A broadband semiconductor optical amplifier (SOA) has been realized for the coarse WDM (CWDM) based systems operating over the O-band range. The SOA exhibits low polarization sensitivity, 23 dB gain and low noise based on the asymmetric multi-quantum well (AMQW) technique. Reflective SOAs (for modulating the upstream data in passive optical networks (PONs)) as well as in-line or booster amplifiers in CWDM systems are some of the applications of such a SOA.
Abstract-The objective of this research is to propose two new optical procedures for packet routing and forwarding in the framework of transparent optical networks. The single-wavelength label-recognition and packet-forwarding unit, which represents the central physical constituent of the switching node, is fully described in both cases. The first architecture is a hybrid opto-electronic structure relying on an optical serial-toparallel converter designed to slow down the label processing. The remaining switching operations are done electronically. The routing system remains transparent for the packet payloads. The second architecture is an all-optical architecture and is based on the implementation of all-optical decoding of the parallelized label. The packet-forwarding operations are done optically. The major subsystems required in both of the proposed architectures are described on the basis of nonlinear effects in semiconductor optical amplifiers. The experimental results are compatible with the integration of the whole architecture. Those subsystems are a 4-bit time-to-wavelength converter, a pulse extraction circuit, a an optical wavelength generator, a 3 8 all-optical decoder and a packet envelope detector.Index Terms-Optical header processing, optical logic gate, optical packet switching, semiconductor optical amplifier (SOA), transparent optical network.
The energy resolution in hyperspectral imaging techniques has always been an important matter in data interpretation. In many cases, spectral information is distorted by elements such as instruments’ broad optical transfer function, and electronic high frequency noises. In the past decades, advances in artificial intelligence methods have provided robust tools to better study sophisticated system artifacts in spectral data and take steps towards removing these artifacts from the experimentally obtained data. This study evaluates the capability of a recently developed deep convolutional neural network script, EELSpecNet, in restoring the reality of a spectral data. The particular strength of the deep neural networks is to remove multiple instrumental artifacts such as random energy jitters of the source, signal convolution by the optical transfer function and high frequency noise at once using a single training data set. Here, EELSpecNet performance in reducing noise, and restoring the original reality of the spectra is evaluated for near zero-loss electron energy loss spectroscopy signals in Scanning Transmission Electron Microscopy. EELSpecNet demonstrates to be more efficient and more robust than the currently widely used Bayesian statistical method, even in harsh conditions (e.g. high signal broadening, intense high frequency noise).
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