bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on The Current Situation and Development Trend of Optical Fiber Communication technology
Authors: Yuan Bingjie , Cai Han 
Published: April 2021

Abstract
The technology uses light wave as carrier and optical fiber as transmission medium to realize the function of transmitting information from one end to the other. Optical fiber communication technology has become one of the important means of information transmission in today’s era. It can be imagined that in the near future, optical fiber technology will have more rapid development and application. This paper briefly introduces the principle and characteristics of optical fiber communication technology, analyzes its current application fields, and discusses the future development trend of this technology, so as to let more people understand this technology.
Design of Monitoring System for Electric Vehicle Charging Facilities Based on Cloud Platform
Authors: Jian lv , Li Bo , Chen Jingyun , Han Luyu 
Published: April 2021

Abstract
Faced with the increasingly severe pressure of traditional energy consumption and environmental problems, electric vehicles have become the hotspot and trend of the development of the world automobile industry due to their energy-saving and environmental protection characteristics. The cloud platform in the paper was introduced into the monitoring system of electric vehicle charging facilities, and a model of electric vehicle charging facility monitoring system based on cloud platform was proposed. Aiming at the difficulty of charging facility monitoring system in large concurrent data processing, a M/M/C queuing theory model was presented. Data concurrency control algorithm. The transmission process of the data frame in the monitoring system was analyzed, and the concurrent processing algorithm was applied to the large charging station for analysis experiments. The experimental results showed that the proposed algorithm not only satisfied the real-time and concurrency requirements of data frame transmission, but also proved that the gateway optimization method could accurately derive the optimal number of gateways.
Compression of Person Re-identification Model Based on Depthwise Separable Convolutional Network
Authors: Zhu Liming , Xiao Junsheng 
Published: April 2021

Abstract
In practical application, the network depth and the number of parameters of person re-identification(re-ID) model bring great challenges to its deployment on devices with weak computing capabilities such as cloud desktop, mobile terminal and embedded terminal. To solve the above problem, this paper proposed a model compression method based on a depthwise separable convolutional network. Knowledge distillation is used to the core idea of model compression. Knowledge distillation is to transfer knowledge from a complex model to a simple model. The complex model is called the teacher model. The simple model is called the student model. This paper proposed ResNet18 based on depthwise separable convolution (ResNet18-DSC). ResNet50 is used as a teacher network and ResNet18-DSC is used as a student network. To narrow the performance gap between student and teacher networks. KL divergence loss function is used to approximate the soft label distribution of student network to that of teacher network. With a slight decrease in recognition rate, this method reduces the number of parameters by about 20 times and improves the calculation speed by about 20 times.
Remote sensing image registration based on full convolution neural network and k-nearest neighbor ratio algorithm
Authors: Wang Dongzhen , Chen Ying , Li Jipeng 
Published: April 2021

Abstract
Aiming at the problem of low accuracy of remote sensing image registration caused by the negative effects of noise and imaging in some traditional algorithms, an effective remote sensing image registration method based on depth feature from coarse to fine is proposed. In the coarse registration stage, the full convolution neural network is used to extract the features of the input image, then the nearest neighbor distance ratio algorithm is used to coarse match the features and finally an approximate transformation matrix is obtained. In the stage of fine registration, firstly, the image features are extracted by the improved convolutional neural network based on shortcut connection, then the affine transform coefficients are obtained by the combination of approximate transform matrix and k-nearest neighbor ratio algorithm. Finally, the image to be registered can be transformed according to the coefficients to achieve the purpose of registration. Experimental results show that, compared with the comparison method, the proposed method can increase the correct matching correspondence, so as to improve the accuracy of registration.
Analysis Of Protocol Convergence For Satellite And 5G Communication
Authors: Xiong Yue , Wang Quan , Chen Xuqiong , Li Yuanjie , Li Wenting 
Published: April 2021

Abstract
In recent years, with the commercial application of fifth-generation mobile communication (5G), the integration of satellite communication and 5G system has become a hot topic in the industry. The first thing to be considered in the integration of two communication systems is the integration and conversion of communication protocols between different systems. However, there is no final conclusion on how to integrate satellite and 5G communication protocol. By analyzing the differences and characteristics of satellite and 5G communication protocol, this paper creates a new protocol stack model from the direction of protocol system integration, and realizes the integration from the protocol level. According to the characteristics of different network applications, three network deployment modes are proposed. Finally, the application scenarios of satellite communication and 5G integration are given.
Real-time Object Tracking Method based on Multi-Core DSP
Authors: Li Lidan , Zhang Yi , Guo Qianya , Lin Qing , Zhou Jing , Wang Yanyong , Zhao Bo 
Published: April 2021

