Informatics Publishing Limited

Sahar Aboshosha , O. Zahran ¹, M. El-Kordy ², F. E. Abd El-Samie ³

Affiliations
1 Electronics and Electrical Communications Department, Menoufia University, Menouf, EG
2 Department of Electronics and Electrical Communications, Menoufia University, Menouf, EG
3 Department of Electronics and Electrical Communications, Menoufia University, Menouf, EG

Abstract

In this paper histogram equalization with image interpolation are presented. Histogram equalization is a very important widely used contrast-enhancement technique in image processing. Image enhancement is a means of improvement of an image appearance by increasing luminance of some a distinctive attribute or by decreasing ambiguity between different regions of the image to improve quality. This paper proposes simultaneous interpolation and equalization of digital images. The best performance has been achieved when performing the equalization on interpolated images instead of performing the interpolation on equalized images. Finally, we can say that the proposed techniques achieve better performance compared to other techniques. 

Keywords

Histogram Equalization, Interpolation, and Image Enhancement.

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E. A. El-Shazly ¹, O. Zahran ², S. M. Elaraby ¹, M. El-Kordy ², F. E. Abd El-Samie ²

Affiliations
1 Engineering Department, Nuclear Research Center, Atomic Energy Authority, Cairo, EG
2 Electrical Communications Department, Menoufia University, Menouf, EG

Abstract

In this paper new identification techniques for buried landmine objects are presented.  Most of the existing supervised identification methods are based on traditional statistics, which can provide ideal results when sample size is tending to infinity. However, only finite samples can be acquired in practice. In this paper, two proposed learning methods; Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), are applied on landmine images. The complete identification technique consists of two stages to perform both the training of the input image models and the evaluation of the testing image sets. In the 1st stage, the 2-D images are transformed into 1-D signals by a spiral scan, and then the Mel Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients are extracted from these 1-D signals and/or their transforms. In the 2nd stage, the ANN and SVM are used to match the extracted features in the testing phase to those of the training phase. Experimental results have shown that the proposed techniques are effective with landmines. The best performance has been achieved with features extracted from the Discrete Cosine Transform (DCT) signals using ANN and from the DCT of images contaminated by AWGN and speckle noise and from the Discrete Sine Transform (DST) of images contaminated by impulsive noise using SVM. Finally, we can say that the proposed techniques achieve better performance compared to other techniques.

Keywords

Spiral Scan, Landmines Identification, ANNs, SVMs, MFCC, Kernel Functions.

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R. Ali ¹, O. Zahran ¹, M. El-Kordy ¹, F. E. Abd El-Samie ¹

Affiliations
1 Department of Electronics and Electrical Communication, Menoufia University, Menouf, EG

Abstract

This paper addresses the problem of blind signal separation (BSS) for the system of multiple input and multiple output signals (MIMO). We use different modulation techniques such as quadrature phase shift keying (QPSK), minimum shift keying (MSK), and Gaussian minimum shift keying (GMSK). Several methods have been used to solve this problem such as principle component analysis (PCA), independent component analysis (ICA), and multi user kurtosis (MUK) algorithms. We use different modulation techniques and different algorithms in the separation to compare between results and take into consideration the good separation. In this paper, we propose wavelet denoising with PCA, ICA and MUK methods. We consider the instantaneous mixture of two sources. The simulation results show a considerable improvement in extracted signals when compared to original signals. We assume mean square error (MSE) between extracted and original signals to compare between them to give the better result. 

Keywords

BSS, MIMO, PCA, ICA, MUK, MSK, GMSK, QPSK, MSE.

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