On the Influence of Spatial Dispersion on the Performance of Graphene-Based Plasmonic Devices

We investigate the effect of spatial dispersion phenomenon on the performance of graphene-based plasmonic devices at THz. For this purpose, two different components, namely a phase shifter and a low-pass filter, are taken from the literature, implemented in different graphene-based host waveguides, and analyzed as a function of the surrounding media. In the analysis, graphene conductivity is modeled first using the Kubo formalism and then employing a full-$k_\rho$ model which accurately takes into account spatial dispersion. Our study demonstrates that spatial dispersion up-shifts the frequency response of the devices, limits their maximum tunable range, and degrades their frequency response. Importantly, the influence of this phenomenon significantly increases with higher permittivity values of the surrounding media, which is related to the large impact of spatial dispersion in very slow waves. These results confirm the necessity of accurately assessing non-local effects in the development of practical plasmonic THz devices.Comment: 5 pages, 18 figures, 2 table

An intuitive control space for material appearance

Many different techniques for measuring material appearance have been proposed in the last few years. These have produced large public datasets, which have been used for accurate, data-driven appearance modeling. However, although these datasets have allowed us to reach an unprecedented level of realism in visual appearance, editing the captured data remains a challenge. In this paper, we present an intuitive control space for predictable editing of captured BRDF data, which allows for artistic creation of plausible novel material appearances, bypassing the difficulty of acquiring novel samples. We first synthesize novel materials, extending the existing MERL dataset up to 400 mathematically valid BRDFs. We then design a large-scale experiment, gathering 56,000 subjective ratings on the high-level perceptual attributes that best describe our extended dataset of materials. Using these ratings, we build and train networks of radial basis functions to act as functionals mapping the perceptual attributes to an underlying PCA-based representation of BRDFs. We show that our functionals are excellent predictors of the perceived attributes of appearance. Our control space enables many applications, including intuitive material editing of a wide range of visual properties, guidance for gamut mapping, analysis of the correlation between perceptual attributes, or novel appearance similarity metrics. Moreover, our methodology can be used to derive functionals applicable to classic analytic BRDF representations. We release our code and dataset publicly, in order to support and encourage further research in this direction

Exploring opportunities in TinyML

Internet of Things (IoT) has acquired useful and powerful advances thanks to the Machine Learning (ML) implementations. But the implementation of Machine Learning in IoT devices with data centers has some serious problems (data privacy, network bottleneck, etc). Tiny Machine Learning (TinyML) arose in order to have an independent edge device executing the ML program without the necessity of any data center. But there is still the need for high performance computers to train the ML model. But, can this situation improve? This project goes through TinyML and two TinyML techniques capable to train the ML model on-device (what we call TinyML On-Device Learning or TinyODL): TinyML with Online-Learning (TinyOL) and Federated Learning (FL). We study both techniques in a theoretical analysis and try to develop one TinyODL app.Internet of Things (IoT) ha obtingut uns forts avantatges molt usables gràcies a les implementacions del Machine Learning (ML). Però la implementació del Machine Learning en dispositius IoT utilitzant centres de dades porta una sèrie de problemes a tenir en compte (privacitat de les dades, el coll d'ampolla de la xarxa, etc.). Tiny Machine Learning (TinyML) va sorgir amb l'objectiu de tenir dispositious IoT independents executant el programa d'ML sense la necessitat d'un centre de dades. Però encara hi ha la necessitat de fer servir ordinadors d'alta potència per poder entrenar el model d'ML. Així i tot, es pot millorar aquesta situació? Aquest projecte estudia el TinyML i dues de les seves tècniques, del que anomenem TinyML On-Device Learning o TinyODL, capaces d'entrenar el model d'ML en el mateix dispositiu (on-device learning): TinyML with Online Learning (TinyOL) i Federated Learning (FL). S'estudien les dues tècniques des d'una anàlisi teòrica i provem de desenvolupar una aplicació TinyODL.Internet of Things (IoT) ha obtenido unas muy buenas y usables mejoras gracias a las implementaciones del Machine Learning (ML). Pero la implementación de Machine Learning en dispositivos IoT utilizando centros de datos conlleva una serie de problemas a tener en cuenta (privacidad de los datos, el cuello de botella de la red, etc.). Tiny Machine Learning (TinyML) surgió con el objetivo de tener dispotivios IoT independientes ejecutando el programa de ML sin la necesidad de un centro de datos. Pero aún existe la necesidad de usar ordenadores de alta potencia para poder entrenar el modelo de ML. Aún así, se puede mejorar esta situación? Este proyecto estudia el TinyML y dos de sus técnicas, de lo que llamamos TinyML On-Device Learning o TinyODL, capaces de entrenar el model de ML en el mismo dispotivio (on-device learning): TinyML with Online Learning (TinyOL) y Federated Learning (FL). Se estudian las dos técnicas desde un anáisis teórico y probamos de desarrollar una aplicación TinyODL