281 research outputs found
Spectral and spatial power evolution design with machine learning-enabled Raman amplification
No full textWe present a machine learning (ML) framework for designing desired signal
power profiles over the spectral and spatial domains in the fiber span. The
proposed framework adjusts the Raman pump power values to obtain the desired
two-dimensional (2D) profiles using a convolutional neural network (CNN)
followed by the differential evolution (DE) technique. The CNN learns the
mapping between the 2D profiles and their corresponding pump power values using
a data-set generated by exciting the amplification setup. Nonetheless, its
performance is not accurate for designing 2D profiles of practical interest,
such as a 2D flat or a 2D symmetric (with respect to the middle point in
distance). To adjust the pump power values more accurately, the DE fine-tunes
the power values initialized by the CNN to design the proposed 2D profile with
a lower cost value. In the fine-tuning process, the DE employs the direct
amplification model which consists of 8 bidirectional propagating pumps,
including 2 second-order and 6 first order, in an 80 km fiber span. We evaluate
the framework to design broadband 2D flat and symmetric power profiles, as two
goals for wavelength division multiplexing (WDM) system performing over the
whole C-band. Results indicate the framework's ability to achieve maximum power
excursion of 2.81 dB for a 2D flat, and maximum asymmetry of 14% for a 2D
symmetric profile
Experimental validation of machine-learning based spectral-spatial power evolution shaping using Raman amplifiers
No full textWe experimentally validate a real-time machine learning framework, capable of
controlling the pump power values of Raman amplifiers to shape the signal power
evolution in two-dimensions (2D): frequency and fiber distance. In our setup,
power values of four first-order counter-propagating pumps are optimized to
achieve the desired 2D power profile. The pump power optimization framework
includes a convolutional neural network (CNN) followed by differential
evolution (DE) technique, applied online to the amplifier setup to
automatically achieve the target 2D power profiles. The results on achievable
2D profiles show that the framework is able to guarantee very low maximum
absolute error (MAE) (<0.5 dB) between the obtained and the target 2D profiles.
Moreover, the framework is tested in a multi-objective design scenario where
the goal is to achieve the 2D profiles with flat gain levels at the end of the
span, jointly with minimum spectral excursion over the entire fiber length. In
this case, the experimental results assert that for 2D profiles with the target
flat gain levels, the DE obtains less than 1 dB maximum gain deviation, when
the setup is not physically limited in the pump power values. The simulation
results also prove that with enough pump power available, better gain deviation
(less than 0.6 dB) for higher target gain levels is achievable
Colloque international : “Islamic Alternatives. Non-Mainstream Religion in Persianate Societies” — Georg-August-Universität Göttingen (Allemagne), 11-12/04/2014
No full textEn attente du programme complet Participants : Mehran Afshari, Mohammad Ali Amir-Moezzi, Martin van Bruinessen, Juergen Wasim Frembgen, Stéphane A. Dudoignon, Yiannis Kanakis, Philip Kreyenbroek, Ulrich Marzolph, Alexandre Papas, Shahrokh Raei, Mohammadali Soltani, Razia Sultanova, Khanna Usoyan, Mohsen Zakeri, Thierry Zarcon
Colloque international : “Islamic Alternatives. Non-Mainstream Religion in Persianate Societies” — Georg-August-Universität Göttingen (Allemagne), 11-12/04/2014
No full textEn attente du programme complet Participants : Mehran Afshari, Mohammad Ali Amir-Moezzi, Martin van Bruinessen, Juergen Wasim Frembgen, Stéphane A. Dudoignon, Yiannis Kanakis, Philip Kreyenbroek, Ulrich Marzolph, Alexandre Papas, Shahrokh Raei, Mohammadali Soltani, Razia Sultanova, Khanna Usoyan, Mohsen Zakeri, Thierry Zarcon
Correction to: Predominance of Fourth Panzootic Newcastle Disease Virus Subgenotype VII.1.1 in Iran and Its Relation to the Genotypes Circulating in the Region
No full textThe original version of this article contained a mistake in the co-author names “Mohammad Sotani and Esameel Allahyari”. The correct co-author names should be Mohammad Soltani and Esmaeel Allahyari
ZnSnO3 - SnO2 nanocomposite as a catalyst for efficient hydrogen production through sodium borohydride methanolysis
