Automation, electronics, elecrical engineering and space technology
Control problems and dynamic systems: linear and non-linear dynamic systems with lumped and distributed parameters; methods of their description and basic properties. Systems identification, static and dynamic properties of open and closed control systems, control algorithms including PID and tuning methods. Optimal control problems such as time-optimal, minimal energy and LQR. Intelligent control. Designing a digital control system and its implementation, including the real time application. Hierarchical control systems. Control of Discrete Event Systems.
Embedded systems: FPGA systems, heterogeneous programmable devices (e.g. Zynq SoC), ASICs, ASSPs (architecture, programming, typical applications), GPUs and embedded GPUs (architecture, programming and typical applications), microprocessors and microprocessor architectures (design, differences, partitioning, properties, key functional blocks), real-time systems: partitioning and properties, embedded systems programming (specifications, languages, variables, interrupts, DMA channels), printed circuit boards: technology and design.
Robotics: configurations of industrial robots, kinematics and dynamics of industrial robots, planning of the end effector's trajectory, planning of the trajectory of autonomous robots, autonomous vehicle, identification of the environment, control.
Computer vision algorithms and their hardware implementations: algorithms (preprocessing, foreground object segmentation, optical flow, stereovision, detection, tracking), methods for quality assessment of perception algorithms in vision, radar and lidar systems, concept, methods and applications of multi-domain sensory data fusion.
Autonomous vehicles: SAE classification, sensors, functionalities, parameters describing the static and dynamic aspects of large data sets used in the process of machine learning for the perception of the environment in autonomous vehicles, driver assistance systems (discussion of selected functionalities), the traffic planning task and its solution for a vehicle moving in autonomous mode with a defined start and end point.
Automation of industrial processes: structures of real process control systems. The elements of automation systems, the real devices and processes. Distributed control systems. Event control. Industrial Internet of Things (IIoT) and Industry 4.0.
Machine learning and artificial intelligence methods: machine learning methodology, machine learning algorithms (regression, SVM, decision trees, PCA, naive Bayes classifier). The concept of reinforcement learning in planning issues for highly automated robotic systems.
Deep learning: structure and operation methodology of deep neural networks (including CNN, RNN, autoencoders). The use of deep neural networks in the processing of video signals and in the detection of anomalies in diagnostic systems. Optimization of neural network structures in the context of their effective implementation in real-time systems. Interpretable and explainable of AI. Challenges related to the implementation of such solutions in embedded systems, methods of model size reduction: limiting the precision of calculations, pruning.
Computational methods in automation: Fundamentals of numerical methods in the field of approximation, numerical algebra and calculus. Knowledge of the basic methods of static optimization with and without the constraints. Basics of operations research. Modeling and optimization of continuous and discrete problems (basic differences, the types of optimization methods), dedicated exact algorithms, NP difficult and NP complete problems - basic computational complexity classes, dynamic programming, the methods of constraints consideration in approximate optimization algorithms. Multi-criteria analysis of decisions: preference structures, modeling the consequences of decisions made, substitution coefficients, reference sets.
Basic semiconductor devices - diodes and their special types, bipolar and unipolar transistors, thyristor, IGBT - principle of operation, models, characteristics. Analog circuits: single and double transistor amplifiers, filters, current and voltage sources based on transistors. Amplifiers with active load. Darlington circuit and cascodes. Differential amplifier. Power amplifiers. Internal structure of operational amplifiers. Frequency response of amplifiers. Feedback theory. Generators, PLL, stability criteria. Noises. RF systems. Digital circuits: Switching transistors. Inverter, construction of static and dynamic gates. FPGA circuits. Multiplexers. Sequential logic systems. Registers. Counters. Semiconductor memories. Arithmetic systems. Parasitic elements in digital circuits. Synchronization. Internal structure of the microprocessor. Digital circuit modeling: behavioral models, synthesizable models. Hardware description languages. Simulation and design of VLSI circuits: environment and simulation of electronic circuits, types of analysis, and design methods. CMOS technology, scaling. Integrated circuit mask plan drawing rules. Design rule verification, simulations including parasitic elements. Simulations taking into account technological dispersion. Designing digital blocks. Circuit testing. Analog-to-digital and digital-to-analog conversion: converter architectures and their parameters. Voltage comparators. Control and measurement systems. Methods of designing control and measurement systems. Measurement cards and their parameters. Measurement data analysis. Sensor technique. Types of sensors, their parameters, and applications. MEMS technology. Signal theory. Fourier series. Fourier's transform. Laplace transform. Modulation. Sampling. Discrete Fourier transform. Z-transform. CAD tools in the design of electronic circuits. Radio communication. Wireless techniques and systems. Microwave technique. Optical communication and optical networks. Architecture of computer systems. Operating systems - basic issues. Computer networks.
