Characterisation of millimetre wave multimode radio-over-fibre systems

Millimetre wave radio-over-fibre links using both singlemode and multimode fibres are demonstrated over a 0-50 GHz bandwidth operating at 1550 nm. Results show that good link gain can be achieved with both single mode and multimode detectors.Millimetre wave radio-over-fibre links using both singlemode and multimode fibres are demonstrated over a 0-50 GHz bandwidth operating at 1550 nm. Results show that good link gain can be achieved with both single mode and multimode detectors

A millimetre wave phase shifter using a 40GHz hybrid mode locked laser

In this paper, electrical injection locking of a modelocked laser is used to create a millimetre wave phase shifter. The phase shift is measured directly using a vector network analyzer enabling straightforward characterization of such systems. Both magnitude and phase of the modulation response are measured and for sufficiently high RF input power a "plateau" is observed in the magnitude response which corresponds to the locking range of the system. Phase shifts of greater than 90o are observed and such devices could have applications in millimetre wave radio-over-fibre phased array antenna systems.In this paper, electrical injection locking of a mode-locked laser is used to create a millimetre wave phase shifter. The phase shift is measured directly using a vector network analyzer enabling straightforward characterization of such systems. Both magnitude and phase of the modulation response are measured and for sufficiently high RF input power a ldquoplateaurdquo is observed in the magnitude response which corresponds to the locking range of the system. Phase shifts of greater than 90deg are observed and such devices could have applications in millimetre wave radio-over-fibre phased array antenna systems

On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor

Recent work suggests that synaptic plasticity dynamics in biological models of neurons and neuromorphic hardware are compatible with gradient-based learning (Neftci et al., 2019). Gradient-based learning requires iterating several times over a dataset, which is both time-consuming and constrains the training samples to be independently and identically distributed. This is incompatible with learning systems that do not have boundaries between training and inference, such as in neuromorphic hardware. One approach to overcome these constraints is transfer learning, where a portion of the network is pre-trained and mapped into hardware and the remaining portion is trained online. Transfer learning has the advantage that pre-training can be accelerated offline if the task domain is known, and few samples of each class are sufficient for learning the target task at reasonable accuracies. Here, we demonstrate on-line surrogate gradient few-shot learning on Intel's Loihi neuromorphic research processor using features pre-trained with spike-based gradient backpropagation-through-time. Our experimental results show that the Loihi chip can learn gestures online using a small number of shots and achieve results that are comparable to the models simulated on a conventional processor

Caperton\u27s Next Generation: Beyond the Bank

The article looks at a panel discussion on judicial responsibility and the U.S. Supreme Court\u27s decision in \u27Caperton v. A.T. Massey Coal Co.\u27 discussed by several law professionals including Jed Shugerman, Debra Lyn Bassett and Dmitry Bam at a 2014 symposium held in the New York University