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B-huset

Institutionen för systemteknik (ISY) är central inom olika ingenjörsutbildningar både vad gäller baskunskaper och tillämpade kurser. Forskningen baseras främst på industriella behov och spänner från helt grundläggande frågor till mera applikationsnära frågor.

Grundutbildning

Institutionen erbjuder cirka 100 olika kurser inom fyra grundutbildningsområden: Bild, Elektronik, Reglersystem och Telekommunikation. Inom universitetets program finns ett antal inriktningar som vi koordinerar.

Forskning

Forskning och forskarutbildning bedrivs inom ämnesområdena: Datorseende, Elektronik och datorteknik, Fordonssystem, Informationskodning, Kommunikationssystem och Reglerteknik.

Examensarbete

Här finns också information om hur man hittar och gör examensarbete hos oss.

Framläggningar

2026-06-03 kl 10:00 i Visionen Stora Konferensrummet

Tool Use for Vision-Language Models in Building Analysis with Street View Imagery

Författare: Ture Pontén
Opponent: Henry Yström
Handledare: Johannes Hägerlind
Examinator: Per-Erik Forssén
Nivå: Avancerad (30hp)

Förtränade visionsspråkmodeller kan användas för byggnadsanalys från gatubilder. Samtidigt har deras ökade resoneringsförmåga gjort det möjligt att låta modellerna använda visuella verktyg under analysen. I detta examensarbete undersöks om mindre visionsspråkmodeller kan förbättra sin förmåga att analysera byggnader genom verktygsanvändning.
Studien använder panoramabilder från gatunivå där modellen uppskattar antalet synliga våningar i markerade byggnader. Modellen kan antingen svara direkt eller använda verktyg för att zooma in, segmentera byggnader, detektera fönster och markera möjliga våningsgränser. Arbetet jämför promptstyrd verktygsanvändning, övervakad finjustering och förstärkningsinlärning som metoder för att lära modellerna när och hur verktygen bör användas.
Resultaten visar att mindre modeller behöver en inlärd verktygsstrategi för att verktygen ska bli användbara. Övervakad finjustering bidrar mest, särskilt genom att förbättra hur verktygen anropas och hur modellen tolkar verktygens resultat. En starkare referensmodell har däremot nytta av verktygen även utan särskild träning.
2026-06-03 kl 10:15 i Transformen

Fuzzing IPv4 modules on FPGAs: Design and comparison of on-chip and host-based fuzzers

Författare: Alexander Lindskog, Filip Ripstrand
Opponenter: Cornelia Calota, Daniel Söderström
Handledare: Petter Källström
Examinator: Kent Palmkvist
Nivå: Avancerad (30hp)
2026-06-03 kl 13:15 i Stora Visionen

Deep Learning-based Identification of Anatomical Landmarks

Författare: Valdemar Bång, Philip Gustafsson
Opponenter: Simon Hansson, Edvard Nilsson
Handledare: Ioannis Athanasiadis
Examinator: Maria Magnusson
Nivå: Avancerad (30hp)
2026-06-03 kl 13:15 i Transformen (B-hus, A-korridor, ing. 27, övre plan)

Complete Coverage Path Planning for Multiple UAVs

Författare: Adam Mejri, Jacob Persson
Opponenter: David Forslund, Axel Johansson
Handledare: David Axelsson
Examinator: Erik Frisk
Nivå: Avancerad (30hp)
2026-06-04 kl 10:15 i Nollstället

Positive-Unlabeled Graph Learning for Financial Crime Detection - Using Graph Neural Networks to Identify Financial Crime in Sparsely-Labeled, Real-World Data

Författare: Karl Duckert Karlsson, Lukas Olof Ingemarsson
Opponenter: Alice Stattin, Sofie Wiklund
Handledare: Adrian Edin
Examinator: Danyo Danev
Nivå: Avancerad (30hp)
2026-06-04 kl 10:15 i SH63

System-Level Optimisation of Heat and Electricity Utilisation from Gas Turbine Testing Using Thermal Storage

Författare: Sami Chouman, William Elfström Mackintosh
Opponenter: Jonathan Byman, Axel Stockhaus
Examinator: Daniel Axehill
Nivå: Avancerad (30hp)
2026-06-04 kl 10:15 i C3