Basic laws and methods of analyzing electric circuits. Linear and nonlinear systems. Systems with lumped and dispersed elements. Stationary and non-stationary systems. Commutation in electrical circuits. AC and DC circuits - analyzes and measurements. Transients in electrical circuits. Power theories in electrical circuits. Measurements in electrical circuits. Maxwell's equations, electric/magnetic field theory. Materials used in electrical engineering; properties of conductive materials, dielectrics, magnetic materials, semiconductors and superconductors. Electrical equipment insulation systems - materials, structures, diagnostic methods. Generation of electricity - conventional and unconventional energy sources, distributed energy resources. Transmission, distribution and use of electric power, power losses in electric networks. Smart grids - concept, technologies, challenges. Quality and reliability of power delivery, Directions and problems of power system development. E-mobility - problems and challenges. Basic semiconductor devices: diode, bipolar transistor, thyristor, IGBT. Power electronic systems: AC/DC, DC/DC, AC/AC. Power electronics interfaces in renewable energy sources. Electric machines and electric drives: DC, AC. Building automation.
Information and communication technology
Algorithmics - definition of the algorithm, time and space complexity, classes of complexity, examples of algorithms differ in complexity classes. Asymptotic notations, running time estimation. Sorting algorithms, BFS and DFS algorithm, a minimal spanning tree of a graph, the shortest path problem and algorithms. The concept of a data structure. Different types of data structures, i.e., single-linked and double-linked lists, hash tables, binary search trees (BST), red-black trees, representations of a directed/undirected graph, and their pros and cons
Programming languages – procedural, object-oriented, and functional languages. Popular control structures/phrases: if, for, while, do, return, break, new, delete, super, etc. Their meaning and use. The overall structure of a program providing in object-oriented and functional languages. Effective use of data structures in various programming languages. Object-oriented programming - concepts of inheritance, polymorphism, and projection. Throwing and handling exceptions.
Parallel processing - the concept of a thread and a process. The idea of shared memory, mutual exclusion, thread, and processes synchronization. Synchronization errors, deadlock, and livelock issues. Models of concurrent systems: dining philosophers' problem, readers and writers, producers and consumers, etc. Synchronization mechanisms: semaphore, monitor, and CAS (compare-and-swap) mechanism. Their meaning and implementations in the contemporary programming languages.
Formal languages - Chomsky's taxonomy of formal languages and automata corresponding to these languages. Turing machine as a computation model. Classes of computability: NP, NP-complete, NP-hard, and others. Examples of problems belonging to these classes. Halting problem. The relationship between formal languages and programming languages.
Databases - types of databases. The architecture of a relational database: tables, relations, keys, indexes, views, component procedures, etc. Basics of SQL, types of queries, and their syntax. Database normalization, normal forms. Effective use of databases. Integration of programming languages and DBs.
Software engineering - requirements engineering, product engineering. Acquisition and analysis of requirements. Models of the software development process. Structural software analysis and modeling. ERD, DFD, STD, FHD diagrams. Object-oriented design (OOD) and analysis (OOA). The concept of object, class, method, message, pattern, encapsulation, interface. UML language - basic diagrams. Software quality - evaluation methods, software metrics, quality management in the software development process.
