Informatics Publishing Limited

E. M. El-Bakary ¹, O. Zahran ², S. EL-Rabaie ³, F. E. Abd El-Samie ⁴

Affiliations
1 Department of Electronics and Electrical Communications, Menoufia University, Menouf 32952, EG
2 University of Liverpool, GB
3 Electronics and Communications Eng., Menoufia University, EG
4 Menoufia University, EG

Abstract

This paper presents a chaotic interleaving approach for efficient video transmission with orthogonal frequency division multiplexing (OFDM) with equalization over fading channels. The SPIHT compressed video is converted to luminance and chrominance approach and converted to binary data. The binary data is interleaved with the proposed approach prior to the modulation step. The chaotic Baker map is used in the proposed interleaving approach. In addition to reducing the channel effects on the transmitted data, the proposed chaotic interleaving approach adds a degree of encryption to the transmitted data. The performance of the proposed approach is tested by the transmission of SPIHT compressed video frames over Rayleigh fading channels with chaotic interleaving. Experimental results show that video frames are received with higher peak signal-to-noise ratios (PSNRs) if chaotic interleaving is applied. We also propose an efficient technique to reduce the effects of both multiple access interference (MAI) and intersymbol interference (ISI) in wireless OFDM systems. A hybrid scheme comprising randomization of SPIHT video frames at the transmitter and a regularized equalizer at the receiver unit is suggested and studied. Then, the regularized equalizer is used to reduce the effect of ISI and provide a better estimate of the data. The performance of the proposed scheme is studied and compared with traditional schemes. Our simulation results show a noticeable performance improvement by using the proposed scheme.

Keywords

Video Processing, SPIHT Video Compression, OFDM, and Chaotic Map.

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M. E. Keshk , M. Abd El-Naby ¹, S. Elrabie ², M. I. Dessouky ¹, F. E. Abd El-Samie ³

Affiliations
1 Department of Communication, Menoufia University, IN
2 Department of Communication, Menoufia University, EG
3 Department of Communication, Menoufia University, EG

Abstract

Automatic Digital Modulation Recognition (ADMR) is becoming an interesting problem with various civil and military applications. In this paper, an ADMR algorithm in Orthogonal Frequency Division Multiplexing (OFDM) systems using Discrete Transforms (DT) and Mel-Frequency Cepstral Coefficients (MFCCs) is proposed. The proposed algorithm uses various DT techniques as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) with MFCCs to extract the features of the modulated signal and Support Vector Machine (SVM) to classify the modulation orders. The proposed algorithm avoids over fitting and local optimal problems that appear in artificial neural networks (ANNs). Simulation results show the classifier to be capable of recognizing the modulation scheme with high accuracy (90-100% when using DWT, DCT and DST for some modulation schemes) over a wide Signal-to-Noise Ratio (SNR) range in the presence of Additive White Gaussian Noise (AWGN) and Rayleigh fading channel, particularly at a low signal to noise ratio (SNR).

Keywords

ADMR, OFDM, MFCC, PR, SVM, ANN.

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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. M. El-Bakary ¹, O. Zahran ², S. El-Rabaie ³, F. E. Abd El-Samie ⁴

Affiliations
1 Department of Electronics and Electrical Communications, Menoufia University, 32952, Menouf, EG
2 University of Liverpool, GB
3 Menoufia University, EG
4 Electrical Communications, Menoufia University, EG

Abstract

Video is compressed to move the redundancies for efficient communication. In this paper, we use a steganography method for verification of the video content at the receiver side. The main idea of the proposed steganography method is based on the Discrete Cosine Transform (DCT). It is based on creating a signature for each block of the DCT of the compressed video sequence to be embedded in another block. This is attributed to the fact that most DCT coefficients of the compressed video sequence are either zero or close to zero. Simulation results reveal the robustness of the suggested steganography method.

Keywords

Video Compression, Data Hiding, Steganography.

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Mohsen A. M. El-Bendary ¹, H. Abdellatif ², M. El-Tokgy ², T. E. Taha ², Sayed M. EL-Rabaie ², O. F. Zahran ², W. Al-Nauimy ³, Saleh Ahmad ², F. E. Abd El-Samie ²

Affiliations
1 Department of Electronics Technology, Helwan University, Cairo, EG
2 Menofia University, EG
3 Electrical Engineering and Electronics, University of Liverpool, Brownlow Hill, L693GJ, Liverpool, GB

Abstract

Mammography is well known method for detection of breast tumors. Early detection and removal of the primary tumor is an essential and effective method to enhance survival rate and reduce mortality. Breast tumor segmentation is needed for monitoring and quantifying breast cancer. However, automated tumor segmentation in mammograms poses many challenges with regard to characteristics of an image. In this paper we propose a fully automatic algorithm for segmentation of a breast masses, using two types of image segmentation, Normalized graph cuts to delineate pectoral muscle and optimal threshold based on the two-dimensional entropy for masses detection.

