This paper evaluates a design procedure which is able to scale one-dimensional quadratic-residue diffusers, with integrated Helmholtz resonators. These acoustic structures can be tuned to room modes while fitting within a specified volume. An algorithmic solver is used to control geometric parameters in order to achieve a target frequency. The effect of the diffuser on a room is estimated using Pachyderm. Values obtained with simplified models, that make use of analytically derived coefficients, are compared with those obtained by simulating the full geometry. The predictive power of the simplified modeling made it preferable over simulating the full geometry in comparable scenarios. CFD simulations and measurements taken from a 1:1 scale prototype, are used to evaluate the applicability of lumped mass models to predict resonance frequency and absorption of slit Helmholtz resonators. Although the obtained results remain inconclusive, they indicate a higher inertial attached length for semi-infinite slit resonators, than typically found in literature. If these results can be validated, then the procedure should provide reliable designs.
Micro Ring Resonators (MRRs) have become the workhorse in photonics, both for data/telecomas well as bio-chemical sensing applications. In this contribution the use of MRRs as sensors for food-safety applications will be discussed.
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Studying images in social media poses specific methodological challenges, which in turn have directed scholarly attention toward the computational interpretation of visual data. When analyzing large numbers of images, both traditional content analysis as well as cultural analytics have proven valuable. However, these techniques do not take into account the contextualization of images within a socio-technical environment. As the meaning of social media images is co-created by online publics, bound through networked practices, these visuals should be analyzed on the level of their networked contextualization. Although machine vision is increasingly adept at recognizing faces and features, its performance in grasping the meaning of social media images remains limited. Combining automated analyses of images with platform data opens up the possibility to study images in the context of their resonance within and across online discursive spaces. This article explores the capacities of hashtags and retweet counts to complement the automated assessment of social media images, doing justice to both the visual elements of an image and the contextual elements encoded through the hashtag practices of networked publics.
