Search

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Practical Electrocardiology is the property of Journal of Practical Electrocardiology Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Practical Medicine / Shiyong Yixue Zazhi is the property of Journal of Practical Medicine Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Medical Education Technology is the property of China Medical Education Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Objective] Power Line Carrier(PLC)communication adopts the frame burst transmission mode. Due to the carrier frequency offset between transceivers, various noise and time-varying characteristics of PLC channel and the system has no dedicated reference signal. The traditional channel estimation has no tracking and prediction ability for the channel, which leads to the deterioration of the PLC system performance. [Methods] Aiming at the existing problems, this paper proposes a Denoising Long Short Term Memory (DnLSTM) neural network based on Long Short Term Memory (LSTM) neural network and Denoising Convolutional Neural Network (DnCNN), which is used for PLC channel estimation. First, offline training is performed on DnLSTM and the parameters are saved after training. Then the trained parameters are deployed in PLC system. After loading parameters, online prediction is performed to obtain the predicted PLC system channel estimation. In the simulation of PLC system, this paper uses Least Squares (LS) algorithm, Minimum Mean Square Error (MMSE) algorithm and DnLSTM to estimate the channel response, and gives the simulation results under the conditions of Additive White Gaussian Noise (AWGN), combined noise, impulsive noise and colored noise. Meanwhile, simulations for different number of preamble symbols for channel estimation are performed. [Results] The results show that there is a relationship between the accuracy of DnLSTM channel estimation and the number of preamble symbols. Using four preamble symbols for channel estimation, its estimation accuracy is better than LS and close to MMSE algorithm. DnLSTM has a good ability to resist carrier frequency offset and channel time-varying. When the number of preamble symbols for channel estimation increases, PLC system performance with low Singnal to Noise Ratio (SNR) gets better and PLC system performance is similar with high SNR. [Conclusion] According to simulation results above, it can be concluded that DnLSTM, which is based on DnCNN and LSTM, can predict PLC system channel response with frequency offset very well and it can track the varying PLC system channel response in real time [ABSTRACT FROM AUTHOR]

Copyright of Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Atomic & Molecular Physics (1000-0364) is the property of Journal of Atomic & Molecular Physics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: Aiming at the low accuracy and high model complexity of object detection algorithms in foggy scenes, a lightweight foggy object detection method based on YOLOv5 is proposed. Firstly, this paper adopts the receptive field attention module (RFAblock) to add an attention mechanism to the receptive field by interacting with the receptive field feature information to improve the feature extraction ability. Secondly, the lightweight network Slimneck is used as the neck structure to reduce the model parameters and complexity while maintaining the accuracy. The angle vector between the real frame and the predicted frame is introduced in the loss function to improve the training speed and inference accuracy. PNMS (precise non-maximum suppression) is used to improve the candidate frame selection mechanism and reduce the leakage detection rate in the case of vehicle occlusion. Finally, the experimental results are tested on the real foggy day dataset RTTS and the synthetic foggy day dataset Foggy Cityscapes, and the experimental results show that the mAP50 is improved by 4.9 and 3.5 percengtage points, respectively, compared with YOLOv5l, and the number of model parameters is only 54.6% of that of YOLOv5l. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Zhejiang University (Science Edition) is the property of Journal of Zhejiang University (Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rendering realistic virtual scenes from images has been a long-standing research goal in the fields of computer graphics and computer vision. NeRF (neural radiance fields) is an emerging method based on deep neural networks, which achieves realistic rendering by learning the radiance field of each point in the scene. By using neural radiance fields, not only realistic images but also realistic three-dimensional scenes can be generated, making it have a wide range of application prospects such as virtual reality, augmented reality and computer games. However, its basic model has shortcomings such as low training efficiency, poor generalization ability, insufficient interpretability, susceptible to lighting and material changes, inability to handle dynamic scenes, and other deficiencies that may result in suboptimal rendering results in certain situations. With the continuous popularity of this field, a large amount of research has been carried out, yielding impressive results in terms of efficiency and accuracy. In order to track the latest research in this field, this paper provides a review and summary of the key algorithms in recent years. This paper first outlines the background and principles of neural radiance fields, and briefly introduces the evaluation metrics and public datasets in this field. Then, a classification discussion is conducted on the key improvements to the model, mainly including: the optimization of basic NeRF model parameters, the improvement in rendering speed and inference ability, the enhancement of spatial representation and lighting ability, the improvement in camera pose and sparse view synthesis methods for static scene, and the development in dynamic scene modeling field. Subsequently, the speed and performance of various models are classified, compared and analyzed, and the main model evaluation indicators and open datasets in this field are briefly introduced. Finally, the future development trend of neural radiance field is prospected. [ABSTRACT FROM AUTHOR]

