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Design and Development of a Novel Sensorized Orthosis for Orthopedic Rehabilitation
No full textThis thesis presents the design, development, and implementation of a novel sensorized
orthosis for orthopedic rehabilitation. The system addresses the lack of
quantitative feedback in traditional passive braces by integrating compact sensing
and data communication modules into a lightweight and modular structure. An
absolute rotary encoder and two six-axis IMUs were employed to measure knee joint
angle and motion dynamics in real time. The embedded controller, based on an
ESP32-S3 microcontroller, supports high-frequency data acquisition, local microSD
logging, and wireless communication with the ROS 2 framework, enabling both
offline analysis and interactive applications.
The system was designed as a fully independent and non-invasive add-on to a commercial
postoperative brace, maintaining clinical compatibility while adding sensing
capability. The complete system weighs approximately 180 grams and allows rapid
attachment and removal without altering the brace’s mechanical properties. Experimental
validation confirmed stable and synchronized sensor performance, accurate
joint-angle estimation, and reliable differentiation between correct and incorrect rehabilitation
motions.
The integration of the existing Unity3D-based rehabilitation game with the proposed
system successfully validated its feasibility and immense potential in intelligent,
interactive rehabilitation. This system bridges the technological gap between
conventional orthopedic braces and intelligent robotic rehabilitation devices, establishing
a solid foundation for achieving intelligent, quantitative, and personalized
rehabilitation assessment and training
Optimized Internal Logistics for Non-Standard Parts
No full textThis report focuses on optimizing internal logistics for non-standard parts in Volvo Trucks’ production plant in Tuve. More specifically, the customer adapted (CA) materials are emphasized. The thesis aims to identify the issues in the current logistics flow and propose solutions to improve material availability, reduce lead times, and support production goals. Furthermore, the research covers the challenges induced by having a logistics flow with low-volume and customer specific components.
By analyzing the root causes of delays, errors, and poor coordination, the study identifies areas for improvement, such as better information flow, system support, and quality control measures. Key findings suggest that addressing underlying issues like unclear material specifications, manual handling, and weak communication between departments can significantly improve the robustness of the CA material flow, leading to improved performance and better customer satisfaction. The proposed solutions include implementing real-time scanning systems, enhancing buffer management, and improving coordination across departments. This report shows the importance of adapting logistics systems to handle a high variability and ensure
on-time deliveries of non-standard parts in a complex manufacturing environment
Modeling of Teeth Forces in Electrical Machines - A Comparison between different types of machines
No full textElectromagnetic forces are the primary excitation sources of vibration and noise in electrical machines. Their generation mechanisms and transmission processes are closely related to pole–slot combinations, winding configurations, and structural characteristics of the machine. This thesis investigates permanent-magnet synchronous machines (PMSMs) with different pole–slot configurations, focusing on the characteristics of electromagnetic (EM) forces and their associated vibration and noise mechanisms. A comparative study is conducted between an integralslot distributed-winding interior permanent-magnet synchronous machine (ISDW IPMSM) and fractional-slot concentrated-winding surface-mounted permanent-magnet
synchronous machines (FSCW SPMSMs), highlighting their differences in electromagnetic fields, electromagnetic forces, torque ripple, and vibro-acoustic performance. First, from a theoretical perspective, analytical models of radial and tangential airgap electromagnetic fields and lectromagnetic forces are established under both slotless and slotted conditions. The influence of different pole–slot combinations on air-gap flux density distributions and the spatio-temporal harmonic characteristics of electromagnetic forces is systematically analyzed. Based on these models, the
effects of electromagnetic forces on cogging torque and load torque ripple in different machines are investigated, clarifying the impact of pole–slot combinations on torque performance. Second, to address the discontinuous nature of force transmission from air-gap electromagnetic forces to stator tooth forces, a theoretical model of the mechanical modulation effect of stator teeth is developed. By analyzing the force transmission process from the perspectives of distributed forces and concentrated forces, the sampling and modulation characteristics exhibited by stator teeth during force transformation are revealed. The differences of this effect among various machine structures and its role in reshaping the tooth force spectrum are also clarified. In terms of numerical simulations, two-dimensional (2D) finite-element electromagnetic simulations are employed to obtain electromagnetic field distributions, electromagnetic force waves, and torque characteristics under no-load and load conditions. Three-dimensional (3D) finite-element models are used to perform modal analysis of the stator and housing, yielding their natural frequencies and mode shapes. Furthermore, the electromagnetic forces obtained from 2D simulations are mapped onto 3D structural models to conduct coupled multiphysics harmonic response analyses. Equivalent radiated power (ERP) is used to evaluate vibration and noise responses over the entire operating speed range.
