As an assistant professor, I teach university courses. Here, you will find information about the university courses that I teach.
EE2T21 – Data Communications Networking
Data Communications Networking is an introductory course to telecommunication networks. Telecommunication networks include local area networks, the Internet, and telephone networks. This is an obligatory course for second year students that are following the Bachelor Electrical Engineering at the faculty of Electrical Engineering, Mathematics & Computer Science.
Learning Objectives
In this university course, students learn to:
- Explain the functionalities with which modern end-to-end communication in data communication networks is achieved.
- Explain the architecture and design concepts that underlie the internet and its protocols.
- Perform elementary calculations and performance analyses on small networks.
- Implement basic mathematical coding and routing algorithms that have inspired error correction methods and routing protocols in networks.
Course material
This course covers Chapters 1 – 7.5 of the book Data Communications Networking, written by professor Piet Van Mieghem. The topics covered are therefore:
- Chapter 1. Introduction to data communication networks
- Chapter 2. Local Area Networking
- Chapter 3. Error control and retransmission protocols
- Chapter 4. Architectural principles of the internet
- Chapter 5. Flow control protocols in the internet
- Chapter 6. Routing algorithms
- Chapter 7. Routing protocols
Educational load
The students can earn 2 ECTS by taking this course. This is roughly equivalently to a load of 56 hours. The course is taught in just under 4 weeks. The course is therefore lecture intensive, and a high degree of self-study is expected from the students.
Prerequisites and dependants
No prerequisite university courses are specified for this course. Familiarity is expected with the basic courses taught in the BSc program up to that point. The reason is that this course sits within the second year of our BSc program. The relevant courses include:
- EE1M11 – Linear Algebra and Analysis A,
- EE1M12 – Linear Algebra and Analysis B, and
- EE1M31 – Probability Theory and Statistics.
Follow-up university courses that specify this course as a prerequisite include:
- EE4C06 – Networking, and
- IN4341 – Performance Analysis.
Sample exam
Here is the exam that students did on July 6th, 2018. This gives you an impression of the contents of the course.
Reading Seminars
In the fall of 2018, I will organize a reading seminar on part one of the book Graph Spectra for Complex Networks, written by Piet Van Mieghem.
Reading Seminar Fall 2018
| Date | Topic | Location | Presenter |
|---|---|---|---|
| Chapter 2: Algebraic graph theory | |||
| October 3rd, 2018 | 1. adjacency matrix; 2. admittance matrix, Laplacian; 7-8. line graph; 10-13. permutation matrix, isomorphism and automorphism; 14-15. partitions, quotient matrix; 17-20. walks, paths, diameter, shortest path | HB 09.120 | Albert Senen-Cerda |
| Chapter 3: Eigenvalues of graphs | |||
| October 10th, 2018 | 21. eigenvalues; 23. Gerschgorin’s theorem; 27-28,31. identities relating graphs properties to eigenvalues; 30. identity relating number of links in a line graph to the number of connected triplets in a graph | HB 09.120 | Qiang Liu |
| October 17th, 2018 | 33-35. number of walks of length k; 43-44. lower bounding eigenvalues; 50,54. eigenvalue bounds in connected graphs; 64. random walk on a graph, relation between concentration and spectral gap | HB 09.120 | Bastian Prasse |
| Chapter 5: Spectra of special types of graphs | |||
| January 23rd, 2019 | 5.1. complete graph; 5.2. small-world graph; 5.3. circuit; 5.4-5.5. path; 5.6. wheel; 5.7. complete bipartite graph; | HB 09.120 | Albert Senen-Cerda |
| Chapter 6: Density functions of eigenvalues | |||
| 121. eigenvalue density function; 123. integral form; 124. trace relation; 128. limiting density function on paths; 130. limiting density function on small world graphs; | HB 09.120 | Albert Senen-Cerda | |
| January 30th, 2019 | 131-133. large sparse regular graphs, McKay’s law; 134-135. random matrices, Wigner’s Semicircle Law; 137. spectrum of the Erdos-Renyi random graph 6.5.2 Marcenko-Pastur’s law | HB 09.120 | Jaron Sanders |