School of Engineering and Digital Sciences
The Master of Science in Electrical and Computer Engineering (MSc-ECE) degree program is a specialized degree program offered by School of Engineering and Digital Sciences at Nazarbayev University (NU). Students are required to complete 120 ECTS credits, in 4 semesters, which satisfies requirements stipulated by the Bologna Process and the European Credit Transfer and Accumulation System (ECTS) for Master’s Degrees. The program is focusing on research and has been designed to provide advanced skills and a detailed knowledge base at the graduate level for individuals working in academia, industry, or research settings in Kazakhstan or throughout the world.
The MSc ECE provides a comprehensive technological and scientific preparation for engineers in the area of Electrical and Computer Engineering in four different main majors, 1) Devices and Circuits, 2) Power Engineering and Control Systems, 3) Signal Processing and Communication Systems, 4) Computer Engineering in perfect alignment with today’s technological challenges. A set of mandatory core courses guarantees competence in advanced mathematics, the 4 (four) key areas of the program, classic and novel research methods and effective oral and written communication in the science and technology domain.
By delivering an MSc at an international standard level, we are offering our master’s students future opportunities in terms of PhD and positions in research centers, locally or abroad.
Aims and Objectives
The mission of the School of Engineering and Digital Sciences at Nazarbayev University is to contribute to the development of Kazakhstan in terms of:
- Educating students with engineering expertise to lead organizations and provide innovative solutions for complex technical issues of enterprises.
- Conducting innovative and pioneering basics of applied research that evolve the body of knowledge in Engineering through interdisciplinary collaboration with other schools and research centers at Nazarbayev University and leading universities worldwide.
- Advancing the professional development in engineering through our service to the professional community and providing lifelong learning opportunities for practitioners.
The M.Sc. (ECE) program aims to reflect the mission of the School of Engineering and Digital Sciences and accomplishes this by pursuing the following objectives:
1) Extend the knowledge and skills in advanced Electrical and Computer Engineering analysis and design, particularly in using latest modelling and analysis techniques;
2) Develop solutions to Electrical and Computer Engineering problems using in-depth knowledge and innovative ideas and techniques necessary for specialist engineers; 3) Develop skills to conduct high quality research in the field of Electrical and Computer
Engineering;
3) Develop skills to conduct high quality research in the field of Electrical and Computer
Engineering;
4) Provide awareness of the challenges in the profession and integrate engineering and non-engineering topics for leading organizations.
Total Number of Credits: 120 ECTS
Year 1: Fall Semester (Semester 1)
| TYPE | COURSE CODE & TITLE | ECTS | |
|---|---|---|---|
| Core | MECE 606, EMBEDDED SYSTEMS AND APPLICATIONS | 6 | |
| Core | MSC 602, ADVANCED APPLIED MATHEMATICS | 6 | |
| Core | MECE 603, ADVANCED DATA STRUCTURES AND ALGORITHMS | 6 | |
| Core | MECE 724, SYSTEM MODELLING AND CONTROL | 6 | |
| Core | MECE 605, PROBABILITY AND STATISTICS FOR ELECTRICAL AND COMPUTER ENGINEERS | 6 |
Year 1: Spring Semester (Semester 2)
| TYPE | COURSE CODE & TITLE | ECTS | |
|---|---|---|---|
| Core | MSC 600, RESEARCH METHODS AND ETHICS | 6 | |
| Core | MECE 600, RESEARCH SEMINAR | 6 | |
| Core | MSC 601, TECHNICAL COMMUNICATION | 6 | |
| Elective | Elective 1 (Pick on From Electives Pool) | 6 | |
| Elective | Elective 2 (Pick on From Electives Pool) | 6 |
Year 2: Fall Semester (Semester 3)
| TYPE | COURSE CODE & TITLE | ECTS | |
|---|---|---|---|
| Core | MECE601, MASTER THESIS I | 24 | |
| Elective | ELECTIVE 3 (PICK ON FROM ELECTIVES POOL) | 6 |
Year 2: Spring Semester (Semester 4)
| TYPE | COURSE CODE & TITLE | ECTS | |
|---|---|---|---|
| Core | MECE 602, MASTER THESIS II | 24 | |
| Elective | ELECTIVE 4 (PICK ON FROM ELECTIVES POOL) | 6 |
Program Core Courses
This course addresses the primary need for graduate students to undertake formal training that will help them in understanding how to conduct their research. The course will develop student’s understanding of research plan and engender skills enhancement for reading, interpreting, writing, and presenting key ideas. The course will also instill an understanding of a variety of research methods and ethics and implement appropriate strategies in lecture and workshop settings.
