Photonic Extreme Learning Machine based on frequency multiplexing

The optical domain is a promising field for physical implementation of neural networks, due to the speed and parallelism of optics. Extreme Learning Machines (ELMs) are feed-forward neural networks in which only output weights are trained, while internal connections are randomly selected and left untrained. Here we report on a photonic ELM based on a frequency-multiplexed fiber setup. Multiplication by output weights can be performed either offline on a computer, or optically by a programmable spectral filter. We present both numerical simulations and experimental results on classification tasks and a nonlinear channel equalization task.Comment: 22 pages, 16 figure

Plectranthus zeylanicus: A rich source of secondary metabolites with antimicrobial, disinfectant and anti-inflammatory activities

Plectranthus zeylanicus Benth is used in Sri Lankan folk medicine as a remedy for inflammatory conditions and microbial infections. Our previous investigations revealed potent 5-lipoxygenase (5-LO) inhibitory activity in lipophilic extracts of this plant, supporting its anti-inflammatory potential. In-depth studies on the antimicrobial activity have not been conducted and the bioactive ingredients remained elusive. As a continuation of our previous work, the present investigation was undertaken to evaluate the antimicrobial activity of different extracts of P. zeylanicus and to isolate and characterize bioactive secondary metabolites. Different organic extracts of this plant were analyzed for their antibacterial activity, and the most active extract, i.e., dichloromethane extract, was subjected to bioactivity-guided fractionation, which led to the isolation of 7α-acetoxy-6β-hydroxyroyleanone. This compound displayed strong antibacterial activity against methicillin-resistant Staphylococcus aureus with a minimum inhibitory concentration of 62.5 µg/mL, and its disinfectant capacity was comparable to the potency of a commercial disinfectant. Moreover, 7α-acetoxy-6β-hydroxyroyleanone inhibits 5-LO with IC50 values of 1.3 and 5.1 µg/mL in cell-free and cell-based assays, respectively. These findings rationalize the ethnopharmacological use of P. zeylanicus as antimicrobial and anti-inflammatory remedy

Modenverhalten von Quantenkaskadenlasern in miniaturisierten externen Resonatoren

Viele spektroskopische Anwendungen erfordern sowohl hohe spektrale Auflösung als auch geringe Messzeiten des Spektrometers, z. B. die Detektion giftiger Gase oder die Atemgasanalytik. Quantenkaskadenlaser in externen Resonatoren (EC-QCLs) eignen sich besonders als Lichtquelle für Spektroskopie im mittleren Infrarotbereich, da sie sowohl über hohe spektrale Intensitäten als auch einen breiten Durchstimmbereich verfügen. Für schnelles und hochauflösendes spektrales Abstimmen des Lasers gibt es jedoch noch Hindernisse, die in dieser Arbeit behandelt werden. Ein schnell durchstimmbarer EC-QCL wird vorgestellt, realisiert mit einem Beugungsgitter, das auf einem mikro-elektro-mechanischen System (MOEMS) basiert. Dieses Gitter schwingt mit 1 kHz und erlaubt damit einzelne Spektren in 500 µs. Diese Arbeit liefert einen Überblick über die Resonatorbedingungen, die für einen stabilen Betrieb der longitudinalen Moden dieses Lasers notwendig sind. Darauf aufbauend wird der Einfluss des Resonators auf das Abstimmverhalten dieser Moden untersucht, was schließlich zu einem schnellen und hochauflösenden Spektroskopie-Verfahren führt

Frequency Multiplexed Optical Extreme Learning Machine

We propose an optical implementation of an Extreme Learning Machine (ELM) inspired by frequency-multiplexing techniques previously employed for Reservoir Computing. The input layer of the ELM is encoded in the lines of a frequency comb and the hidden layer is generated by making comb lines interfere. Multiplication by output weights can be performed optically. This approach combines the potential high-speed, low-power and paral- lelization advantages of Optical Neural Networks with the cheap training (both in terms of speed and power) of ELMs, which do not require slow gradient descent and error backpropagation algorithms. We present preliminary experimental results compared with simulations.info:eu-repo/semantics/publishe

Frequency-multiplexed Photonic Reservoir Computing

We present a coherent wavelength division multiplexed reservoir computer based on intra-cavity phase modulation. We report preliminary results on a signal classification task using a small number (7) of frequency sidebands.info:eu-repo/semantics/publishe

Photonic reservoir computer based on frequency multiplexing

Reservoir computing is a brain-inspired approach for information processing, well suited to analog implementations. We report a photonic implementation of a reservoir computer that exploits frequency domain multiplexing to encode neuron states. The system processes 25 comb lines simultaneously (i.e. 25 neurons), at a rate of 20 MHz. We illustrate performances on two standard benchmark tasks: channel equalization and time series forecasting. We also demonstrate that frequency multiplexing allows output weights to be implemented in the optical domain, through optical attenuation. We discuss the perspectives for high-speed, high-performance, low-footprint implementations.info:eu-repo/semantics/publishe