Modelling and Performance Evaluation of Cell-to-Cell Variations in Series-Connected Battery Systems for Automotive Applications

Författare: Simon Gustafsson
Handledare: Arvind Balachandran
Examinator: Lars Eriksson
Nivå: Avancerad (30hp)
2026-06-04 kl 13:15 i Transformen

Time Series Anomaly Detection for Server Monitoring Data Using Unsupervised Machine Learning

Författare: Elias Axelsson, Joar Wiklund Hellström
Opponenter: Oscar Peter Hjelm, Johan von Axelson
Handledare: Kelvin Arana
Examinator: Danyo Danev
Nivå: Avancerad (30hp)
2026-06-04 kl 13:15 i Systemet

Reactive Power Control in a Pulp and Paper Industry

Författare: Oscar Ljungberg, Gabriel Lyberg
Handledare: Arezou Safdari-Vaighani
Examinator: Christofer Sundström
Nivå: Avancerad (30hp)
2026-06-04 kl 14:30 i Systemet

Performance Evaluation and Control Strategy Analysis of a Microgrid

Författare: Elisa Rylander, Hannah Schmid
Handledare: Oskar Lind Jonsson
Examinator: Christofer Sundström
Nivå: Avancerad (30hp)
2026-06-04 kl 15:15 i ISY-Visionen

Map based drone positioning

Författare: Mikael Lundgren, Edvard Wetind
Opponenter: Viktor Axén, Filip Nygren
Handledare: Justus Karlsson
Examinator: Amanda Berg
Nivå: Avancerad (30hp)
2026-06-05 kl 09:15 i Visionen Stora Konferensrummet

Directional Visual Perception for Object Sonification in Indoor Environments

Författare: Edvard Nilsson
Opponent: Valdemar Bång
Handledare: Arvind Balachandran, Lars Nielsen
Examinator: Per-Erik Forssén
Nivå: Avancerad (30hp)
2026-06-05 kl 09:15 i Transformen

Real-Time Capable Simulation and Control of a Tractor-Implement System

Författare: Jonathan Ruter, Oscar Tengbert
Handledare: Viktor Uvesten
Examinator: Martin Enqvist
Nivå: Avancerad (30hp)
2026-06-05 kl 10:15 i BL34

Model Predictive Control for Thermal Management Systems in Commercial Battery Electric Vehicles

Författare: David Forslund, Axel Johansson
Handledare: Max Johansson
Examinator: Lars Eriksson
Nivå: Avancerad (30hp)
2026-06-05 kl 10:15 i Transformen

Visualization of multiple duel simulations

Författare: Viktor Thellgren
Handledare: Souad Mohaoui
Examinator: Ingemar Ragnemalm
Nivå: Avancerad (30hp)
2026-06-05 kl 13:15 i BL32 (Nobel)

Evaluation of Multiphase Buck Converter for FPGA Power Delivery Networks

Författare: David Lindgren
Handledare: Arvind Balachandran
Examinator: Lars Eriksson
Nivå: Avancerad (30hp)
2026-06-05 kl 13:15 i Visionen Stora konferensrummet

High Frame Rate Video Synthesis using Event Cameras

Författare: Gabriel Bülow
Opponent: Alexander Nyström
Handledare: Fredrik Lundell
Examinator: Per-Erik Forssén
Nivå: Avancerad (30hp)
2026-06-05 kl 13:15 i Transformen

Computationally Efficient Angle-of-Arrival Estimation on FPGA

Författare: Daniel Söderström
Handledare: Theodor Lindberg
Examinator: Oscar Gustafsson
Nivå: Avancerad (30hp)

The ability to track mobile devices and vehicles using signal direction estimation is a critical component for modern communications and signal intelligence purposes. This thesis investigates the implementation and optimisation of angle-of-arrival (AoA) estimation algorithms on a Field-Programmable Gate Array (FPGA). The theory focuses on three algorithms: Bartlett's method, Capon's method and Multiple Signal Classification (MUSIC), utilising a uniform circular array (UCA) antenna geometry. The objective is to find a balance between mathematical complexity, execution time, and algorithm precision to meet real-time requirements.

