arXiv subjects

Bruno Miranda

Publications and source records attributed to Bruno Miranda.

7 recordsLinked to original sources

arXiv2026

Robust Fano-like Antiresonances in Large-Area Au-coated Ag-Nanoisland Ensembles

We report robust Fano-like spectral profiles in substrate-supported Au-coated Ag nanoislands fabricated by solid-state dewetting followed by Au overgrowth. Combining electrostatic modal analysis, full-wave simulations, and linear transmission spectroscopy, we investigate the origin of these profiles, their tunability, and their robustness against morphological disorder. Increasing the Au coating thickness progressively red-shifts the low-energy resonance and transfers spectral weight to it from the high-energy resonance, whereas the Fano-like antiresonant dip undergoes a limited spectral displacement. The electrostatic modal analysis identifies this dip as the signature of destructive interference between superradiant and subradiant hybridized modes, and its weak spectral evolution is consistently reproduced by theory, simulations, and experiments. These results establish Au-coated Ag nanoislands as a scalable, lithography-free platform for engineering robust yet tunable Fano-like optical responses accessible through standard far-field spectroscopy.

physics.optics↗

arXiv2026

Theory of Supercritical Coupling And Generalized Bound States in the Continuum

Bound states in the continuum (BICs) arise from destructive interference suppressing radiation despite spectral overlap with the continuum. Here we show that Friedrich--Wintgen interference naturally emerges from a bright--dark supermode decomposition of resonances coupled through a shared radiation channel. In this basis, any finite leakage of a quasi-BIC induces a causality-driven reactive coupling enabling non-Hermitian pumping of the dark sector. We derive the optimal condition for this process and show that it corresponds to the supercritical coupling regime previously identified in [Nature 626, 765 (2024)], while naturally recovering universal quasi-BIC asymmetry scaling. Extending the theory to Dirac-like dispersions in photonic crystal slabs, we identify an open-Dirac singularity where the Dirac gap matches the supercritical regime. A four-wave Hamiltonian quantitatively reproduces rigorous coupled-wave analysis, revealing the breakdown of conventional critical coupling. Near this regime, absorptive cross-coupling induces coherent absorption interference and enables suppression of effective dissipative losses beyond conventional material limits. These results motivate the concept of a generalized bound state in the continuum (gBIC) as a limiting non-Hermitian state where radiative and effective gain compensate, producing a true divergence of the total quality factor. Overall, this work establishes a unified framework connecting BIC interference, Dirac topology, and non-Hermitian physics for ultra-high-Q enhancement and loss engineering in open photonic systems.

physics.optics↗

arXiv2025

Scenario Discovery for Urban Planning: The Case of Green Urbanism and the Impact on Stress

Urban environments significantly influence mental health outcomes, yet the role of an effective framework for decision-making under deep uncertainty (DMDU) for optimizing urban policies for stress reduction remains underexplored. While existing research has demonstrated the effects of urban design on mental health, there is a lack of systematic scenario-based analysis to guide urban planning decisions. This study addresses this gap by applying Scenario Discovery (SD) in urban planning to evaluate the effectiveness of urban vegetation interventions in stress reduction across different urban environments using a predictive model based on emotional responses collected from a neuroscience-based outdoor experiment in Lisbon. Combining these insights with detailed urban data from Copenhagen, we identify key intervention thresholds where vegetation-based solutions succeed or fail in mitigating stress responses. Our findings reveal that while increased vegetation generally correlates with lower stress levels, high-density urban environments, crowding, and individual psychological traits (e.g., extraversion) can reduce its effectiveness. This work showcases our Scenario Discovery framework as a systematic approach for identifying robust policy pathways in urban planning, opening the door for its exploration in other urban decision-making contexts where uncertainty and design resiliency are critical.

cs.CY↗

arXiv2023

Deep-seeded Clustering for Emotion Recognition from Wearable Physiological Sensors

According to the circumplex model of affect, an emotional response could characterized by a level of pleasure (valence) and intensity (arousal). As it reflects on the autonomic nervous system (ANS) activity, modern wearable wristbands can record non-invasively and during our everyday lives peripheral end-points of this response. While emotion recognition from physiological signals is usually achieved using supervised machine learning algorithms that require ground truth labels for training, collecting it is cumbersome and particularly unfeasible in naturalistic settings, and extracting meaningful insights from these signals requires domain knowledge and might be prone to bias. Here, we propose and test a deep-seeded clustering algorithm that automatically extracts and classifies features from those physiological signals with minimal supervision - combining an autoencoder (AE) for unsupervised feature representation and c-means clustering for fine-grained classification. We also show that the model obtains good performance results across three different datasets frequently used in affective computing studies (accuracies of 80.7% on WESAD, 64.2% on Stress-Predict and 61.0% on CEAP360-VR).

cs.LG↗

arXiv2021

Electromagnetic Scattering by Networks of High-Permittivity Thin Wires

The electromagnetic scattering from interconnections of high-permittivity dielectric thin wires with sizes smaller than (or almost equal to) the operating wavelength is investigated. A simple lumped element model for the polarization current intensities induced in the wires is proposed. The circuit elements are capacitances and inductances between the wires. An analytical expression for the induced polarization currents in terms of the magneto-quasistatic current modes is obtained. The connection between the spectral properties of the loop inductance matrix and the network's resonances is established. The number of the allowed current modes and resonances is deduced from the topology of the circuit's digraph. The coupling to radiation is also included, and the radiative frequency shifts and the quality factors are derived. The introduced concept and methods may find applications both at the microwaves and in nanophotonics.

physics.class-ph↗

arXiv2020

Enhancing Electric Fields in High-Index Resonators by Flux Conservation of the Displacement Current Density

Concentrating light within subwavelength spatial regions is a central topic in nanophotonics. In this letter, we introduce a general principle for the subwavelength localization and enhancement of electric fields in high-index resonators, based on the flux conservation of the displacement current density. We apply this design rule to a ring resonator by locally squeezing its section: since the flux is conserved, the electric field is necessarily enhanced to compensate the reduction of the section area. The introduced principle may constitute an important step toward the control of the displacement current density at the nanoscale, guiding the design of the topology and the geometry of complex dielectric structures.

physics.optics↗

arXiv2010

Pattern Recognition in Collective Cognitive Systems: Hybrid Human-Machine Learning (HHML) By Heterogeneous Ensembles

The ubiquitous role of the cyber-infrastructures, such as the WWW, provides myriad opportunities for machine learning and its broad spectrum of application domains taking advantage of digital communication. Pattern classification and feature extraction are among the first applications of machine learning that have received extensive attention. The most remarkable achievements have addressed data sets of moderate-to-large size. The 'data deluge' in the last decade or two has posed new challenges for AI researchers to design new, effective and accurate algorithms for similar tasks using ultra-massive data sets and complex (natural or synthetic) dynamical systems. We propose a novel principled approach to feature extraction in hybrid architectures comprised of humans and machines in networked communication, who collaborate to solve a pre-assigned pattern recognition (feature extraction) task. There are two practical considerations addressed below: (1) Human experts, such as plant biologists or astronomers, often use their visual perception and other implicit prior knowledge or expertise without any obvious constraints to search for the significant features, whereas machines are limited to a pre-programmed set of criteria to work with; (2) in a team collaboration of collective problem solving, the human experts have diverse abilities that are complementary, and they learn from each other to succeed in cognitively complex tasks in ways that are still impossible imitate by machines.

cs.AI↗