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  Subjects -> ELECTRONICS (Total: 207 journals)
Showing 1 - 200 of 277 Journals sorted by number of followers
IEEE Transactions on Aerospace and Electronic Systems     Hybrid Journal   (Followers: 281)
Control Systems     Hybrid Journal   (Followers: 236)
IEEE Transactions on Geoscience and Remote Sensing     Hybrid Journal   (Followers: 174)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 165)
Electronic Design     Partially Free   (Followers: 125)
Electronics     Open Access   (Followers: 125)
Advances in Electronics     Open Access   (Followers: 122)
Electronics For You     Partially Free   (Followers: 114)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 112)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 90)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 88)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 87)
IEEE Transactions on Industrial Electronics     Hybrid Journal   (Followers: 85)
IEEE Transactions on Software Engineering     Hybrid Journal   (Followers: 84)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 79)
IET Power Electronics     Open Access   (Followers: 76)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 65)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 62)
IEEE Embedded Systems Letters     Hybrid Journal   (Followers: 60)
IEEE Transactions on Industry Applications     Hybrid Journal   (Followers: 57)
Advances in Power Electronics     Open Access   (Followers: 56)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 52)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 50)
IEEE Nanotechnology Magazine     Hybrid Journal   (Followers: 45)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 45)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 41)
IET Microwaves, Antennas & Propagation     Open Access   (Followers: 35)
IEEE Transactions on Biomedical Engineering     Hybrid Journal   (Followers: 35)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 31)
International Journal of Power Electronics     Hybrid Journal   (Followers: 30)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 27)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 27)
Microelectronics and Solid State Electronics     Open Access   (Followers: 27)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 26)
Journal of Sensors     Open Access   (Followers: 25)
Electronics Letters     Open Access   (Followers: 25)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 23)
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 22)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 19)
IEEE Reviews in Biomedical Engineering     Hybrid Journal   (Followers: 19)
IEEE/OSA Journal of Optical Communications and Networking     Hybrid Journal   (Followers: 19)
Journal of Artificial Intelligence     Open Access   (Followers: 18)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 18)
IET Wireless Sensor Systems     Open Access   (Followers: 17)
Circuits and Systems     Open Access   (Followers: 16)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 16)
IEEE Transactions on Signal and Information Processing over Networks     Hybrid Journal   (Followers: 14)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 14)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 14)
Archives of Electrical Engineering     Open Access   (Followers: 14)
International Journal of Control     Hybrid Journal   (Followers: 13)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 13)
Advances in Microelectronic Engineering     Open Access   (Followers: 12)
IEEE Transactions on Learning Technologies     Full-text available via subscription   (Followers: 12)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 12)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 12)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 12)
IEEE Solid-State Circuits Magazine     Hybrid Journal   (Followers: 11)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 11)
IEEE Women in Engineering Magazine     Hybrid Journal   (Followers: 11)
IEEE Transactions on Broadcasting     Hybrid Journal   (Followers: 11)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 10)
International Journal of Advanced Electronics and Communication Systems     Open Access   (Followers: 10)
International Journal of Antennas and Propagation     Open Access   (Followers: 10)
IETE Journal of Research     Open Access   (Followers: 10)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 9)
IETE Technical Review     Open Access   (Followers: 9)
Batteries     Open Access   (Followers: 8)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 8)
IEEE Transactions on Autonomous Mental Development     Hybrid Journal   (Followers: 8)
Journal of Power Electronics     Hybrid Journal   (Followers: 8)
China Communications     Full-text available via subscription   (Followers: 8)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 8)
Open Journal of Antennas and Propagation     Open Access   (Followers: 8)
Metrology and Measurement Systems     Open Access   (Followers: 8)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 8)
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal   (Followers: 8)
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 8)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 8)
Solid-State Electronics     Hybrid Journal   (Followers: 7)
Nanotechnology, Science and Applications     Open Access   (Followers: 7)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 7)
International Journal of Electronics     Hybrid Journal   (Followers: 7)
IEEE Magnetics Letters     Hybrid Journal   (Followers: 7)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 7)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 6)
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Annals of Telecommunications     Hybrid Journal   (Followers: 6)
Electronic Markets     Hybrid Journal   (Followers: 6)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 6)
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 6)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 6)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 5)
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access   (Followers: 5)
Journal of Electronics (China)     Hybrid Journal   (Followers: 5)
Journal of Field Robotics     Hybrid Journal   (Followers: 5)
Energy Storage Materials     Full-text available via subscription   (Followers: 5)
IEEE Pulse     Hybrid Journal   (Followers: 5)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 5)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 5)
Batteries & Supercaps     Hybrid Journal   (Followers: 5)
IEEE Transactions on Services Computing     Hybrid Journal   (Followers: 5)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 4)
Wireless and Mobile Technologies     Open Access   (Followers: 4)
Superconductivity     Full-text available via subscription   (Followers: 4)
IEEE Transactions on Haptics     Hybrid Journal   (Followers: 4)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 4)
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 4)
Networks: an International Journal     Hybrid Journal   (Followers: 4)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 4)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Journal of Optoelectronics Engineering     Open Access   (Followers: 4)
Sensors International     Open Access   (Followers: 3)
Nature Electronics     Hybrid Journal   (Followers: 3)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
EPE Journal : European Power Electronics and Drives     Hybrid Journal   (Followers: 3)
Machine Learning with Applications     Full-text available via subscription   (Followers: 3)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 3)
Informatik-Spektrum     Hybrid Journal   (Followers: 3)
IETE Journal of Education     Open Access   (Followers: 3)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 2)
Power Electronics and Drives     Open Access   (Followers: 2)
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal   (Followers: 2)
Advancing Microelectronics     Hybrid Journal   (Followers: 2)
Transactions on Electrical and Electronic Materials     Hybrid Journal   (Followers: 2)
Energy Storage     Hybrid Journal   (Followers: 2)
Journal of Information and Telecommunication     Open Access   (Followers: 2)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 2)
Advanced Materials Technologies     Hybrid Journal   (Followers: 2)
EPJ Quantum Technology     Open Access   (Followers: 2)
e-Prime : Advances in Electrical Engineering, Electronics and Energy     Open Access   (Followers: 2)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 2)
IET Smart Grid     Open Access   (Followers: 2)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 2)
Journal of Nuclear Cardiology     Hybrid Journal   (Followers: 1)
Transactions on Cryptographic Hardware and Embedded Systems     Open Access   (Followers: 1)
ACS Applied Electronic Materials     Open Access   (Followers: 1)
Frontiers in Electronics     Open Access   (Followers: 1)
IEEE Letters on Electromagnetic Compatibility Practice and Applications     Hybrid Journal   (Followers: 1)
Semiconductors and Semimetals     Full-text available via subscription   (Followers: 1)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 1)
IET Energy Systems Integration     Open Access   (Followers: 1)
ECTI Transactions on Electrical Engineering, Electronics, and Communications     Open Access   (Followers: 1)
International Journal of Hybrid Intelligence     Hybrid Journal   (Followers: 1)
Open Electrical & Electronic Engineering Journal     Open Access   (Followers: 1)
Ural Radio Engineering Journal     Open Access   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
Електротехніка і Електромеханіка     Open Access   (Followers: 1)
Edu Elektrika Journal     Open Access   (Followers: 1)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 1)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
Automatika : Journal for Control, Measurement, Electronics, Computing and Communications     Open Access  
npj Flexible Electronics     Open Access  
Elektronika ir Elektortechnika     Open Access  
Emitor : Jurnal Teknik Elektro     Open Access  
IEEE Solid-State Circuits Letters     Hybrid Journal  
IEEE Open Journal of Industry Applications     Open Access  
IEEE Open Journal of the Industrial Electronics Society     Open Access  
IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology     Hybrid Journal  
IEEE Open Journal of Circuits and Systems     Open Access  
Journal of Electronic Science and Technology     Open Access  
Australian Journal of Electrical and Electronics Engineering     Hybrid Journal  
Solid State Electronics Letters     Open Access  
Industrial Technology Research Journal Phranakhon Rajabhat University     Open Access  
Journal of Engineered Fibers and Fabrics     Open Access  
Jurnal Teknologi Elektro     Open Access  
IET Nanodielectrics     Open Access  
Elkha : Jurnal Teknik Elektro     Open Access  
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Jurnal Teknik Elektro     Open Access  
IACR Transactions on Symmetric Cryptology     Open Access  
Acta Electronica Malaysia     Open Access  
Bioelectronics in Medicine     Hybrid Journal  
Chinese Journal of Electronics     Open Access  
Problemy Peredachi Informatsii     Full-text available via subscription  
Technical Report Electronics and Computer Engineering     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Visión Electrónica : algo más que un estado sólido     Open Access  
Telematique     Open Access  
International Journal of Nanoscience     Hybrid Journal  
International Journal of High Speed Electronics and Systems     Hybrid Journal  

