New doctoral dissertation

February 10, 2023
VILNIUS TECH Library invites you to follow the published new dissertations. The dissertation „Investigation of detection properties of planar microwave diodes based on A3B5 semiconductor compounds in millimeter–wavelength range“ („Planarinių mikrobangų diodų A3B5 puslaidininkinių junginių pagrindu detekcinių savybių tyrimai milimetrinių bangų ruože“) prepared by VILNIUS TECH, Maksimas Anbinderis. The dissertation was prepared in 2016–2022, supervisor – Prof. Dr Algirdas Sužiedėlis.

The dissertation was defended at the public meeting of the Dissertation Defense Council of Electrical and Electronic Engineering in the Senate Hall of Vilnius Gediminas Technical University at 10 a.m. on 10 February 2023.

„Successful development of microwave technologies requires electromagnetic detectors capable of sensing high frequency signals at low levels of microwave power. Bulk barrier planar microwave diodes operating based on the major carrier phenomena are promising in high frequency electromagnetic radiation sensing applications. The dissertation aimed to develop and investigate new original planar microwave diodes with a lower spread of their electrical parameters and capable of detecting an electromagnetic signal in the millimeter wavelength range. The first chapter reviews the physical properties of microwave diode based detectors with quasi linear and non linear current voltage characteristics and microwave diodes with a two dimensional electron gas channel. Then, the application technologies for microwave detectors and methods for their investigation using appropriate probing systems are discussed. The second chapter covers the aspects of the development of planar semiconductor microwave diodes based on GaAs, AlGaAs, and AlGaAs/GaAs compounds. Next, the methodology for investigation methods of electrical parameters and detection properties of the microwave diodes is presented. The third chapter presents the results of experimental investigations of the electrical parameters and detection properties of planar dual microwave diodes based on a semi insulating or low resistivity GaAs substrate, including current voltage characteristics, detected voltage on power characteristics and dependence of voltage sensitivity on frequency in the millimeter wavelength range. The fourth chapter presents theoretical estimations and experimental investigations of the electrical and detection properties of bow tie type microwave diodes with partial gate above a two dimensional electron gas channel based on a selectively doped GaAs/AlGaAs heterostructure. The dissertation presents new developed planar microwave diodes, advanced techniques for investigating their properties, and ways for enhancing their detection properties. Five scientific papers were published on the topic of the dissertation: three papers in scientific journals included in the list of Clarivate Analytics Web of Science database with an impact factor, and two papers in conference proceedings included in the Clarivate Analytics Web of Science and Scopus databases. A European patent based on the results of the dissertation has been granted, and twelve reports, including the results of the dissertation, were presented at national and international scientific conferences.“

Doctoral dissertation readers can search via VILNIUS TECH Virtual Library.

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New doctoral dissertation
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VILNIUS TECH Library invites you to follow the published new dissertations. The dissertation „Research and application of machine learning methods for migraine attack prediction“ prepared at VILNIUS TECH by Viroslava Kapustynska. The dissertation was prepared in 2021–2026. Scientific consultant – Prof. Dr Šarūnas Paulikas. The dissertation was defended at the public meeting of the Dissertation Defense Council of the Scientific Field of Electrical and Electronic Engineering in the Aula Doctoralis Meeting Hall of Vilnius Gediminas Technical University at 2 p.m. on 9 June 2026. Migraine is a complex neurological disorder characterized by strong inter- and intra-individual variability, which makes early forecasting difficult using only clinical observations. Wearable biosensors combined with machine learning offer new opportunities to detect subtle physiological changes that may precede migraine attacks and to develop individualized prediction models. This dissertation investigates migraine analysis and next-day prediction using physiological recordings collected under real-life monitoring conditions. Data were obtained with the Empatica Embrace Plus wearable device and include electrodermal activity, pulse rate, skin temperature, and movement-related signals. The analysis focuses on nocturnal recordings, since the night period provides a more stable physiological context with fewer external disturbances. Nights were standardized using sleep-based contextual selection and consistent night-level rules. The experimental framework is organized in two stages. In the first stage, a window-level binary classification task is used as an exploratory methodological analysis to examine how design choices influence model performance. Night recordings are segmented into analysis frames ranging from 5 to 120 minutes, statistical features are extracted, and the influence of signal preprocessing and feature representation is evaluated across several classifier families, including Random Forest, XGBoost, histogram-based gradient boosting, support vector machines, and k-nearest neighbors. In the second stage, the research evaluates next-day migraine prediction based on whole-night recordings. This stage refines the experimental methodology to obtain more reliable estimates of predictive performance under a stricter validation framework. The analysis focuses on the effect of temporal aggregation while comparing the same classifier families under consistent evaluation conditions. The results demonstrate considerable variability across participants in achievable prediction performance and optimal modeling configurations. Shorter analysis frames generally preserve informative short-term physiological changes, whereas longer windows tend to smooth these variations. Signal preprocessing shows a window-dependent effect and does not consistently improve performance. Overall, the results highlight the importance of temporal resolution, rigorous validation, and individualized modeling for wearable-based migraine prediction systems. Doctoral dissertation readers can search via VILNIUS TECH Virtual Library.
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