Keywords

Mammography, Image Segmentation, Thresholding, Entropy, Normalized Graph Cuts.

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E. M. El-Bakary ¹, O. Zahran ², S. El-Rabaie ¹, F. E. Abd El-Samie ³

Affiliations
1 Department of Electronics and Electrical Communications Engineering, Menoufia University, Menouf-32952, EG
2 Department of Electronics and Electrical Communications Engineering, Menoufia University, Menouf-32952, EG
3 Department of Electronics and Electrical Communications Engineering, Menoufia University, Menouf-32952, EG

Abstract

The videois compressed to move the redundancies for reducing the number of bits whichneed to show the video for reducing the size of video to reduce the band width. We embed the hiding data in redundancies of the video frame withoutvisibility manner using Steganography technique. In our research we focus in increasing the improvement of compressed video frame through using steganography. In our research we use two mixed techniques to compress the video, the first is digital steganography and the second is data compression. Hence, the suggested scheme is mixed the algorithm ofthe steganography algorithm with the DCT compressionvideo frame.The suggested scheme in our study compression of data is made in two steps. In the first step,redundant data isminimized by compaction of energy using video. In the second step, with using steganography bit blocks isembedded in its blocks which call subsequent of the same video frame. Theadvantage of thebits which it is embedded isdecrease the size of the file and increase thesize of the file of the compressed video frame, but also decrease the file size further more. Theresults of simulation results explainthat,the suggestedschemehave more potential in coding of the frame of the video.

Keywords

Video, Data Hiding and Compression and Steganography, DCT Compression.

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Hayam A. Abd El-Hameed ¹, Emad S. Hassan ², Sami A. El-Dolil ², F. E. Abd El-Samie ²

Affiliations
1 Dept. of Electronics and Electrical Comm., Menoufia Univ.,32952, Menouf, EG
2 Dept. of Electronics and Electrical Comm., Menoufia Univ.,32952, Menouf, EG

Abstract

Since all the multimedia products are released via internet so it‟s very important need today to protect the data from malicious attacks. This directs us to look at digital watermarking which intends to protect the copyright information of the users. This paper presents a Discrete Cosine Transform (DCT) based Signature scheme which provides higher resistance to image processing attacks such as speakle noise, rotation, Gaussian, etc. The invasive distribution of digital images and the growing concern on their integrity and originality in need of authenticating corrupted images by transmission. To meet this need, this paper proposes a new technique that is make digital signatures in image robust to image degradations. The DCT is used to improve the security against forgery attacks due to its precise localization properties and excellent multiscale. Several experiments are carried out to test the proposed scheme in terms of Peak Signal to Noise Ratio (PSNR) and Normalized Cross-Correlation (NC).The obtained results confirm that the proposed scheme can achieve good efficiency against transmission errors. It also is very robust to counterfeiting attacks.

Keywords

Discrete Cosine Transform (DCT), Signature, Attacks, Normalized Cross-Correlation (NC).

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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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A. A. Mahmoud ¹, S. EL. Rabaie ², T. E. Taha ², F. E. Abd El-Samie ³, O. Zahran ², W. Al-Nauimy ⁴

Affiliations
1 Department of Electronics and Electrical Communications, Menoufia University, 32952, Menouf, EG
2 Department of Electronics and Electrical Communications, Menoufia University, 32952, Menouf, EG
3 Department of Electronics and Electrical Communications, Menoufia University, 32952, Menouf, EG
4 University of Liverpool, GB

Abstract

Image denoising involves processing of the image data to produce a visually high quality image. The denoising algorithms may be classified into two categories, spatial filtering algorithms (Wiener filter, Gaussian filter, Gabor filter and Median filter) and wavelet based algorithms (Wiener filter in the wavelet domain and Log Gabor filter in the wavelet domain). In this paper a comparative study for the previous mentioned algorithms based on calculating the Peak Signal to Noise Ratio (PSNR) value as a metric since for such images the visual evaluation is not appropriate. The quantitative results of comparison are tabulated by calculating the PSNR of the output image.

Keywords

PSNR, DWT.