This graduate level course combines the application of rhetorical analysis to stylistic conventions of writing in engineering, with a focus on clarity, conciseness, and coherence. Students will employ process writing to produce genre specific writing familiar to Engineers, including research reports scientific papers designed for specific audiences. This course also trains students to deliver effective and appealing professional and scientific presentations, with attention to best practices in the use of technical English and oral communication.
This course reviews and deepens the advanced analytical and numerical methods to solve ODEs and PDEs. The whole course will be supported by a mathematical software package capable to perform symbolical calculations. The module is designed for graduate students to cover their research needs concerning mathematical modeling via analytical, semi-analytical or numerical techniques.
This course enables students to gain and apply basic research knowledge and skills to select their re- search projects in Electrical and Computer Engineering by attending a series of expert presentations and by assessing, preparing and presenting his or her own preliminary research ideas. The course develops the following knowledge and skills:
1. Identifying the area of research interest;
2. Surveying of state of the art for the area of interest;
3. Identifying the research topic and associated issues;
4. Understanding of the social, cultural, global, and environmental issues associated with chosen research topic;
5. Writing a research paper based on the literature survey under the supervision of a faculty member;
6. Presentation of a seminar and being assessed by a panel.
1. Identifying the area of research interest;
2. Surveying of state of the art for the area of interest;
3. Identifying the research topic and associated issues;
4. Understanding of the social, cultural, global, and environmental issues associated with chosen research topic;
5. Writing a research paper based on the literature survey under the supervision of a faculty member;
6. Presentation of a seminar and being assessed by a panel.
This course intends to give students the opportunity to develop the research proposal. The course develops the following knowledge and skills:
1. Preparation of a thorough and comprehensive literature review to support the research proposal;
2. Formulation of the research hypothesis;
3. Developing and justifying the methods to conduct the research.
1. Preparation of a thorough and comprehensive literature review to support the research proposal;
2. Formulation of the research hypothesis;
3. Developing and justifying the methods to conduct the research.
This course intends to give students the opportunity to fully implement the research proposal and bring it to a conclusion. The course develops the following knowledge and skills:
1. Planning and conducting, independently, research at an advanced level;
2. Critically analyzing research results;
3. Effectively presenting their results to a wide audience;
4. Effectively compiling their results in the form of an authoritative thesis.
1. Planning and conducting, independently, research at an advanced level;
2. Critically analyzing research results;
3. Effectively presenting their results to a wide audience;
4. Effectively compiling their results in the form of an authoritative thesis.
The course provides a survey of computer algorithms, examines fundamental techniques in algorithm design and analysis, and develops problem-solving skills required in all programs of study involving computer science. Topics include advanced data structures, recursion and mathematical induction, algorithm analysis and computational complexity, sorting and searching, design paradigms (divide and conquer, greedy heuristic, dynamic programming, amortized analysis), and graph algorithms. Advanced topics are selected from areas like randomized algorithms, information retrieval, string and pattern matching, and computational geometry. A final project is required form each student.