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Similar Journals
Journal Cover
Journal of Sensors
Journal Prestige (SJR): 0.288
Citation Impact (citeScore): 1
Number of Followers: 25  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1687-725X - ISSN (Online) 1687-7268
Published by Hindawi Homepage  [339 journals]
  • Innovation and Discrete Dynamic Modeling of College Music Teaching Model
           Based on Multiple Intelligences Theory

    • Abstract: The development of music teaching mode in colleges and universities needs to take music as the main body and carrier to spread and inherit the music theory system. With the continuous innovation and development of science and technology, the teaching mode and teaching system have also ushered in new changes. How to let students understand the process of music teaching and better appreciate the charm of music is the main problem faced by educators. Faced with the above situation, starting from the theory of multiple intelligences, this paper studies the innovation and discrete dynamic modeling of music teaching mode in colleges and universities. Firstly, this paper discusses the application effect of this method in music teaching based on the theory of multiple intelligences. This paper investigates the actual development of multiple intelligences theory in college music teaching. Combined with the characteristics of multiple intelligences theory, modeling and analysis of students’ interest changes in the intelligent music education model represented by the space vector model are carried out. Finally, this paper studies the discrete dynamic modeling of students’ learning effect after the optimization and innovation of music teaching mode in colleges and universities under the theory of multiple intelligences. The results show that in the innovation of music teaching mode, personalized learning services should be provided based on students’ interests. The theory of multiple intelligences can help teachers to effectively analyze the diversity characteristics and changes of students in teaching activities, and it is of great help to improve students’ musical performance.
      PubDate: Tue, 24 May 2022 11:35:01 +000
  • Multiobjective Optimization Strategy of WSN Coverage Based on IPSO-IRCD

    • Abstract: The nonuniform distribution characteristic of randomly deployed mobile nodes will lead to the coverage hole and redundancy in wireless sensor networks (WSNs). To solve this problem, we propose a multiobjective optimization algorithm for WSN based on Improved Particle Swarm Optimization-Increment of the Ratio of Coverage Rate to Move Distance (IPSO-IRCD), and a network node coverage optimization model is formulated to maximize the coverage rate of the target area while reducing the moving distance of nodes. In each iteration of IPSO, the population fitness value is calculated and compared with the historical optimal value, when the arbitrary dimensional location information of each node is updated, which can avoid the standard PSO algorithm loses the optimal solution, and IPSO will determine the candidate deployment location of nodes. Based on which, IRCD node coverage scheduling optimization algorithm is proposed, so that the final deployment location can be determined iteratively by calculating IRCD of nodes. Simulation results indicate that, for the nodes initial coverage state follows random distribution and Gaussian distribution, IPSO-IRCD can, respectively, improve 4.6% and 7.4% coverage ratio compared with the suboptimal algorithm in other five similar algorithms and reduce 809.59 m and 626.63 m nodes moving distance.
      PubDate: Tue, 24 May 2022 04:35:02 +000
  • Optimization and Design of General Machinery Production Line Management
           Process Based on Intelligent Computing Model

