Computer Science

With the rapid development of information technology, the amount of information data has grown exponentially. The analysis and processing of massive amounts of complex data requires advanced visualization techniques. This course explores how to create effective visualization methods, including science and information visualization. This course is for students who are interested in using visualizations or engaging in visual research.

The course gives a deep understanding of principles, functions, and techniques that form the foundation of communication networks with an emphasis on wireless communication systems. In particular, the course covers these functions' behavior and performance based on the stochastic nature of the data streams in modern communication networks. The course covers both public systems (3G, LTE) and technologies for wireless local networks (WLAN, Ad-Hoc, and mesh networks). The course gives an understanding of how these systems integrate more and more, advantages and disadvantages, as well as problems and their solutions in connection with this integration. The courses also discusses current and future trends in network systems such as Internet of Things and Tactile Internet, and the enabling technologies being developed for them. The course structure contains lectures and exercises, as well as a lab on network simulation and data analysis. Further, students complete a group-based system design project. The course is divided into the following modules: review of probability, stochastic processes and basic computer networking; medium access control using reservation schemes and random access schemes; network architectures for licensed and unlicensed spectrum; modelling for performance analysis; traffic management: queueing systems and congestion and flow control. The following systems and technologies are covered: cellular systems (GSM, UMTS, LTE and a discussion of 5G); MAC protocols: ALOHA, CSMA, 802.11 (WiFi), including the Bianchi model of 802.11; congestion and flow control techniques such as Random Early Detection, token bucket schemes; queueing disciplines such as priority queueing, weighted fair queueing, class based queueing TCP flow and congestion control and retransmission strategies.

This course provides an introduction to embedded and real-time systems. Students gain practical experience using a small micro-controller development board, programmed in C, and industrial development tools. The course examines the interface between the digital micro-controller and physical sensors and actuators, and the process of analyzing, designing, and testing systems that react to external events.

The purpose of the course is to introduce discrete event simulation, basic optimization approaches, and heuristic methods such as simulated annealing, tabu search, and evolutionary algorithms. The course begins by studying discrete event simulation. Students learn to write process-oriented and event-scheduling simulation programs in general programming languages. Estimation of accuracy, random number generation, methods for studying rare events, verification and validation are also covered. The next topic covered is optimization techniques. Linear programs (LP) and the simplex algorithm are studied. This is followed by integer programming (IP) and Mixed Integer Programming (MIP), the relation between IP and LP, and the branch-and-bound method for IP. Finally, heuristic and meta-heuristic methods for combinatorial optimization problems are viewed as optimization through simulation. The local search and its most common variations are explained along with the Monte Carlo techniques.

This course covers the entire software development lifecycle from design to deployment and maintenance, with an emphasis on quality, industry standards, and professional issues. Topics include software in business; software development processes and technologies; modeling, architecture and design; configuration, change, versioning, and release management; implementation, deployment, and maintenance; legacy architectures, technologies, and systems; software quality, standards, and processes; project management, resourcing, and control; project risk management; and software documentation.

This course introduces basics of quantum computing and covers various well known quantum algorithms, such as Deutsch-Jozsa algorithm, Simon's algorithms, quantum Fourier transform, phase estimation, Shor's algorithm and Grover's algorithm.