusion algorithm measurement prediction

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Adaptive　Multisensor　Tracking　fusion　algorithm　for　Air－borne　Distributed　Passive　Sensor　Network

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Journal of Systems Engineering and Electronics 1996年第3期7卷 15-23页

作者： Zhen Ding Hongcai Zhang & Guanzhong Dai (Department of Automatic Control, Northwestern Polytechnical UniversityShaanxi, Xi’an 710072, P.R.China) Department of Automatic Control Northwestern Polytechnical University Shaanxi Xi'an 710072 P.R. China

Single passive sensor tracking algorithms have four disadvantages: bad stability, longdynamic time, big bias and sensitive to initial conditions. So the corresponding fusion algorithm results in bad performance. A new error analysis method for two passive sensor tracking system is presented and the error equations are deduced in detail. Based on the equations, we carry out theoretical computation and Monte Carlo computer simulation. The results show the correctness of our error computation equations. With the error equations, we present multiple 'two station'fusion algorithm using adaptive pseudo measurement equations. This greatly enhances the tracking performance and makes the algorithm convergent very fast and not sensitive to initial *** results prove the correctness of our new algorithm.

关键词： Passive tracking system Error analysis fusion algorithm Distributed passive sensornetwork Distributed estimation.

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Polarization Image fusion algorithm Using NSCT and CNN

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JOURNAL OF RUSSIAN LASER RESEARCH 2021年第4期42卷 443-452页

作者： Wang, Shifeng Meng, Jin Zhou, Yuan Hu, Qinglong Wang, Zhiwei Lyu, Jiahang Changchun Univ Sci & Technol Natl Demonstrat Ctr Expt Optoelect Engn Educ Sch Optoelect Engn Weixing Rd 7089 Changchun 130022 Jilin Peoples R China Changchun Univ Sci & Technol Key Lab Optoelect Measurement & Opt Informat Tran Weixing Rd 7089 Changchun 130022 Jilin Peoples R China

fusion is beneficial to improve the accuracy of target detection in polarization images. In this paper, we propose an image fusion algorithm that combines nonsubsampled contourlet transform (NSCT) and Convolutional Neural Network (CNN). First, the fast guide filter and pulse-coupled neural network (PCNN) are employed to eliminate the noise of the polarization angle images. Second, the degree of linear polarization and polarization angle images are fused to obtain the polarization characteristic image. Finally, the polarization characteristic image is fused with the intensity image by NSCT. The CNN network is involved to extract mutilayer features from the high-frequency components of NSCT. The zero-phase component analysis (ZCA) is applied to normalize these features. The fusion coefficients of the high-frequency subband fusion coefficients are obtained by calculating the activity energy map of feature image. The low-frequency subband fusion coefficients are obtained by the strategy of regional energy. The result image is reconstructed by inverse NSCT. The experimental results show that the average gradient of the fused image is 51.3% higher than that of polarized intensity image, while the spatial frequency is improved by 35.1%. This algorithm is superior to others in terms of subjective visual effect and objective evaluation parameters, which is suitable for target detection.

关键词： fusion algorithm polarization imaging CNN network target detection

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