    • Abstract: Machinery is the most important basic tool for modern times, and it is also an indispensable sharp weapon for creating big country projects. The production line management of general machinery is particularly important. Traditional production line management causes a lot of labor waste, time waste, and low production efficiency, so the efficiency of production line management also determines the quality and output of general machinery. Therefore, it has become an industry consensus to realize intelligent management in the manufacturing process of general machinery. General machinery is not only a complex industrial structure but also has a series of preconditions that are not conducive to production, such as the diversity of parts and the low precision of preparation in the early stage. Therefore, to realize automation in the field of construction machinery manufacturing, it usually faces more challenges. Whether it is traditional production process or automatic production process, the realization of intelligent production line is the primary problem, because both production efficiency and product quality are determined by the efficient production line management efficiency and exquisite process. The workload of production line management is heavy, which is time-consuming, labor-consuming, and expensive. Adhere to the goal of quality first, and how to improve the efficiency of the production line has become the biggest problem at present. Based on the above problems, this paper adopts intelligent computing model to improve the efficiency of production line management and optimize the process. A series of tedious processes from product adoption to final shipment of production lines need intelligent technology to simplify the process, which can effectively improve the production efficiency of general machinery, reduce production costs, and improve the production quality of machinery. Supply chain management theory is used to manage suppliers’ production behavior, so as to reduce costs and improve quality and service, thus improving the competitiveness of batch production lines and enterprises. Advanced manufacturing technology is used to realize automation and flexible production of batch production lines, thus improving the rapid response ability of production lines to market demand.
      PubDate: Tue, 24 May 2022 04:35:02 +000
  • Research on UI Design and Optimization of Digital Media Based on
           Artificial Intelligence

    • Abstract: At present, artificial intelligence technology has developed rapidly and has been gradually applied to various fields of society. With the popularization and development of computers, under the influence of artificial intelligence technology, digital media art and artificial intelligence technology merge with each other, which makes modern culture bloom with greater charm. Based on the era of artificial intelligence, this paper discusses the development core of the integration of artificial intelligence technology and digital media art, analyzes the current development status of digital media art and technology, and finally puts forward the innovative development direction and future trend. At present, China’s artificial intelligence technology has gradually entered a prosperous stage in the continuous development and has become one of the high and new technologies in the new era, bringing many changes to people’s work and life. With the development of society, modern people are pursuing the two-way needs of material and spirit, and art, as a special field, has attracted much attention. By involving artificial intelligence in the art field, it has become a new challenge to be faced at present, which will promote art and technology to present different forms of expression. Of course, the artistic content of digital media art will be richer, and the attention will be higher. Under the background of artificial intelligence era, the integration of artificial intelligence and digital media is the core industry at present, which promotes the development of China’s economy in a better direction.
      PubDate: Tue, 24 May 2022 03:35:01 +000
  • Implementing Fusion Technique Using Biorthogonal Dwt to Increase the
           Number of Minutiae in Fingerprint Images

    • Abstract: Biometric devices identify persons based on the minutiae extracted from fingerprint images. Image quality is very important in this process. Usually, fingerprint images have low quality and in many cases they are obtained in various positions. The paper focuses on increasing minutiae detected number by fusing two fingerprint images obtained in various positions. Biorthogonal wavelets have advantages compared to orthogonal wavelets. Fusion is performed in wavelet domain by implementing biorthogonal wavelet. Terminations and bifurcations are extracted from the original and fused images using licensed software Papillon 9.02 and manually extraction by an expert. Biorthogonal Wavelet transform is implemented in the image fusion process, yielding in the increased number of the minutiae compared to the original one. Different biorthogonal wavelets are experimented and various results are obtained. Finding the appropriate wavelet is important in the fusion process since it has a direct impact in the number of minutiae extracted. Based on the number of minutiae and MSE results, the appropriate wavelet to be used in the fusion process is defined.
      PubDate: Tue, 24 May 2022 03:20:00 +000
  • Urbanization Detection Using LiDAR-Based Remote Sensing Images of Azad
           Kashmir Using Novel 3D CNNs

    • Abstract: An important measurable indicator of urbanization and its environmental implications has been identified as the urban impervious surface. It presents a strategy based on three-dimensional convolutional neural networks (3D CNNs) for extracting urbanization from the LiDAR datasets using deep learning technology. Various 3D CNN parameters are tested to see how they affect impervious surface extraction. For urban impervious surface delineation, this study investigates the synergistic integration of multiple remote sensing datasets of Azad Kashmir, State of Pakistan, to alleviate the restrictions imposed by single sensor data. Overall accuracy was greater than 95% and overall kappa value was greater than 90% in our suggested 3D CNN approach, which shows tremendous promise for impervious surface extraction. Because it uses multiscale convolutional processes to combine spatial and spectral information and texture and feature maps, we discovered that our proposed 3D CNN approach makes better use of urbanization than the commonly utilized pixel-based support vector machine classifier. In the fast-growing big data era, image analysis presents significant obstacles, yet our proposed 3D CNNs will effectively extract more urban impervious surfaces.
      PubDate: Mon, 23 May 2022 07:05:01 +000
  • Intelligent Running Posture Detection Based on Artificial Intelligence
           Combined with Sensor

