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.

Abstract
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.

Abstract

Abstract

Abstract
In this paper, we describe Fourier-based Wave Front Sensors (WFS) as linear integral operators, characterized by their Kernel. In a first part, we derive the dependency of this quantity with respect to the WFS’s optical parameters: pupil geometry, filtering mask, tip/tilt modulation. In a second part we focus the study on the special case of convolutional Kernels. The assumptions required to be in such a regime are described. We then show that these convolutional kernels allow to drastically simplify the WFS’s model by summarizing its behavior in a concise and comprehensive quantity called the WFS’s Impulse Response. We explain in particular how it allows to compute the sensor’s sensitivity with respect to the spatial frequencies. Such an approach therefore provides a fast diagnostic tool to compare and optimize Fourier-based WFSs. In a third part, we develop the impact of the residual phases on the sensor’s impulse response, and show that the convolutional model remains valid. Finally, a section dedicated to the Pyramid WFS concludes this work, and illustrates how the slopes maps are easily handled by the convolutional model.

Abstract
We present PRIME (PSF Reconstruction and Identification for Multi-sources characterization Enhancement) as a novel hybrid concept to improve the PSF estimation based on Adaptive optics (AO) control loop data. PRIME uses both focal and pupil plane data to jointly estimate the model parameters, which are both the atmospheric (Cn2 (h), seeing), system (e.g. optical gains, residual low-order errors). The parametric model in use is flexible enough to be scaled with field location and wavelength, making it a proper choice for optimized on-axis and off-axis data-reduction across the spectrum. We review the methodology and on-sky validations on NIRC2 at Keck II. We also present applications of PSF model parameters retrieval using PRIME: (i) calibrate the PSF model for observations void of stars on the acquired images, i.e. optimize the PSF reconstruction process (ii) update the AO error breakdown mutually constrained by the telemetry and the images in order to speculate on the origin of the missing error terms and evaluate their magnitude (iii) measure photometry and astrometry in stellar fields.