Electrical Engineering

The widespread adoption of deep learning methods has been largely driven by the availability of easy-to-use systems such as PyTorch and TensorFlow. However, it is less common for users to explore the internals of the libraries and understand how they function, as well as how to optimize the high-level code for hardware systems. When deep learning algorithms are deployed into custom hardware, they are often modified to run faster and more efficiently. This course provides students with the basic concepts and principles of modern deep learning systems, and it explores how optimizations can be applied from both the software and hardware aspects of the system stack.

This course gives a fundamental understanding and an experimental experience of ultrasound and gain insight in an expanding research area. Topics covered include ultrasound physics, transducer technology, diagnostic equipment technology, doppler, bio-acoustics, field characterization, airborne ultrasound, diagnostic application, non-destructive testing, sonar, and research projects at the department. Assumed prior knowledge: First Course in Physics

This course introduces the calculation methods and experience of manufacturing options used by engineers to design electromagnetic devices such as transformers, actuators, and electric machines. The aim of the design of an electromagnetic device is the desired function, integration, and rational manufacturing method, and thus this course develops the related and relevant skills and experience. The course provides theoretical knowledge though lectures, and the acquisition of modelling skills and experience through assignments and course projects. Assumed prior knowledge: EIEF15 Electrical Engineering (EE), ETE055, EITF85 Electromagnetic Field Theory (PhyE), MIE012, EIEF35 Electrical Engineering, basic course (ME), ETEF01 Electromagnetic Field Theory (MathE).

This course is driven by time series perspectives and short term statistical behavior, underpinned by digital signal processing and machine learning, which is appropriate for cash assets (stocks, bonds, currencies, portfolios) and futures. It therefore answers the needs of the rapidly changing global financial system which requires financial models to be adaptively inferred from the data. Students gain hands-on experience through structured Python assignments based upon time series models, robust estimators, subspace techniques, portfolio optimization, and data analytics on graphs. There are no particular prerequisites, although knowledge of estimation theory and statistics would be useful. 

The course providers the knowledge, skills, and experience from taking part in an industrially based mechatronic development project, which is conducted up to a working prototype. The principal design of the product has been formed in the course Applied Mechatronics. It is essential that the work is done in a team with competences from various fields. The project is done during two study periods. The course participants should develop the mechatronic parts of those projects or other purely mechatronic products. The development process starts with extensive information search, brainstorming, and evaluation, activities which often encompass 30-40% of the total workload. This has been done in the course EIEN65 Applied Mechatronics. Then follows in this course selection of concept, constructive design of the product idea, ordering of components, building, testing, and adjustments. The course concludes with the official presentation of the designed products, where representatives from industry, course leaders, and the press take part. Assumed prior knowledge: Applied Mechatronics.

The course covers how solar cells and photovoltaic systems technology work in different applications, especially when integrated into the built environment. In developing countries where many live outside the electric grid, standalone PV systems are also of great interest. The ability to design and optimize the performance of PV systems through computer simulations is an important part of the course. The course covers: energy knowledge and the problems connected to the use of energy; radiation physics, the annual irradiance distribution and the climatic conditions for using solar energy in Sweden; calculation of solar angels and the irradiance on different surfaces; the PN-junction and Solar cell physics and construction and function of a PV-module; function and performance of the components in the PV-system; batteries, power point tracker, DC-AC inverter, charge regulator; system design of standalone and grid connected systems; building integration of PV-system; hand calculation of economic profitability of PV-systems; calculations of climate impact of solar cells in carbon dioxide emissions; use of simulation programs; laborations and computer simulations; and studievisits to PV installations.  Assumed prior knowledge: Basic courses in electricity and electronics. Experience from the use of calculation program like Matlab and Excel.

This course provides knowledge about the most important power electronic circuit configurations, including both power semiconductors and passive components like inductors and capacitors in addition to how modulation and current control is done in the most relevant circuits. The course includes a project to design a power electronic circuit with a specific set of functional specifications. Participants develop and verify (electrically and thermally) a power electronic converter for a low power application like an electric scooter or bicycle motor drive. The battery supply and the motor are given, but the power electronic converter with its modulation and control are developed as a part of the course. Topics include diodes, transistors (BJT, IGBT, MOSFET), materials (silicon, silicon carbide), inductors, capacitors, sensors (current, voltage). Function, mechanical and thermal design, aging, drive and protection circuits, various bridges such as 1Q, 2Q and 3-phase 2- and multi-level converters, parasitic components, load currents, and earth currents, carrier modulation, sampled current control, tolerance band control of current, voltage control, switching power supplies, motor drive systems for DC and AC motors, solar cell converters, electric vehicle chargers, "Unified Power Flow Controllers" (UPFC), active power filters, and high voltage direct current (HVDC). Assumed prior knowledge: ESSF01 Analogue Circuits, ESS030, ESSF20 Physics of Devices, ESSF15 Electrical Engineering (EE) or MIE012, EIEF35 Electrical Engineering, basic course (ME) and FRT010, FRTF05 Automatic Control, Basic Course.