To achieve this, an architecture was developed using systolic array-based processing elements. The main contribution to the implementation is an optimised grid-search approach for estimating the angle-of-arrival utilising adaptive angle spaces to reduce the amount of iterations required without sacrificing essential resolution. The algorithm was verified using Matlab and Python simulations, and analysed through synthesis using AMD Vivado. Results show that Bartlett's method provides the required balance between execution time and AoA accuracy. Furthermore, a Pareto front analysis was utilised to determine the optimal parameter configurations, showing the trade-off between execution time and resource usage based on wordlength and grid search parallelisation. This thesis thus provides a robust proof-of-concept for efficient angle-of-arrival estimation in different communications sectors.
2026-06-05 kl 14:15 i ISY Visionen Stora konferensen

Weakly Supervised Segmentation of Breast Tissue in Mammograms using Image-level Breast Density Labels

Författare: Oscar Peter Hjelm, Johan von Axelson
Opponenter: Elias Axelsson, Joar Wiklund Hellström
Handledare: Gulnaz Zhambulova
Examinator: Yonghao Xu
Nivå: Avancerad (30hp)
2026-06-05 kl 14:30 i BL32 (Nobel)

Switching Loss Optimization of a Three-Phase Motor Drive Through MOSFET Selection and Gate Circuit Design

Författare: Lukas Eliasson
Handledare: Arvind Balachandran
Examinator: Lars Eriksson
Nivå: Avancerad (30hp)
2026-06-05 kl 15:15 i Transformen

Re-Identifying User Equipment in LTE-Networks Using Machine Learning

Författare: Erik Karlstedt
Opponent: Martin Castro Bildhjerd
Handledare: Martin Dahl
Examinator: Danyo Danev
Nivå: Avancerad (30hp)

This thesis investigates the re-identification of user equipment (UE) in LTE networks following
temporary identity changes, utilizing machine learning to analyse unencrypted
RNTI and TMSI metadata. Data was collected by connecting a target UE operated by the
author to a real base station. Unencrypted metadata from the base station was collected
using a software defined radio and data from the special UE was used to label samples as
positive if they belonged to the target UE and negative if they did not. Importantly, real
users were connected to the base station during collection but no user traffic was collected.
Further, unencrypted metadata related to other UE were considered negative samples and
no UE other than the target UE was re-identified. This ensured the privacy and integrity
of real users connected to the base station. Two machine learning models – a Long Short
Term Memory (LSTM) model and a Siamese neural network model – were employed and
compared for the task. The results showed that the LSTM model performed well on RNTI
data, managing to detect over 90% of all samples belonging to the target UE with a precision
of around 20%. The models based on Siamese neural networks performed much
worse. On RNTI data, around 70% of all positive samples were detected, but the precision
was only 0.001%. On TMSI data the Siamese models performed better, detecting around
90% of all positive samples with a precision of 0.0045%. Not enough TMSI data was collected
for training LSTM models. The thesis concludes that it may be possible to re-identify
UE in LTE networks after a change of temporary identity by analysing unencrypted metadata
in RNTI traffic with the use of machine learning, but that more research is needed to
definitively answer.
2026-06-05 kl 15:15 i Nollstället

Enhancing Subsea Inertial Navigation: Underwater current estimation

Författare: Jonathan Norrestam
Opponent: Oscar Johansson
Handledare: Joel Wendin
Examinator: Gustaf Hendeby
Nivå: Avancerad (30hp)

The absence of GPS/GNSS in the underwater domain makes accurate long-term navigation challenging. Inertial navigation systems (INS) are commonly used, but unaided, they suffer from inherent drift caused by sensor noise and bias, which is further exacerbated by unknown water currents. The Doppler velocity log (DVL) sensor can provide accurate velocity aiding if the seabed is within range, effectively reducing the amount of drift. However, its range is limited, and rough bathymetry can lead to measurement dropouts. In these scenarios, the DVL can measure the vehicle's velocity relative to the surrounding water using its water track (WT) mode. This thesis investigates the feasibility of utilizing these WT measurements for INS aiding.

The study addresses the problem by implementing current estimation capabilities driven by the DVL WT measurements. Different filtering techniques based on the error state Kalman filter (ESKF) architecture are used to model the unknown dynamics of the current. An extended version of the baseline ESKF with a fixed current dynamics model is first established. To address the challenge of correctly tuning this fixed model to unknown dynamics, two different interacting multiple model (IMM) approaches are then investigated: one using 2 models, and another using 4 models that decouple the current dynamics in the global navigation axes.