    • Abstract: In order to avoid injuries caused by incorrect running posture to a greater extent and reduce the impact on athletes’ performance and physical health, on the basis of artificial intelligence sensors, the author studies the accurate detection of intelligent running motion posture. Using artificial intelligence sensors, an adaptive error quaternion unscented Kalman filter (DAUKF) algorithm is designed. The attitude analysis and recognition system based on the inertial measurement unit can not only measure the motion information of human body but also obtain the motion characteristic data and movement state of the human body through the analysis of posture data. Use the error quaternion and gyroscope drift error to establish the equation of state, the measurement values of the accelerometer and magnetometer are used to establish the observation equation, and the fading memory method is introduced to adaptively adjust the observation noise covariance, so as to reduce the interference of the system itself and the environment on attitude detection. Experimental results show that the proposed method improves the attitude detection accuracy, effectively suppresses the influence of drift error and dynamic observation noise, and provides a foot attitude detection scheme suitable for long-distance running.
      PubDate: Mon, 23 May 2022 07:05:01 +000
  • The Time-Dependent Reliability Analysis of Brake Piston Special-Shaped
           Seal of the Caliper Disc Brake

    • Abstract: To solve the brake caliper disc brake piston sealing ring in the high temperature, pressure, and changeful, complex working environments, such as vibration failure cause brake short service life, low reliability, in the original brake piston O seal ring and cross section, the research, based on the standard of sealing ring, such as special-shaped seal structure is put forward in order to improve the reliability of the caliper disc brake piston sealing performance. Based on the basic concept of time-varying reliability and the theoretical basis of stress-strength interference model, the time-varying reliability model of the plum blossom seal ring of the brake piston under shear stress failure and leakage failure modes was established. The reliability of the plum blossom seal ring under single failure mode and multiple failure modes is obtained. The results show that under the same conditions, the reliability of the plum blossom seal ring is greater than that of the O seal ring, and its sealing performance is better than that of the O seal ring.
      PubDate: Mon, 23 May 2022 06:20:03 +000
  • A Globe Calibration Method for Optical Multisensor in 3D Complex Surface
           Measurement System

    • Abstract: There are few existing omnipotent sensors that handle a complex surface inspection task in an accurate and effective way. The prevailing solution is integrating multiple sensors and taking advantage of their strengths. One key task is the extrinsic parameter calibration (global calibration) of the multiple sensors before measurement. This paper proposes a method of optimal extrinsic calibration for a structured light sensor (SLS) and conoscopic holography sensor (CHS). In adopting this method, a common planar calibration board is placed with different poses in front of the multisensory system, and the extrinsic calibration problem is solved through a three-dimensional reconstruction of the calibration board and using geometric constraints of the views from the SLS and CHS. This calibration method, which uses only the plane calibration board, is simple. Physical experiments demonstrate that the proposed method is robust and accurate in the calibration of multiple inhomogeneous optical sensors for the measurement of a complex surface.
      PubDate: Mon, 23 May 2022 06:05:04 +000
  • Application of the VR Sensor Image Combined with Sports Games in the
           Treatment of Autistic Children

    • Abstract: In order to study the application of the VR sensor image combined with sports games in the treatment of autistic children, this study mainly takes qualitative research as the main research method and uses physical intervention based on sensory integration training to study autistic children aged 6-9. Three children, A, B, and C, are mainly selected in order to promote the improvement of children’s physical function and motor skills and further have a positive impact on behavior and psychology. By analyzing the effects measured before and after the experiment, this paper explores the impact of sports intervention based on sensory integration training on the motor ability of autistic children over the age of 6, as well as its impact on their sensory integration function, daily behavior, and psychological activities, so as to enrich the empirical research on the content, principles, implementation methods, and specific implementation process of sensory integration training, to provide reference and enlightenment for schools, institutions, and children’s parents engaged in educational intervention for autistic children.
      PubDate: Mon, 23 May 2022 05:50:02 +000
  • Model Analysis of Applying Computer Monitoring to College Students’
           Mental Health

    • Abstract: In the past 20 years, although there are many achievements in the model analysis and research, there are still problems of low data utilization and low accuracy. This paper analyzes the mental health level of college students based on chaotic algorithm. At the same time, the application of computer monitoring algorithm to students’ real life psychology is discussed. According to different types of mental health analysis models, the high-precision matching analysis of different students is realized. At the same time, according to the personality characteristics and psychological changes of different students, the model is established and analyzed. Finally, an experiment is designed to carry out practical application and data analysis of the mental health analysis model. The results show that the intelligent analysis model based on computer chaos algorithm has better classification effect. In addition, the algorithm can also make different evaluation strategies according to the different personality of students and can carry out multidimensional classification for college students of different majors. It has effectively increased the proportion of college students’ mental health groups. Compared with the current mainstream algorithms, the algorithm used in this study can adaptively classify college students of different majors. The accuracy of the experimental results is improved by at least 37% compared with the traditional method, and the error is low.
      PubDate: Sat, 21 May 2022 10:35:00 +000
  • Assessing Usability and Accessibility of Indian Tourism Websites for
           Visually Impaired

    • Abstract: The tourism industry cannot ignore the needs of people with special needs. Providing accessible tourism is essential because of social and legal obligations, but also because they have large business opportunities. These people with special needs face challenges in every social, economic, and digital environment. One of the greatest barriers they face is the lack of accessible and usable information on the Internet, which thwarts their travel plans. This research is aimed at identifying the usability and accessibility status of official state tourism websites of India. The usability evaluation was done on various web quality parameters using automated online tools. The accessibility evaluation was done to check the compliance of Web Content Accessibility Guideline version 2.0 by the tourism website using the automated tool TAW. Further manual inspection was applied to identify accessibility and language options on the webpage. The result revealed that Indian state tourism websites had low usability and accessibility status, and they need much improvement to make them accessible to people with special needs.
      PubDate: Sat, 21 May 2022 09:05:02 +000
  • A Collaborative Filtering Method for Operation Maintenance Behavior in
           Power Monitoring Systems