No full textIn this work, we describe a straightforward modified sol gel approach for producing zinc stannate-tin oxide (ZnSnO3-SnO2) nanocomposite particles by combining tin chloride and zinc acetate using EDTA ammonium salt as an electrosteric inhibition agent. The acquired samples were characterized using simultaneous thermal gravimetric and differential scanning calorimetry (TGA/DSC), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), UV-Vis spectroscopy and scanning electron microscopy (SEM). The catalyst activity of ZnSnO3-SnO2 particles in hydrogen production was determined by the methanolysis reaction from NaBH4. The hydrogen generation rate TOF (Turnover Frequency) The hydrogen generation rate, activation energy (Ea), enthalpy (ΔH) and entropy (ΔS) values of the hydrogen production reaction were calculated as 374.11 ml min−1.g−1 (832.38 h−1), 43.19 kJ mol−1, 40.65 kJ mol−1 and -178.96 J/mol.K, respectively. The prepared ZnSnO3-SnO2 composite can be recycled and used without obvious loss of activity; This makes the procedure economical and environmentally friendly.The author would like to acknowledge the financial support provided by the Algerian Ministry of Higher Education and Scientific Research and the University of Biskra. We are also grateful to Soltani Mohamed Toufik, Head of the Laboratory of “Photonic Physics and Multifunctional Nanomaterials” for providing TG analysis. Lastly, we extend our appreciation to our colleagues for their valuable feedback during manuscript preparation, their constructive comments helped improve the quality of this article.Peer reviewe
Enhancing optical fiber transmission performance through advanced link and system design
No full textDistributed Raman amplification (DRA) is a key technology that can improve the performance of fiber optic communication systems. This amplification scheme provides several advantages over the Erbium-Doped Fiber Amplifiers (EDFAs), in terms of Noise Figure (NF), broadband gain, and flexibility in design by means of multi-pumping schemes. Due to its distributed amplification, DRA enables to control of the shape of signal power evolution in both frequency and fiber distance. This is crucial for attaining some of the long-term objectives in fiber optic communications, including optimization of Signal-to-Noise Ratio (SNR) and compensating for nonlinear impairments. However, the optimization of the pump power and wavelength values poses a challenge to DRA configurations. In this thesis, we utilize Machine Learning (ML) and optimization techniques to design signal power evolution in two dimensions (2D), i.e. frequency and fiber distance, using Raman amplifiers. First, an inverse system model based on a Convolutional Neural Network (CNN) is used to map the 2D signal power profiles to their corresponding Raman pump power and wavelength values. The CNN model has shown a statistically low error in learning the inverse mapping. However, its performance is not accurate for designing 2D profiles of practical interest, such as a 2D flat or a 2D symmetric (with respect to the midpoint in the distance). To accurately design the practical 2D profiles, we use an online optimization framework based on Differential Evolution (DE). In this framework, the DE adjusts the pump power values online on the setup aiming to reduce the cost value between the desired and the designed 2D profiles. The DE framework is also combined with the CNN inverse model to achieve better accuracy, more reliable optimum values, and faster convergence. Finally, we experimentally validate the performance of the CNN model, the DE, and the CNN-assisted DE framework using an amplifier setup employing four counter-propagating Raman pumps. Different target power profiles defined jointly in the entire C-band and in fiber distance are aimed to be designed. Moreover, the DE framework is tested and showed promising performance in an experimental multi-objective design scenario to achieve 2D profiles with flat gain levels at the end of the span, jointly with minimum spectral excursion over the entire fiber length
Modeling optical amplifiers: from inverse design to full system optimization
No full textOptical amplifiers play a critical role in the optimization of communication systems striving to achieve maximum throughput. Here, we review recent efforts in amplifier modeling – from physics-based to black-box modeling – for amplifier inverse design to full system optimization
Eremopeza reducta Uvarov 1934, stat. nov.
No full textEremopeza reducta (Uvarov, 1934) stat. nov. (Figs. 118, 163) Material examined. IRAN: Persia, Bushir to Buruzdjun, 21.5.1927, 1♀ (Holotype) (M. M. Siazov) (ZIN); S. Persia, Masjid-i Sulaiman, N. of Ahwaz, 1932, 1♂, 2♀ (Paratypes) (leg. F. Marsh); ibidem, 16– 28.6.1932, 1♀ (Paratype) (S.V. Pill); Masjid-i-Sulaiman, 5.6.1938, 1♀ [not a type] (S. V. Pill); Dehloran, 10.5.1973, 6♂ (leg. A. A. Soltani); Mehran, 400 m, 8– 9.5.1973, 3♂, 2♀ (leg. A. A. Soltani); Iran, Lar, 4.5.1950, 2♂, 2♀; 25 mls. N. of Bandar Abbas, 1.4.1950, 1♂ (leg. G. B. Popov) (all in NHMUK). Remarks. This taxon was described by Uvarov (1934: 106) as a subspecies of E. gibbera a status followed by subsequent authors. I think it is an independent species by the distinct hind wing patterns, having a very narrow Lshaped dark band, lacks of apical spot (or has only the veins darkened) and the bright yellow disc. It is most closely related to E. soltanii Ünal, sp. nov. (see next species).Published as part of Ünal, Mustafa, 2016, Pamphagidae (Orthoptera: Acridoidea) from the Palaearctic Region: taxonomy, classification, keys to genera and a review of the tribe Nocarodeini I. Bolívar, pp. 1-223 in Zootaxa 4206 (1) on page 31, DOI: 10.11646/zootaxa.4206.1.1, http://zenodo.org/record/20826
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