Analysis of possibilities to use neural network for remote control of electronic devices

Authors

DOI:

https://doi.org/10.15587/2312-8372.2018.149539

Keywords:

remote control, neural network, hardware interfaces to communicate, wireless communications

Abstract

The object of research in the work is the systems of remote control of electronic devices. There are wired and wireless means of implementing a remote communication channel between the slave and control devices. Analysis of existing means of creating a communication channel, found a low value of the ratio of system flexibility and data transfer rate within the created network. One of the reasons for the low ratio is the use of modules as part of a system with a high minimum operating time. Such modules are modules for filtering and decoding the received signal at the receiver side, encoding and modulation at the transmitter side. Replacing these modules with one with a significantly lower time spent will significantly improve the value of the ratio of system flexibility and data transfer rate. The ability to create a module that will have the necessary properties of time spent on work, provides a neural network.

The model of a remote control system obtained during the study has several advantages, in particular, the presence of a neural network, makes it possible to reduce the time spent and to improve the accuracy of the system during the entire system operation time. This is achieved thanks to the ability of the neural network to self-learning without human intervention and its ability to analyze any input signals with different background noise values. These properties allow the replacement of elements that do not allow to increase the rate of exchange for elements of the neural network that will perform the same functions with greater speed, reliability and accuracy.

The data obtained during the work proves the expediency of integrating the elements of the neural network into the remote control systems of electronic devices. Also, possible places for the integration of a neural network into the remote control system of electronic equipment have been proposed, which will improve the stability, accuracy, speed of the system.

Author Biographies

Maksym Holikov, Kharkiv National University of Radio Electronics, 14, Nauky ave., Kharkiv, Ukraine, 61166

Department of Design and Operation of Electronic Devices

Pavlo Galkin, Kharkiv National University of Radio Electronics, 14, Nauky ave., Kharkiv, Ukraine, 61166

