Magistracy
Speciality Code:
7M07125
Speciality Name:
Еlectronics and Control Systems
Faculty:
of Physics and Technology
Qualification:
- Scientific and pedagogical direction - Master of Engineering Sciences
- Model of graduating student
- Mandatory disciplines
- Elective disciplines
- Professional
ON1. Explain the principles of building devices for automated control systems, in order to determine their functions and characteristics of the structural units of power, digital devices and electronic sensors;
ON2. Apply design methods for non-linear adaptive systems and neural networks to create intelligent control systems using deep machine learning mechanisms;
ON3. Design embedded systems using modern digital integrated circuits for use in control units of intelligent and multi-agent systems.
ON4. Use modern digital data transmission systems to create wired and wireless communication channels using the technology of the Internet of things and sensor networks in automated control systems;
ON5. Develop power and electronic functional blocks of control systems of embedded systems, characterize their structural modules, ensure uninterrupted communication between them for remote control and monitoring in real time using the Internet of things technology;
ON6. To analyze the applicability of intelligent systems using neural networks and machine learning methods for data processing, in order to improve the efficiency of the process;
ON7. Solve problems associated with the creation of automated dispatch control and monitoring of data from the sensor system using modern technologies of various data transfer technologies using the Internet of things;
ON8. Identify key features and functional characteristics of the developed adaptive automated control systems based on neural networks in order to optimize the process;
ON9. Demonstrate a high level of competence in the development of project documentation, educational and methodological complexes, define goals and methods for solving analytical and technical problems in the field of automated control systems;
ON10. Integrate the skills of designing and developing electronic and power devices to create hardware and software for intelligent control systems;
ON11. Evaluate adaptive and non-linear methods used in embedded control systems, identify advantages and disadvantages of these systems in order to improve the existing system;
ON12. Demonstrate a civic and ideological position, formulate problems, goals, tasks in the theoretical and practical areas of management systems, work with foreign scientific and technical literature, participate in international cooperation in the field of professional activity, be able to organize the work of scientific and technical personnel, use an individual approach to students in the implementation of pedagogical activities in the field of electronics and control systems.
ON2. Apply design methods for non-linear adaptive systems and neural networks to create intelligent control systems using deep machine learning mechanisms;
ON3. Design embedded systems using modern digital integrated circuits for use in control units of intelligent and multi-agent systems.
ON4. Use modern digital data transmission systems to create wired and wireless communication channels using the technology of the Internet of things and sensor networks in automated control systems;
ON5. Develop power and electronic functional blocks of control systems of embedded systems, characterize their structural modules, ensure uninterrupted communication between them for remote control and monitoring in real time using the Internet of things technology;
ON6. To analyze the applicability of intelligent systems using neural networks and machine learning methods for data processing, in order to improve the efficiency of the process;
ON7. Solve problems associated with the creation of automated dispatch control and monitoring of data from the sensor system using modern technologies of various data transfer technologies using the Internet of things;
ON8. Identify key features and functional characteristics of the developed adaptive automated control systems based on neural networks in order to optimize the process;
ON9. Demonstrate a high level of competence in the development of project documentation, educational and methodological complexes, define goals and methods for solving analytical and technical problems in the field of automated control systems;
ON10. Integrate the skills of designing and developing electronic and power devices to create hardware and software for intelligent control systems;
ON11. Evaluate adaptive and non-linear methods used in embedded control systems, identify advantages and disadvantages of these systems in order to improve the existing system;
ON12. Demonstrate a civic and ideological position, formulate problems, goals, tasks in the theoretical and practical areas of management systems, work with foreign scientific and technical literature, participate in international cooperation in the field of professional activity, be able to organize the work of scientific and technical personnel, use an individual approach to students in the implementation of pedagogical activities in the field of electronics and control systems.
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Psychology of management
- Number of credits: 3
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: Purpose: to form in undergraduates the competencies necessary for the application of modern psychological approaches to scientific management. The following will be studied: the theoretical foundations of managerial interaction, the psychological features of the implementation of basic managerial functions, the psychology of the subject of managerial activity, the methods of psychological research in the field of managerial activity and interaction.
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Organization and Planning of Scientific Research (in English)
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to form the research culture of the future specialist, and to study the theoretical and methodological principles of organizing research activities in the context of the development of science and society. The discipline is aimed at developing the ability to conduct independent scientific research using methods and techniques of analysis, and information scientific resources.
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Optimal control systems
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: To form the skills of students in the analysis and synthesis of optimal control systems. Problem of optimal control and classification; solution of linear problems of optimal control; method of Lagrange multipliers; calculus of variations and Pontragin's minimum principle; Euler equations; the principle of optimality and dynamic programming; numerical methods for solving the problem of optimal control using direct and indirect methods; Hamilton Jacobi-Bellman equation; algebraic Riccati equation.
