iduzel: 64004
idvazba: 76348
šablona: stranka
čas: 9.5.2024 18:23:53
verze: 5351
uzivatel:
remoteAPIs: https://studuj-api.cis.vscht.cz/cms/program?weburl=/studies/degree/phd_theses/
branch: trunk
Server: 147.33.89.153
Obnovit | RAW
idvazba: 76348
šablona: stranka
čas: 9.5.2024 18:23:53
verze: 5351
uzivatel:
remoteAPIs: https://studuj-api.cis.vscht.cz/cms/program?weburl=/studies/degree/phd_theses/
branch: trunk
Server: 147.33.89.153
Obnovit | RAW
iduzel: 64004
idvazba: 76348
---Nová url--- (newurl_...)
domena: 'fchi.vscht.cz'
jazyk: 'en'
url: '/studies/degree/phd_theses/program/locale/en/22340/D405'
iduzel: 64004
path: 8547/4156/1394/4158/10908/12634/12725/64004
CMS: Odkaz na newurlCMS
branch: trunk
Obnovit | RAW
idvazba: 76348
---Nová url--- (newurl_...)
domena: 'fchi.vscht.cz'
jazyk: 'en'
url: '/studies/degree/phd_theses/program/locale/en/22340/D405'
iduzel: 64004
path: 8547/4156/1394/4158/10908/12634/12725/64004
CMS: Odkaz na newurlCMS
branch: trunk
Obnovit | RAW
|
Measurement and Signal Processing in Chemistry
Doctoral Programme,
Faculty of Chemical Engineering
--- Careers--- Programme Details
Ph.D. topics for study year 2024/25Multi-Channel Data Analysis in Biomedicine
AnnotationThe dissertation is devoted to multi-channel data acquisition by selected sensor systems and their processing using specific artificial intelligence tools. A special attention is devoted to sensors for simultaneous data acquisition and the use of wireless communication links for their recording and organization in the selected database system. The associated mathematical processing includes data symmetry evaluation, machine learning application, and pattern recognitiion in engineering and biomedicine. The application is devoted to monitoring of neurological signals, evaluation of motion symmetry, and deep learning application for classification of patterns. Results will enable real time data processing using computational intelligence for dynamic access to records through Internet connection.
Contact supervisor
Study place:
Department of Mathematics, Informatics and Cybernetics, FCE, VŠCHT Praha
Digital Data Processing for Motion Kinematics
AnnotationThe dissertation is devoted to motion kinematics based on multi-channel data analysis using computational intelligence and digital multidimensional signal processing tools both in the time, frequency, and scale domains. The methodology includes discrimination methods, machine learning, and pattern vector recognition tools for data classification and motion signals modelling in engineering and biomedicine. The application is devoted to monitoring of physiological signals, recognition of motion patterns, and gait data evaluation using selected wearable sensors including accelerometers, positioning GNSS satellite receivers, and thermal cameras. Results will enable real time data processing for diagnostics and artificial intelligence treatment.
Contact supervisor
Study place:
Department of Mathematics, Informatics and Cybernetics, FCE, VŠCHT Praha
Detection of released gases as early warning of Li-batteries disfunction
AnnotationThe operation of lithium-ion batteries (LIB) always brings a certain risk of the so-called "thermal runaway of batteries". To specify it in more details the short-circuit inside the battery may cause local overheating which destroys internal insulation. Then the increasing electric current stimulates further increase of temperature, leading to release of explosive or toxic gases to the environment with the danger of fire or explosion. It was experimentally proved, however, that focusing on detection of small concentrations of released gaseous species, we can reliably identify early stages of battery destruction, which can be a basis of early-warning systems indicating LIB malfunction. The gaseous species which are released from LIB during thermal runaway, are mostly CO2, CO, H2 and CH4. The student will induce the conditions which take place during LIB malfunction and will be focused to development of sensors (preferably chemiresistors) that enable to detect the above mentioned gaseous markants.
