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Investigation of Magnetometer-Inertial SLAM for Autonomous Railway Robot Navigation
No full textIn GPS-denied railway environments, such as tunnels or underground stations, estimating
the speed of a moving platform remains a challenge. This thesis presents a
sensor-based approach using magnetometers and inertial measurement units (IMUs)
to estimate velocity without relying on external infrastructure.
We first simulate realistic magnetometer and IMU data based on motion profiles,
adding noise such as Gaussian jitter, temperature drift, spatial jitter, and random
spikes. The synthetic magnetic field is created using a combination of sinusoidal
functions to reflect real-world magnetic variations.
Velocity is then estimated by comparing signals from front and rear magnetometers.
By measuring the time delay between them, speed can be calculated using the
known distance between sensors. The method includes filtering and window-based
lag detection for stability under dynamic conditions.
The estimated speed is fused with IMU acceleration using an Extended Kalman
Filter (EKF). Results show that IMU-only fusion performs well, but direct use of
noisy magnetometer speed may degrade accuracy. To improve this, we propose a
complementary filter as a pre-fusion step before EKF.
This work demonstrates a lightweight and effective method for speed estimation in
low-GPS or GPS-free railway scenarios. Future work will include improving signal
quality, implementing adaptive fusion, and hard-in-loop testing in real robots
Conceptualizing the Competitiveness of Pharmaceutical R&D Sites A Case Study of AstraZeneca’s R&D Site in Gothenburg
No full textIn the context of increasingly decentralized innovation models,
understanding what makes individual pharmaceutical R&D sites
competitive has become strategically important for multinational
corporations. While most research on global R&D focuses on
firm-level outcomes, this thesis addresses the underexplored
question of how competitiveness is shaped and sustained at the
level of the individual site. Through a qualitative case study of
AstraZeneca’s R&D site in Gothenburg, the study investigates how
internal and external factors contribute to site competitiveness within
a global pharmaceutical R&D network.
Drawing on 21 interviews with internal and external stakeholders, as
well as written material analysis, the study explores site competitiveness as
a dual construct: external competitiveness, defined by the site’s ability
to deliver high-value projects to the global pipeline and internal relevance,
its perceived strategic importance within the multinational corporation.
The findings reveal that pipeline delivery and site perception are the key
drivers of site competitiveness. These are shaped by internal success
factors, such as talent, infrastructure, operations, and culture and identity,
and external conditions such as ecosystem dynamics and quality of life.
The study also highlights several tensions that site managers must
navigate, including the balance between external competitiveness and
internal relevance, the paradox of pipeline delivery, how to increase
visibility without losing trust, talent loyalty and renewal, and being a
builder of a local ecosystem while needing to demonstrate global value.
The thesis contributes to the literature by providing a conceptual
framework for understanding site-level competitiveness in pharmaceutical
R&D. It offers practical insights for managers seeking to sustain relevance
in an increasingly globalized innovation landscape
Quasiparticle tunneling detection in superconducting qubits
No full textSuperconducting quantum circuits are sensitive to decoherence caused by quasiparticle
tunneling events, where broken Cooper pairs tunnel across Josephson junctions.
While these events typically affect individual qubits, high-energy cosmic ray impacts
can generate correlated relaxations and decoherence events across multiple qubits
on the same chip, a detrimental problem for quantum error correction protocols
that assume independent errors. In this thesis, we studied correlated quasiparticle
tunneling using charge-sensitive sensors. The quasiparticle detectors consisted of
single-island qubits directly coupled to a waveguide.
By recording simultaneous time traces from both quasiparticle detectors, we identified
burst events with elevated tunneling rates consistent with cosmic ray impacts.
Our correlation analysis found evidence that these high-energy events affect
both sensors within the same timeframe, supporting the hypothesis of correlated
quasiparticle generation across the chip. The main limitation was relatively low
signal-to-noise ratios which affected our ability to distinguish tunneling events from
background noise.
Additionally, we explored the feasibility of using a conventional transmon qubit
as a quasiparticle sensor by exploiting the enhanced charge-sensitivity of higherorder
energy transitions. We successfully demonstrated control of the |3⟩ → |4⟩
energy transition, but found that the charge dispersion was below our measurement
resolution limit for the device tested, highlighting the need for qubits with lower
EJ/EC ratios for this approach
Future System for High-Pressure Fuel Leakage Detection in Marine Commercial Engines
No full textThe project explores the development of novel double-walled high-pressure fuel injection
pipes used in marine commercial engines. Current double-walled pipes meet
the safety standard, but they suffer from higher costs and increased manufacturing
complexity.
The project aimed to offer the industry a superior alternative to existing solutions in
certain Volvo Penta engines, with the intent of improving the manufacturability of
the components, reducing costs, and improving tolerances. The project was driven
towards developing a robust leak detection and containment mechanisms to mitigate
hazards such as fuel spray or contamination in the engine room. It also investigates
on potential suppliers for procuring the materials and components for the system.
It also aims to propose recommendations for future systems.
The project was conducted using a stage gate product development process. Initially,
an analysis of existing systems and Volvo Penta marine engines was performed,
followed by a review of the regulations applicable to current engines, as these regulations
played a major role in the concept development. The final design was validated
through Finite Element Analysis (FEA) while cost calculations were performed and
material alternatives were explored
Sensitivity Study of Parameters in a Mathematical Model of HIV Infection Using Clinical Data
No full textModeling the early stages of HIV infection is essential for understanding virus dynamics
and improving treatment strategies. In this project, we solve a system of
three ordinary differential equations (ODEs) that describe the interactions between
uninfected immune cells, infected cells, and the viral load. Our goal is to reconstruct
key parameters and optimize the model using real clinical data from four patients.
