S. Leonard Gibson Moses ¹, S. Ganesan ², C. Vimala ³, I. Rabiya Begam ⁴, M. Nivetha Priya ⁴

Affiliations
1 Department of ECE, Periyar Maniammai University, Thanjavur - 613403, Tamil Nadu, IN
2 Department of ECE, Mailam Engineering College, Mailam – 604304, Tamil Nadu, IN
3 Department of Mathematics, Periyar Maniammai University, Thanjavur – 613403, Tamil Nadu, IN
4 Department of ECE, Jei Mathaajee College of Engineering, Siruvakkam - 631552, Tamil Nadu, IN

Abstract

Objectives: Vedic mathematics contains 16 formulae or Sutras. Employing these shortcut formulae in the computations of Elgamal cryptographic algorithm will reduce the logic elements, layout area and power consumption. Methods/Statistical Analysis: Simulation programming for the paper has been done through Verilog HDL code in MODELSIM software and performance parameters like speed, power etc., using XILINX software. Findings: In this paper, when employing these Vedic formulae, lesser number of logic elements are consumed when compared with conventional method. Improvements/ Applications: By investigating the various Sutras in Vedic mathematics, it has been observed that speed is increased and layout area is minimized.

Keywords

Elgamal Algorithm, Multiplier and Accumulate Unit, Urdhva Tiryagbhyam Sutra, Vedic Multiplier.

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C. Vimala ¹, P. Aruna Priya ¹

Affiliations
1 Department of Electronics and Communication Engineering, SRM University, Kancheepuram - 603203, Tamil Nadu, IN

Abstract

Background/Objectives: Denoising is the first pre-processing step in image processing. Image denoising is the removal of noise from the corrupted image without deleting the useful information. Methods/Statistical Analysis: Wavelet transform is a main tool for image processing applications in modern existence. In this paper Double Density Dual Tree Discrete Wavelet Transform is used and investigated for image denoising. The proposed techniques give the better performance when comparing other two wavelet techniques. Findings: Images are considered for the analysis purpose and the performance is compare with other two wavelet transform discussed in this paper. Peak Signal to Noise Ratio values and Root Means Square error are calculated in all the three wavelet techniques for denoised images and the performance has evaluated. Applications/Improvements: The reduced RMSE and increased PSNR value resultant shows the good visual perception of the image which is used to image analysis.

Keywords

Denoising, Discrete Wavelet Transform (DWT), Image processing, Wavelet Transform.

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