Towards an Implantable Vestibular Prosthesis: The Surgical Challenges

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Spiking Chemical Sensor (SCS): A new platform for neuro-chemical sensing

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An auto-input-offset removing floating gate pseudo-differential transconductor

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A Partial-Current-Steering Biphasic Stimulation Driver for Vestibular Prostheses

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An Ultra-Low-Power Micro-Optoelectromechanical Tilt Sensor

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A Partial-Current-Steering Biphasic Stimulation Driver for Neural Prostheses

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A Neural Implant ASIC for the Restoration of Balance in Individuals with Vestibular Dysfunction

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Application of liquid-air and pumped-thermal electricity storage systems in low-carbon electricity systems

In this study, w e consider two medium - to large - scale electricity storage systems currently under development, namely ‘Liquid - Air Energy Storage’ (LAES) and ‘Pumped - Therma l Electricity Storage’ (PTES). Consistent t hermodynamic models and costing methodologies for the two systems are presented , with the object ive of integrating the characteristics of these technologies in to a whole - electricity system assessment model , and assess ing the ir system - level value in different scenarios for power system decarbonisation . It is found that the value of storage varies greatly depending on the cumulative installed ca pacity of storage in the electrical system, with the s torage technologies provid ing greater marginal benefits at low p enetrations . T wo carbon target scenarios showed similar results, with a limited effect of the carbon target on the system value of storage (although it is noted that this may change for even more ambitious carbon targets). On the other hand, the location and installed capacity of storage plants is found to have a significant impact on the syste m value and acceptable cost of the se technologies. The w hole - system value of PTES was found to be slightly higher than that of LAES, driven by a higher storage duration and efficiency, however, due to the higher power capital cost of PTES, this becomes les s attractive for implementat ion at lower volumes than LAES

iMapD: intrinsic Map Dynamics exploration for uncharted effective free energy landscapes

We describe and implement iMapD, a computer-assisted approach for accelerating the exploration of uncharted effective Free Energy Surfaces (FES), and more generally for the extraction of coarse-grained, macroscopic information from atomistic or stochastic (here Molecular Dynamics, MD) simulations. The approach functionally links the MD simulator with nonlinear manifold learning techniques. The added value comes from biasing the simulator towards new, unexplored phase space regions by exploiting the smoothness of the (gradually, as the exploration progresses) revealed intrinsic low-dimensional geometry of the FES