Informatics Publishing Limited

M. E. Hammad ¹, H. Kasban ¹, O. Zahran ², M. I. Dessouky ², S. M. Elaraby ¹

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

Abstract

This paper presents a proposed approach for distillation column malfunction identification using Higher Order Statistics (HOS). Gamma ray scanning techniques have been used for examining the inner details of a distillation column. In the proposed method, the signals are firstly divided into frames; each frame contains only the signal of one column tray. Secondly, HOS are estimated for these frame signals. Thirdly, features are extracted from the HOS estimate. Finally, features are used for training and testing of Support Vector Machine classifier to identify the distillation column malfunctions. The simulation results show that the HOS can be used efficiently for the distillation column malfunction identification especially at high noisy scanning conditions.

Keywords

Bispectrum, Cumulant, Moment, and Trispectrum.

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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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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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