Electrical and Information Technology

Faculty of Engineering LTH | Lund University

Event archive

SoS Workshop 2019

Published: 2019-04-08

Save the date

When: 2019-09-19 09:00 to 2019-09-20 15:00
Location: Grand Hotel dag 1, LTH dag 2


AIML@LU WS: AI & ML Technologies

Published: 2019-03-20

This AIML@LU fika-to-fika workshop focuses on the development of the technologies that form the basis of Artificial Intelligence and Machine Learning. Possible topics to discuss are the research front for different types of AI, but also to look at different techniques for machine learning.

Room E:A, E-huset, Ole Römers väg 3 LundWhen: 30 August at 9.30 - 15.30  

Where: E:A, E-building, Ole Römers väg 3, LTH, Lund University


9.30 Fika and mingle

10.15 Introduction and update regarding the AIML@LU network

10.30 Ongoing projects

Collaborative reading robotMartin Karlsson, Lund University: Robot Programming by Demonstration Based on Machine Learning

Abstract: Whereas humans would prefer to program on a high level of abstraction, for instance through natural language, robots require very detailed instructions, for instance time series of desired joint torques. In this research, we aim to meet the robots half way, by enabling programming by demonstration.

Marcus Klang, Lund University:  Finding Things in Strings

Najmeh Abiri, Lund University: Variational Autoencoders

Joakim Johnander, Linköping University: Deep Recurrent Neural Networks for Video Object Segmentation

12.00 Lunch, mingle and poster session

13.00 Future trends and interesting examples

Mikael GreebMikael Green, Desupervised2: Bayesian Deep Probabilistic Programming: Are we there yet?

Abstract: Not many would argue against the Bayesian paradigm being the most useful one in modeling problems where parameter estimations are inherently uncertain. But unfortunately most interesting models, especially the ones we know from deep learning, have been very hard to fit in any reasonable amount of time. When dealing with +10 million parameters and +100 thousand data points, Markov Chain Monte Carlo just isn't a viable option. This is why almost every practitioner in deep learning defaults to maximum likelihood estimates through optimization via stochastic gradient descent, because it's much faster. In this talk we'll explore a promising way of doing full Bayesian inference on large scale models via stochastic black box variational inference.

ErikErik Gärtner, Lund University: Intrinsic Motivation - Exploration, curiosity and learning for learning's sake

Abstract: Humans as well as other animals are curious beings that develop cognitive skills on their own without the need for external goals or supervision.
Inspired by this, how can we encourage AIs to learn and solve tasks by themselves?
This talk presents the fascinating area of intrinsic reward in the context of reinforcement learning by showcasing recent articles and results.

14.30 Summary and conclusions

15.00 Fika and mingel



To participate is free of charge, but please register no later than 28 August 12.00 at:


If you have any questions, suggestions or would like to contribute to the program please contact one of:

More AIML@LU events at | Join the AIML@LU Network at:

When: 2019-08-30 09:30 to 2019-08-30 15:30
Location: E:A, E-building, Ole Römers väg 3, LTH, Lund University


ELLIIT Distinguished Lecture by Bill Dally: The Future of Computing: Domain-Specific Accelerators

Published: 2019-06-17

Speaker: Bill Dally, Chief Scientist and Vice President of NVIDIA Research and Inez Kerr Bell Professor of Computer Science and Electrical Engineering at Stanford University

Title of talk:  The Future of Computing:  Domain-Specific Accelerators

Abstract: Scaling of computing performance enables new applications and greater value from computing. With the end of Moore?s Law and Dennard Scaling, continued performance scaling will come primarily from specialization. Graphics processing units are an ideal platform on which to build domain-specific accelerators. They provide very efficient, high performance communication and memory subsystems - which are needed by all domains. Specialization is provided via ?cores?, such as tensor cores or ray-tracing cores that accelerate specific applications. This talk will describe some common characteristics of domain-specific accelerators via case studies.