This course provides the practical understanding of microcontrollers in the design and implementation of embedded systems for engineering applications using open hardware and software by considering modern industry standard specifications and design issues. In this course, the students will learn the formal specification models along with system level requirements for implementing embedded systems and get exposed to a range of practical hardware-software co-design and mapping issues. The students will gain an understanding of the design trade-off of real-time systems working on the constraints of low power, computational speed, and low memory requirements. The design verification methods required for embedded system design will be introduced. Students will work on introductory projects and labs covering example engineering applications related to (1) Devices, and Circuits, (2) Power Systems and Control Engineering, (3) Signal Processing and Communication Systems, and (4) Computer Engineering. These areas can cover a range of topics such as cyber-physical systems, smart homes, communications systems, consumer electronics, automotive systems, energy management and conservation, power systems, smart grids, and robotics. Laboratory assessment will be by program demonstration, code review, re-ports, and practical skills testing.
The main goal of this course is to provide a mathematical introduction to the theory of stochastic processes. Topics covered in this module include:
1. Overview of Probability and Random Variables: axioms of probability theory; conditional probability; density, distribution and characteristic functions; random vectors and random sequences;
2. Introduction of Stochastic Processes: Definitions; processes with independent increment, Markov and Martingale processes; stationarity and its properties; frequently used processes, systems with random input; ergodicity;
3. Power Spectra and Orthogonal Expansions: Autocorrelation function and power spectral density; power spectrum analysis for linear time-invariant systems; Fourier series and Karhunen–Loève expansions of processes; expansion of stationary processes using innovation process;
4. Bandlimited and Discrete-time Processes: Definition of bandlimited processes and their properties; sampling theorem for stochastic processes; probability functions, moments and power spectra of discrete-time processes; i.i.d. white noise, AR, MA and ARMA models;
5. Introduction to Estimation Theory: Minimum mean-squared error (MMSE) and linear MMSE criteria; Orthogonality principle and its applications; causal and non-causal Wiener filters and their applications.
1. Overview of Probability and Random Variables: axioms of probability theory; conditional probability; density, distribution and characteristic functions; random vectors and random sequences;
2. Introduction of Stochastic Processes: Definitions; processes with independent increment, Markov and Martingale processes; stationarity and its properties; frequently used processes, systems with random input; ergodicity;
3. Power Spectra and Orthogonal Expansions: Autocorrelation function and power spectral density; power spectrum analysis for linear time-invariant systems; Fourier series and Karhunen–Loève expansions of processes; expansion of stationary processes using innovation process;
4. Bandlimited and Discrete-time Processes: Definition of bandlimited processes and their properties; sampling theorem for stochastic processes; probability functions, moments and power spectra of discrete-time processes; i.i.d. white noise, AR, MA and ARMA models;
5. Introduction to Estimation Theory: Minimum mean-squared error (MMSE) and linear MMSE criteria; Orthogonality principle and its applications; causal and non-causal Wiener filters and their applications.
This course introduces students to recently developed and advanced techniques for solving modeling and control problems. The course presents theory and methodology for analysis and modeling of systems and signals, and methods for design and synthesis of feedback controllers. The emphasis of the course will be on mathematical foundations and numerical methods for system modeling from physical properties, identification, and advanced control of dynamical systems. In particular, it explores state space representation, dynamical modeling from Lagrange equations, model validation, model approximation, Kalman filtering, real-time system identification. The course also discovers control techniques such as state-feedback controller, output feedback controller, state observer, stabilizing controllers, automatic Tuning, gain scheduling, and auto-calibration.
Program Elective Courses
This course presents advanced analytical techniques for formulating and solving problems in applied electromagnetics. A number of techniques are presented including basic electromagnetic theorems, Green's functions, spectral domain representations, electric and magnetic field integral equations, and approximate boundary conditions. Applications of these methods are also presented in the areas of radiation, radiowave propagation in stratified media, and scattering. The mathematical techniques required, particularly those not found in other courses, are included in this course.
This course reviews the classical control systems, system modelling, time and frequency domain analyses, feedback characteristics, stability analysis, state space representation, sampled data control systems, z-transform and pulse transfer function, stability analysis of the sampled data control systems, fuzzy control systems, neural network based controllers.
