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1. A novel key frame selection method for aerial image stitching by integrating navigation information and trusted key points | |||
Zheng Yongji,Wang Guoyou | |||
Computer Science and Technology 01 April 2021 | |||
Show/Hide Abstract | Cite this paper︱Full-text: PDF (562K B) | |||
Abstract:Fast and high-precision video based image stitching plays an important role in many machine vision applications, such as UAV mapping and reconnaissance. Due to the large number of frames and high redundancy of video sequence, image stitching is very time-consuming. Therefore, from the perspective of reducing the number of redundant frames, this paper proposes a novel video sequence key frame selection method based on rough camera external parameters and key point fusion, which selects the appropriate key frames by optimizing the overlap rate and the number of reliable key points between two adjacent key frames. The algorithm not only greatly reduces the number of key frames, but also ensures the reliable video mosaic. The experimental results on Bu S\' videos show that our method can reduce the number of key frames by 92%. In addition, compared with the key frame selection method based only on navigation information, this method also overcomes the problem of missing stitched images caused by insufficient key points in overlapping regions. | |||
TO cite this article:Zheng Yongji,Wang Guoyou. A novel key frame selection method for aerial image stitching by integrating navigation information and trusted key points[OL].[ 1 April 2021] http://en.paper.edu.cn/en_releasepaper/content/4754321 |
2. Ship Detection via Multi-scale Graph Convolutional Network | |||
Muyan Feng,Ming Wu,Xin Jiang,Chuang Zhang | |||
Computer Science and Technology 26 March 2021 | |||
Show/Hide Abstract | Cite this paper︱Full-text: PDF (3405K B) | |||
Abstract:In the existing high resolution optical remote sensing images, objects are often located in a complex environment and most datasets have serious problems of imbalance in the number and size of samples, especially the ship samples which located in a changeable marine environment. Our research is mainly to solve the problem of object detection of different scales in unbalanced datasets in complex background. Ship datasets are typical datasets with these characteristics, so we will choose ship targets as experimental dataset. We introduce a Multi-scale Graph Convolutional Network(MGCN), which is formed by a multi-scale module and GCN module to get a better performance on ship detection. For the multi-scale module, we add dilated convolutions combinations with suitable dilated rates on feature maps of different sizes to obtain context and global information, which improves the detection accuracy of multi-scale objects. For the GCN module, we try to design a co-occurrence matrix as the input of GCN to summarize the relationship from the dataset as the prior knowledge. By updating features from related objects, it can enhance local representations to get more accurate result. MGCN outperforms than existing methods on ship detection and provides a new baseline for the dataset. Experiments verify the effectiveness of our method, e.g. achieving around 16.7\% on ship detection dataset FGSD in terms of mAP. We also visualize ship detection results and show the improvement of our method. Our network is generalized and can be applied to different types of datasets. | |||
TO cite this article:Muyan Feng,Ming Wu,Xin Jiang, et al. Ship Detection via Multi-scale Graph Convolutional Network[OL].[26 March 2021] http://en.paper.edu.cn/en_releasepaper/content/4754159 |
3. An extended model of group chase and escape based on refuges | |||
ZHANG Xinglei,LIU Shaohua | |||
Computer Science and Technology 24 March 2021 | |||
Show/Hide Abstract | Cite this paper︱Full-text: PDF (634K B) | |||
Abstract:In this paper,an extended model is proposed to describe the motion trajectory of group chase and escape based on refuges.The rich dynamic behaviors of chaser and escaper are demonstrated by using a cellular automata model and the protective effect of refuge on escaper is explored in both long-term and short-term modes. The protective effect of different refuge density and distribution is compared in this paper. A critical refuge density which provides 100% protection for escaper is founded as refuge density increases and the optimal refuge distribution for prey\'s survival is concluded. These findings can provide references for the establishment of endangered animal refuges and the modeling of crowd evacuation under attack, and have profound significance on the topic of group chase and escape. | |||
TO cite this article:ZHANG Xinglei,LIU Shaohua. An extended model of group chase and escape based on refuges[OL].[24 March 2021] http://en.paper.edu.cn/en_releasepaper/content/4754164 |
4. Design and implementation of campus care system based on wechat small program | |||
Wang Yanlong,Liu Jun | |||
Computer Science and Technology 23 March 2021 | |||
Show/Hide Abstract | Cite this paper︱Full-text: PDF (431K B) | |||
Abstract:Due to the large number of students and the lack of timely access to information, Chinese college counselors have been facing enormous pressure to manage students for a long time. In order to improve the efficiency of student management and reduce the pressure of managers at the same time, many colleges and universities have begun to develop some applications, which is focusing on some important points of student management. Starting from the management of undergraduates, we design and implement a campus care system. It collects some applications to facilitate the work of counselors,achieving the effect of precise management. | |||
TO cite this article:Wang Yanlong,Liu Jun. Design and implementation of campus care system based on wechat small program[OL].[23 March 2021] http://en.paper.edu.cn/en_releasepaper/content/4754280 |
5. Research on power allocation in V2X network based on Reinforcement Learning | |||