    • Abstract: As an important part of power infrastructure, a power monitoring system provides real-time data acquisition, state detection, and remote control of power equipment for the power grid and can deal with sudden anomalies in time. The operation and maintenance of the power monitoring system are very important to ensure the stable operation of power grid. The current mainstream remote operation and maintenance mode has internal threats such as misoperation of operation and maintenance personnel or malicious damage caused by attackers stealing operation and maintenance authority. Meanwhile, the existing operation and maintenance audit has the problems of high human resource cost and limited supervision of operation and maintenance personnel. To solve this problem, this paper proposes a collaborative filtering method for operation and maintenance behavior of power monitoring system called CFomb. Exploiting a keyword matching algorithm, CFomb determines the power resources accessed by operation and maintenance users from multiple operation instructions and extracts operation and maintenance behaviors. Referring to the collaborative filtering idea, the feature matrix decomposition scheme is introduced to train the access probability model based on the historical normal behavior of multiple operation and maintenance users, which provides a basis for real-time prediction of the access behavior probability of target operation and maintenance users. The OTSU binarization technique is used to determine the probability threshold of abnormal operation and maintenance behaviors, identify abnormal behaviors through threshold comparison, and send real-time alarms to operation and maintenance audit. The simulation experiment results show that the method in this paper can effectively identify the abnormal behavior of operation and maintenance users, reduce the overhead of manual audit, and help improve the power monitoring system’s ability to respond to internal threats of operation and maintenance.
      PubDate: Fri, 20 May 2022 11:35:01 +000
  • Effects of Variable Proportions of Concrete Fragments on Urban Soil
           Moisture Transport: An Experimental and Simulation Study

    • Abstract: In order to investigate the effects of typical anthropogenic concrete fragments on moisture infiltration and evaporation in urban soils, the effects of typical anthropogenic concrete fragments on wetting peak transport distance, cumulative infiltration, cumulative evaporation, evaporation rate, and soil profile moisture at four levels (0, 5%, 10%, and 20%) were investigated by indoor soil column experiments. The results showed that the presence of concrete fragments promoted the wetting peak transport distance and cumulative infiltration, and the promotion effect increased gradually with the increase of the ratio, but there was a threshold value, and the promotion effect was least when the ratio was 20%. When the evaporation period was 35 d, concrete fragment treatment can increase the cumulative evaporation and promote the evaporation of urban soil moisture; the promotion effect increases with the increase of the proportion, but there is a threshold value; when the proportion is 20%, the promotion effect is the smallest. The evaporation rate was consistent with the different stages of evaporation process during evaporation. The concrete fragment treatment reduced the time required for moisture to reach the same depth during infiltration; the moisture coefficient of variation of the concrete fragment treatment during evaporation showed a trend of decreasing, then increasing, and then decreasing, which increased the uncertainty of moisture in the evaporation process. The model simulation results show that the models such as the power function, Kostiakov model, and Rose model fit well, and the coefficient of determination is greater than 0.99, among which the Kostiakov model fits best. The research results can provide a theoretical scientific basis for building an efficient ecological city.
      PubDate: Fri, 20 May 2022 10:35:01 +000
  • Nonlinear Identification of PMSM Rotor Magnetic Linkages Based on an
           Improved Extended Kalman Filter

    • Abstract: The permanent magnet synchronous motor (PMSM) has complex nonlinear, strongly coupled characteristics and the variation of motor parameters makes its control more difficult. Therefore, parameter identification is of great significance for the stable operation of its closed-loop control system. In this paper, a method based on an improved extended Kalman filter (EKF) for the identification of the rotor flux () of a permanent magnet synchronous motor is investigated for this nonlinear and strongly coupled model. Simulation results show that the method has a more fast convergence rate and more accurate identification result than traditional EKF algorithm.
      PubDate: Thu, 19 May 2022 17:50:05 +000
  • A Deep Domain-Adversarial Transfer Fault Diagnosis Method for Rolling
           Bearing Based on Ensemble Empirical Mode Decomposition

    • Abstract: In recent years, the deep learning-based fault diagnosis methods for rotating mechanical equipment have attracted great concern. However, because the data feature distributions present differences in applications with varying working conditions, the deep learning models cannot provide satisfactory performance of fault prediction in such scenarios. To address this problem, this paper proposes a domain adversarial-based rolling bearing fault transfer diagnosis model EMBRNDNMD. First of all, an EEMD-based time-frequency feature graph (EEMD-TFFG) construction method is proposed, and the time-frequency information of nonlinear nonstationary vibration signal is extracted; secondly, a multi-branch ResNet (MBRN) structure is designed, which is used to extract deep features representing the bearing state from EEMD-TFFG; finally, to solve the model domain adaptation transfer problem under varying working conditions, the adversarial network module and MK-MMD distribution difference evaluation method are introduced to optimize MBRN, so as to reduce the probability distribution difference between the deep features of source domain and target domain, and to improve the accuracy of EMBRNDNMD in state diagnosis of target domain. The results of experiments carried out on two bearing fault test platforms prove that EMBRNDNMD can maintain an average accuracy above 97% in fault transfer diagnosis tasks, and this method also has high stability and strong ability of scene adaptation.
      PubDate: Wed, 18 May 2022 11:35:01 +000
  • Optimal Arrangement of Structural Sensors in Landfill Based on Stress Wave
           Detection Technology