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Modern methods of project management in engineering
- Number of credits: 6
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: To teach master students modern principles of life cycle management of engineering projects for their successful implementation. Project management process; project risk management analysis; project planning and control; critical path method; program evaluation review method; aspects and applications of CPM and PERT; project lifecycle concepts; concept of forward tariffs and payback time; planning and organization the job; resource alignment and resource limitation; graphical assessment and review technique; Q-GERT; queue analysis and constraint theory.
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Foreign Language (professional)
- Number of credits: 6
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose is to acquire and improve competencies by international standards of foreign language education and to communicate in an intercultural, professional, and scientific environment. A master's student must integrate new information, understand the organization of languages, interact in society, and defend his point of view.
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History and Philosophy of Science
- Number of credits: 3
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: Purpose: Understanding of modern philosophy as a system of scientific knowledge, including worldview in rational-theoretical comprehension. The discipline includes aspects of the evolution and development of scientific thinking, historical moments, the contribution of scientists and scientific schools to the formation of science, and ethical and social aspects of scientific activity.
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Nonlinear and adaptive control systems
- Number of credits: 6
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: To teach master students methods of operational identification of parameters and methods of adaptive control of nonlinear systems with uncertain parameters. Weak Lyapunov functions; connection with observability; minimum-phase systems and universal regulators; relative degree and minimum phase; control of Lyapunov functions; Sontag's universal formula; gradient method; adaptive laws with normalization; linear parameterization of the installation; model of reference adaptive control; stability of state entry; design of adaptive controller; stability of slowly changing time system time; adaptive control switching.
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Organization and Planning of Scientific Research
- Number of credits: 6
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the subject is to form the ability to plan and conduct scientific research in the field of philology based on knowledge of the theoretical and methodological bases of organizing research activity. The training course forms the research culture of the future specialist. The subject is aimed at studying the methodology of scientific research, modern methods used in philology.
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Pedagogy of higher education
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose is the formation of the ability of pedagogical activity through the knowledge of higher education didactics, theories of upbringing and education management, analysis, and self-assessment of teaching activities. The course covers the educational activity design of specialists, Bologna process implementation, acquiring a lecturer, and curatorial skills by TLA-strategies.
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Design of embedded control systems
- Number of credits: 9
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: To give practical knowledge in the field of modern embedded control systems for their further design. Embedded processor architecture; tracing and reliability of embedded systems; memory organization; object-oriented programming of microcontrollers; control registers; peripheral modules of the microprocessor; interrupt handling; processing of analog and digital signals; synchronous and asynchronous data exchange interfaces; tact and time optimization; emulation and debugging technologies; memory protection; real-time operating systems; systems in one crystal; collaborative multitasking; multithreading.
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Digital control systems
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: To teach master students the basics of analysis and design of digital controllers with continuous or discrete working time. Discrete time systems; mathematical modelling of the control process; modeling of systems with discrete time on the impulse transmission function; analysis of stability of systems with discrete time; Time response of discrete systems; design of sampling data control systems; space discrete state model; characteristic equation; state transition matrix; controllability; observability and stability of the models of the space of discrete states; feedback simulation.
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Deep neural networks
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: To teach master students deep learning models of artificial neural networks and their practical applications. Improvement and optimization of the learning process of neural networks; cross-entropy error function; retooling and regularization; the problem of vanishing gradient; unstable gradients in deep learning of neural networks; problems of the deep learning; convolutional networks; programming of convolutional neural networks, speech and image recognition; deep learning methods.
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Distributed control systems
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: To give master students advanced knowledge and practical skills in automation of distributed control systems in industry. Distributed computing systems; SCADA system; remote end devices; base controller; identification of controller boards; discrete and logical control; sequential and batch control; DCS controllers; tracking and initialization in control slots; used for cascade control; phase logic programming; phase logic interface; logic block functions in the extended controller; balanced and unbalanced transmission networks; EIA-485 interface; MODBUS protocol; HART protocol; interaction between FieldBus and DeviceNet.
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Intelligent multi-agent systems
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: To teach master students the basics of multi-agent systems to optimize the decision-making process in industrial complexes. Theory of multi-agent systems; characteristics of intelligent agents; architecture of multi-agent systems; collective behavior of agents; interaction between agents; modeling the interaction of multi-agent systems; coordination of agents in multi-agent systems; examples of multi-agent systems; an auction model for coordinating behavior; multi-agent decision support systems in production; design of multi-agent systems; software and hardware for designing multi-agent systems; prospects for the development of multi-agent systems.
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Intelligent data processing systems
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: To teach master students how to process and analyze data using modern intelligent systems. Varieties of intelligent systems; intellectual information system; expert system; neural networks for data processing; data collection and analysis; decision tree; genetic algorithm; artificial neural networks; feedforward and feedback networks; networks of self-organization; the process of obtaining data based on a neural network; image and video data processing; recognition, prediction, processing of data received from sensors; parallel processing of predictive analytics.