The implemented filters are evaluated in simulation using an autonomous underwater vehicle and compared to a baseline unaided INS under varying current conditions, sensor qualities, and movement patterns. The results demonstrate that current estimation has the potential to significantly reduce the long-term position drift of an unaided INS. While the fixed ESKF can perform well if tuned correctly, the 2-model IMM demonstrates superior adaptability to changing conditions and proves to be the most robust solution across all evaluated scenarios. This work concludes that DVL WT-aided current estimation, particularly using IMM techniques, can contribute to a more robust navigation solution for underwater missions.
2026-06-08 kl 13:15 i Stora Visionen

Mer än en nödlösning: En teknisk och ekonomisk studie av gasturbinbaserad reservkraft

Författare: Ida Edin, Karl Johansson
Handledare: Carl Steen
Examinator: Christofer Sundström
Nivå: Avancerad (30hp)
2026-06-08 kl 15:00 i ISY Kärnan

Data-Driven Analysis of Complex Measurement Data to Support Efficient Testing Processes

Författare: Varun Gurupurandar
Opponent: Adam Roos
Handledare: Supratim Manna
Examinator: Arunava Naha
Nivå: Avancerad (30hp)

The work is conducted in collaboration with Toyota Material Handling in Mjölby and focuses on developing a scalable, end-to-end data pipeline capable of transforming raw multi-source sensor data into a structured format suitable for machine-learning-based energy estimation. Despite the availability of large heterogeneous data for electric forklifts, there is a lack of efficient methods to handle such data. This thesis investigates methods for handling heterogeneous time-series data with varying update frequencies in the context of predicting the energy consumption of electric forklifts.
The primary contribution of the thesis is a data-processing framework that aligns and integrates time-series signals — such as current, voltage, and vehicle speed — originating from different sampling rates and acquisition systems. A segmentation and phase-identification methodology is proposed to isolate operational states within the travel scenario, enabling targeted feature extraction relevant to energy-use prediction. Machine learning techniques are applied at multiple stages of the workflow, including data fusion, phase classification, and supervised estimation of energy consumption. The transformed and derived results are stored and visualized using structured Excel outputs to support further analysis and validation.
The outcomes demonstrate that consistent preprocessing of heterogeneous sensor streams is critical for accurate energy-consumption modeling, and provide a foundation for future predictive systems in material-handling applications.
2026-06-08 kl 15:15 i ISY Visionen Stora konferensrummet

Semantic Segmentation of LiDAR Point Clouds Using Image Annotations

Författare: Johanna Nilsson
Opponent: Alexander Berntsson
Handledare: Anmar Karmush
Examinator: Yonghao Xu
Nivå: Avancerad (30hp)
2026-06-09 kl 08:15 i ISY Visionen, stora konferensrummet

Investigation of Using Weak Labels for Instance Segmentation

Författare: Alexander Berntsson
Opponent: Johanna Nilsson
Handledare: Nathaniel Helgesen
Examinator: Maria Magnusson
Nivå: Avancerad (30hp)
2026-06-09 kl 10:00 i Systemet

Analysis of Compromising Emanations in a Commercial Quantum Key Distribution System

Författare: Arvid Sjöblom
Handledare: Martin Clason
Examinator: Joakim Argillander
Nivå: Avancerad (30hp)
2026-06-09 kl 10:00 i Stora Visionen

Semi-Autonomous Aircraft Tug Docking Function

Författare: Oscar Johansson
Opponent: Jonathan Norrestam
Handledare: David Axelsson
Examinator: Erik Frisk
Nivå: Avancerad (30hp)
2026-06-09 kl 10:00 i Transformen

Accurate Replaying of Simulations in Non-Deterministic Physics Engines

Författare: Anton Nilsson
Handledare: Daniel Spegel-Lexne
Examinator: Ingemar Ragnemalm
Nivå: Avancerad (30hp)
2026-06-09 kl 14:15 i Visionen Stora Konferensrummet

Algorithms for Sonifying Objects into Spatial Audio using Head-Related Transfer Functions

Författare: Simon Hansson
Opponent: Philip Gustafsson
Handledare: Arvind Balachandran, Lars Nielsen
Examinator: Per-Erik Forssén
Nivå: Avancerad (30hp)
2026-06-09 kl 15:15 i Nollstället, ISY