Transformer is a revolutionary neural network architecture initially designed for natural language processing. However, its outstanding performance and versatility have led to widespread applications in the field of computer vision. While there is a wealth of research and literature on Transformer applications in natural language processing, there remains a relative scarcity of specialized reviews focusing on low-level visual tasks. In light of this, this paper begins by providing a brief introduction to the principles of Transformer and analyzing several variants. Subsequently, the focus shifts to the application of Transformer in low-level visual tasks, specifically in the key areas of image restoration, image enhancement, and image generation. Through a detailed analysis of the performance of different models in these tasks, this paper explores the variations in their effectiveness on commonly used datasets. This includes achievements in restoring damaged images, improving image quality, and generating realistic images. Finally, this paper summarizes and forecasts the development trends of Transformer in the field of low-level visual tasks. It suggests directions for future research to further drive innovation and advancement in Transformer applications. The rapid progress in this field promises breakthroughs for computer vision and image processing, providing more powerful and efficient solutions for practical applications. [ABSTRACT FROM AUTHOR]

This paper aims to address the problems of low accuracy and high leakage rate due to the influence of different insulator sizes and background interference in the target detection task of power systems. Firstly, a convolutional block attention module (CBAM) is added to the YOLO v7 backbone network to make the network model pay more attention to the insulator features from both channel and space aspects and reduce the leakage rate in insulator detection. Secondly, a concentrated feature pyramid (CFP) is added to the deeper layer of the network model to allow the information exchange and aggregation of feature maps at different scales, thus obtaining more comprehensive insulator features and improving insulator detection accuracy. Finally, the k-means algorithm is used to cluster the preselected frames to obtain the most suitable insulator preselected frame size. The experimental results show that the improved YOLO v7 network model has a detection mAP (mean average precision) of 96.2%, a precision of 90.8%, and a recall of 93.8%. The improved method in this paper has a wide application prospect in the insulator detection of power systems. [ABSTRACT FROM AUTHOR]