Abstract
In this paper, we propose a fast object tracking method used in real-time application. The tracking method is based on compact kernelized correlation filters. Traditional, gray values are used to describe target feature. In order to achieve relatively fast performance, we adopt Principal Component Analysis (PCA) to simplify Histogram of Gradient (HOG) as feature descriptor, which can significantly reduce computation resources. Our method is implemented on Texas Instruments (TI) multi-core digital signal processor (DSP) TMS320 C6657, which can achieve 60 frame per-second (fps). Finally, we conduct both quantitative and qualitative experiment to show the robustness and real-time performance of our method.
Unsupervised registration of intravascular ultrasound images combined with attention mechanism
Authors: Liu JiRong , Wang YouSheng , Man KaiLiang , Gao Xue 
Published: April 2021

Abstract
For intravascular ultrasound (IVUS) images, the registration technology is used to calculate coronary arteries displacement to analyze vascular elasticity. It not only provides evidence for the prevention and treatment of cardiovascular diseases, but also has important significance for guiding interventional surgery and monitoring the placement of surgical stents. Aiming at the high computational cost of current traditional registration methods and the insufficient accuracy of common deep learning registration methods for IVUS images, this paper proposes a fast unsupervised registration of IVUS images combined with an attention mechanism. The proposed method directly learns to estimate a displacement vector field (DVF) from a pair of input images of the training set. The spatial transform network (STN) uses the DVF to transform the moving image into the fixed image. Finally, the model is trained by minimizing a similarity metric loss function between the deformed moving image and the fixed image. Compared with the previous deep learning method, the registration performance improved after implementing the proposed method. The proposed method can accurately register the inner and outer membranes of IVU S images and provide a reliable basis for vascular elasticity analysis.
Realization of amplitude-type hologram based on nanostructured metasurfaces
Authors: Qu Kening 
Published: April 2021

Abstract
Conventional Amplitude-type holograms are generated by recording the interference fringes on a photosensitive material. In this paper, taking advantage of the characteristics of metasurfaces that can continuously control the amplitude of electromagnetic waves, a silver-nanopolarizer-based metasurface is proposed to realize amplitude-type holograph. An off-axis meta-hologram is designed and fabricated with e-beam lithography. And experimental results show that it can project a holographic image in the far field with high-fidelity, indicating that this amplitude hologram has the ability of continuous amplitude modulation and is safe and stable, so it has application spaces in holography, optical holography, holographic encryption, anti-counterfeiting, etc.
Transfer Learning Technique with VGG-16 for Near-Infrared Facial Expression Recognition
Authors: Atabansi Chukwuemeka C , Chen Tong , Cao Ranlei , Xu Xueming 
Published: April 2021

Abstract
In this paper, we investigate a deep learning vgg-16 network architecture for facial expression recognition under active near-infrared illumination condition and background. In particular, we consider the concept of transfer learning whereby features learned from high resolution images of huge datasets can be used to train a model of relatively small dataset without loosing the generalization ability. The pre-trained vgg-16 network architecture with transfer learning technique has been trained and validated on the Oulu-CASIA NIR dataset comprising of six (6) distinct facial expressions, and average test accuracy of 98.11% was achieved. The validation on our test data using the confusion, the precision, and the recall matrix reveals that our method achieves better results in comparison with the other methods in the literature.
Low power design method of medical image display system based on FPGA
Authors: Yang Qiankun , Yang Yong , Li Guohui , Fan Jiebiao 
Published: April 2021

Abstract
A novel low power design scheme of medical image display system is proposed, based on the research of FPGA(Field Programmable Gate Array) low power design technology and medical image display system. Firstly, the power consumption of video and image processing system based on FPGA is analyzed. Then a method to reduce the dynamic power consumption of FPGA is proposed based on the tonal and spatial characteristics of medical images. Finally, taking radiographic medical images as an example, the signal flip ratio is reduced from 37.95% to 9.68% by using software statistics, which proves that the proposed scheme can effectively reduce the dynamic power consumption of FPGA.
Study On Book Quality Evaluation Based On Neural Network And Data Visualization
Authors: Xiaobin Bi 
Published: April 2021

Abstract
In order to improve the quality of library books evaluation, neural network visualization and feature model technology were used to establish a feature model for the quality evaluation system. Model processing of neural network in the paper was applied to the book information of library, and various information indicators related to the quality of book evaluation were used as neurons. The convergence speed was improved by optimizing global optimization of neural network weights, and the relationship between the meta-nodes nerves were more accurately displayed, and the data analysis function with visual effects was provided, thus, an objective and vivid display of the book quality of library was shown. Finally, the neural network structure algorithm designed in this paper directly represented the mapping relationship between various indicators and evaluation scores, which was a reasonable and feasible method for library book quality evaluation.
UAV ranging method based on monocular vision
Authors: Rong Yicheng , Zhang Zhiyong , Qiu Changzhen , Ye Zezheng 
Published: April 2021

