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Tapered slot antennas (TSAs) have seen considerable application in the millimeter-wave portion of the spectrum. Desirable characteristics of TSAs include symmetric E- and H-plane antenna patterns, and broad non-resonant bandwidths. We investigate extension of TSA operation toward higher frequencies in the thermal infrared (IR), using a metal-oxide-metal diode as the detector. Several different infrared TSA design forms are fabricated using electronbeam lithography and specially developed thin-film processes. The angular antenna patterns of TSA-coupled diodes are measured at 10.6 micrometer wavelength in both E- and H-planes, and are compared to results of finite-element electromagnetic modeling using Ansoft HFSS. Parameter studies are carried out, correlating the geometric and material properties of several TSA design forms to numerical-model results and to measurements. A significant increase in antenna gain is noted for a dielectric-overcoat design. The traveling-wave behavior of the IR TSA structure is investigated using scattering near-field microscopy. The measured near-field data is compared to HFSS results. Suggestions for future research are included

Yes / This study assesses experimentally the role of gas viscosity in controlling the minimum film thickness in slot coating in both the slot over roll and tensioned web modes. The minimum film thickness here is defined with respect to the onset of air entrainment rather than rivulets, the reason being that rivulets are an extreme form of instabilities occurring at much higher speeds. The gas viscosity effects are simulated experimentally by encasing the coaters in a sealed gas chamber in which various gases can be admitted. An appropriate choice of two gases was used to compare performances: air at atmospheric pressure and helium at sub-ambient pressure (25mbar), which we establish has a significantly lower “thin film” viscosity than atmospheric air. A capacitance sensor was used to continuously measure the film thickness on the web, which was ramped up in speed at a fixed acceleration whilst visualizations of the film stability were recorded through a viewing port in the chamber. The data collected show clearly that by coating in rarefied helium rather that atmospheric air we can reduce the minimum film thickness or air/gas entrainment low-flow limit. We attribute this widening of the stable coating window to the enhancement of dynamic wetting that results when the thin film gas viscosity is reduced. These results have evident practical significance for slot coating, the coating method of choice in many new technological applications, but it is their fundamental merit which is new and one that should be followed with further data and theoretical underpinning.

Yes / The antenna array designed to operate over 0.450-0.475 Terahertz comprises two dielectric resonators (DRs) that are stacked vertically on top of each other and placed on the surface of the slot antenna fabricated on a silicon substrate using standard CMOS technology. The slot created in the silicon substrate is meandering and is surrounded by metallic via-wall to prevent energy dissipation. The antenna has a maximum gain of 4.5dBi and radiation efficiency of 45.7% at 0.4625 THz. The combination of slot and vias transform the antenna to a metamaterial structure that provides a relatively small antenna footprint. The proposed series-fed double DRs on-chip antenna array is useful for applications in THz integrated circuits. / Partially supported by innovation programme under grant agreement H2020-MSCA-ITN-2016 SECRET-722424 and the financial support from the UK Engineering and Physical Sciences Research Council (EPSRC) under grant EP/E0/22936/1.

Данная работа посвящена исследованию резонансных и нерезонансных волноводных щелевых антенных решеток. Описан расчет размеров антенной решетки. Проведено моделирование волноводных щелевых антенных решеток. Исследовано влияния перекрытия щелей на характеристики антенной решетки. Исследовано влияние покрытия поверхности антенной решетки слоем диэлектрика. Исследована частотная зависимость волноводных щелевых антенных решеток. / This work is devoted to research of resonant and not resonant waveguide slot-hole antenna lattices. Calculation of the sizes of an antenna lattice is described. Modeling of the waveguide slot-hole antenna lattices is carried out. It is investigated influences of overlapping of cracks on characteristics of an antenna lattice. Influence of a covering of a surface of an antenna lattice is investigated by a dielectric layer. Frequency dependence of the waveguide slot-hole antenna lattices is investigated.

Task-oriented dialogue (ToD) systems need to interpret the user's input to understand the user's needs (intent) and corresponding relevant information (slots). This process is performed by a Natural Language Understanding (NLU) component, which maps the text utterance into a semantic frame representation, involving two subtasks: intent classification (text classification) and slot filling (sequence tagging). Typically, new domains and languages are regularly added to the system to support more functionalities. Collecting domain-specific data and performing fine-grained annotation of large amounts of data every time a new domain and language is introduced can be expensive. Thus, developing an NLU model that generalizes well across domains and languages with less labeled data (low-resource) is crucial and remains challenging. This thesis focuses on investigating transfer learning and data augmentation methods for low-resource NLU in ToD. Our first contribution is a study of the potential of non-conversational text as a source for transfer. Most transfer learning approaches assume labeled conversational data as the source task and adapt the NLU model to the target task. We show that leveraging similar tasks from non-conversational text improves performance on target slot filling tasks through multi-task learning in low-resource settings. Second, we propose a set of lightweight augmentation methods that apply data transformation on token and sentence levels through slot value substitution and syntactic manipulation. Despite its simplicity, the performance is comparable to deep learning-based augmentation models, and it is effective on six languages on NLU tasks. Third, we investigate the effectiveness of domain adaptive pre-training for zero-shot cross-lingual NLU. In terms of overall performance, continued pre-training in English is effective across languages. This result indicates that the domain knowledge learned in English is transferable to other languages. In addition to that, domain similarity is essential. We show that intermediate pre-training data that is more similar – in terms of data distribution – to the target dataset yields better performance.

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