Here we propose a novel design of fiber-optic reservoir computing (FORC) and demonstrate it applicability for QAM-signal processing. The FORC enables over 5 dB improvement due to mitigating nonlinear distortions and supports high complexity QAM-formats
Reservoir computing is a brain-inspired approach for information processing, well suited to analog i...
Despite ever increasing computational power, recognition and classification problems remain challeng...
Photonic reservoir computing has evolved into a viable contender for the next generation of analog c...
Here we propose a novel design of fiber-optic reservoir computing (FORC) and demonstrate it applicab...
Nonlinearity mitigation in optical fiber networks is typically handled by electronic Digital Signal ...
In recent years, various methods, architectures, and implementations have been proposed to realize h...
Photonic reservoir computing is a hardware implementation of the concept of reservoir computing whic...
Machine learning techniques have proven very efficient in assorted classification tasks. Nevertheles...
All-optical platforms for recurrent neural networks can offer higher computational speed and energy ...
We propose photonic reservoir computing as a new approach to optical signal processing in the contex...
We propose a design for high (including THz) bandwidth neuromorphic signal processing based on fiber...
The efficacy of data decoding in contemporary ultrafast fiber transmission systems is greatly determ...
For many challenging problems where the mathematical description is not explicitly defined, artifici...
Reservoir computing is a recent bio-inspired approach for processing time-dependent signals. It has ...
Optical reservoir computing is a machine learning technique in which a photonic chip can be trained ...
Reservoir computing is a brain-inspired approach for information processing, well suited to analog i...
Despite ever increasing computational power, recognition and classification problems remain challeng...
Photonic reservoir computing has evolved into a viable contender for the next generation of analog c...
Here we propose a novel design of fiber-optic reservoir computing (FORC) and demonstrate it applicab...
Nonlinearity mitigation in optical fiber networks is typically handled by electronic Digital Signal ...
In recent years, various methods, architectures, and implementations have been proposed to realize h...
Photonic reservoir computing is a hardware implementation of the concept of reservoir computing whic...
Machine learning techniques have proven very efficient in assorted classification tasks. Nevertheles...
All-optical platforms for recurrent neural networks can offer higher computational speed and energy ...
We propose photonic reservoir computing as a new approach to optical signal processing in the contex...
We propose a design for high (including THz) bandwidth neuromorphic signal processing based on fiber...
The efficacy of data decoding in contemporary ultrafast fiber transmission systems is greatly determ...
For many challenging problems where the mathematical description is not explicitly defined, artifici...
Reservoir computing is a recent bio-inspired approach for processing time-dependent signals. It has ...
Optical reservoir computing is a machine learning technique in which a photonic chip can be trained ...
Reservoir computing is a brain-inspired approach for information processing, well suited to analog i...
Despite ever increasing computational power, recognition and classification problems remain challeng...
Photonic reservoir computing has evolved into a viable contender for the next generation of analog c...
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