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This paper proposes enhanced storage management in Augmented Reality (AR) and Virtual Reality (VR) Systems using Residue Number Systems (RNS) to improve teaching and learning in computing and engineering education. In recent times, computing and engineering have evolved tremendously due to technology-driven applications that support experiential learning. Therefore, this paper developed and utilized Python programming to simulate and enhance memory management solutions for AR and VR systems when integrated with Discrete Cosine Transform (DCT)–Residue Number Systems (RNS) based on a moduli set. The findings from the study reveal that the RNS has made the images that were kept in the AR and VR systems’ memory smaller and the original image size was decreased by 95.28%, from 1,628,571 Kb to 76,834 Kb. An analysis of the data showed that each technique had a significant variance after the computation comparison with existing algorithms like PCA-DWT-CHC (0.0695), Relative Huffman Coding (5.79), and Relative DCT Residual States (0.0182). Consequently, the proposed method for integrating DCT-RNS into AR and VR systems enhances storage utilization, improves overall system performance, and provides more dynamic and interactive memory capacity optimization opportunities.

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Enhanced Storage Management in Augmented Reality and Virtual Reality Systems in Engineering and Computing Education Using Discrete Cosine Transform (DCT)–Residue Number Systems (RNS)

  • 会议论文

  • 发表日期：

    2025年

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  • Emmanuel Freeman,
  • Ernest Mnkandla,
  • Lateef Adesola Akinyemi,
  • Donatien Koulla Moulla,
  • David Sena Attipoe

摘要

This paper proposes enhanced storage management in Augmented Reality (AR) and Virtual Reality (VR) Systems using Residue Number Systems (RNS) to improve teaching and learning in computing and engineering education. In recent times, computing and engineering have evolved tremendously due to technology-driven applications that support experiential learning. Therefore, this paper developed and utilized Python programming to simulate and enhance memory management solutions for AR and VR systems when integrated with Discrete Cosine Transform (DCT)–Residue Number Systems (RNS) based on a moduli set. The findings from the study reveal that the RNS has made the images that were kept in the AR and VR systems’ memory smaller and the original image size was decreased by 95.28%, from 1,628,571 Kb to 76,834 Kb. An analysis of the data showed that each technique had a significant variance after the computation comparison with existing algorithms like PCA-DWT-CHC (0.0695), Relative Huffman Coding (5.79), and Relative DCT Residual States (0.0182). Consequently, the proposed method for integrating DCT-RNS into AR and VR systems enhances storage utilization, improves overall system performance, and provides more dynamic and interactive memory capacity optimization opportunities.