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Measurement and Signal Processing in Chemistry
Doctoral Programme,
Faculty of Chemical Engineering
--- Careers--- Programme Details
Ph.D. topics for study year 2026/27Bacterial resistance in the context of the antimicrobial effect of non-thermal plasma
AnnotationThis dissertation focuses on studying the potential for the emergence and mechanisms of development of bacterial resistance to non-thermal plasma. The experimental part will monitor the effect of repeated exposure to non-thermal plasma on selected bacterial strains in order to determine whether and how adaptive changes occur that may affect the long-term effectiveness of this technology. The thesis also includes an assessment of the practical implications of these findings for the future use of non-thermal plasma in decontamination and antimicrobial applications. Traffic Image Quality Assessment for Traffic Surveillance in Smart Cities Using Artificial Intelligence
AnnotationThis dissertation focuses on the application of modern machine learning and artificial intelligence methods for analyzing the quality of image data acquired by specialized vehicles in real operational scenarios. The goal is to optimize the selection of the most suitable images for subsequent transmission to superior traffic surveillance, control, and processing systems. The work is based on practical operational scenarios where image sequences are processed under variable lighting conditions, with motion artifacts from the vehicle and surrounding objects, and transmission channel compression limitations. The aim of this work is to design an automated system for evaluating and classifying image quality, with a particular focus on predicting the utility and informational value of the image, especially regarding its suitability for automated license plate recognition and traffic sign reading. Statistical and Machine Learning Models for Multidimensional Data Processing in Chemistry
AnnotationThis work concentrates on the processing, reconstruction, and analysis of multidimensional signals. The analysis of mixed chemical samples, utilizing techniques such as mass spectrometry, generates a vast amount of data, often affected by numerous undesirable physical factors. The objective is to focus on identifying and optimizing suitable statistical and machine learning models. This includes comparing various models and refining them to emphasize the filtering of unwanted components, reconstruction of optimal signals, and direct extraction of significant values. Machine learning in biomedical data analysis
AnnotationThe dissertation focuses on the design and implementation of a comprehensive system for the analysis of biomedical data. The data will be obtained from the Faculty Hospital Královské Vinohrady in Prague and the Pardubice Region Hospital. The system aims to serve as a support tool for physicians, enabling objective assessment of patients' health conditions, while also providing the capability to analyze both univariate and multivariate data, such as ECG, heart rate, motion data, and imaging data from CT and MRI. The analysis will leverage traditional statistical methods (e.g., OLR, RF) as well as modern deep learning approaches. Computational Intelligence in Motion Analysis
AnnotationThe dissertation is devoted to motion analysis using appropriate sensor systems for data acquisition, and methods of computational intelligence for their processing with applications in biomechanics and engineering. The methodology includes the use of database systems with data recorded by wearable sensors, implementation of communication technologies, functional transforms, the use of anatomical frames, definition of a pattern matrix with features in the time and frequency domains, and the use of machine learning for pattern vectors recognition during signal processing. The application part is devoted to motion monitoring, classification of motion patterns, and data analysis. The proposed general methodology, based on common theoretical foundations of digital signal processing, spectral analysis, and artificial intelligence, will be used in the diagnosis of motion patterns with selected interdisciplinary applications. Utilisation of aerogels for gas sensors
AnnotationSignificant development of technology of nanomaterials in the last two decades has enabled the preparation of a wide range of materials for sensoric applications with unique structure and properties. Relatively simple supercritical drying technique, can be used to prepare active layers from the materials used for gas sensors in the form of aerogels. From the point of view of chemical sensors, such nanostructured materials show unique properties in many ways (high sensitivity and selectivity, large active surface). The aim of the work will be the design and implementation of sensors based on aerogels formed by inorganic oxides and their possible chemical (selective organic receptors, surface tension modifiers) and physical modification (laser annealing, incorporation of catalytically active nanoparticles). Impedance spectroscopy and UV-VIS-NIR spectrometry will be used to evaluate the sensor response. Development of diagnostic methods using vibrational spectroscopy and machine learning
AnnotationThe application of vibrational spectroscopy in clinical research opens new opportunities for the development of rapid and reliable diagnostic tools. This dissertation will focus on the design, development, and validation of methods for rapid automated measurement and processing of spectral data obtained from biological samples, with the aim of accelerating and streamlining the use of these techniques in clinical settings. The work will include the development and optimization of measurement parameters, the selection and implementation of algorithmic approaches for data pre-processing, and multivariate statistical analysis using modern machine learning and deep learning methods. The research will reflect current clinical needs and will be conducted with an emphasis on the translational potential of the results into routine clinical practice. The project will be carried out in collaboration with the Department of Analytical Chemistry, FCE, UCT Prague and partnering clinical institutions, particularly Bulovka University Hospital. Development of modern electromagnetic radiation shields as passive protection of information against eavesdropping
AnnotationThe proliferation of modern electronics, integrated circuits, microprocessors and communication and computer technology in general brings with it a high risk of disclosing critical information about the infrastructure in which these elements are used. In the extreme case, there may be a leak or takeover of administrative privileges, which can be misused for digital vandalism, disclosure of important information or attacks on the infrastructure itself. One of the very effective and difficult to detect methods of these attacks is the remote eavesdropping on information that is emanated from electronic devices in the form of electric or magnetic fields. With the development of inexpensive radio technology and as a result of readily available libraries and signal processing algorithms, such an attack may no longer be the sole domain of rich, state-sponsored organizations, but may gradually be adopted by the mainstream hacking community and misused for criminal purposes. The aim of this work is to explore the possibilities and develop and test light and flexible protective shields based on modern nanomaterials, which will serve as an effective passive protection of electronic devices against remote eavesdropping. For this purpose, new composite materials based on electrically conductive nanoparticles with magnetic properties will be prepared. The possibilities of their compatibility with the carrier, chemical structure and morphology, mechanical, electrical and magnetic properties and methods and the possibilities of their processing into the required shape and form suitable for use in miniature electronics will be studied. The experiments will also include testing passive shields in simulated and real conditions and evaluating their ability to dampen electromagnetic waves emitted by electronic devices. |
Updated: 20.1.2022 16:26, Author: Jan Kříž