The course provides advanced topics of optics and photonics, with the aim of giving a complete vision of photonics to use in engineering and systems. The course describes some important aspects of optics and photonics components along with their applications, describing both the main devices and the main formulation of light propagation and interaction with matter. The course will touch the following topics: Classical formulation of light-matter interaction; Semi-classical theory of light matter interaction; Introduction of quantum theory of interaction of radiation field with matter; Advanced lasers; Wave-equation description of nonlinear optical interactions; Model of refractive index intensity dependence; Non-linear optics application; Non-linear fiber optics; Soliton propagation; Ultrafast optics; Scattering theory; Stimulated Brillouin and stimulated Rayleigh scattering; Stimulated Raman scattering; Applications, Raman amplification, Raman spectroscopy and microscopy. Numerical method in nonlinear optics.
The course aims to provide the student with the fundamentals of electrical industrial and commercial power systems: design, demand calculation, maintenance, load calculation, management and operation. In specific, it provides practical and essential knowledge for designing the electrical distribution infrastructure in large commercial buildings and industrial sites. Particular emphasis is on compliance with current practices and regulations within Eurasia. The course also touches on some aspects of utilization. Regulatory aspects; switch-board design; underground cabling systems; designing neutral system in industrial and commercial structure, surge protection; lightning rod system design and calculation; electrical lighting systems; industrial heating; energy efficiency and energy management; power factor correction; power quality and the effects of voltage and current harmonics; escalator and lift traffic analysis; transformer loading and over-load in industrial and commercial sites; and introduction to the smart grid will be discussed in this course. This is an essential course for power engineers which are planning to work in industry or doing re-search in smart grid field in distribution system.
This course studies integrated network architecture of service, control signaling, performance evaluation, and management. Examples of high-speed LAN/WAN, next generation Internet and mobile wireless network, ATM Systems and Networks, and PANs will be given and analyzed. Each student is required to prepare and present a term paper and a final project.
This course deals with fundamentals to advances in the field of power systems protection and relaying. In particular, it covers the types of faults and their calculation using symmetrical components methods; industrial voltage and current transformers; transmission and distribution line protection over-current, directional, distance, out-of-step protection; primary and backup protection zones, discrimination of relay time and current settings, Digital relaying: Microprocessor based relays, Phasor measuring units and Wide Area monitoring Systems (WAMs), fault detection and isolation procedures, power system restoration and self-healing systems".
The course covers traditional parallel architecture topics—such as programming models and communication and synchronization issues in message passing and shared-memory architectures—as well as a variety of recent and/or advanced ones such as datacenters and heterogeneous architectures. Topics like multicore systems, interconnection networks and algorithms, cache coherence, victor processors, pipelining, multiprocessor systems will be covered. The course features a semester-long course project in order to introduce the students to research in the fields of parallel computer architecture and systems, and advances in the state of the art in parallel computer design and performance evaluation.
Optical Communication course provides an in-depth overview of fiber-optic communications for high bit-rate/high-throughput. The course provides a description of fiber-optic communication network and devices, and performance calculations. The effect of attenuation, dispersion, and non-linear effects is quantified. Protocols for 10-100 Gbit/s data communication over long-haul links are discussed.
The main goal of this course is to provide an in depth understanding of design issues in analog and digital integrated circuits. Topics covered in this module include:
1. Mixed-mode design flow; Top-down and bottom-up, constraint-driven design methodologies; Design reuse (IPs);
2. High-level design and optimization ̧ Behavioral modeling and simulation; Macro-modeling algorithms;
3. Multilevel and mixed-domain simulation; AHDL macro models;
4. Computer-aided design considerations for mixed-signal coupling; Functional verification;
5. Floor planning and physical implementation; Layout tools for analog ICs and mixed-signal SoCs;
6. Specific simulation methods for mixed-mode integrated circuits.
1. Mixed-mode design flow; Top-down and bottom-up, constraint-driven design methodologies; Design reuse (IPs);
2. High-level design and optimization ̧ Behavioral modeling and simulation; Macro-modeling algorithms;
3. Multilevel and mixed-domain simulation; AHDL macro models;
4. Computer-aided design considerations for mixed-signal coupling; Functional verification;
5. Floor planning and physical implementation; Layout tools for analog ICs and mixed-signal SoCs;
6. Specific simulation methods for mixed-mode integrated circuits.
The general purpose of the module is to have the students exposed to modern DC-DC (Switched Capacitor and Flying Capacitor) and DC-AC (Neutral Point Clamped, Flying Capacitor, Cascade H-bridge) power converter topologies, their operation principles, methods of analysis and computer simulation.