    • Abstract: Sensor arrangement is the primary link of landfill structure health monitoring, and the number of measuring points and the quality of data directly affect the effect of modal identification. Therefore, how to ensure the service safety of landfill structure has become an important research content in the field of landfill structure health monitoring. In this paper, the stress wave propagation principle and stress wave detection theory are analyzed, and an optimal sensor arrangement method based on AGSA (adaptive gravity search algorithm) algorithm is proposed. Use CS (Cuckoo search) algorithm to optimize the objective function value. The corresponding response value is calculated by the finite element model, and the initial proxy model is constructed. By comparing and analyzing the results of model correction, it is found that the error of parameters before and after correction is less than 2.5%. It is further verified that AGSA algorithm can be used to solve the optimal sensor placement problem. In this paper, the use of structural health monitoring technology for health diagnosis and performance evaluation is an important means to ensure structural safety, prolong service life, and reduce maintenance cost. As the primary link of structural health monitoring, sensor system directly determines the accuracy of structural safety diagnosis.
      PubDate: Wed, 18 May 2022 08:35:03 +000
  • Sensor and Attitude Analysis of Track and Field Training Action
           Recognition Based on Artificial Intelligence

    • Abstract: In order to provide effective information support to athletics training and to increase the effectiveness of athletics training, in-depth analysis of motion recognition sensors and attitudes based on artificial intelligence was conducted. First of all, the basic conditions of type analysis, cognitive technology, and the current situation were studied, and the basic theory related to it was studied, and on this basis, a human position analysis and recognition system based on artificial intelligence movement training sensors was developed. We studied the technology in depth. Experiments have shown that the approach data collected by the system’s inertia node is transmitted wirelessly to the computer-side software to restore the trend and identify each trend and parameter with high accuracy. During the 30-minute test, the static error was within 1° and the dynamic error was within 5°, which is acceptable and adheres well to dynamic conditions. The system can overcome the limitations of traditional wired or optical methods and be widely used in sports training, human-computer interaction monitoring, rehabilitation medicine, games, film, and television production.
      PubDate: Tue, 17 May 2022 11:35:03 +000
  • Shape Reconstruction of Eccentric Defect in Cylindrical Component by
           Modified Born Approximation Method

    • Abstract: In this paper, a cylindrical aluminum sample with an eccentric circular hole was prepared, and ultrasonic measurements were carried out by experimental means. The measurement area was limited to a plane edge perpendicular to the axis of the cylindrical component. The measured waveform data were input into the formula of approximate correction method, and the section image was obtained by using a modified Born approximation (MBA) method. Then, the three-dimensional (3D) shape of the defects in the aluminum sample was reconstructed by superimposing the cross-sectional images. Results showed that the defect reconstruction effect of the two-dimensional section and the 3D defect reconstruction effect were significantly improved by the MBA method.
      PubDate: Tue, 17 May 2022 10:20:04 +000
  • Crop Yield Maximization Using an IoT-Based Smart Decision

    • Abstract: Today, farmers are suffering from the low yield of crops. Though right crop selection is the main boosting key to maximize crop yield by doing soil analysis and considering metrological factors, the lack of knowledge about soil fertility and crop selection is the main reason for low crop production. In the changed current climate, the farmers having primitive knowledge about conventional farming are facing challenges about making sagacious decisions on crop selection. The selection of the same crop in every seasonal cycle makes the low soil fertility. This study is aimed at making an efficient and accurate system using IoT devices and machine learning (ML) algorithms that can correctly select a crop for maximal yield. Such a system is reliable as compared to the old laboratory testing manual systems, which bear the chances of human errors. Correct selection of a crop is predominantly a priority in agricultural arena. As a contribution, we propose an ML-based model, Smart Crop Selection (SCS), which is based on data of metrological and soil factors. These factors include nitrogen, phosphorus, potassium, CO2, pH, EC, temperature, humidity of soil, and rainfall. Existing IoT-based systems are not efficient as compared to our proposed model due to limited consideration of these factors. In the proposed model, real-time sensory data is sent to Firebase cloud for analysis. Its results are also visualized on the Android app. SCS ensembles the following five ML algorithms to increase performance and accuracy: Decision tree, SVM, KNN, Random Forest, and Gaussian Naïve Bayes. For rainfall prediction, a dataset containing historical data of the last fifteen years is acquired from Bahawalpur Agricultural Department. This dataset and an ML algorithm, Multiple Linear Regression leverages prediction of the rainfall in future, a much-desired information for the health of any crop. The Root Mean Square Error of the rain fall prediction model is 0.3%, which is quite promising. The SCS model is trained for 11 crops’ prediction, while its accuracy is 97% to 98%.
      PubDate: Tue, 17 May 2022 10:20:04 +000
  • Research on Learner Modeling and Curriculum Recommendation Based on
           Emotional Factors

    • Abstract: With the increasing with the number of courses, learners cannot find the courses they need quickly. Therefore, the primary problem to change the efficiency of online courses is to recommend corresponding courses for a certain group of people according to their needs. Learner characteristics are an important aspect of reflecting learner preferences, and learner models are abstract representations and descriptions of learner characteristics. It is necessary to enhance the use of online courses among students; we must build a relatively comprehensive curriculum model. At present, the construction of learner model is mostly based on cognitive level and learning style, ignoring the emotion expressed by learners to the curriculum, and emotion is a very important characteristic of learners. In order to establish a perfect learner model, it is necessary to incorporate learners’ aspect emotion into the learner model to make the course recommendation process more accurate. Firstly, based on the attention mechanism long-term and short-term memory network, this paper extracts the learner’s aspect emotion to the curriculum from the learner’s curriculum review. At the same time, it studies various characteristics, such as demography, cognitive level, motor behavior, and learning style. By establishing a perfect model integrating researchers’ emotional state, finally, the complex interaction between learner characteristics and curriculum characteristics is modeled by using deep factor natural decomposition, so as to achieve accurate curriculum recommendation. In this study, the learner’s aspect emotion is included in the construction of learner model and enriched and perfected the learner model. It provides a reference for the theoretical research and applied research of learner model and has reference significance. At the same time, combining Deep learning can improve the accuracy of course recommendation, help learners’ learning efficiency and personalized learning quality, and also contribute to the long-term development of online platform. The mathematical modeling in this paper uses learning analysis technology and general factor model based on matrix factorization to calculate and uses factorization machine to reduce the dimension of high-dimensional data, which is efficient and accurate.
      PubDate: Tue, 17 May 2022 10:20:03 +000
  • Financial Risk Assessment of Enterprise Management Accounting Based on
           Association Rule Algorithm under the Background of Big Data