Room: MH:309A

When: Wednesday June 19, 14:30-15:00

When: 2019-06-19 14:30 to 2019-06-19 15:00
Location: MH:309A, Mattehuset, Sölvegatan 18, Lund


AI* Nordic Powwow

Published: 2019-03-18

Exploring AI ? The future benefits and challenges. Beyond the traditional conference we present the unique POWWOW experience.

During this POWWOW we gather across sectors and competences around the hot topic Artificial Intelligence. Together we explore the impact it will have on society and our common future, but we also look into specific industries and their applications.

One of the purposes of the day will be the meeting between people and building an active platform for collaboration and innovation moving forward. All while having fun.

Some speakers:

  • ANDERS BORG, AI Adviser IPsoft, Fintech investor and Former Minister of Finance in Swedish Government

  • JOSÉ VAN DIJCK, Professor in media and digital society at Utrecht University

  • LIJO GEORGE, Business Lead at Sony AI

  • LISELOTT LADING, Serial entrepreneur, board member and Business Integrator IT, Axis Communications AB

  • KALLE ÅSTRÖM, Professor in Mathematics at Lund University

  • DAVID POLFELDT, CEO Massive Entertainment AB

More speakers, deatils of the program and how to register at the conference website: 


Conference fee: SEK 1.950 (Fee excluding VAT). Employees at Lund university are given a discount of 25%. Contact cecilia [at] skanemotor [dot] se (Cecilia Löfberg) at cecilia [at] skanemotor [dot] se or +46 707 88 40 48 for discount code.


Nordic Artificial Intelligence Powwow  is a collaboration between Skånemotor and Lund University.

More AIML@LU events at:

When: 2019-05-23 09:00 to 2019-05-23 22:00
Location: Central Lund, Sweden


Massive MIMO: Prototyping, Proof-of-Concept and Implementation - PhD Defence by Steffen Malkowsky

Published: 2019-04-23

Author: Steffen Malkowsky, Department of EIT

Location:  E:1406, E-building, Ole Römers väg 3, LTH, Lund University

Faculty opponent:  Professor Joseph R. Cavallaro

Thesis for download (PDF) 


Wireless communication is evolving rapidly with ever more connected devices and significantly increasing data rates. Since the invention of the smartphone and the mass introduction of mobile apps, users demand more and more traffic to stream music, watch high-definition video or to simply browse the internet. This tremendous growth is more pronounced by the introduction of the Internet of Things (IoT) in which small devices, such as sensors, are interconnected to exchange data for all sorts of applications. One example are smart homes in which a user can for instance, check temperature at home, verify if windows are closed or open, or simply turn on and off distributed loud speakers or even light bulbs in order to fake a busy household when on vacation. With all these additional devices demanding connectivity and data rates current standards such as 4G are getting to their limits. From the beginning 5G was developed in order to tackle these challenges by offering higher data rates, better coverage as well as higher energy and spectral efficiencies. Massive Multiple-Input Multiple-Output (MIMO) is a technology offering the benefits to overcome these challenges. By scaling up the number of antennas at the Base Station (BS) side by the order of hundred or more it allows separation of signals from User Equipments (UEs) not only in time and frequency but also in space. Exploiting the high spatial degrees-of-freedom it can focus energy with spotlight precision to the intended UE, thereby not only achieving higher energy being received per UE but also lowering the interference among different UEs. Utilizing this precision, massive MIMO may serve a multitude of UEs within the same time and frequency resource, thereby achieving both higher data rates and spectral efficiency. This is a very important feature as spectrum is very crowded and does not allow for much higher band-widths, and more importantly is also very expensive. 

The promised gains, however, do come at a cost. Due to the significantly increased number of BS antennas, signal processing and data distribution at the BS become a challenging task. Signal processing complexity scales with the number of antennas, thus requiring to distribute different tasks properly to still achieve low-latency and energy efficient implementations. The same holds for data movement among different antennas and central processing units. Processing blocks have to be distributed in a manner to not exceed hardware limits, especially at points where many antennas do get combined to perform some kind of centralized processing. 