Contact supervisor
Study place:
Department of Physics and Measurement, FCE, VŠCHT Praha
Dynamic Models of Chromatography
Annotation
Contact supervisor
Study place:
Department of Mathematics, Informatics and Cybernetics, FCE, VŠCHT Praha
Hybrid and adaptive software sensors for advanced monitoring of bioprocesses
AnnotationThe quality of process control of biotechnological production processes used in the pharmacy and food industry is often constrained by the limited possibilities of on-line measurement of key process parameters (e.g. cell concentration, growth rate, production rate, etc.). One possible solution is the use of software sensors to continually estimate the values of key process indicators from on-line measurable process variables. The proposed PhD thesis is focused on the development of hybrid software sensors and data-driven software sensors with dynamically switched structure, that will be able to evaluate the quality of their estimation during the estimation process and continuously adjust the composition of their data inputs, i.e. use a different on-line measured variable or set of variables for each individual phase of the process.
Contact supervisor
Study place:
Department of Mathematics, Informatics and Cybernetics, FCE, VŠCHT Praha
Mathematical Modeling and Identification via Machine Learning Techniques
Annotation
Contact supervisor
Study place:
Department of Mathematics, Informatics and Cybernetics, FCE, VŠCHT Praha
Measurement of physical and chemical characteristics of corona discharges with respect to some biological applications
AnnotationThe aim of the work is to intensify selected properties of existing non-thermal, especially corona discharges with regard to their possible use for surface decontamination and other biological applications. Intensification means achieving higher discharge powers or qualitative change in the discharge mode without undesirable phenomena such as transition to spark or arc, excessive heating in the discharge area, etc. Modification (intensification) can generally be accomplished, for example, by including a suitable element in the electrical circuit of the discharge, by changing the geometry of the electrodes, by changing the nature of the supply voltage of the discharge, or by influencing the plasma already formed between the discharge electrodes. The latter option includes, for example, additional introduction of a flowing gas into the space between the electrodes, application of an electromagnetic field or ultrasound to the plasma, etc. The intensified discharge will then be tested for various biological applications.
Contact supervisor
Study place:
Department of Physics and Measurement, FCE, VŠCHT Praha
Modern machine learning methods in biomedical data analysis
AnnotationThe aim of the dissertation is the design and implementation of a complex system for the analysis of biomedical data. Data for analysis will be provided/measured at the University Hospital of Královské Vinohrady Prague and the Hospital of the Pardubice Region. The system will (i) serve as an auxiliary tool for the specialist (MD) in the objective assessment of the patient's current condition, (ii) enable the analysis of one- and multi-dimensional data (mainly ECG, heart rate, movement data, possibly CT and NMR). The methodology used for the analysis will be based on classical statistical methods (OLR, RF, etc.) and will also use deep learning methods.
Contact supervisor
Study place:
Department of Mathematics, Informatics and Cybernetics, FCE, VŠCHT Praha
Multivariable chemical gas sensors
AnnotationThe topic of the thesis is the research and development of new types of chemical gas sensors – multivariable sensors based on photoluminescence combined with electrical conductivity measurement approaches or using quartz crystal microbalances (QCM). Multivariable sensing will improve sensor selectivity, detection limit, and reduce drift to increase their overall accuracy. Inorganic materials based on nanostructured metal oxides doped with lanthanides (e.g. SnO2, TiO2, ZnO and WO3) as well as organic materials, e.g. porphyrin derivatives, can be used as luminescent sensitive layers. Rare earth dopants have a high luminescent yield and a long lifetime, which facilitates optical detection, especially detection based on luminescence decay time. In the QCM scanning approach, it is advantageous to use layers of black metals (BM), e.g. Au, Al, Ag, Pd, Ti, ..., which exhibit an extremely large surface area due to the high porosity of BM, which is much larger than its geometric area. BM layers ensure a lower detection limit and increase the selectivity of the QCM sensor while maintaining a high quality factor value. QCM benefits from its robust nature, affordability, and affordable interface electronics. BM can also be used in chemiresistors, where there is a possibility of decorating their surface using the above-mentioned luminescent layers and other materials (e.g. 2D nanomaterials). Thin film structures will be prepared by PVD techniques (evaporation, magnetron sputtering, pulsed laser deposition). To modify the properties of the structures, the interaction with intense laser radiation will also be investigated. The use of AI technologies is beneficial for evaluating sensor response. The functional properties of the sensors will be optimized based on the characterization of thin films including optical, structural, electronic structural and sensor properties. The results will be important for the development of more efficient devices for chemical gas sensors. Devices based on the developed sensors could provide fast and reliable detection of trace amounts of chemicals and explosives, which is a high priority for security, defense, critical infrastructure protection, industrial process and environmental monitoring. The work will be carried out in cooperation with the Institute of Physics of the Czech Academy of Sciences, v.v.i., with the possibility of involvement in the project of excellent research of the OPJAK program "Sensors and detectors for the information society of the future", projects supported by the Czech Science Foundation and the Technology Agency of the Czech Republic and international cooperation.