    • Abstract: Under the background of the rapid development of data informatization, the evaluation and analysis of enterprise management accounting risk are the key link of enterprise sustainable development. Based on the theoretical basis of enterprise management accounting risk analysis, this paper selects 11 representative corporate financial risk indicators from financial risk indicators through correlation analysis and then selects 8 key indicators that affect corporate financial risk through association rules, to assess and analyze the company’s financial risks. Finally, this paper takes 20 ST companies in China as the research object to carry out empirical research. According to the assessment and analysis of financial risks in the company’s management accounting, it proposes measures from four modules to strengthen the company’s ability to resist risks.
      PubDate: Tue, 17 May 2022 10:20:03 +000
  • Research and Implementation of Sports Entity Simulation Based on
           Heterogeneous Binocular Vision

    • Abstract: To study the value of heterogeneous binocular vision in the detection of sports targets, a Zynq-based joint software and hardware design method is proposed, and a mobile object detection system for binocular vision is developed based on it. This paper introduces the relevant technologies and theories used in the system design. First, the basic principles and various modules of the binocular stereo vision process are introduced, including camera tuning, stereo correction, stereo tuning, and telescope stereo vision. Based on the depth information, a method to detect and measure motor targets was developed. Finally, combined with the improved mobile target detection algorithm, the real-time methodology research and algorithm design to determine the target range are completed. The performance of the moving target detection system based on binocular stereo vision and the moving target detection and ranging algorithm are tested and analyzed. We know that the front and rear width of the moving target (the person tested in this paper) is at least 10 cm, while the error value of the ranging algorithm in this paper is within 6 meters, and the average error is less than 10 cm. According to the characteristics of binocular stereo vision, the farther the viewing angle is, the smaller the parallax value of the left and right image pixels will be. Therefore, the insignificant change of depth information will bring some errors to the ranging algorithm. The greater the measurement error is, the lower the percentage deviation of the measurement result is within 2%. It also shows that the target ranging algorithm in this paper can ensure good accuracy within a certain distance. Through this method, we can obtain the accurate data of moving objects in sports competition, so as to improve the training method of athletes and improve the competition results.
      PubDate: Mon, 16 May 2022 11:20:02 +000
  • Multi-Inertial Sensor-Based Arm 3D Motion Tracking Using Elman Neural

    • Abstract: Recent years have witnessed the rapid development of microelectromechanical systems, and human motion tracking technology based on IMU (inertial measurement unit) has attracted much attention. However, the magnetic field varies with time and position, which makes it necessary to calibrate sensors before tracking. To address the poor adaptability of IMU to the environments and improve the accuracy of estimated traces, this paper presents an ENN-based (Elman neural network) method to track human arm motions, which consists of two steps. First, the data derived from IMUs are preprocessed for the rough Euler angles; then, an ENN is trained to estimate motions. We explore the initially estimated position to calibrate the acceleration measurements as the input of the ENN. Real-world experiments of arm motion tracking are carried out with the ground truth from an optical motion tracking system. The experimental results show that the mean tracking errors are around 35 mm, with a strong ability to eliminate the effect of extreme measurement and environment noises, avoiding calibrating the magnetometer. The implementation of the well-trained model to independent motions indicates that the robustness of the proposed method is excellent, and the errors reduce by 37.2% on the -axis and perform similarly on the -axis compared with 4 traditional methods. This method quite suits those situations where trajectory tracking of the standardized motions is required, such as the medical habilitation.
      PubDate: Mon, 16 May 2022 04:50:01 +000
  • Prediction and Optimization of Stability Parameters of Borehole Sensor for
           Deep Water Drilling Based on Genetic Algorithm

    • Abstract: In order to study the prediction and optimization of borehole stability parameters in deepwater drilling based on genetic algorithm. First, a genetic hybrid algorithm based on pattern search is proposed. Then, based on the adaptive genetic algorithm, the evolutionary population is searched for patterns, which makes the hybrid algorithm not only has a strong global search ability but also improves the local optimization accuracy. Finally, the unit footage cost in the drilling process is taken as the objective function, and the algorithm is verified by taking the drilling in Karamay area as an example. The calculation results show that if the bit wear reaches 0.8-0.9 and then the bit is pulled out, the utilization rate of the bit can be increased, the design efficiency and accuracy can be improved, and the drilling cost can be reduced. The wear amount of the optimized bit is higher than that of the actual bit. Increasing the utilization rate of the bit can reduce the cost of drilling meters to a certain extent and improve the economic benefits of drilling. The objective function and constraint conditions for the optimization of drilling parameters are determined, and the algorithm is verified with the drilling data of Karamay Oilfield. The results show that the algorithm improves the stability and speed of iterative convergence and improves the reliability of data analysis results. Based on the regional three-dimensional formation rock parameter data volume, the optimization method can be used to optimize the drilling parameters before drilling and provide a basis for formulating the drilling design scheme.
      PubDate: Sat, 14 May 2022 16:50:05 +000
  • LETR: An End-to-End Detector of Reconstruction Area in Blades Adaptive
           Machining with Transformer