Protecting Digital Game Integrity: Exploring Similarity Detection Methods to Ensure Authenticity for Small Game Platforms

Författare: Hannah Bahrehman, Jenny Hanås
Opponent: Thea Borg
Handledare: Martin Clason
Examinator: Joakim Argillander
Nivå: Avancerad (30hp)

In a world of computers, data integrity is of utmost importance. To know where the information published online truly originates is becoming an increasingly difficult task. One place where this may be a problem is in the gaming world, where piracy is a common issue.
In this master's thesis, the work is performed on a platform for small games, where the authenticity of games has been compromised, as games can be copied, modified, and republished under a different author's name.
To determine if a malicious copy has been made, this report explores pairwise similarity detection methods to ensure authenticity.
Similarity metrics were constructed from raw game data, game logic, and graphical appearance using fuzzy hashing, Abstract Syntax Trees (AST:s) with the Zhang–Shasha algorithm, and color histograms compared using cosine distance and earth mover's distance.
The methods were evaluated using four datasets: popular game groups, near-copy games, manually selected distinct games, and 100 unlabeled games.
Pairwise similarity metrics generally distinguished games within the same group from those outside it.
Combined pairwise similarity measures analyzed using Kernel Principal Component Analysis (KPCA) and Multi-Dimensional Scaling (MDS) preserved meaningful group structure and successfully detected near-copy games.
KPCA on the quantitative dataset showed short distances between games of similar level design and color scheme.
The main contribution of this thesis is the demonstration that combined feature-based similarity metrics can capture meaningful relationships between games and show promise as a tool for detecting copied games, particularly near-copy variants.
2026-06-09 kl 15:15 i Transformen

Implementation of a Framework for Real-Time Model Predictive Control

Författare: Astrid Lauenstein
Opponent: Leo Jarhede
Handledare: Joel Wikner
Examinator: Daniel Axehill
Nivå: Avancerad (30hp)
2026-06-09 kl 15:15 i Systemet

Estimation of Rolling Resistance of Heavy-Duty Battery Electrical Vehicle with Physics Informed Neural Network Variations

Författare: Emil Alakulju
Opponent: Patrik Modorato
Handledare: Oskar Lind Jonsson
Examinator: Lars Eriksson
Nivå: Avancerad (30hp)
2026-06-10 kl 09:00 i ISY Systemet

Radar for Agricultural Machinery Applications

Författare: Amanda Falk, Oskar Persson
Handledare: Pratiti Paul
Examinator: Diana Pamela Moya Osorio
Nivå: Avancerad (30hp)
2026-06-10 kl 10:15 i Transformen, ISY

Externalizing State in Radio Access Network (RAN)

Författare: Sandra Faraj
Opponent: Roberto Wilnerzon Thörn
Handledare: Didrik Bergström
Examinator: Joakim Argillander
Nivå: Avancerad (30hp)
2026-06-10 kl 13:15 i Systemet

SE-VF: Security Estimations for Video Fingerprinting

Författare: David Rotander
Opponent: William Kaul Aronzon
Handledare: Gustaf Åhlgren
Examinator: Joakim Argillander
Nivå: Avancerad (30hp)
2026-06-10 kl 15:15 i Stora Visionen

Excitation and Estimation for Adaptive Control of a Supersonic Missile

Författare: Mattias Uvesten, Emil Wallbom
Examinator: Anders Hansson
Nivå: Avancerad (30hp)

Most missile systems today use model based controllers. The performance of
such controllers will be determined by the accuracy of the model compared to
the true system. If production is scaled up or the production cost of the mis-
siles produced needs to be decreased, there will be a larger deviation between
individual missiles. One way to mitigate the problem of model uncertainties is
to use an adaptive controller where the control law changes in order to improve
performance mid flight.
In this thesis, such methods will be explored in combination with methods that
force adaptation to be rapid and accurate. For the missile to gather useful infor-
mation to be used during adaptation of the control law it will have to manoeuvre
somehow and compare its motion to the predicted motion. Given that the goal
of a missile launch is to strike a target the performance in the later stage is more
important than the initial performance, therefore it is important that adaptation
happens early. Thus, initial excitation through Optimal Experiment Design is
considered. Another method of mitigating the risk of poor performance late in
the mission stage is to enforce persistence of excitation throughout the entire
flight and constantly adapt to changes in parameters.
The adaptive controllers in this thesis are indirect, meaning that aerodynamic
coefficients are estimated and then used in a new model based control synthesis.
Two estimation methods are considered, Recursive Least Squares and the Predic-
tion Error Method. The considered controllers are: an Adaptive Gain Scheduled
Linear Quadratic Controller, Adaptive Model Predictive Controller and a Persis-
tently Exciting Adaptive Model Predictive Controller.
The results show that performance is improved with the adaptive strategies. What
is also found is that the robustness of the adaptive schemes is higher than the
pre-implemented Gain Scheduled Linear Quadratic Controller via Monte Carlo
Simulations.
2026-06-11 kl 10:00 i ISY Nollstället