Contact supervisor
Study place:
Department of Physics and Measurement, FCE, VŠCHT Praha
Protective 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.
Contact supervisor
Study place:
Department of Mathematics, Informatics and Cybernetics, FCE, VŠCHT Praha
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 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.
Contact supervisor
Study place:
Department of Mathematics, Informatics and Cybernetics, FCE, VŠCHT Praha
Model order reduction and optimization in engineering applications
AnnotationThe work is focused on the application of modern methods of model order reduction (MOR) in engineering practice, including chemical and material engineering. The full order models (FOM) are based on the methods of computational continuum dynamics (both fluids and solids). The FOM-generated data are processed via a posteriori data-driven MOR methods such as proper orthogonal decomposition (POD) or its shifted variant (shiftedPOD). The reduced-order model is prepared either in a standard, projection-based, manner or utilising machine learning. The MOR methodology developed within the dissertation will be applied in a number of engineering-driven optimisation problems.
Contact supervisor
Study place:
Department of Mathematics, Informatics and Cybernetics, FCE, VŠCHT Praha
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.
Contact supervisor
Study place:
Department of Mathematics, Informatics and Cybernetics, FCE, VŠCHT Praha
Study of the degree of internal order in quantum confined silicon nanostructures
AnnotationNanomaterials became an indisputable part of technological development in the 21st century. The modification of the mechanical, chemical or physical properties arising from their decreasing size is so significant that it can play a fundamental role in the usability of the given materials. A nanomaterial with high application potential are silicon nanostructures. They show high lithium storage, high stress resistance and efficient light energy conversion and generation. The extremely low size of this material in the range of several nanometers is, on the one hand, beneficial as a property tuning mechanisms. On the other hand, it also makes them difficult to characterize structurally. In theory, a small nanoparticle can exist in a large number of structural variations with different surface facets, or it can even be partly or fully amorphous lacking a longer-range order. Such structural variants will inevitably impact physical and possibly also chemical properties. In reality, several methods can be applied to study the structure of materials, for example X-ray diffraction (XRD), Raman spectroscopy, or electron microscopy (TEM). In such small dimensions, however, these methods are limited by the low degree of long-range arrangement and high surface to volume ratio. In particular in silicon nanoparticles, even such well-established methods as XRD and TEM were shown to lead to contradictory results, raising fundamental questions about the real structural order in such small objects. Thus, the aim of this thesis will be the synthesis and subsequent characterization of silicon nanostructures while tuning their size and structure. The synthesis will be performed mainly in non-thermal plasma. After the synthesis, the produced nanostructures will be structurally characterized and the characterization methods will be assessed with regards to their ease of application, accuracy and possible limitations. In this way, nanostructures with on-demand structure will be produced and questions about the possible structural variations which can be experimentally realized in these structures will be answered.
Contact supervisor
Study place:
Department of Physics and Measurement, FCE, VŠCHT Praha
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.
Contact supervisor
Study place:
Department of Physics and Measurement, FCE, VŠCHT Praha
Application of machine learning methods for interdisciplinary analysis of geographic data
AnnotationIn the past decade, there has been a dynamic development of airborne laser scanning of the altimeter of the territory of most European countries, including the Czech Republic. Digital terrain models, which are one of the results of this laser scanning, provide an extremely large amount of detailed information about the nature of the earth's surface within the given territory. Manual analysis of these data sets is very laborious and lengthy and, in the case of examining larger areas, relatively inefficient, especially from the point of view of human labor costs. In this context, machine learning methods offer a promising alternative for solving this very topical problem. This work is specifically focused on the analytical processing of digital terrain models using machine learning methods for the purpose of identifying and classifying the relics of terrain objects created by human activity (use e.g. in archaeology, nature conservation, etc.).
Contact supervisor
Study place:
Department of Mathematics, Informatics and Cybernetics, FCE, VŠCHT Praha
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.
Contact supervisor
Study place:
Department of Mathematics, Informatics and Cybernetics, FCE, VŠCHT Praha
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.
Contact supervisor
Study place:
Department of Physics and Measurement, FCE, VŠCHT Praha
|
Updated: 25.3.2022 18:16, Author: Jan Kříž