Utilizing Artificial Intelligence to Collect Pavement Surface Condition Data

  • H. Joni H
  • A. Alwan I
  • A. Naji G
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Abstract

Recently the Discrete-Wavelet-Transform (DWT) has been represented as signal processing powerful tool to separate the signal into its band frequency components. In this paper, improvement of the steganography techniques by hiding the required message into the suitable frequency band is presented. The results show that the increase of the message length will reduce the Peak Signal to Noise Ratio (PSNR), while the PSNR increases with the increasing the DWT levels. It should be noted that the PSNR reduction was from -13.8278 to -17.77208 when increasing the message length from 161 to 505 characters. In this context, the PSNR is increased from -13.8278 to 7.0554 and from -17.7208 to 1.7901 when the DWT increased from level (1) to level (2).

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H. Joni, H., A. Alwan, I., & A. Naji, G. (2020). Utilizing Artificial Intelligence to Collect Pavement Surface Condition Data. Engineering and Technology Journal, 38(1), 74–82. https://doi.org/10.30684/etj.v38i1a.251

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