Model Predictive Path-Following Control for Fixed-Wing UAVs

Författare: Henric Johansson, Oliver Nimnuan Almgren
Opponenter: Mattias Uvesten, Emil Wallbom
Handledare: Duy-Nam Bui
Examinator: Johan Löfberg
Nivå: Avancerad (30hp)
2026-06-11 kl 10:00 i Systemet

Effective use of the Electomagnetic Spectrum

Författare: Niklas Lennarth Greger Eriksson
Opponent: Oscar Wilkens
Handledare: Martin Andersson
Examinator: Danyo Danev
Nivå: Avancerad (30hp)
2026-06-11 kl 13:15 i Zulu

Sim-to-Real Evaluation of Pallet Slot Corner Detection and Tracking Using Deep Neural Networks

Författare: Ferdinand Kouhia, Adil Shamji
Opponenter: Tobias Berglind, Oscar Sandblom
Handledare: Bryan Adams
Examinator: Mårten Wadenbäck
Nivå: Avancerad (30hp)
2026-06-11 kl 15:15 i ISY Systemet, B-huset

FPGA Implementation of a Non-Negative Least Squares Accelerator Using Floating-Point Arithmetic - A Fast Projected-Gradient Approach

Författare: Wilhelm Hedestad
Opponent: Carl Jörgensen
Handledare: Simon Bjurek
Examinator: Oscar Gustafsson
Nivå: Avancerad (30hp)
2026-06-11 kl 15:15 i Stora Visionen

Optimal Control-Based Motion Planning for Autonomous Forklift Pallet Pick-up

Författare: Matej Brtan, Oskar Herling
Opponenter: Ferdinand Kouhia, Adil Shamji
Handledare: David Axelsson
Examinator: Erik Frisk
Nivå: Avancerad (30hp)
2026-06-12 kl 09:00 i Systemet

Implementation and Evaluation of Tire Mounted Sensors for Tire Load Estimation

Författare: Victor Snarberg, Ture Valtonen
Handledare: Emanuel Herberthson
Examinator: Fredrik Gustafsson
Nivå: Avancerad (30hp)
2026-06-12 kl 10:15 i Nollstället

How Different Circumstances Affect Random Number Generators on FPGA

Författare: Cornelia Calota
Handledare: Theodor Lindberg
Examinator: Kent Palmkvist
Nivå: Avancerad (30hp)
2026-06-12 kl 13:15 i Systemet

Online Boot Load Estimation Using Barometer and GNSS

Författare: Max Frejd, Jesper Jansson
Handledare: Jakob Åslund
Examinator: Fredrik Gustafsson
Nivå: Avancerad (30hp)
2026-06-15 kl 10:00 i Transformen

Robust On-board Communication for Forklifts

Författare: Martin Castro Bildhjerd
Opponent: Erik Karlstedt
Handledare: Martin Dahl
Examinator: Danyo Danev
Nivå: Avancerad (30hp)
2026-06-15 kl 14:15 i Transformen

Formula-Driven Supervised Learning for Vehicle Classification in SAR Imagery

Författare: Simone Edman
Opponent: Anna Jonsson
Handledare: Pavlo Melnyk
Examinator: Leif Haglund
Nivå: Avancerad (30hp)
2026-06-17 kl 10:15 i Systemet

Performance Evaluation of GUWMANET and Zigbee-Based Protocols for Underwater Communication

Författare: Thea Antonson
Handledare: Henrik Åkesson
Examinator: Håkan Johansson
Nivå: Avancerad (30hp)