Plasmonic properties of metallic nanostructures with reduced symmetry

In this thesis, we theoretically study the plasmonic properties of metallic nanostructures with reduced symmetry using the Plasmon Hybridization (PH) and the Finite Difference Time Domain (FDTD) methods. Both methods provide efficient and accurate results for calculating physical properties of metallic nanostructures, including the optical cross section spectra, the local electromagnetic fields and induced charge densities around the surface of the nanostructures. The PH method is applied to a nanoshell with an offset core (nanoegg). The results show that the reduction in symmetry relaxes the selection rules in the hybridization of primitive plasmon modes, allowing for an admixture of dipolar components in higher multipolar plasmon modes of the particle. The hybridization therefore makes higher multipolar nanoshell plasmon modes dipole active, resulting in a core offset-dependent shift for the plasmon energies and a multipeaked feature in the optical spectrum. The polarization dependence of the optical absorption spectra is found to be relatively weak. The calculations also show significantly larger local-field enhancements on nanoegg's external surface than the equivalent concentric spherical nanostructure. The results agree very well with results from FDTD simulations and experiments, suggesting applications of nanoeggs as substrates for surface enhanced Raman spectroscopy (SERS) Another comprehensive investigation of the plasmonic interactions of individual metallic nanoshells with dielectric substrates is performed using the FDTD method. The results show that the adjacent dielectric breaks the spherical symmetry of individual nanoshell and lifts the degeneracy of the dipole and quadrupole plasmon modes, introducing significant polarization dependent redshifts and hybridization of the nanoparticle plasmon resonances. The results also show that, for small nanoparticle-substrate separations and substrates with large dielectric permittivities, the hybridized quadrupolar nanoparticle plasmon resonances also appear in the scattering spectrum. We discuss different numerical approaches in FDTD simulations for calculating the scattering spectrum in typical dark-field scattering geometries. We also discuss issues of numerical convergence and show that the scattering spectra can be calculated using finite substrate slab models. The results agree very well with experiments, showing that dielectric substrates matter in optical measurements of plasmonic nanoparticles. FDTD method is also applied to a bowtie-shaped nanostructure (nanobowtie). The calculations show significantly large SERS enhancements across a broad bandwidth of exciting wavelengths because of the complicated mode structure possible in the interelectrode gap. Nanometer-scale asperities in the gap area break the inter-electrode symmetry of the structure, resulting in optical excitations of many inter-electrode modes besides the simple dipolar plasmon mode commonly considered. The broken symmetry also leads to much less dependence of the calculated enhancement on polarization direction, as seen experimentally. The calculations confirm that the electromagnetic enhancement is confined in the normal direction to the film thickness and to a region comparable to the radius of curvature of the asperity. The calculated electromagnetic enhancements can exceed 1011, approaching that sufficient for single-molecule sinsitivity. We also compare the calculated extinction spectra for various values of interelectrode conductance connecting the source and drain. The results show that negligible charge transfer occurs between the two electrodes until junction conductance approaches the conductance quantum, G 0 = 2e2/h

Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection

Effective fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e.g. daytime and nighttime). In this paper, we present a novel box-level segmentation supervised learning framework for accurate and real-time multispectral pedestrian detection by incorporating features extracted in visible and infrared channels. Specifically, our method takes pairs of aligned visible and infrared images with easily obtained bounding box annotations as input and estimates accurate prediction maps to highlight the existence of pedestrians. It offers two major advantages over the existing anchor box based multispectral detection methods. Firstly, it overcomes the hyperparameter setting problem occurred during the training phase of anchor box based detectors and can obtain more accurate detection results, especially for small and occluded pedestrian instances. Secondly, it is capable of generating accurate detection results using small-size input images, leading to improvement of computational efficiency for real-time autonomous driving applications. Experimental results on KAIST multispectral dataset show that our proposed method outperforms state-of-the-art approaches in terms of both accuracy and speed

Fresnel diffraction patterns as accelerating beams

We demonstrate that beams originating from Fresnel diffraction patterns are self-accelerating in free space. In addition to accelerating and self-healing, they also exhibit parabolic deceleration property, which is in stark contrast to other accelerating beams. We find that the trajectory of Fresnel paraxial accelerating beams is similar to that of nonparaxial Weber beams. Decelerating and accelerating regions are separated by a critical propagation distance, at which no acceleration is present. During deceleration, the Fresnel diffraction beams undergo self-smoothing, in which oscillations of the diffracted waves gradually focus and smooth out at the critical distance