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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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R. Ali ¹, O. Zahran ², M. Elkordy ³, F. E. Abd El-Samie ²

Affiliations
1 Department of Electronics and Electrical Communications, 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

This paper studied the problem of blind signal separation (BSS) for the system of multiple input and multiple output signals (MIMO) of noisy signals. It uses the separation algorithm for the discrete cosine transforms (DCT) for blind of mixed signals, instead of separating the mixtures themselves, as a technique that achieves a great result in eliminating the noise.  We used the proposed algorithm with multi user kurtosis (MUK) that used for blind of mixed signals. 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 signal to noise ratio (SNR) between extracted and original signals to compare between them to give the better result. The separation of signals in a noisy environment is studied with and without the using the new technique. The simulation results confirm the usefulness of this technique.

Keywords

BSS, MIMO, MUK, SNR.

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S. Abd El-Moneim ¹, M. A. Nassar ², M. Abdelnaby ², F. E. Abd El-Samie ³

Affiliations
1 Department of Electronics and Electrical Communications, Faculty of Electronic Engineering. Menoufia University, Menouf, 32952, EG
2 Department of Electronics and Electrical Communications, Faculty of Electronic Engineering. Menoufia University, Menouf, 32952, EG
3 Department of Electronics and Electrical Communications, Menoufia University, Menouf, 32952, EG

Abstract

Speech signal is a signal that conveys information about the identity, words, age and emotional state of the speaker. If this signal is corrupted by noise, this information may be lost or become difficult to hear.There are several speech enhancement methods such as spectral subtraction method, Wiener filter method and adaptive wiener filtering method. A proposed method of speech enhancement to reduce the degradation of speech signal is introduced in this paper depending on speech averaging, median filtering or minimum periodogram. This averaging method can be used as a preprocessing step in speaker identification. Simulation results show a good performance of the proposed speech enhancement method.

Keywords

Speech Recognition, Speech Enhancement, Speech Averaging, Spectral Subtraction,Wiener Filter, Adaptive Wiener Filter, Speech Quality Metrics.

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Hala Shawky ¹, A. Nassar ², M. Abdelnaby ², F. E. Abd El-Samie ³

Affiliations
1 Department of Electronics and Electrical Communication, Menouf University, Menouf, IN
2 Department of Electronics and Electrical Communication, Menouf University, Menouf, EG
3 Department of Electronics and Electrical Communication, Menouf University, Menouf, EG

Abstract

Speech compression is a process of compressing speech signal to reduce its size for transfer. This paper proposed a new technique to compress the speech signal. This technique is called the decimation process. It is opposite of interpolation. This process reduces the sampling rate and thus save time, storage capacity, and cost. Decimation contains two stages, processes of lowpass filtering followed by downsampling. The benefit of using a filter is to avoid aliasing effect. The reconstruction of the original speech signal can be performed using inverse interpolation techniques such as maximum entropy and regularization theory. Finally, we assess the quality of the reconstructed signal using quality metrics such as signal-to-noise ratio (SNR), signal to noise ratio segmental (SNRseg), spectral distortion (SD) and log-likelihood ratio (LLR).

Keywords

Decimation, Interpolation, Maximum Entropy, Regularisation Theory.

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Mohamed M. Al-Abasy ¹, M. I. Dessouky ², M. Abdelnaby ³, F. E. Abd El-Samie ²

Affiliations
1 Department of Electronics and Electrical Communications, Menoufia University, Menouf, 32952, EG
2 Department of Electronics and Electrical Communications, Menoufia University, Menouf, 32952, EG
3 Department of Electronics and Electrical Communications, Menoufia University, Menouf, 32952, EG

Abstract

The importance of seismic wave researches concentrate of the ability to predict earthquake and also obtain information on the Earth’s composition and features. Compression is the reduction of signal size to save storage capacity, transmission time, and cost. In this paper, we propose a technique for seismic signal compression and two techniques for seismic signal reconstruction. Compression is performed with a decimation process, which reduces the sampling rate. The reconstruction of the original seismic signal can be performed using inverse interpolation techniques such as maximum entropy and regularization theory. In addition, we apply compression through multiplication of a certain non-square matrix followed by a pseudo-inverse process for reconstruction. Finally we assess the quality of the reconstructed signals using quality measures such as Signal-to-Noise Ratio (SNR), segmental Signal-to-Noise Ratio (SNRseg), Spectral Distortion (SD), and Log-Likelihood Ratio (LLR).

Keywords

Seismic Signal, Decimation, Maximum Entropy, Regularization Theory.

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