Finally, by combining modal analysis and harmonic response results, the generation mechanisms and evolution of vibration and noise in PMSMs with different pole–slot configurations over the full speed range are systematically elucidated. The dominant electromagnetic force components and their coupling with structural modes are identified. This thesis establishes a complete physical chain from electromagnetic force generation, through stator tooth mechanical modulation, to structural vibration and noise radiation, providing theoretical foundations and engineering guidance for the
low-vibration and low-noise design of PMSMs with different pole–slot combinations
Reducing downlink reference signal overhead for CSI acquisition in massive MIMO systems
No full textThe number of antennas used in massive multiple input multiple output (MIMO) systems
is expected to increase significantly to meet requirements of future radio access networks
(RANs). In legacy 5G New Radio, scaling the number of antennas imposes a proportional
increase in overhead associated with the acquisition of channel state information (CSI)
through downlink (DL) transmission of reference signals (CSI-RS). This thesis investigates
methods to reduce the overhead of CSI-RS transmission using a twofold approach:
optimising pilot placement for sparse sounding of the reference signals, and reconstructing
the full channel information from these sparse measurements at the user equipment (UE).
First, the sparse sampling of CSI-RS is formulated as a submodular optimisation
problem, and a cost function is presented based on the frame potential of the DL channel
estimated by the UE. A greedy algorithm for solving the sparse pilot placement problem is
proposed and evaluated for a simulated 3rd Generation Partnership Project (3GPP) MIMO
urban microcell environment with a uniform planar array (UPA), achieving near-optimal
pilot placement for subsets of antenna ports.
The second part of the thesis investigates the recovery of the full channel information
from the sparsely sounded CSI-RS using an artificial neural network (ANN). A
physics-informed U-Net architecture is developed, that leverages the sparse angular
representation of the DL channel to recover the full-rank channel. The ANN is trained on
a large dataset of simulated noiseless DL channels for the same 3GPP environment and
for several different spatial pilot configurations and muting levels.
The results of the experiments show that the ANN model can achieve a reconstruction
accuracy comparable to basis pursuit denoising (BPDN), while outperforming BPDN
in computational efficiency. In addition, the choice of spatial antenna port muting
pattern has a noticeable impact on the reconstruction performance of both methods in
the considered scenario, with the found near-optimal sampling patterns gives the closest
spectral similarity to the full-rank channel in terms of the Itakura-Saito distance. The
combined approach of optimising sparse pilot placement and using a neural network
for reconstruction demonstrates the potential of AI functionality for CSI-RS overhead
reduction and for improving the performance of massive MIMO systems in upcoming 6G
networks and beyond
Survey on seamless on-board and cloud connectivity for transport missions
No full textThe reliable connectivity required for mission-critical transport systems, such as
autonomous driving, remains a challenge in areas with limited terrestrial network
coverage. Non-Terrestrial Networks (NTNs), particularly Low Earth Orbit (LEO)
satellites, have emerged as a promising solution to fill this gap.
The study employs a comprehensive MATLAB-based simulation framework informed
by 3GPP TR 38.811 and ITU-R channel models. The methodology involves a
systematic approach where free-space path loss, atmospheric attenuation, Doppler
shift, and environmental fading are integrated into a complete link budget. The
primary contribution of this research is its integrated analysis of these factors
specifically for vehicular links, providing a unified assessment of performance through
key metrics including Bit Error Rate (BER) versus Carrier-to-Noise Ratio (CNR),
BER versus Eb/N0, throughput, latency, and Doppler shift.
