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Maternal-Fetal Hepatitis E Transmission:Is It Underestimated?

Journal of Clinical and Translational Hepatology 2014年第2期2卷 117-123页

作者： Maysaa el Sayed Zaki mohammed magdy abd el razek Hassan magdy abd el razek Clinical Pathology Mansoura Faculty of Medicine Egypt Mansoura University Hospital Egypt

Hepatitis E virus (HEV) is an enterically transmitted virus;and several modes of transmission have been proposed, including blood transfusion, person to person transmission, and transplacental transmission. HEV during pregnancy is asso-ciated with an unfavorable prognosis for mothers and in severe cases can cause acute fulminate hepatitis and death. Transplacental transmission of HEV usually results in unfa-vorable outcomes of pregnancy, mainly fetal loss, preterm labor, and hepatic dysfunction in neonates. In this review, we will summarize the effects of HEV on maternal-fetal health in various clinical situations.

关键词： Hepatitis E virus Vertical transmission Prevention

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Blind Modulation Recognition in Wireless MC-CDMA Systems Using a Support Vector Machine Classifier

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Wireless Engineering and Technology 2013年第3期4卷 145-153页

作者： Mohamed el-Hady magdy Keshk Keshk el-Sayed elrabie Fathi el-Sayed abd el-Samie mohammed abd el-Naby Egyptian Space Program National Authority for Remote Sensing and Space Science Cairo Egypt Faculty of electronic Engineering Menoufia University Menouf Egypt.

Automatic Digital Modulation Recognition (ADMR) is becoming an interesting problem with various civil and military applications. In this paper, an ADMR algorithm in Multi-Carrier Code Division Multiple Access (MC-CDMA) systems using Discrete Transforms (DTs) and Mel-Frequency Cepstral Coefficients (MFCCs) is proposed. This algorithm uses various DT techniques such as the Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) with MFCCs to extract features from the modulated signal and a 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 shows the classifier is capable of recognizing the modulation scheme with high accuracy up to 90% - 100% 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 ratios (SNRs).

关键词： ADMR MC-CDMA MFCC DT SVM ANN

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