General IT knowledge - computer architecture and design. Problems and challenges of AI, Turing test vs. the Chinese room idea. Computer-aided decision-making. The idea of heuristics. Examples of heuristic algorithms. Binary arithmetic. Basics of formal logic and discrete mathematics. Examples of computer applications.
Biomedical engineering
Knowledge expected from all candidates
The domain range of "biomedical engineering". Concepts of: biocybernetic model, simulation of biological system and examples of their application to selected problems of biology and medicine. The role of biocybernetics and biomedical engineering in progress of technology, biology and medical sciences as well as civilization achievements.
Knowledge representation methods. Concepts of incomplete and tentative knowledge. Expert systems. Inference rules in systems with rule-based representation of knowledge. Fuzzy logic, evolutionary algorithms. Biomedical engineering systems and applications for diagnostics, therapy, rehabilitation and prosthetics of various organs and body parts – examples and general design rules.
Domain range I: electronics and computers in medicine
Backgrounds of theoretical neurocybernetics, goals and methods of brain modeling, various types of artificial neural networks with applications, basics of cognition sciences. Models of biological and technical perception systems (auditory and visual systems in human), regulatory systems (the concept of homeostasis and structure of management systems), and control systems (control and coordination of motor system, control with the gamma loop, cooperation of synergic and antagonist muscles). Population models.
Computer methods for biomedical signal processing and methods for automated analysis and image recognition. Selected issues of artificial intelligence in biomedical applications.
Methods applied in biological and physiological measurements, monitoring of blood circulation, muscle stress, fetal wellbeing, brain function, visual and auditory perception. Examples of digital supportive tools for signal and image-based diagnostics. Multidimensional and multimodal signals. Computer methods for feature extraction and objects / events classification. Methods of surveillance of human in daily living activities (assisted living), ordination and particular characteristics of sensors. Sensor networks. Data security and privacy-related problems in physiological measurement and data transmission. Hospital information systems, therapy planning automatic and telematic triage. Problems of telemedicine: data secyrity and reliability, seamless data access, aspects of mobility and energetic efficiency of equipment. Brain-Computer Interfaces: paradigms and particular characteristics of BCIs.
Domain range II: biomaterials engineering
Basic concepts and definitions: biomaterial, biocompatibility, bioactivity, medical device, implant, transplant, artificial organ, hybrid organ. The relationship between the structure, properties and manufacturing methods of different types of biomaterials: metallic, polymeric, ceramic and composite. Classification of biomaterials by: material type (metals and alloys, ceramics, polymers, carbons, composites, hybrids) and behavior in the biological environment (biostable, degradable, resorbable). Application of metals, polymers, carbons, composites, calcium phosphate bioceramics, bioactive glasses in medicine, e.g. in surgery, orthopedics, cardiac surgery, dentistry. Surface engineering and surface modification techniques. Methods of analysis: structure, microstructure and properties of biomaterials. Biological response to the implant. Biomaterials testing in vitro and in vivo. Tissue engineering and regenerative medicine.
Domain range III: biomechanics
Basic concepts and definitions: Biomechanics and mechanobiology. Fields and directions of research in biomechanics. Structure – function relationship of tissues. Research fields in biomechanics, Division of joints due to type of movement, Biotribiology and issues related to the exploitation of joints and tissues, Bones – structure and mechanical properties, Models of mechanical properties of bones, Functions and properties of articular cartilage, Models of articular cartilage, Structure and properties of connective tissues based on tendon example, Models describing tendon properties, Structure and functions of the spine, Natural and synthetic biomaterials, Modeling of biomaterials as a viscoelastic elements, Experimental methods in tissue biomechanics (including measurements of stress, strain, displacement etc.). Basics of mechanics of tissue and other biological materials – ultimate tensile, compression, bending and torsional strength.