International Journal of Educational Innovation and Science, 2020, 1(3); doi: 10.38007/IJEIS.2020.010302.
Developed in Harbin Preschool Teachers College Experimental Kindergarten, Harbin, China
Major in Preschool Education, Normal University, Heilongjiang, China
Preschool education is the foundation of education, and it is the key for students to quickly enter the learning state before school. How to carry out preschool education for preschool children at a high speed and effectively is the issue that this article will discuss. The main purpose of this article is to accelerate the development of preschool education based on the network communication of embedded microprocessor. This paper proposes a multi-core computing method for embedded microprocessors. By combining the multi-core computing of embedded processors and the basis of the EPA protocol, the template of the embedded microprocessor network communication model can be constructed. Combined with OPC technology, the network communication capabilities of embedded microprocessors can be greatly improved. Experiments have proved that the network communication efficiency of the new embedded microprocessor can reach an increase of 5%-14%, and it is analyzed by collecting information on the use of equipment and receiving new education methods by students in preschool education. A new kind of preschool education method designed in this paper can improve students' thinking ability by 32%, improve students' hands-on ability by 34%, and improve students' EQ by up to 47%. This shows that the new type of pre-school education based on embedded microprocessor network communication in this article is very effective in helping students’ pre-school education.
Network Communication Technology, Embedded Structure, Embedded Microprocessor, Preschool Education
Huiyu Jiang. Network Communication Based on Embedded Microprocessor Accelerates the Development of Preschool Education. International Journal of Educational Innovation and Science (2020), Vol. 1, Issue 3: 8-26. https://doi.org/10.38007/IJEIS.2020.010302.
 Filipe, M, Lins, et al. Register File Criticality and Compiler Optimization Effects on Embedded Microprocessor Reliability. IEEE Transactions on Nuclear Science, 2017, 64(8):2179-2187.
 Clark L T, Patterson D W, Ramamurthy C, et al. An Embedded Microprocessor Radiation Hardened by Microarchitecture and Circuits. IEEE Transactions on Computers, 2016, 65(2):382-395.
 Halfhill T R. Embedded Processors Embrace AI. Microprocessor report, 2018, 32(12):29-35.
 Bello O, Zea Da Lly S, Badra M. Network layer inter-operation of Device-to-Device communication technologies in Internet of Things (IoT). Ad Hoc Networks, 2016, 57(MAR.):52-62.
 Bian, Desong, Kuzlu, et al. Performance evaluation of communication technologies and network structure for smart grid applications. IET Communications, 2019, 13(8):1025-1033.
 Lehrl S, Kluczniok K, Rossbach H G. Longer-term associations of preschool education: The predictive role of preschool quality for the development of mathematical skills through elementary school. Early Childhood Research Quarterly, 2016, 36(Complete):475-488.
 Oguz K, Isil T, Durmus A. Metaphor Perceptions of Pre-Service Teachers towards Mathematics and Mathematics Education in Preschool Education.. Educational Research & Reviews, 2016, 11(14):1338-1343.
 Ogbodo E U, Dorrell D, Abu-Mahfouz A M. Cognitive Radio based Sensor Network in Smart Grid: Architectures, Applications and Communication Technologies. IEEE Access, 2017, 5(9):19084-19098.
 Hida I, Takamaeda-Yamazaki S, Ikebe M, et al. A High Performance and Energy Efficient Microprocessor with a Novel Restricted Dynamically Reconfigurable Accelerator. Circuits & Systems, 2017, 08(5):134-147.
 Berdahl E, Blessing M. Physical modeling sound synthesis using embedded computers: More masses for the masses. The Journal of the Acoustical Society of America, 2016, 139(4):2204-2204.
 Sato R, Hatanaka Y, Ando Y, et al. High-Speed Operation of Random-Access-Memory-Embedded Microprocessor With Minimal Instruction Set Architecture Based on Rapid Single-Flux-Quantum Logic. IEEE Transactions on Applied Superconductivity, 2017, 27(4):1-5.
 Boussadi M A, Tixier T, Landrault A, et al. HNCP: A many-core microprocessor ASIC approach dedicated to embedded image processing applications. Microprocessors & Microsystems, 2016, 47(NOV.):333-346.
 Tom, R, Halfhill. Epyc Embedded 3000 Family Adds Ethernet, South Bridge. Microprocessor report, 2018, 32(2):19-24.
 David, Kanter. Everspin MRAM Targets Enterprise New Nonvolatile Memory Attracts Storage and Embedded Customers. Microprocessor report, 2016, 30(12):15-18.
 Chen J. Composition Rule Perception Algorithm of National Art Plane System Based on Wireless Sensor Network Communication Technology. International Journal of Wireless Information Networks, 2021, 12(2):1-9.
 Zeng J, Sun J, Wu B, et al. Mobile edge communications, computing, and caching (MEC3) technology in the maritime communication network. China Communications, 2020, 17(5):223-234.
 Farnsworth C, Clark L T, Gogulamudi A R, et al. A Soft-Error Mitigated Microprocessor with Software Controlled Error Reporting and Recovery. IEEE Transactions on Nuclear Science, 2016, 63(4):2241-2249.
 Tom, R, Halfhill. MT3620 Runs Azure Sphere for IoT: MediaTek Adopts Microsoft's Security Engine and Embedded OS. Microprocessor report, 2018, 32(5):22-27.
 Halfhill T R. More Embedded Mergers in 2016 Consolidation Creates New Giants, but Some Products Suffer. Microprocessor Report, 2016, 30(12):9-14.
 TR Halfhill. Splashdown for Intel's Apollo Lake. Microprocessor Report, 2016, 30(11):23-27.
 Bian D, Kuzlu M, Pipattanasomporn M, et al. Performance Evaluation of Communication Technologies and Network Structure for Smart Grid Applications. IET Communications, 2019, 13(8):1025-1033.
 Amiripalli S S, V Bobba. An Optimal TGO Topology Method for a Scalable and Survivable Network in IOT Communication Technology. Wireless Personal Communications, 2019, 107(2):1019-1040.
 Benites L, Benevenuti F, Oliveira A, et al. Reliability Calculation With Respect to Functional Failures Induced by Radiation in TMR Arm Cortex-M0 Soft-Core Embedded Into SRAM-Based FPGA. IEEE Transactions on Nuclear Science, 2019, 66(7):1433-1440.
 Peng W, Zhang Y, Dan J, et al. ARM-based Embedded Detection System of Cardiovascular Function Parameters. Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation, 2017, 41(2):79-83.
 Konopko K, Janczak D. Nonlinear signal processing with minimization of spectral distortion for embedded systems - ScienceDirect. IFAC-PapersOnLine, 2018, 51(6):450-455.