Abstract
Aiming at the problem that the unmanned aerial vehicle (UAV) cannot complete the positioning and ranging in the complex background and the signal is interfered, we proposed a monocular measurement scheme of assigning 4 LED fiducials (also called landmarks) with 3 colors in total on the target UAV. Firstly, feature areas are screened out by using high saturation, high brightness and specific hue range of LED in HSV (Hue, Saturation, Value) color space. After morphological processing, the background that does not meet the features is removed. Then, the mapping relationship between target LED fiducials and image fiducials is established by using the average hue of connected domain and special geometric layout. The improved Lambda-Twist is used to find the distance from the target UAV to the center of the camera, and the performance of the UAV recognition and ranging from 90m to 245m is tested. The experimental results show that the method can quickly and accurately measure the distance to the target, and can be used for UAV auxiliary measurement.
Mutual Attention Learning Network for Domain Adaptive Person Re-identification
Authors: Zhu Liming , Xiao Junsheng 
Published: April 2021

Abstract
Owing to its important role in video surveillance, person re-identification(re-ID) has become a hot research field of computer vision. The purpose of person re-ID is to identify the same person under different cameras. Domain adaptive person re-ID is to transfer the training model from labeled dataset to unlabeled dataset. Due to the neglect of key channels and key regions, the performance of most existing domain adaptive person re-ID methods is limited. To solve the above problem, this paper proposed a mutual attention learning network model (MALNet). It is based on channel attention and spatial attention mechanism. It consists of two channel-spatial attention convolutional neural networks (channel-spatial attention CNNs). Two networks are pre-trained in a source domain. They adapt to the target domain by the mutual attention learning strategy afterward. They distill and supervise each other to enhance the complementarity of MAL-Net. Moreover, a channel-spatial attention loss (CSAL) function is proposed to constrain and approximate the classification prediction distributions and feature triplet distributions. Extensive comparative experiments are carried out on two large-scale re-ID datasets (Market-1501 and DukeMTMC-reID). The performance of MAL-Net is better than other state-of-the-art methods, which proves the superiority and effectiveness of our method.
Historical Corpora Correlation based on RNN and DCNN
Authors: Lin Wei , Lin Zhaoquan 
Published: April 2021

Abstract
Correcting historical corpora in digital version is a crucial task for the historical research, however, scan quality, book layout, visual character similarity can affect the quality of the recognizing. OCR is at the forefront of digitization projects for cultural heritage preservation. The main task is to identify characters from their visual form into their textual representation. In this paper, we propose a model combining recurrent neutral network(RNN) and deep convolutional network(DCNN) to correct OCR transcription errors. The experiment on a historical book corpus in German language shows that the model is very robust in capturing diverse OCR transcription errors greatly.
An Analysis on Urban Land Use Efficiency Based on Super-efficiency DEA
Authors: Qiu Yingying , Sheng Jia , He Xiaoyu 
Published: April 2021

Abstract
The purpose of this research is to, by taking Wuhan city as an example, build the super-efficiency DEA to calculate and analysis the urban land use efficiency so as to provide reference for improving urban land use efficiency. Methods employed include super-efficiency DEA. The results show that: 1) the overall land use efficiency of Wuhan city in 2010-2016 is relatively high, most of the regional land use efficiency is relatively effective, but the difference of land use efficiency is relatively large, 2) super-efficiency DEA model can effectively measure and distinguish the urban land use efficiency from the quantitative point of view.
A novel linguistic steganalysis method for hybrid steganographic texts
Authors: Xu Yimin , Yang Liran , Zhao Tengyun , Zhong Ping 
Published: April 2021

Abstract
Most of the existing linguistic steganalysis methods mainly focus on detecting steganographic texts which are generated by embedding secret information into a type of text medium using one steganographic algorithm. But in practical applications, a large number of the steganographic texts may be hybrid ones which are generated by embedding secret information into different types of text media using different steganographic algorithms. In this paper, inspired by transfer learning, a novel linguistic steganalysis method is proposed to detect hybrid steganographic texts. The proposed method first uses the pre-trained BERT language model to obtain initial context-dependent word representations. Then the extracted features are fed into attentional Long Short-Term Memory (LSTM) to get the final contextual representations of sentences. The experimental results show that the proposed method can better satisfy the practical application demands than the existing linguistic steganalysis methods.
Research on Data Security Encryption Based on Extracting Evaluation Index from Multiple Angles
Authors: Hu Pan , Hu Nan , Jiao Chuanming , Zhang Fuliang , Qu Ruiting 
Published: April 2021

