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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 2025/26Dynamic Models of Chromatography
AnnotationDeep 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. Measurement of physical and chemical characteristics of non-thermal plasma for modification of solid materials
AnnotationThe aim of the work is to study, characterize and design selected sources of non-thermal plasma for the purpose of modification of solids and their surfaces. Plasma as a source of numerous reactive particles is suitable for targeted modification of materials sensitive or inert to chemical interaction. The modification can represent a chemical change of the entire material volume or only its surface properties. Typical modifications are the targeted termination of surface bonds with a suitable functional group. The latter option includes, for example, the additional introduction of a flowing gas into the space between the electrodes, the application of an electromagnetic field or ultrasound to the plasma, etc. The modified source will then be tested for various applications. Deep learning methods in the diagnosis of voice pathology
AnnotationThe dissertation focuses on the application of deep learning in the field of diagnosis of voice pathology through the analysis of voice recordings. Part of the data for the analysis will be measured at the University Hospital of Královské Vinohrady, Prague. The methodological part of the work includes the research of methods for selecting symptoms and possibilities for the synthesis of new symptoms. Another aspect of the work is the use of Kolmogovor-Arnold networks for the classification of voice recordings. It is assumed that the resulting classifier and feature extractor will be implemented within the SW, which is currently used at FNKV to record voice recordings and analyze them. The resulting SW will enable the automatic classification of pathology within the patient examination. Modeling, Simulation and Optimization of Solid Rocket Motor
AnnotationProtective shields for autonomous systems against electromagnetic interference
AnnotationThe rapid advent of autonomous systems such as robotic assistants, drones or self-driving vehicles has inevitably brought with it an increase in the use of positioning devices, such as microwave sensors, or advanced lidar, radar or radio technology. This also increases the likelihood of the occurrence of undesired interferences of this electromagnetic wave with the integrated circuits of the autonomous device, which may in turn lead to an increased probability of the occurrence of dangerous phenomena, including accidents and loss of life. The aim of this work is therefore to develop new materials for the attenuation of electromagnetic interference and to apply them as protective shields in the operating area of the electromagnetic spectrum of existing positioning systems. The work will focus on the search, synthesis and characterization of suitable electrical and magnetic materials and their nanostructured analogues and the subsequent design, manufacture and testing of new lightweight and flexible shields. Part of the work will also be modelling and evaluation of the shielding efficiency of protective shields in simulated and real conditions of operation of autonomous systems. Optimization of Statistical and Machine Learning Models for Multidimensional Data Processing in Chemistry
AnnotationThis work concentrates on the processing, reconstruction, and analysis of multidimensional signals, particularly those with significant interfering components. The analysis of mixed chemical samples, utilizing techniques such as mass spectrometry and capillary electrophoresis, 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 also includes comparing various models and refining them to emphasize the filtering of unwanted components, reconstruction of optimal signals, and direct extraction of significant values. The project involves collaboration with the Department of Biochemistry and Microbiology, leveraging their extensive experience in protein analysis through mass spectrometry. Sensor arrays of tactile temperature and pressure sensors
AnnotationTactile temperature or pressure sensors are devices used in robotics to evaluate the robot's interaction with other objects. These include, for example, manipulating an object, measuring the slip of a gripped object, determining the coordinates of the position of the object or measuring the magnitude of the force acting on the object. The extreme case is complex tactile systems, the purpose of which is to simulate and replace human touch. The sensors used for these purposes must be sufficiently miniature, sensitive to small changes in pressure, must have favorable dynamic properties and time and operational stability of the parameters. Due to the expected high density of tactile sensors connected in simple applications, there must be the possibility of their operation in the form of sensor arrays and data processing using advanced mathematical and statistical algorithms. Last but not least, the cost of producing them must be reasonable so that they can be easily replaced in the event of wear. The aim of this work is therefore to develop new types of tactile temperature and pressure sensors based on modern nanomaterials, which can be used in experiments with the measurement of temporally and spatially distributed forces acting on the matrix of sensors. Part of the work will be the preparation, characterization and processing of thermoelectric and piezoresistive materials based on organic nanostructured semiconductors and carbon nanostructures. Testing of these substances will include, inter alia, structural, chemical and mechanical analysis and measurement of electrical properties in both direct and alternating electric fields. Selected materials will then be processed into sensitive sensors. Part of this work will also be the design of sensor arrays and their testing and signal processing using advanced algorithms. Transport of charge carriers in nanostructured and nanocomposite materials
AnnotationThe topic of the thesis is theoretical and practical study of charge transfer mechanisms in nano-structured and nano-composite materials prepared in the form of thin films, coatings and aerogels. The aim of the thesis is to design models describing the charge transfer in real materials used for chemical sensors. The properties of the nanostructured samples will be measured in the Quantum Design - PPMS system, depending on the temperature and intensity of the magnetic field. The work involves (i) modeling and simulating the transport of charge carriers using the finite element method, (ii) designing and implementing software for managing, collecting and processing data obtained from PPMS system; (iii) seeking an analytical model describing the real (measured) properties of the samples depending on their nanostructure. 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. Processing of chemical sensor signals using artificial intelligence algorithms
AnnotationOne way to improve the selectivity and detection properties of modern chemical sensors is to use artificial intelligence algorithms. The topic of the thesis is to design, prepare and test new approaches for processing and extracting data from multi-component sources such as GC / IMS spectrometer, sensors and sensor arrays with response in the visual, infrared and radio-frequency fields of the electromagnetic spectrum. The solution assumes usage of hardware acceleration of data processing and software-defined radio. |
Updated: 25.3.2022 18:16, Author: Jan Kříž