The focus of this thesis can be divided into three different aspects, first, building a real-time prototype for massive MIMO, second, conducting measurement campaigns in order to verify theoretically promised gains, and third, implementing a fully programmable and flexible hardware platform to efficiently run software defined massive MIMO algorithms. In order to construct a prototype, challenges such as low-latency signal processing for huge matrix sizes as well as task distribution to lower pressure on the interconnection network are considered and implemented. By partitioning the overall system cleverly, it is possible to implement the system fully based on Commercial off-the-shelf (COTS) Hardware (HW). The working testbed was utilized in several measurement campaigns to prove the benefits of massive MIMO, such as increased spectral efficiency, channel hardening and improved resilience to power variations. Finally, a fully programmable Application-Specific Instruction Processor (ASIP) was designed. Extended with a systolic array this programmable platform shows high performance, when mapping a massive MIMO detection problem utilizing various algorithms, while post-synthesis results still suggest a relatively low-power consumption. Having the capability to be programmed with a high-level language as C, the design is flexible enough to adapt to upcoming changes in the recently released 5G standard.

When: 2019-05-17 09:15 to 2019-05-17 09:15
Location: E:1406, E-building, Ole Römers väg 3, LTH, Lund University


5G - An Antenna and Measurements Perspective

Published: 2019-01-25

An exciting event oreganised by The Antenna Measurement Techniques Association. Spend the day with the Antenna Measurement Techniques Association listening to top experts present the most recent developments in the industry.

Technical Tour

May 6, 2019, 18:00?20:00

Arrive a day early so you can plan to join us as we take a tour of the MAX IV Laboratory. Transportation and tour are included in the price of registration.
Technical Program

Programme May 7, 08:00?19:00

DTU-ESA Spherical Near-Field Antenna Test Facility ? Past, Present, and Future Activities
by Prof. Olav Breinbjerg, Technical University of Denmark, Lyngby, Denmark

Near-Field Measurement Technique for Electromagnetic Exposure of 5G Devices
by Prof. Mats Gustafsson, Lund University, Sweden

Far-Field OTA Testing of User Equipment Using Plane Wave Generators
by Mr. Lars Foged, Microwave Vision Group (MVG), Italy

5G Over-The-Air Conformance Testing
by Dr. Jonas Fridén, Ericsson Research, Gothenburg, Sweden

5G: Challenges for Human Exposure Assessment and Virtual-Drive Over-the-Air Testing
by Dr. Christian Bornkessel, Technische Universität Ilmenau, Germany

High-Resolution Dynamic Characterization of mm-Wave Channels
by Prof. Fredrik Tufvesson, Lund University, Sweden

Organizing Committee

Christer Larsson
Donnie Gray
Manuel Sierra Castañer
Michael Havrilla
Michelle Taylor
Fredrik Tufvesson
Lars Foged
Jan Zackrisson

Registrationis now open at:

The full program and information about fees (PDF)


When: 2019-05-07 08:00 to 2019-05-07 19:00
Location: Lund University Student Union (Kårhuset) LTH John Ericssons väg 3, Lund Sweden


POSTPONED to August 30: AIML@LU WS: AI & ML Technologies

Published: 2019-01-16

This fika-to-fika workshop is postponed to 30 August 2019

Please save-the-date and check out the program in progress at:

More AIML@LU events at




When: 2019-04-11 09:30 to 2019-04-11 09:30
Location: E:A, E-building, Ole Römers väg 3, LTH, Lund University


PhD dissertation by William Tärneberg: The confluence of Cloud computing, 5G, and IoT in the Fog

Published: 2019-03-11

Author: William Tärneberg, Department of EIT

Location:  E:C, E-Building, John Ericssons väg 4, Lund University, Faculty of Engineering LTH

Faculty opponent: Professor Maarten van Steen, University of Twente, Nederländerna

Thesis for download (PDF)


In the wake of the arrival of cloud computing, future applications are poised to be- come more resilient and adaptive by embracing elasticity in an osmotic manner. Although cloud computing is a strong attractor for application developers, there
are still unconquered performance frontiers. Latency-sensitive and mission-critical ap- plications make up a significant portion of all software systems, and their owners are eager to reap the benefits of cloud computing. However, they are hindered by signific- ant delay, jitter in the delay, and relatively low resilience when operating on traditional, distant, cloud data centres.

