46 resultados para division rules
Resumo:
An all-optical polarization rotation technique was demonstrated for demultiplexing a 40 Gb/s return-to-zero optical time division de/multiplexing (OTDM) signal. A sensitivity penalty of 3.5 dB was achieved for the total multiplexing/demultiplexing process from 10Gb/s to 40 Gb/s and back again.
Resumo:
Specific fibre modes are deliberately excited in a few-mode and multimode fibre using holography. The same system is also used to demonstrate holography's ability to detect and route individual fibre modes. © 2011 Optical Society of America.
Resumo:
Each mode of a multimode fibre is excited using binary phase patterns on a Spatial Light Modulator and verified by observation of the near-field leaving the fibre and analysis of the step response. © 2011 OSA.
Resumo:
A Spatial Light Modulator and a non-specialized multimode coupler are used together to provide sufficient channel isolation and modal bandwidth for 2x12.5Gbps NRZ over 2km of standard graded-index multimode fibre without DSP. © 2012 IEEE.
Resumo:
The design of an SLM-based mode demultiplexer is discussed and mode division multiplexing is performed using the LP0,1 and LP 0,2 modes, representing the first demonstration to propagate channels on modes with the same azimuthal index. Mode multiplexed transmission over 2 km of 50-μm OM2 fiber demonstrates a modal selectivity of 16 dB and an OSNR penalty of 1.5 dB for the transmission of 2×56 Gb/s QPSK signals. © 2012 IEEE.
Resumo:
Abstract (40-Word Limit): A novel method for sending MIMO wireless signals to remote antenna units over a single multimode fibre is proposed. MIMO streams are sent via different fibre modes using mode division multiplexing. Combined channel measurements of 2km MMF and a typical indoor radio environment show in principle a 2x2 MIMO link at carrier frequencies up to 6GHz.
Resumo:
Although it is widely believed that reinforcement learning is a suitable tool for describing behavioral learning, the mechanisms by which it can be implemented in networks of spiking neurons are not fully understood. Here, we show that different learning rules emerge from a policy gradient approach depending on which features of the spike trains are assumed to influence the reward signals, i.e., depending on which neural code is in effect. We use the framework of Williams (1992) to derive learning rules for arbitrary neural codes. For illustration, we present policy-gradient rules for three different example codes - a spike count code, a spike timing code and the most general "full spike train" code - and test them on simple model problems. In addition to classical synaptic learning, we derive learning rules for intrinsic parameters that control the excitability of the neuron. The spike count learning rule has structural similarities with established Bienenstock-Cooper-Munro rules. If the distribution of the relevant spike train features belongs to the natural exponential family, the learning rules have a characteristic shape that raises interesting prediction problems.