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In this work we focus on the influence of salt water as the medium between two coupling coils to design a wireless power transfer system. An electrical circuit model and an adequate characterization approach is presented to account for the power losses in the conductive medium. Optimum values for the load and efficiency of the power link are determined. Experimental results are provided to compare the performance of the coupling coils between different coupling mediums (air, fresh and salt water).

Abstract
Triboelectric nanogenerators (TENGs) have been recognized as a promising harvesting technology to satisfy the power requirements of some marine activities. Our current work addresses the TENG design for the purpose of energy harvesting in the sea. In this paper we present a prototype currently under development, in which spheres made of a triboelectric material move within a limited volume due to external mechanical excitation. The spheres move along a printed circuit board having several electrodes and a PDMS film deposited on top, producing numerous voltage spikes. Experimental results are provided with time-domain wave forms and power characterization of the TENG.

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We explore the task of computational beat tracking for musical audio signals from the perspective of putting an end-user directly in the processing loop. Unlike existing “semi-automatic” approaches for beat tracking, where users may select from among several possible outputs to determine the one that best suits their aims, in our approach we examine how high-level user input could guide the manner in which the analysis is performed. More specifically, we focus on the perceptual difficulty of tapping the beat, which has previously been associated with the musical properties of expressive timing and slow tempo. Since musical examples with these properties have been shown to be poorly addressed even by state of the art approaches to beat tracking, we re-parameterise an existing deep learning based approach to enable it to more reliably track highly expressive music. In a small-scale listening experiment we highlight two principal trends: i) that users are able to consistently disambiguate musical examples which are easy to tap to and those which are not; and in turn ii) that users preferred the beat tracking output of an expressive-parameterised system to the default parameterisation for highly expressive musical excerpts. © 2021, Springer Nature Switzerland AG.

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