The study is divided into two phases. In the first phase, we focus on estimating
the viral load V (t) by solving an inverse problem with a time-adaptive optimization
method. Two test cases, Test 1 and Test 2, are evaluated, and we find that Test 2
provides better accuracy across all four patients. The results show a good fit between
the computed and measured viral load up to day 50, confirming the effectiveness of
Test 2 in capturing early infection dynamics.
In the second phase, we extend the model by including the total immune response
Σ = T + I, which represents the sum of uninfected and infected CD4+ T cells. We
continue using Test 2, as it provided the best accuracy in the first phase. Additionally,
we modify the adjoint equations by incorporating an extra term derived from
the adjoint problem to improve the optimization process. The model is applied to
clinical data from four patients to analyze how well it reconstructs their immune
response and viral load dynamics.
An interesting result emerges from the numerical experiments. In many cases, the
optimization improves the fit for both the viral load V (t) and the immune response
Σ = T+I at the same time. This suggests that the model and the conjugate gradient
algorithm can reconstruct both variables in parallel with good accuracy. However, in
more challenging situations such as for Patient 1 and 4, it is still difficult to achieve
a perfect match for both quantities. These findings highlight the importance of
having a good initial guess and confirm the reliability of the optimization method,
even though further refinements may still be necessary.
From a theoretical point of view, we implement a time-adaptive reconstruction
method that improves numerical stability and allows better control of the parameters.
The optimization is based on minimizing a Tikhonov functional, where the
regularization term is chosen to balance data fitting and smoothness. Adding extra
terms from the adjoint equations improves the reconstruction but also makes it
harder to achieve good results for both state variables at the same time.
Overall, our results show that choosing test cases and optimization strategies carefully
is important in order to reconstruct both V (t) and Σ = T +I effectively. Time
adaptivity improves numerical performance, but more improvements are needed to
consistently match both outputs. This study contributes to building better mathematical
models for HIV infection, which can be useful in clinical prediction and
treatment planning
Automatic System Identification of Equivalent Circuit Parameters for Lithium Ion Batteries
No full textLithium-ion batteries are a critical component of electric vehicles (EVs), directly influencing their performance, range, and longevity. Battery management algorithms often rely on equivalent circuit models (ECMs) to describe the voltage response of cells to applied currents. However, accurately identifying ECM parameters is challenging due to their dependency on factors such as temperature, state of charge (SOC), and hysteresis. These challenges are further compounded by the timeconsuming nature of the parameter identification process.
This thesis presents a novel system for identifying ECM parameters by executing realistic drive cycle current profiles on lithium-ion cells. The method accounts for temperature effects, SOC variations, and the hysteresis effect to improve modeling accuracy. The identified parameters were validated and stored in lookup tables for integration into battery management systems (BMS). The results demonstrated consistent parameter identification across various SOCs and temperatures, with a notable enhancement in ECM accuracy. Despite some limitations, such as challenges in parameter estimation at low temperatures, this work provides a robust foundation for more accurate and efficient BMS algorithms, particularly in automotive
applications
Evaluation of Neural-Network and Large-Language Model Approaches for Generating Instructions for Animations
No full textConversational agents are used more and more in customer service, health care, for
educational purposes. The fundamental problems of conversational agents are many,
including limitations in interpretation of complex queries and lack of emotional intelligence.
Despite this, there are distinct advantages of conversational agents, such as
efficient data analysis, reduction of operational costs and aid in interactive learning
for personalized teaching. The most significant challenge this project aims to undertake
is to generate realistic and complex animations in the context of interactive
learning with a real-time constraint. The investigation includes how to select machine
learning tools and models to aid in the advancement of animation generation,
by using both Large-Language Models and purposely constructed Neural Networks.
While Large-Language Models are convenient when used in straightforward conditions,
Neural Networks are more dependable in an operative application thanks to
their consistent format, adaptability and specifically developed purpose
Challenges of Large-Scale Agile Transformation A Case Study at a Manufacturing Firm
No full textThis master’s thesis explores the challenges associated with large-scale agile transformation
within a manufacturing firm. Adopting a qualitative single-case study
approach, the research draws on data from semi-structured interviews, internal documents,
and field notes, focusing on a single platform within the organization to
provide an in-depth understanding of the transformation process. Several key challenges
were identified across multiple dimensions, which impacted the overall success
and effectiveness of the agile transformation. These challenges, though varied stem
from factors related to process, technology, organizational structure, and people.
The discussion emphasizes the most significant challenges within each dimension,
exploring their impact on the agile transformation and highlighting the complex
nature of implementing agile at scale in a non-IT-centric environment.
The thesis presents a set of practical recommendations to address these challenges,
including the need to improve communication and collaboration across teams, engage
stakeholders more effectively, ensure strong leadership involvement, and promote
organizational alignment throughout the transformation process. Additionally,
the study highlights the importance of cultivating an agile mindset at all levels of
the organization, addressing both cultural and psychological barriers to change. The
research concludes that successful agile adoption goes beyond changing processes—it
requires a fundamental shift in organizational culture and alignment at all levels,
emphasizing the importance of leadership, collaboration, and continuous learning.
This study contributes to the body of knowledge on agile adoption in manufacturing
and other non-IT-centric environments, offering insights into the unique challenges
faced by organizations in these contexts. The findings also underscore the necessity
of adaptive governance structures, integration strategies, and tailored approaches to
agile transformation. The thesis provides managerial implications that can guide organizations
through the complexities of agile adoption, focusing on the key aspects
of leadership, engagement, and cultural transformation necessary for sustainable success