Wang Yanlong,Liu Jun,Yang Jie | |||
Computer Science and Technology 23 March 2021 | |||
Show/Hide Abstract | Cite this paper︱Full-text: PDF (507K B) | |||
Abstract:With the rapid development of IoT (Internet of Things), the application scenarios of wireless devices accessing the network has been greatly enriched. Vehicle-to-Everything (V2X) is one of the most typical scenarios. This paper proposes a model of the V2X power distribution problem in a multi-base station scenario for the power allocation problem, and designs three types of reinforcement learning algorithms. The specific optimization problems are publicized and deduced and the elements of the reinforcement learning algorithm are sorted out. The results show that the performance of the DDPG algorithm is the best, which verifies the feasibility of the reinforcement learning algorithm in the V2X scenarios. | |||
TO cite this article:Wang Yanlong,Liu Jun,Yang Jie. Research on power allocation in V2X network based on Reinforcement Learning[OL].[23 March 2021] http://en.paper.edu.cn/en_releasepaper/content/4754266 |
6. User Authentication from Smartwatch Photoplethysmography sensor | |||
TAN ZhiHao,HUANG Qinlong,YANG Yixian | |||
Computer Science and Technology 22 March 2021
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Show/Hide Abstract | Cite this paper︱Full-text: PDF (1054K B) | |||
Abstract:With the rapid proliferation of smartwatch, a secure and convenient smartwatch-based user authentication scheme are desired. As the widely deployed bioelectrical signal sensor in smartwatch, Photoplethysmography (PPG) sensors have shown potentials for authentication. Existing authentication solutions usually have some limitations. They require the user to provide an amount of registration data from user to reflect the profile of user, which may impact the experience of user. In this paper, we propose a PPG-based smartwatch authentication scheme. We leverage the Siamese Network to extract the feature of user from the PPG signal affected by the finger-level gesture for authentication. We conduct some experiments to evaluate the performance of the scheme. The experiment results show that our model has an average accuracy rate of 92.43\%. In addition, the authentication model can achieve high authentication accuracy with a small amount of user registration data. | |||
TO cite this article:TAN ZhiHao,HUANG Qinlong,YANG Yixian. User Authentication from Smartwatch Photoplethysmography sensor[OL].[22 March 2021] http://en.paper.edu.cn/en_releasepaper/content/4754180 |
7. FGSD: A Dataset For Fine-Grained Ship detecion In High Resolution Satellite Images | |||
Kaiyan Chen,Kaiyan Chen,Ming Wu,Jiaming Liu,Chuang Zhang | |||
Computer Science and Technology 19 March 2021 | |||
Show/Hide Abstract | Cite this paper︱Full-text: PDF (3519K B) | |||
Abstract:Ship detection using high-resolution remote sensing images is an important task, which contribute to sea surface regulation. The complex background and special visual angle make ship detection relies in high quality datasets to a certain extent. However, there is few works on giving both precise classification and accurate location of ships in existing ship detection datasets. To further promote the research of ship detection, we introduced a new fine-grained ship detection datasets, which is named as FGSD. The dataset collects high-resolution remote sensing images that containing ship samples from multiple large ports around the world. Ship samples were fine categorized and annotated with both horizontal and rotating bounding boxes. To further detailed the information of the dataset, we put forward a new representation method of ships’ orientation. For future research, the dock as a new class was annotated in the dataset. Besides, rich information of images were provided in FGSD, including the source port, resolution and corresponding GoogleEarth's resolution level of each image. As far as we know, FGSD is the most comprehensive ship detection dataset currently and it'll be available soon. Some baselines for FGSD are also provided in this paper. | |||
TO cite this article:Kaiyan Chen,Kaiyan Chen,Ming Wu, et al. FGSD: A Dataset For Fine-Grained Ship detecion In High Resolution Satellite Images[OL].[19 March 2021] http://en.paper.edu.cn/en_releasepaper/content/4754102 |
8. Design and Experiment of Reconstructing NANO Grid System by Java | |||
Huang Mengyan,Zhang Yuyan | |||
Computer Science and Technology 18 March 2021 | |||
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Abstract:In recent years, blockchain technology has developed rapidly. Not only Bitcoin is widely known, but also Ethereum and Hyperledger technology are gradually being applied to life. However, the current technology has problems with high time delay and low throughput. The Nano block grid structure solves the above problems by virtue of its concurrent transactions and account transactions. However, the Nano blockchain project is too redundant and large, and it is not easy to learn. In order to make the project easier to support the development of application and test its performance in lab environment, we choose Java to reconstruct the Nano block grid system, using Socket communication technology, JDBC Database and other technologies. The system includes start-up block chain, construction block, socket communication, system transaction, account information display, and block content display. The experiment results show that the reconstructing system works well. | |||
TO cite this article:Huang Mengyan,Zhang Yuyan. Design and Experiment of Reconstructing NANO Grid System by Java[OL].[18 March 2021] http://en.paper.edu.cn/en_releasepaper/content/4754061 |
9. Multi-dimensional social relationship analysis model based on LDA | |||
Ming Xuyang,Zhang Siyang | |||
Computer Science and Technology 18 March 2021 | |||
Show/Hide Abstract | Cite this paper︱Full-text: PDF (749K B) | |||
Abstract:The development of location service technology provides the premise for the emergence of location-based socia |