    • Abstract: In the leading/trailing edge’s adaptive machining of the near-net-shaped blade, a small portion of the theoretical part, called the reconstruction area, is retained for securing aerodynamic performance by manual work. The next work is to recognize the reconstruction area of the reconstructed leading/trailing edge’s image. To accelerate this process, an anchor-free neural network model based on Transformer was proposed, named Leading/trailing Edge Transformer (LETR). LETR extracts image features from an aspect of mixed frequency and channel domain. We also integrated LETR with the newest meta-Acon activation function. We tested our model on the self-made dataset LDEG2021 on a single GPU and got an mAP of 91.9%, which surpassed our baseline model, Deformable DETR, by 1.1%. Furthermore, we modified LETR’s convolution layer and named the new model after Ghost Leading/trailing Edge Transformer (GLETR) as a lightweight model for real-time detection. It is proved that GLETR has fewer weight parameters and converges faster than LETR with an acceptable decrease in mAP (0.1%) by test results. The proposed models provide the basis for subsequent parameter extraction work in the reconstruction area.
      PubDate: Sat, 14 May 2022 08:20:01 +000
  • Research on Finite Element Structure of Vehicle Suspension Control Arm
           Based on Neural Network Sensing Control

    • Abstract: To improve the adaptive control of the neural network under the influence of vehicle suspension control, the neural network control method is proposed. The specific content of the method analyzes the nonlinear properties of vehicle suspensions, proposes neural network-based adaptive control strategies, and develops neural network-based nonlinear algorithms and neural identifiers. Genetic algorithms perform predictive control of rear suspension through a compensation network. The experimental results show that the model structure is order , the AN1 network node is 4-6-1, the AN2 network node is 5-4-1, the AN3 network node is 6-4-1, and the learning correction rate is . In the actual simulation calculation, the number of nodes in the hidden layer of the network is increased, and the minimum number of nodes is chosen to determine the structure of the network, since the control effect obtained is not fundamentally changed. The suspension, which is controlled by the neural network’s adaptive control, has a vibration-reducing effect and is more effective by increasing the control of the rear suspension. The neural network has been shown to be able to effectively control the vehicle’s control arm.
      PubDate: Sat, 14 May 2022 08:20:01 +000
  • Efficiency Analysis of Sports Equipment Batch Management Based on
           Antimetal RFID Tag

    • Abstract: Based on the antimetal RFID tag model, a sports equipment batch management system is established. The establishment process of the model and system is discussed in detail, and the principle of metal-resistant RFID tag is revealed. Then, the antimetal RFID tag sports equipment batch management system is applied to colleges, middle schools, and primary schools, and the use of teachers and students on the efficiency of sports equipment batch management is studied, highlighting the advantages of the management system. In general, the antimetal RFID tag sports equipment batch management system has timeliness, universality, and reliability. It can adapt to the sports equipment management of different schools, improve its management efficiency, and play a positive role in the development of modern school sports.
      PubDate: Fri, 13 May 2022 11:35:01 +000
  • Polarization Characteristics of Electromagnetic Wave Sensing in Confined
           Space Based on Hybrid Algorithm

    • Abstract: In order to make better use of electromagnetic waves in communication, navigation, and radar, this invention offers a feature of software to study the polarization of electromagnetic waves in a limited space based on a hybrid algorithm. Using the double Gaussian model, the polarization software model was developed according to vector analysis, and the characteristics of the software at different angles of electromagnetic polarization under the plasma sheath, different peak electron densities, and different collision frequencies were studied in -band. The results show that at peak electron densities of 1017/m3 and collision frequencies of 0.01 GHz, 0.1 GHz, and 1 GHz, the reverse polarity angles are 53°, 76°, and 77°, respectively, and the higher the electron peak densities, the higher the ratio, and the more severe the degradation of the properties: at an angle of 60° to 1017/m3, 1018/m3, and 1019/m3, the axial ratio of the electron peak density is -1.1.dB, -6.2 dB and -10.1 dB, respectively; the greater the frequency of the electromagnetic wave than the frequency of the collision, the more severe the axial degradation of the electromagnetic wave as the frequency of the electromagnetic wave approaches its frequency corresponding to the maximum electron density. Relationship characteristics were as follows: 2.8 GHz frequency equal to 1017/m3 is the closest to 2.3 GHz electromagnetic frequency, and the axial ratio characteristics deteriorate and are more severe than other conditions; the heterologous medium designed in this paper has simple structure, thin thickness, and easy process preparation, which will have potential applications in radar stealth.
      PubDate: Fri, 13 May 2022 11:20:01 +000
  • Superresolution Reconstruction Method of Software Remote Sensing Image
           Based on Convolutional Neural Network

    • Abstract: In order to solve the problem of long training time for remote sensing image super-resolution reconstruction algorithm, a method for remote sensing image superresolution reconstruction based on convolutional neural network is proposed, which combines dense convolutional network, parallel CNN structure, and subpixel convolution. The features of low-resolution images are extracted using dense convolutional networks, parallel CNNs are used to reduce network parameters, and subpixel convolutions are used to complete feature reconstruction. The results show that the final PSNR value of the black curve with the number of iterations of the three methods in the training process is the highest 27.3, followed by the middle curve, and the worst curve is 27.0. It is proved that the method extracts more features, retains more image details, and improves the reconstruction effect of the image; it greatly reduces the parameters in the network and avoids the phenomenon of overfitting in the deep network.
      PubDate: Fri, 13 May 2022 09:35:01 +000
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