Understanding the Physical Behavior of Plasmonic Antennas Through Computational Electromagnetics

This chapter focuses on understanding the electromagnetic response of nanoscopic metallic antennas through a classical computational electromagnetic algorithm: volumetric method of moments (V‐MoMs). Under the assumption that metals only respond to external electromagnetic disturbance locally, we rigorously formulate the light‐nanoantenna interaction in terms of a volume integral equation (VIE) and solve the equation by using the method of moments algorithm. Modes of a nanoantenna, as the excitation independent solution to the volume integral equation (VIE), are introduced to resolve the antenna’s complex optical spectrum. Group representation theory is then employed to reveal how the symmetry of a nanoantenna defines the modes’ properties and determines the antenna’s optical response. Through such a treatment, a set of tools that can systematically treat the interaction of light with a nanoantenna is developed, paving the road for future nanoantenna design

RCS-based Quasi-Deterministic Ray Tracing for Statistical Channel Modeling

This paper presents a quasi-deterministic ray tracing (QD-RT) method for analyzing the propagation of electromagnetic waves in street canyons. The method uses a statistical bistatic distribution to model the Radar Cross Section (RCS) of various irregular objects such as cars and pedestrians, instead of relying on exact values as in a deterministic propagation model. The performance of the QD-RT method is evaluated by comparing its generated path loss distributions to those of the deterministic ray tracing (D-RT) model using the Two-sample Cramer-von Mises test. The results indicate that the QD-RT method generates the same path loss distributions as the D-RT model while offering lower complexity. This study suggests that the QD-RT method has the potential to be used for analyzing complicated scenarios such as street canyon scenarios in mmWave wireless communication systems

Development of an auto-calibrated interfacing circuit for thick film multi-gas sensor

A simple, cheap, and integrated architecture is introduced to measure gases with a thick film gas sensor. The temperatures of the sensors are stabilized by controlling the heaters of the sensors. The heaters’ temperatures are measured by sampling the heaters resistance through the use of a voltage divider and ADCs. A microcontroller accordingly adjusts the output of DACs in order to apply the appropriate steering voltage to the heaters. The method employed to measure the gases is to sample the voltage drop over the resistances of the sensors, which are depending on the gases, by ADCs. The innovation lies in the simplicity of the design and the use of different simple methods and commercially available technologies to fabricate the circuit. Also, a single microcontroller is used to drive and control the heaters’ temperature, to compensate ambient temperature of the heaters, to measure and monitor the amount of gases detected by sensors and finally, to select the sensors. This opens the possibility to use these gas sensors for monitoring purposes at a large scale, for example in alarms and computers

Multifunctional blazed gratings for multiband spatial filtering, retroreflection, splitting, and demultiplexing based on C 2symmetric photonic crystals

The concept of multifunctional reflection-mode gratings that are based on rod-type photonic crystals (PhCs) with C 2 symmetry is introduced. The specific modal properties lead to the vanishing dependence of the first-negative-order maximum on the angle of incidence and the nearly sinusoidal redistribution of the incident-wave energy between zero order (specular reflection) and first negative diffraction order (deflection) at frequency variation. These features are key enablers of diverse functionalities and the merging of different functionalities into one structure. The elementary functionalities, of which multifunctional scenarios can be designed, include but are not restricted to multiband spatial filtering, multiband splitting, retroreflection, and demultiplexing. The proposed structures are capable of multifunctional operation in the case of a single polychromatic incident wave or multiple mono-/polychromatic waves incident at different angles. The generalized demultiplexing is possible in the case of several polychromatic waves. The aforementioned deflection properties yield merging demultiplexing with splitting in one functionality. In turn, it may contribute to more complex multifunctional scenarios. Finally, the proposed PhC gratings are studied in transmissive configuration, in which they show some unusual properties.Fil: Serebryannikov, Andriy E.. Adam Mickiewicz University; PoloniaFil: Skigin, Diana Carina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Vandenbosch, Guy A. E.. Katholikie Universiteit Leuven; BélgicaFil: Ozbay, Ekmel. Bilkent University; Turquí

Deep Injective Prior for Inverse Scattering

In electromagnetic inverse scattering, the goal is to reconstruct object permittivity using scattered waves. While deep learning has shown promise as an alternative to iterative solvers, it is primarily used in supervised frameworks which are sensitive to distribution drift of the scattered fields, common in practice. Moreover, these methods typically provide a single estimate of the permittivity pattern, which may be inadequate or misleading due to noise and the ill-posedness of the problem. In this paper, we propose a data-driven framework for inverse scattering based on deep generative models. Our approach learns a low-dimensional manifold as a regularizer for recovering target permittivities. Unlike supervised methods that necessitate both scattered fields and target permittivities, our method only requires the target permittivities for training; it can then be used with any experimental setup. We also introduce a Bayesian framework for approximating the posterior distribution of the target permittivity, enabling multiple estimates and uncertainty quantification. Extensive experiments with synthetic and experimental data demonstrate that our framework outperforms traditional iterative solvers, particularly for strong scatterers, while achieving comparable reconstruction quality to state-of-the-art supervised learning methods like the U-Net.Comment: 13 pages, 11 figure

Micromechanically Tunable Dielectric Rod Resonator

A resonant frequency control method for dielectric rod resonators is discussed. A dielectric rod of cylindrical shape is placed inside a metal cavity. The bottom face of the dielectric rod is fixed at the metal base plate. Resonant frequency tuning is achieved by lifting the top metal plate above the dielectric rod upper face. The paper presents simulations using the mode matching technique and experimental study of this tunable resonator. Resonant frequency of the basic mode can be tuned by more than an octave with displacements of only tens of micrometres, which is in range of piezoactuators, MEMS, etc. A distinct feature of the proposed tuning technique is that the quality factor of the system does not degrade throughout the tuning range