Abstract
In order to solve the problem of low accuracy of ciphertext analysis in traditional power system data encryption methods, this paper presents and designs a power system data encryption method based on multi-angle evaluation index. Firstly, the C/S architecture of security protection is established, and the information node of the management information database is supplemented with the multi-level security management system. Then the 3DES encryption algorithm is introduced to balance the weights of the two factors according to the scaling assignment, to increase the data confidentiality, and the embedded terminal is used to assist decryption. The experimental results show that the decryption result of the design method has a high precision, which verifies the effectiveness of the design method in improving the precision of ciphertext.
A 3D trajectory tracking algorithm for AUV
Authors: Li Wenkui , Zhou Zhu , Lou Jianlu , Zhang Xuguang 
Published: April 2021

Abstract
This paper mainly studies the trajectory tracking problem of six-degree-of-freedom AUV in seafloor three-dimensional space. The mathematical model of AUV is established, the trajectory guidance law is designed by LOS method, the speed and rudder force are compensated by adding PID controller, and the stability of the system is proved by Lyapunov stability criterion. Taking Remus100 as an example, the trajectory tracking simulation of straight line and helix in space is carried out. The simulation results show that the trajectory tracking algorithm can realize the trajectory tracking of AUV.
Application of Bioinformatics in the Internet of Things-Construction of Mushroom Cloud Digital Learning Platform
Authors: Lin Yu-Feng , Lin Chia-Hui , Tsai Sheng-Hsien , Lin M-F(Fanny) 
Published: April 2021

Abstract
In recent three years, the practical spirit of “DIY(Do-It-Yourself)”advocated in European and American education to combine the value of creativity and innovation is developed to a word “Maker” after integrating into the education system, which comes from the idea of “Hacker” and “Self-creator”. The Maker transforms one-way learning model of “listening” and “writing” in the past to school curriculum of “image” and “practice”, causing a reversal of traditional educational concepts. Educational policy in Taiwan naturally moves with the tide of the world. What characteristics of DIY, trouble-shooting, creativity sharing and community link emphasized by the Maker is in consistent with the transformation of technical and vocational education and the innovation of higher education that focus on the spirit of exploration and practice as well as innovation? The school used to participate in High Scope Program Phase II, including the development of “eco-art” course module for the first three years focused on plant observation along with the use of photography, presenting the idea of truth, goodness and beauty with the practice of “scientific drawing” in Fine Art Department; and, elaboration and promotion stage for the following two years focused on the reinforcement of introduction to biological course and the function of “online plants learning platform”, expanding to university and senior high schools, including of the Affiliated Chongli Senior High School of National Central University, Taishan and Sanming Senior High School for the development of more functions. As the theme of mushroom nutrition & health care, the school continues the spirit of exploration and practice by integrating Dept. of Home Economics’ food safety issues, Dept. of Chemistry’s extraction analysis, Dept. of Physics’ spectrograph analysis and Dept. of Computer Science’s internet of things(IoT) into curriculum framework to develop courses module for additional learning platform of mushroom classification based on “online learning platform” within application of bioinformatics in the IoT. The content for logic concept of plant retrieval classification and feature concept of fungi classification is processed in our Mushroom Digital Learning Platform by combining tablet PC and interactive web pages of “Online plants and herbs(mushroom) learning platform” for multiple assessment, in order for teachers to understand the learning effect of each student and analyze the data through the cloud platform.
Modeling for geometry-based massive MIMO V2V channels and analysis of space-time correlation
Authors: Xing Kexin , Guo Aihuang , Song Chunlin 
Published: April 2021

Abstract
This paper proposed a three-dimensional dual-spheres confocal ellipsoid geometry-based model for massive MIMO vehicle-to-vehicle (V2V) communication. In this proposed model, the received signal is composed of non-line-of-sight propagation components generated via effective scatterers and line-of-sight propagation components. According to the geometric relationships in the model, the information of angle and distance can be derived, and then the space-time correlation function expression is obtained. To calculate parameters, modified method of equal areas is employed, and the influence on space-time correlation of scatters in different environments is analyzed. Channel characteristics in different scenarios such as highway scenario, urban road scenario and tunnel scenario are compared and studied. Simulation results show that the scatterers distribution and angle information of antenna arrays make a joint contribution on spatial cross-correlation, and moreover, the degree of anisotropy of scatterers and multipath components have distinct impacts on temporal auto-correlation. Compared with the results of 2D model, the proposed 3D model in this paper is more appropriate to characterize V2V communication in various scenarios.