KAŠPÁREK, T. Adaptivní náhradní modelování pulsarového signálu [online]. Brno: Vysoké učení technické v Brně. Fakulta informačních technologií. .
Ing. Tomáš Kašpárek began his doctoral studies under my supervision in July 2020. Since his enrollment, he has been a valuable member of the Aeroworks research team at the Faculty of Information Technology, Brno University of Technology. As a doctoral candidate, Tomáš Kašpárek made important contributions to a range of multidisciplinary research projects supporting their successful and timely completion. The following projects are particularly relevant to his dissertation: Artificial Intelligence Driven Autonomy (TAČR TN01000029/02, 2019-2020), Tactical cognitive agent (TAČR TN01000029/16, 2021-2022), HERA cubesat implementation phase (ESA 4000131925/20/NL/GLC, 2022-2024), Czech ambitious project - SLAVIA (ESA 4000131925/20/NL/GLC, 2022-2024), Advanced compression and noise reduction for hyperspectral images data (ESA 3-17659/22/NL/GLC/mkn, 2022-2024). Tomáš Kašpárek achieved international acclaim for his role as an Investigation Team Member on the Double Asteroid Redirection Test (DART) project, supervised by the NASA's Planetary Defense Coordination Office. DART was the first-ever space mission to demonstrate asteroid deflection by a kinetic impactor. His visibility increased further after being awarded with an European Space Agency (ESA) project “Advanced compression and noise reduction for hyperspectral images data,” where he held a prime contractor position. Tomáš Kašpárek completed his dissertation in English, focusing on the adaptive surrogate modeling of pulsar signals and closing the gap between astrophysics, experimental radio-telescope observations, and utilization of modern techniques in complex phenomenological modeling of pulsars for future autonomous deep space navigation. He conducted independent multidisciplinary research and presented achieved results at international conferences, including the SPIE Remote Sensing in 2021, the 53rd Lunar and Planetary Science Conference in 2023, and the European Data Handling & Data Processing Conference in 2023. Tomáš Kašpárek has co-authored two journal papers, one for the Planetary Science Journal and the other for Aerospace, both of which are currently under review. In addition, Tomáš Kašpárek co-authored five software applications, including the ESA-contracted "Advanced hyperspectral image compression," which focuses on the on-board preprocessing of hyperspectral images to enable higher compression ratio for the HERA/Milani mission. He also contributed to the "ASPECT hyperspectral camera image on-board processing,” designed to assess the quality of images of the Dimorphos and Didymos asteroids. Tomáš Kašpárek has actively participated in international space consortiums and contributed to international space projects overseen by ESA and NASA, fulfilling the foreign internship requirement as part of his doctoral study. The submitted dissertation is of high quality and complies with requirements.
I recommended that thesis is accepted for PhD defense and the author is awarded the PhD degree.
Mr. Kašpárek's expertise and work are closely aligned with the current challenges in X-ray Pulsarbased Navigation (XNAV) applications and the integration of Machine Learning (ML) algorithms in signal modeling. His thesis present a comprehensive theoretical knowledge spanning space navigation, astrophysics, radio observation, signal processing, modeling, and simulations, complemented by hands-on experience gained from involvement in industrial research projects. This has led to exceptional results that surpass the current state-of-the-art in synthetic pulsar signal generation techniques and their application for pulsar-based positioning and timing. Notably, Mr. Kašpárek has taken into account operational constraints, computational limits for on-board optimization, and the intricacies of hyper-parameter selection, among other considerations. I grade the thesis as “Very Good” (magna cum laude) I do recommend continuation of the Ph.D. degree examination process.
eVSKP id 161901