Fog computing is emerging as a remedy. Fog computing is a heterogeneous hyper- distributed cloud infrastructure paradigm, ranging from small compute nodes close to the end-users to traditional distant data centres. With greater proximity to the end- users, delay and jitter in the delay can be reduced, and intermediate network reliability improved. Additionally, with increased heterogeneity of resources, applications have a richer tapestry of resources to take advantage of for their objectives. However, man- aging and taking advantage of this heterogeneity in resources and objectives is a chal- lenge for both the infrastructure providers and application owners alike. Only where to place and scale application components and how to manage system resources to meet the objectives of both parties, is non-trivial. Application placement implies elaborate optimisation objectives, hard-to-find solutions, and operational conflicts.
The objective of this thesis is to investigate the performance-related properties of fog computing, how such an infrastructure can be managed while applications can osmotic- ally take advantage of the infrastructure, and what Fog computing?s potential practical performance gains are. These are fundamental topics that need to be answered for pro- viders and application owners alike to be able to invest in fog computing. In general terms, the work in this thesis seeks the trade-offs between infrastructure, applications, and software platform in contrast to the traditional cloud offering.

The thesis provides modelling and simulation tools for evaluating the performance and feasibility of Fog computing. Based on which, the thesis goes on to propose holistic infrastructure management algorithms. The requirements of latency-sensitive and mission-critical applications and use cases are discussed for a fog computing paradigm. These requirements are then translated to Fifth Generation Wireless Spe- cifications (5G) Massive Multiple Input Multiple Output (MIMO) specifications. An original 5G-based fog computing test-bed for time-sensitive and mission-critical ap- plications is implemented. The test-bed is used to evaluate the potential application performance gains of fog computing and to what extent the applications can practic- ally take advantage of a fog infrastructure. The thesis also investigates the architecture of the applications that are proposed to benefit from fog computing and how they per- form in traditional cloud offerings.

The included works show that fog computing indeed has a performance advantage over the traditional distant cloud, not only in latency but also in robustness. The be- nefits of 5G on a time-sensitive application deployed in a fog computing infrastructure are shown to be significant. It is also shown that a fog computing infrastructure with a high degree of heterogeneity and with multiple objectives can be successfully managed scalably. Additionally, the thesis sheds some light on the challenges of implementing latency-sensitive and mission-critical applications with traditional cloud service offerings.

When: 2019-03-29 09:15 to 2019-03-29 09:15
Location: Lecture Hall E:1406, E-Building, Ole Römers väg 3, Lund University, Faculty of Engineering LTH


Digit@LTH breakfast seminar: Modelling Intelligent Robot behaviour with Behavioural Trees by Volker Krueger

Published: 2019-01-16

Professor Volker KruegerTitle: Modelling Intelligent Robot behaviour with Behavioural Trees

Speaker:  Professor Volker Krueger, Department of Computer Science  

Location:  M-house, Ole Römers väg 1. M:E (North part of street level)

Abstract: Behavioral Trees (BT) have been used for a long time in computer games to give artificial characters a certain level of intelligence: With BTs the programmer can easily describe very complex behaviour patterns of  the artificial game character, i.e., what the character should do in a given situation or context. This correlates very much with the problems in robotics where modern robots are expected to handle tasks in very different conditions and contexts. 

Traditionally, finite state machines were used in robotics to describe complex behavioural patterns, the use of BTs is relatively new in the robotics community. In my talk, I want to discuss what BTs are, their definition, how they are constructed and how they are used. Then, in order to get a deeper understanding, I want to put the BTs into the context of FSM: Finite state machines (FSMs) are constructs from Theoretical computer science. They are well known from the area of formal language. We know they model the class of regular languages and we can related each FSM directly to a regular expression and vice versa. The question is: can BTs be analysed in the same way as FSMs? Can we also express a BT in form of a formal language? Are BTs even equivalent to FSMs?