Communication systems & technical challenges; Channel modelling (propagation, channel statistics, channel measurements); Modulation & information theory; Antennas (diversity, MIMO, smart); Multiple access schemes; Cooperative & cognitive radio.
This course deals with the key concepts of vulnerabilities, attacks, threats, security measures and mechanisms in computer systems and networks. It will present the essential concepts of cybersecurity technolgy and their applications. Topics include applied cryptography, authentication, digital signatures, digital certificates and network security protocols such as IPSec, SSL, TLS and SET. Students will also learn necessary knowledge about security mechanisms such as firewall, VPN and network intrusion detection systems. Examples and case studies will be covered. A term paper and final project will be required from each student.
This course covers the fundamental techniques in security of E-based Systems. E-based systems are ubiquitous in the modern world with applications spanning e-commerce, e-government, e-services, Virtual Private Networks (VPNs), health care, among others. This course deals with the fundamental concepts and tools of security of e-based systems and computer networks and their range of applications. Among the topics to be covered in this course include: authentication of users, system integrity, confidentiality of communication, availability of business service, non-repudiation of transactions, public key cryptosystems, authentication and digital signature, e-security tools such as Public Key Infrastructure (PKI) systems, biometric-based security systems, wire-less network security, trust management systems in communication networks, intrusion detection systems, protecting against malware and computer network security risk management. Examples and case studies will be given. A term paper and a final project are required from each student.
The ever-increasing demand of power, heating and cooling associated to global population and economic growth, parallel to anthropogenic global warming and fast depletion of fossil fuels, creates an imperative need to incorporate a much larger share of clean and renewable energy. The Renewable Energy Systems course introduces graduate students to the principles of energy conversion, storage, technologies and economy associated to the use of renewable energy sources.
The course presents a global overview of renewable energy sources with a focus on solar and windenergy sources, covering various aspects of the modeling, design and analysis of solar power and heating, and on-shore/off-shore wind power systems, including their economic and environmental impact analyses.
The course presents a global overview of renewable energy sources with a focus on solar and windenergy sources, covering various aspects of the modeling, design and analysis of solar power and heating, and on-shore/off-shore wind power systems, including their economic and environmental impact analyses.
This course presents advanced topics on RF and mm-wave circuits. Frequency synthesizers, transmitter linearization techniques (e.g. polar circuits), MIMO and phase array circuits, power D/As.
This course involves the detection of signals, the prediction and filtering of random processes, the design and analysis of communication systems, the analysis of protocols for communication networks, and statistical processing of images. Specific topics include the use of signal processing and error correction coding, and modulation techniques for both data transmission and digital magnetic recording, the use of spread spectrum techniques for wireless communications, and the design and analysis of multi-user communication networks. Additional areas of re-search include time series analysis, adaptive filtering, sampling design, and wavelet theory. Applications are made to such fields as communications, radar, sonar, oceanography, holography, and image processing. Both theoretical and practical aspects of information processing are studied.
The course is an introduction to semiconductor fundamentals and applications to the electronic devices. Course creates the background in the physics of the compound semiconductor-based electronic devices and also prepare students to advanced courses in solid state and quantum electronics. The course provides an opportunity for students to continue education in undertaking advanced study and research in the variety of different branches of semiconductor device applications. Topics include the background solid state and semiconductor physics, and basic principles of electronic devices operation.