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Neural networks and machine learning mechanisms
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: To teach master students modern models of artificial neural networks and machine learning methods. Artificial neural networks; components of neural networks; distribution function and network input; synchronous and asynchronous activation; teaching with and without a teacher; learning models; supervised network learning paradigms; single-layer and multilayer perceptron; delta rules; elastic backpropagation; adaptation of scales; back propagation of the second order; decreasing weight coefficient; radial basis functions; RBF network training; Elman networks; Hopfield networks.
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Industrial IoT
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: To teach master students the basics of designing and developing industrial systems using the Internet of Things technologies. Various IoT architectures; advantages and disadvantages of IoT systems; IoT system components; sensors, gateways, routers, modems, cloud technologies, servers and their integration; network design for IoT; sensors of various types; special requirements for IOT sensors; data transfer protocols; types of protocols; AMPQ IoT cloud platforms; cloud services; traditional web technologies and relationships with IoT; security requirements and threat analysis for IoT.
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Sensors and sensor systems
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: To teach master students the design and development of embedded modules based on modern sensor systems. Design of sensor systems; measurement and calibration methods; intelligent inertial sensors; built-in magnetic Hall sensors; electronic sensors of physical quantities; amplitude, phase, frequency and time pulse sensors; optimization of design parameters and interfaces of sensors and sensor systems; intelligent temperature sensors; precision instrument amplifiers; multi-electrode capacitive sensors; sensor systems for visualization; intelligent acoustic sensors.
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Power devices and systems
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: To give master students profiling knowledge in the field of transformation, control and monitoring of electrical energy when using power devices. Power electronics; bipolar power transistor; gate turn off thyristor; field-effect transistor on oxide of metals and semiconductors; insulated gate bipolar transistor; transformer for pulsed power supply circuits; rectifiers with asymmetric and step regulation of the output voltage; frequency converters; autonomous and resonant inverters; single-phase uncontrolled rectifiers; reversing converters; autonomous voltage inverters.
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Computer Vision and Pattern Recognition
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: Formation of knowledge about the concepts of image processing, computer vision and their use for the effective implementation of machine vision algorithms in the field of control systems. Image segmentation; image manipulation; detection of signs; generative adversarial networks; autoencoders; neural style transfer; transfer learning and fine-tuning; modern CNN architectures; object detection with YOLO; motion analysis and object tracking; face recognition; optical character recognition; creating a computer vision API; training and testing of neural network models on the GPU; hardware accelerators of deep neural networks.
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Optimization methods for control systems
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: To form students' knowledge and skills in applying optimization methods for various control systems. Static and dynamic optimization; algorithms for unconstrained optimization; one-dimensional search methods; multi-dimensional algorithms; gradient methods; Newton methods; conjugate direction methods; quasi-Newton method; least squares analysis; random search algorithms; genetic algorithm; constrained optimization; convex optimization; nonlinear optimization with constraints and equality constraints.
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Neural networks and machine learning methods
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: To teach master students modern models of artificial neural networks and machine learning methods. Artificial neural networks; components of neural networks; distribution function and network input; synchronous and asynchronous activation; teaching with and without a teacher; learning models; supervised network learning paradigms; single-layer and multilayer perceptron; delta rules; elastic backpropagation; adaptation of scales; back propagation of the second order; decreasing weight coefficient; radial basis functions; RBF network training; Elman networks; Hopfield networks.
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Autonomous control systems
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: To form students' understanding of the theoretical concepts of autonomous systems and practical development experience. The concept of autonomous systems; elements of autonomous control systems; data collection methods; sounding; automatic control strategies; simulation of dynamic systems using transfer functions; stability and reliability of control systems; fault tolerance; manageability and observability of control systems; robot control systems; control system for unmanned aerial vehicles; software for autonomous systems; machine learning and neural networks; artificial intelligence.
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Advanced algorithms and data structures
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: Formation of undergraduate skills to solve complex problems and create high-level software solutions. Object-oriented programming principles; complex classes; object hierarchies; trees; graphs; hash tables; efficient data storage; efficient data processing; complex search algorithms; sorting algorithms; optimization algorithms; big data; code optimization; software solution development; integration components; testing; debugging; code quality assessment.
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Pedagogical
- Type of control: Защита практики
- Description: Aim оf discipline: formation of the ability to carry out educational activities in universities, to design the educational process and conduct certain types of training sessions using innovative educational technologies.
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Research
- Type of control: Защита практики
- Description: The purpose of the practice: gaining experience in the study of an actual scientific problem, expand the professional knowledge gained in the learning process, and developing practical skills for conducting independent scientific work. The practice is aimed at developing the skills of research, analysis and application of economic knowledge.