Bio: Volker Krueger has studied computer science at the University of Kiel, Germany where he graduated with a Diploma degree (M.Sc.) in 1995. Dr. Krueger has completed his Dr. Ing. (PhD) in 2000 at Kiel University in the area of computer vision with a thesis of Gabor Wavelet Networks. He spent is PostDoc time in the Lab of Azriel Rosenfeld and Rama Chellappa at CFAR, University of Maryland before joining Aalborg University (DK) in 2002 as an Associate Professor. Until 2005 Dr. Krueger was researching in the area of Biometrics, Face recognition and Gait recognition. Since 2005, he has focused on cognitive robot in general and since 2012 on cognitive robotics particularly for manufacturing. He became Full Professor at Aalborg University in 2014. He was PI in the EU projects Paco-Plus(FP6), Tapas(FP7) and Scalable (H2020), and he was coordinator of the projects GISA (ECHORD/FP7) and STAMINA (FP7). Dr. Krueger has joined LTH in August 2018 as  WASP professor for Autonomous Systems.

Registration is now closed



When: 2019-03-28 09:00 to 2019-03-28 10:00
Location: M-house, Ole Römers väg 1. M:E (North part of street level)


PhD dissertation by Jakob Helander: Millimeter Wave Imaging and Phased Array Antennas for 5G and Aerospace Applications

Published: 2019-02-28

Author: Jakob Helander, Department of EIT

Location:  E:C, E-Building, John Ericssons väg 4, Lund University, Faculty of Engineering LTH

Faculty opponent: Professor Andrea Massa

Thesis for download (PDF)


Phased array antennas are cornerstones in many proposed antenna solutions concerning the next generation of both airborne radar systems and wireless communication systems (5G). Additionally, millimeter wave (mm-wave) frequencies are expected to play an integral role in 5G, and are deemed well-suited for inspecting structural components used in the aerospace industry.

This dissertation consists of a general introduction (Part I) and six scientific papers (Part II) - of which four have been published and two are under review in peer-reviewed international journals. The introduction comprises the background, the motivation and the subject-specific technical foundation on which the research presented in the included papers is based on. Fundamental theory on antenna arrays, mm-wave imaging systems and computational electromagnetics are presented together with the specific performance metrics, experimental setups, and computational acceleration algorithms that are of interest for the contained research work. The included papers can be divided into three tracks with two distinct applicational overlaps. 

Papers I and II concern electrically large phased arrays for airborne systems, and the numerical techniques that alleviate time-efficient and accurate simulations of such antennas. Paper I investigates the performance of two different approaches to the macro basis function (MBF) method for interconnected subdomains under the harsh electromagnetic conditions that endfire operation implies. Paper II presents a synthesis technique for endfire operation of large scale arrays that utilizes convex optimization to improve the impedance matching performance. 

Papers III and IV concern phased arrays for 5G applications. In Paper III, various array configurations of two microstrip antenna designs are evaluated with respect to two radiation performance metrics introduced specifically for evaluating the beam steering capabilities of phased array systems in the UE. A novel near field measurement technique for running electromagnetic field (EMF) exposure compliance tests of mm-wave phased arrays for future 5G devices is presented in Paper IV. 

Papers V and VI deal with mm-wave imaging systems developed for non-destructive testing (NDT) of composite materials used in the aerospace industry. A transmission-based bistatic imaging system is presented in Paper V, whereas Paper VI presents a further development of this system in a reflection-based measurement scenario. Data is retrieved using a planar scan, and the image retrieval algorithms comprise a numerical technique to separate the sources that contribute to the measured data, and an L1-minimization formulation to exploit potential sparsity of the sought-after solution.

When: 2019-03-26 09:15 to 2019-03-26 09:15
Location: E:C, E-Building, John Ericssons väg 4, Lund University, Faculty of Engineering LTH


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