The results demonstrate that elevation angle is the dominant factor governing link
quality. Performance improves dramatically from near-unusable conditions at 10◦ to
reliable, near-error-free operation (BER < 10−6) at 90◦ elevation. A critical finding
is the establishment of a universal CNR threshold of approximately 15 dB for reliable
operation. The analysis reveals a fundamental design trade-off: Ka-band offers higher
throughput, while S-band provides robustness against impairments. Latency analysis
confirms that LEO systems can meet the delay requirements for connected transport
services.
This study concludes that LEO-based NTNs are a viable complementary technology
for intelligent transportation systems. The findings provide a clear framework for
system design, highlighting the critical importance of elevation-aware planning and
strategic frequency band selection
AI Assisted matching in Mergers And Acquisitions
No full textTraditional buyer identification in M&A relies on manual screening and professional
networks, making it resource-intensive and naturally limiting the buyer pool. This
thesis investigates whether textual embedding models can support the identification
of relevant potential buyers in mergers and acquisitions. The study examines how
different representation methods, including TF-IDF, Doc2Vec with smooth inverse
frequency weighting, and Transformer based models, capture similarity between
companies when applied to standardized summaries of portfolio company descriptions.
The summaries are created using a large language model with information
provided on the portfolio companies websites. The performance of the embedding
models is evaluated through visualization of the embedding spaces, cosine similarity
search experiments, and an expert review of buyer recommendations. The
results indicate that TF-IDF and the Transformer model produced relevant recommendations,
with the Transformer model demonstrating the best performance in
embedding space separation and alignment with expert judgment, while Doc2Vec
models showed weaker differentiation between company types. Overall, the study
shows that embedding based similarity search can serve as a useful first step in buyer
discovery by expanding the range of potential buyers considered and improving efficiency.
The work also highlights that further validation across a larger set of targets
and with a more complete dataset would strengthen confidence in these results
Performance comparison of simulation models and parameters for engine CFD applications.
No full textIn this thesis, the performance of computational fluid dynamics (CFD) simulation models and parameters are systematically compared for an engine model based on the Imperial college experimental engine. A set of simulation models and parameters
were chosen to be studied. For each model and parameter, a few different options were chosen and simulation cases created for each possible different combination of these options. A custom code was written for creating the cases out of all the combinations of model and parameter options.
The simulations for this thesis were performed with OpenFOAM, which is an opensource CFD software. The engine model has been created at Wärtsilä and used in-house meshing tools to create a moving mesh.
After the simulations had ran, the results were post-processed using a custom code written for this thesis. The post-processed results were directly compared to results from a high-fidelity direct numerical simulation (DNS) study based on the Imperial college case, which has great agreement with the experimental results. For each simulation, two metrics were calculated that measured the difference between simulation and DNS results. These metrics were used to assess the performance differences
between cases with different model and parameter combinations, and thus the performance of the models and parameters themselves.
The models and parameters studied in this thesis were the turbulence model, velocity advection scheme, maximum Courant number, mesh resolution and the number and thickness of surface inflation layers of the mesh. These were divided into three
individual studies.
A few of the turbulence models had clearly better performance than the others. Change in the velocity advection scheme, maximum Courant number and surface inflation parameters had only slight effects on performance. An increased mesh
resolution generally lead to better performance
Extensions of Constant Proportion Portfolio Insurance using the Geometric Ornstein-Uhlenbeck process and the Chan-Karolyi-Longstaff-Sanders process
No full textWe investigate performance of the Constant Proportion Portfolio Insurance
(CPPI) strategy and compare it with two of its extensions: Time Invariant
Portfolio Protection (TIPP) and Exponential Proportion Portfolio Insurance
(EPPI).