The course deals with advanced microprocessor systems; embedded control; processor core; system on-chip; power-aware design; media processors; DSP; crypto processors; network processors; trusted processor architectures; and architecture simulation. Both design and programming aspects will be covered. A final project is required from each student.
This course deals with principles of wireless sensor networks (WSNs), architecture, protocols, and performance. Topics include: Wireless technology for distributed sensor networks, clustering techniques in WSNs, routing in WSNs, WSNs security, industrial WSNs protocols, WSNs design, implementation and management and performance evaluation of WSNs including simulation analysis. Trends and challenges of WSNs will be covered. A term paper and final programming/design project are required.
This course provides a core grounding in how science and technology have developed to enable the Internet of Things (IoT) – in a way appropriate for any learner. For those interested in developing further hands-on expertise in designing and developing for the Internet of Things, this course will provide a context to the discoveries and con-verging technologies that will springboard the next round of innovations. Topics covered in this course module include:
-Automation systems: Introduction, networks for automation applications: HART, CAN, PROFIBUS, Industrial Ethernet Capillary networking: Challenges for wireless capillary net-working. Systems addressing these challenges: IEEE 802.15.4, Wireless HART, Low Power Bluetooth, Lower Power WiFi
-Internet of Things (IoT): Introduction of IoT, Integration of constrained node networks into the IPv6 world, 6LoWPAN, ROLL RPL: Routing protocol for heterogeneous low-power and lossy networks, CoRE CoAP: HTTP-like application layer protocol over UDP
-Markov Chains and Basics of Queuing Theory
-Automation systems: Introduction, networks for automation applications: HART, CAN, PROFIBUS, Industrial Ethernet Capillary networking: Challenges for wireless capillary net-working. Systems addressing these challenges: IEEE 802.15.4, Wireless HART, Low Power Bluetooth, Lower Power WiFi
-Internet of Things (IoT): Introduction of IoT, Integration of constrained node networks into the IPv6 world, 6LoWPAN, ROLL RPL: Routing protocol for heterogeneous low-power and lossy networks, CoRE CoAP: HTTP-like application layer protocol over UDP
-Markov Chains and Basics of Queuing Theory
Pattern recognition is the science of assigning an observation to a set of pre-specified categories. The aim of this module is to introduce fundamental concepts used in design and implementation of pattern recognition methods. After a brief review of probability theory, we will cover important and classical families of classification rules, error estimation, and dimensionality reduction techniques.
Our discussion on classical methods is then followed by a set of modern shrink-age-based techniques for performing classification and regression on very large sparse data matrices.
The topics include: Classification Rules: Bayes Decision Rule, Classification, Parametric Classification Rules, Nonparametric Rules, Perceptron, Support vector Machines, Neural networks, Decision Trees, Discrete Classification Error Estimation: Hold-out, Resubstitution, Cross-validation, Boot-strap Dimensionality Reduction: Filter-based feature selection, Wrapper-based feature selection and various search strategies, PCA, Multidi-mensional scaling Shrinkage-based techniques: LASSO, Elastic-net
Our discussion on classical methods is then followed by a set of modern shrink-age-based techniques for performing classification and regression on very large sparse data matrices.
The topics include: Classification Rules: Bayes Decision Rule, Classification, Parametric Classification Rules, Nonparametric Rules, Perceptron, Support vector Machines, Neural networks, Decision Trees, Discrete Classification Error Estimation: Hold-out, Resubstitution, Cross-validation, Boot-strap Dimensionality Reduction: Filter-based feature selection, Wrapper-based feature selection and various search strategies, PCA, Multidi-mensional scaling Shrinkage-based techniques: LASSO, Elastic-net
The objective of this course is to provide the mathematical framework for an understanding of adaptive statistical signal processing. The basic tools of vector spaces, optimization theory, and discrete time stochastic processes are reviewed and applied to the methods of Wiener filtering and least squares filtering. Various types of adaptive filters will be introduced and their properties will be studied, specifically convergence, tracking, robustness and computational complexity. Applications will mainly be addressed through student projects.