Electrical excitation of surface plasmons

We exploit a plasmon mediated two-step momentum downconversion scheme to convert low-energy tunneling electrons into propagating photons. Surface plasmon polaritons (SPPs) propagating along an extended gold nanowire are excited on one end by low-energy electron tunneling and are then converted to free-propagating photons at the other end. The separation of excitation and outcoupling proofs that tunneling electrons excite gap plasmons that subsequently couple to propagating plasmons. Our work shows that electron tunneling provides a non-optical, voltage-controlled and low-energy pathway for launching SPPs in nanostructures, such as plasmonic waveguide

Chirality Changes in Carbon Nanotubes Studied with Near-Field Raman Spectroscopy

We report on the direct visualization of chirality changes in carbon nanotubes by mapping local changes in resonant RBM phonon frequencies with an optical resolution of 40 nm using near-field Raman spectroscopy. We observe the transition from semiconducting-to-metal and metal-to-metal chiralities at the single nanotube level. Our experimental findings, based on detecting changes in resonant RBM frequencies, are complemented by measuring changes in the G-band frequency and line shape. In addition, we observe increased Raman scattering due to local defects associated with the structural transition. From our results, we determine the spatial extent of the transition region to be Ltrans 40−100 nm

Factorization of Discriminatively Trained i-vector Extractor for Speaker Recognition

In this work, we continue in our research on i-vector extractor for speaker verification (SV) and we optimize its architecture for fast and effective discriminative training. We were motivated by computational and memory requirements caused by the large number of parameters of the original generative i-vector model. Our aim is to preserve the power of the original generative model, and at the same time focus the model towards extraction of speaker-related information. We show that it is possible to represent a standard generative i-vector extractor by a model with significantly less parameters and obtain similar performance on SV tasks. We can further refine this compact model by discriminative training and obtain i-vectors that lead to better performance on various SV benchmarks representing different acoustic domains.Comment: Submitted to Interspeech 2019, Graz, Austria. arXiv admin note: substantial text overlap with arXiv:1810.1318