AGH UST Main Page » Doktoranci » Doctoral Schools » AGH Doctoral School » Admissions 2021/2022 » Entry exam topics 2020/2021 » Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering » Automation, and electronics and electrical engineering
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.
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