Remote sensing scene classification is a hot research topic in the field of computer vision, and it is of great significance to semantic understanding of remote sensing images. At present, remote sensing scene classification methods based on deep learning occupy a dominant position in this field. However, it suffers from the lack of samples and poor model generalization ability in actual application scenarios. Therefore, this paper proposes a few-shot remote scene classification method based on attention mechanism, and designs a structure of dual- branches similarity measurement. This method is based on the meta-learning training strategy to divide the dataset into tasks. At the meantime, the input images are divided into blocks in order to preserve the feature distribution in the remote sensing image. Then the lightweight attention module is introduced into the feature extraction network to reduce the risk of overfitting and ensure the acquisition of discriminative features. Finally, based on earth mover's distance (EMD), a dual-branches similarity measurement module is added to improve the discriminative ability of the classifier. The results show that compared with the classic smallsample learning method, the few-shot remote scene classification method proposed in this paper can significantly improve the classification performance. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automotive Engineer (1674-6546) is the property of Auto Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Objective] The rapid development of the intelligent transportation industry brings new opportunities and challenges for talent cultivation. The traditional cultivation system and teaching platforms for transportation majors face the following problems: 1) The cultivation modes of talents are not suitable for the rapid development of the industry. 2) The teaching methods are not coordinated with the development of industrial technologies. 3) The construction of a practical teaching platform is not synchronized with industrial development. 4) There is no sustainable and effective cooperation mechanism in the integration of industry and education. To solve the above problems, a teaching reform method based on the digital twin is proposed in this paper. [Methods] First, this paper analyzed the demand for transportation innovation talents and the significance of technological innovation capabilities, interdisciplinary comprehensive capabilities, and industry foresight capabilities. Meanwhile, a double-closed-loop innovative talent cultivation system of the integration of industry and education was established. One closed loop was theoretical teaching and professional skills practice, and the other closed loop was industrial demand and school supply. Second, a transportation digital twin method was proposed based on the five-dimensional model of the digital twin. The five dimensions of physical space, cyberspace, service system, digital twin data, and connection were deeply integrated with traffic scenarios. Based on the transportation digital twin method, this paper combined hardware (e.g., intelligent roadside devices, driving simulators, and miniature intelligent vehicles) with software (e.g., PreScan, Matlab, and PyCharm) to build the transportation digital twin experimental platform. The platform comprised three parts: the real traffic road, the cyberspace, and the semiphysical traffic simulation scene. Using this platform, real field tests can be replaced by digital twin experiments in the laboratory. Third, a typical experimental example of autonomous driving was selected to demonstrate the operating steps, including data collection and preprocessing, data conversion and traffic scenario construction, and autonomous driving algorithm development. Finally, six indexes were selected to evaluate the transportation digital twin teaching reform method, including learning attitude, cognition of majors, practical participation, course satisfaction, academic performance, and science and technology competition. [Results] The final evaluation results showed the following: 1) The proposed method can meet the learning needs of students, allow students to have a deep understanding of the practical applications of transportation professional courses, and improve their learning attitudes and cognition of majors. 2) Cyber space can provide students with repeated practice opportunities, effectively stimulating their enthusiasm for innovation and creativity, thereby improving their practical participation and course satisfaction. 3) There is a positive effect in promoting students' class performance, experimental results, and exam results, among others. This plays an important role in science and technology competitions. [Conclusions] The proposed teaching reform method based on the digital twin solved the problems existing in traditional transportation talent training systems and teaching platforms, which is of great significance for the cultivation of innovative talents of transportation majors. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Kunming Medical University / Kunming Yike Daxue Xuebao is the property of Kunming Medical Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traditional methods of rice phenological phase feature extraction based on time-series remote sensing images require high temporal resolution, which is difficult to meet due to imaging conditions. Due to the different environmental conditions in different rice growing regions, the rice planting area extraction method based on single image has poor generalization ability. In this paper, similar optical and Synthetic Aperture Radar (SAR) data were selected to reduce the spatiotemporal information differences in rice planting area images. The spatial feature information of optical data and backscatter information of SAR data were effectively used to extract rice features by using a two-structure network model through pan-spatio-temporal feature fusion. Experiments show that the overall test accuracy of the training model validation set on the rice datasets of Sanjiang Plain and Feixi County is 95.66%, and the Kappa coefficient is 0.8805. The results of rice extraction in Nanchang City were in good agreement with the actual field boundaries, and the overall extraction accuracy was 86.78%, which proved the generalization ability and practicability of the pan-temporal feature model. [ABSTRACT FROM AUTHOR]

In order to judge the severity of crop diseases quickly and accurately, a novel online remote diagnosis method was proposed based on multi-task learning in this paper. The classic model MobileNetV3 was improved by introducing convolutional block attention module and feature pyramid module to boost the performance of the recognition of crops, diseases and pests, and disease levels. Besides, some data augment methods were adopted to extend the small samples. The performance of the improved model and other image recognition models in the identification of crop disease was tested, and the performance of different models with and without data enhancement processing was explored. The results showed that the mean average precision of proposed method on such 3 tasks was more than that of the original model by 1.38, 2.24 and 2.03 percentage points, respectively, and the average recall of proposed method on such 3 tasks was more than that of the original model by 2.38, 1.62 and 1.18 percentage points, respectively. The proposed method outperformed the state-of-the-art methods, such as MobileNetV3, InceptionV3 and YOLOv7. [ABSTRACT FROM AUTHOR]