A Review of a Single Neuron Weight Optimization Model for Adaptive Beam Forming

Authors

  • K.S. Senthilkumar Department of Computers and Technology, St. George’s University, Grenada, WI.
  • Lorothy Singkang Department of Mathematics, Science and Computer, Polytechnic Kuching, Sarawak, Malaysia.
  • P.R.P Hoole Department of Electrical and Electronic Engineering, Universiti Malaysia Sarawak, Sarawak, Malaysia.
  • Norhuzaimin Julai Department of Electrical and Electronic Engineering, Universiti Malaysia Sarawak, Sarawak, Malaysia.
  • S. Ang Department of Electrical and Electronic Engineering, Universiti Malaysia Sarawak, Sarawak, Malaysia.
  • Shafrida Sahrani Department of Electrical and Electronic Engineering, Universiti Malaysia Sarawak, Sarawak, Malaysia.
  • Kismet Anak Hong Ping Department of Electrical and Electronic Engineering, Universiti Malaysia Sarawak, Sarawak, Malaysia.
  • K. Pirapaharan College of Engineering, Institute of Engineers Sri Lanka, Sri Lanka.
  • S.R.H. Hoole Department of Electrical and Computer Engineering, Michigan State University, Michigan, USA.

Keywords:

Beam Forming, Neural Network, Single Layer Perceptron, Smart Antenna,

Abstract

In this paper, we review our recent, reported work on using artificial intelligence based software technique to control electronic sensor or wireless communication equipment in narrow and diverging paths such as in underground tunnels and at traffic junctions. In order to make the systems fast as well as needing minimal computational calculations and memory – thus to extend the battery life and minimize cost – we used the single layer Perceptron to successfully accomplish the formation of beams which may be changed according to the nature of the junctions and diverging paths the mobile or stationary system is to handle. Moreover, the beams that survey the scenario around (e.g. in case of guiding a driverless vehicle) or communicating along tunnels (e.g. underground mines) need to be kept narrow and focused to avoid reflections from buildings or rough surfaced walls which will tend to significantly degrade the reliability and accuracy of the sensor or communicator. These requirements were successfully achieved by the artificial intelligence system we developed and tested on software, awaiting prototype development in the near future.

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Published

2017-12-07

How to Cite

Senthilkumar, K., Singkang, L., Hoole, P., Julai, N., Ang, S., Sahrani, S., Anak Hong Ping, K., Pirapaharan, K., & Hoole, S. (2017). A Review of a Single Neuron Weight Optimization Model for Adaptive Beam Forming. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(3-10), 35–40. Retrieved from https://jtec.utem.edu.my/jtec/article/view/3151

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