Waled Hussein Al-Arashi

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Principal Component Analysis (PCA) turns out to be one of the most successful techniques in face recognition systems as a statistical method for dimensionality reduction. Even so, it is yet not optimal from the perspective of classification because the underlying distribution among different face classes in the image space is unpredicted and not known in(More)
We experimentally demonstrate simultaneous optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) in heterogeneous fiber-optic networks by using principal component analysis (PCA) and statistical distance measurement based pattern recognition on scatter plots obtained through asynchronous single channel sampling (ASCS).(More)
We propose a novel technique for simultaneous multi-impairment monitoring and autonomous bit-rate and modulation format identification (BR-MFI) in next-generation heterogeneous fiber-optic communication networks by using principal component analysis-based pattern recognition on asynchronous delay-tap plots. The results of numerical simulations performed for(More)
We experimentally demonstrate the use of deep neural networks (DNNs) in combination with signals' amplitude histograms (AHs) for simultaneous optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) in digital coherent receivers. The proposed technique automatically extracts OSNR and modulation format dependent features of(More)
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