Senior Lecturer

Department of Design and Operation of Electronic Devices

References

  1. Yashchuk, A. (2013). Systemy bezprovidnykh tekhnolohii peredachi danykh. Available at: http://lutsk-ntu.com.ua/sites/default/files/4_yashchuk_sistemi_bezprov_tehnologiy_0.pdf
  2. Halkin, P. V., Holikov, M. O. (2018). Bezkontaktnii metod kontroliu obiektiv. Radyoelektronyka y molodezh v 21 veke. Kharkiv, 56–57.
  3. Tengfei, Z., Qinxiao, L., Fumin, M. (2013). Remote control system of smart appliances based on wireless sensor network. 2013 25th Chinese Control and Decision Conference (CCDC), 3704–3709. doi: http://doi.org/10.1109/ccdc.2013.6561592
  4. Yun, L., Wenxin, L., Jianguo, S., Qidi, W. (2018). Improving wireless devices identification using gray relationship classifier to enhance wireless network security. IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 421–425. doi: http://doi.org/10.1109/infcomw.2018.8406960
  5. Salnyk, S. V., Salnyk, V. V., Symonenko, O. A., Sova, O. Ya. (2015). Metod vyiavlennia vtorhnen v mobilni radiomerezhi na osnovi neironnykh merezh. Nauka i tekhnika Povitrianykh Syl Zbroinykh Syl Ukrainy, 4, 82–90.
  6. Shubham, S., Achyut, H. (2016). WiFi-aware as a connectivity solution for IoT pairing IoT with WiFi aware technology: Enabling new proximity-based services. International Conference on Internet of Things and Applications (IOTA), 2, 137–142. doi: http://doi.org/10.1109/iota.2016.7562710
  7. Mi, L., Bowen, L., Xuping, Z., Yuejiang, S., Yingjie, Z., Guojie, T. (2015). Investigation on the performance of 10 Gb/s on uplink space optical communication system based on MSK scheme. 4th International Conference on Optical Communications and Networks (ICOCN), 1–3. doi: http://doi.org/10.1109/icocn.2015.7203690
  8. Liu, Y. (2016). Wireless Information and Power Transfer for Multirelay-Assisted Cooperative Communication. IEEE Communications Letters, 20 (4), 784–787. doi: http://doi.org/10.1109/lcomm.2016.2535114
  9. Lyu, B., Yang, Z., Gui, G., Feng, Y. (2017). Wireless Powered Communication Networks Assisted by Backscatter Communication. IEEE Access, 5, 7254–7262. doi: http://doi.org/10.1109/access.2017.2677521
  10. Kartashov, V. M., Oleynikov, V. N., Tikhonov, V. A. et. al. (2014). Obrabotka signalov v radioelektronnykh sistemakh distantsionnogo monitoringa atmosfery. Kharkiv: SMIT, 213.
  11. Yurevich, E. (2016). Teoriya avtomaticheskogo upravleniya. Saint Petersburg: BKHV-Peterburg, 560.
  12. Anantaram, V., Paolo, S., Erasmo, V., Silvio, A., Gianmario, P., Vittorio, C. (2018). Remotized Control of Power Electronic Devices Exploiting a Plastic Optical Fiber Photonic Bus. 20th International Conference on Transparent Optical Networks (ICTON), 1–4. doi: http://doi.org/10.1109/icton.2018.8473992
  13. Pavan, K. Y. V., Ravikumar, B., Bogala, R., Anjaneyulu, A. (2014). Switch automation of smart devices between test beds using distributed control system. International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 1330–1333. doi: http://doi.org/10.1109/iccicct.2014.6993168
  14. Sabri, G., Cihan, K. (2017). Remote controllable electronic signboard. International Conference on Computer Science and Engineering (UBMK), 78–83. doi: http://doi.org/10.1109/ubmk.2017.8093561
  15. Jianlong, Z., Chunyu, W., Wenjing, Z., Jiwei, W. (2011). The design and realization of a comprehensive SPI interface controller. Second International Conference on Mechanic Automation and Control Engineering, 4529–4532. doi: http://doi.org/10.1109/mace.2011.5988014
  16. Ali, I., Cho, S. H., Kim, D. G., Rehman, M. R. U., Lee, K.-Y. (2017). A design of ultra-low power I2C synchronous slave controller with interface voltage level independency in 180 nm CMOS technology. International SoC Design Conference (ISOCC), 262–263. doi: http://doi.org/10.1109/isocc.2017.8368885
  17. Chandwani, N., Jain, A., Vyavahare, P. D. (2015). Throughput comparison for Cognitive Radio network under various conditions of primary user and channel noise signals. Radio and Antenna Days of the Indian Ocean (RADIO), 1–2. doi: http://doi.org/10.1109/radio.2015.7323379
  18. Pinku, R., Anand, S., Ravi, K. (2018). Experimental investigation on probe feed equilateral triangular dielectric resonator antenna for 5.8 GHz ISM band (IEEE 802.11). Progress In Electromagnetics Research Symposium – Spring (PIERS), 2195–2199. doi: http://doi.org/10.1109/piers.2017.8262115
  19. Suh, D., Ko, H., Pack, S. (2016). Efficiency Analysis of WiFi Offloading Techniques. IEEE Transactions on Vehicular Technology, 65 (5), 3813–3817. doi: http://doi.org/10.1109/tvt.2015.2437325
  20. Khan, W. M., Zualkernan, I. A. (2018). SensePods: A ZigBee-Based Tangible Smart Home Interface. IEEE Transactions on Consumer Electronics, 64 (2), 145–152. doi: http://doi.org/10.1109/tce.2018.2844729
  21. Galkin, P. V. (2014). Analysis of energy consumption nodes wireless sensor networks. ScienceRise, 2 (2), 55–61. doi: http://doi.org/10.15587/2313-8416.2014.27246
  22. Galkin, P. V. (2014). An algorithm for operating and optimizing information flows in wireless sensor networks. Eastern-European Journal of Enterprise Technologies, 6 (3 (72)), 53–63. doi: http://doi.org/10.15587/1729-4061.2014.30419
  23. Haykin, S. (1998). Neural Networks: A Comprehensive Foundation. Prentice Hall, 842.
  24. Yonghua, Y., Lan, W., Erol, G. (2017). Multi-layer neural networks for quality of service oriented server-state classification in cloud servers. 2017 International Joint Conference on Neural Networks (IJCNN), 1, 1623–1627. doi: http://doi.org/10.1109/ijcnn.2017.7966045
  25. Yasuaki, K., Hitoshi, I. (2016). A model of Hopfield-type octonion neural networks and existing conditions of energy functions. 2016 International Joint Conference on Neural Networks (IJCNN), 2, 4426–4430. doi: http://doi.org/10.1109/ijcnn.2016.7727778

Downloads

Published

2018-05-31

How to Cite

Holikov, M., & Galkin, P. (2018). Analysis of possibilities to use neural network for remote control of electronic devices. Technology Audit and Production Reserves, 6(2(44), 42–49. https://doi.org/10.15587/2312-8372.2018.149539

Issue

Vol. 6 No. 2(44) (2018): Information and control systems

Section

Systems and Control Processes: Original Research

License

Copyright (c) 2018 Maksym Holikov, Pavlo Galkin

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.