Lund University

This course provides an up-to-date introduction to topic information theory. The course emphasizes both the formal development of the theory and the engineering implications for the design of communication systems and other information handling systems. The course includes: Shannon's information measure and its relatives, both for the discrete and continuous case; Three fundamental information theorems: typical sequences, source coding theorem and channel coding theorem; Source coding: optimal coding and construction of Huffman codes, as well as universal source coding such as Ziv-Lempel coding (zip, etc.); Channel coding: principles of error detection and correction on a noisy channel, mainly illustrated by Hamming codes; Gaussian channel: continuous sources and additive white noise over both band limited and frequency selective channels; Derivation of the fundamental Shannon limit; Discrete input Gaussian channel: Maximum achievable rates for PAM and QAM, Coding and Shaping gain, and SNR gap; Rate distortion theory: source coding theorem and the fundamental limit revisited when a certain level of distortion is allowed.

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.

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 focuses on common principles at the heart of the functioning of modern communication networks and shows how the same notions related to connectivity, resilience and fragility, centrality and influence arise in several different domains. It introduces mathematical tools from graph theory, random graphs, dynamical systems, optimization and game theory, and covers a wide variety of applications including: opinion dynamics and learning in social networks; economic and financial networks; communication networks and the Internet; consensus and gossiping; spread and control of epidemics; dynamics and control of transportation and power networks.

This course provides students with a holistic view of the water environment so that it treats water both from physical and biological viewpoints. The main topics are: water resources, the hydrological cycle and processes, lakes and running water, ecological concepts, nutrient cycles, human impact. The course contents cover water resources and water circulation (hydrological processes, precipitation, evaporation, infiltration, soil, and groundwater and runoff) and also the circulation of nutrients, food webs, water chemistry, and species knowledge (ecological relationship between organisms and the aquatic environment). The course is divided in to three blocks: 1) Physical/chemical mechanisms, 2) Organisms, and 3) Restoration. Block 1 and Block 2 are covered mainly during period 1 and block 3 is covered during period 2.

The course provides students with specialized knowledge of the most recent scientific and technological developments within neurobiology. Students learn about the basic mechanisms and experimental strategies within different fields of neurological research. The course consists of two parts: 1) fundamental and basic neurobiology, and 2) brain diseases and treatments. The course covers the following fields: the functional neuroanatomy of the brain; cellular and molecular neurobiology; neurodegenerative diseases; network-related diseases.

This course is comprised of three sub-courses. Students must take all three sub-courses. The first sub-course focuses on analysis. This sub-course provides students with an overview of various advanced methods of data analysis and the statistical tools necessary to analyze current research in the discipline of psychology. The focus is primarily on multivariate analysis. The teaching includes theory and practice in analytical methods, in particular quantitative methods of data analysis.