In order to do this, we model a risky asset (a stock or an index) using
a Geometric Ornstein-Uhlenbeck process, and estimate its parameters using
the likelihood ratio method with historical price data. We model a non-risky
asset (a zero-coupon bound) using a Chan-Karolyi-Longstaff-Sanders process
and estimate its parameters using the maximum likelihood method where
we approximate the transition probability density function using a Hermite
expansion.
We find that both extensions of the CPPI improve performance in different
ways. The resulting distribution of simulated portfolio outcomes for the TIPP
strategy has a lighter tail compared to the CPPI case, and the risk of loss
is lower (this is also true compared to the EPPI strategy, but to a smaller
degree). The EPPI strategy translates the distribution of simulated portfolio
outcomes to the right, so that EPPI performs better than CPPI (and TIPP)
in terms of both mean and median
Data-Driven Automated Reporting Solution for External Collaborations - LLM-driven KPI Definition
No full textThis thesis presents a proof-of-concept, developed with AstraZeneca (AZ), that explores
automating progress reporting for external collaborations by testing whether
a large language model (LLM)-driven system can extract objectives from contracts
and translate them into tailor-made key performance indicators (KPIs). Objective
extraction is quite reliable, reaching several highs of accuracy around the 85%-mark,
but converting objectives into KPIs that stakeholders judge as relevant, clear, actionable,
and measurable, is substantially less solid. Fewer than half of the KPIs
met each quality criterion on average, and 39% met none. Survey responses noted
that KPIs were often unclear, overly generic, or poorly timed, and skewed toward
simple counts (e.g., “number of models”) that miss quality and impact.
From interviews conducted at AZ, a set of general KPIs, that were deemed meaningful
to measure in a collaboration project, could be demonstrated. The final evaluation
suggests that these KPIs (e.g., external engagement and budget coherence)
outperform collaboration-specific KPIs generated directly from objectives. This underscores
the difficulty of creating bespoke target measures in diverse contexts.
Despite these issues, the approach offers practical value. In principle, the pipeline
should be better suited for agreements with explicit milestones (e.g., business or
commercialisation contracts), where more clearly defined expected outcomes support
better-formed KPIs. However, this cannot be conclusively established by the
implementation in this thesis, due to limited data.
Ultimately, translating qualitative objectives into quantitative, decision-grade KPIs
remains inherently difficult. Contemporary LLMs are capable across many aspects of
automation, but evidently less reliable for high-judgement and context-specific KPI
design that balances relevance, clarity, actionability, and measurability, at least by
following the approach outlined in this thesis. Therefore, the most defensible nearterm
usefulness is in metadata extraction and recommendation, while still requiring
a human-in-the-loop as a safeguard. In turn, this can improve customer relationship
management (CRM) metadata completeness and enable collaboration health
insights and automated reporting
Conceptual Design of a NEW Recovery System for Small Fixed-Wing UAVs
No full textUnmanned Aerial Vehicles (UAVs) are becoming an important part of modern rescue
operations where quick access to information can save lives. Small fixed-wing
UAVs help the Swedish Sea Rescue Society (SSRS) gain situational awareness, but
recovering these UAVs after a mission comes with challenges. Today, they land on
water, which works though it slows down urgent missions. Retrieval can also be
difficult in bad weather, and occasionally the UAVs are lost or damaged. The goal
of this thesis is to develop a new land-based recovery system to safely catch SSRS’s
small fixed-wing UAVs without adding extra components to the fixed-wing UAV.
To achieve this, the thesis begins by introducing the problem and reviewing existing
recovery methods (from hooks and nets to more advanced control-based approaches)
before detailing how concepts were generated, compared, and refined through a
structured design process. The final concept combines a precise XY alignment subsystem,
inspired by the motion control in 3D printers, with a mechanical capture
subsystem that captures the UAV without damaging it. Based on kinematic simulation
and subsystem-level evaluations, the recovery system is capable of working
in practice and offers a clear path towards physical prototyping. The thesis ends by
outlining the next steps needed to bring this recovery system closer to real-world
testing and future use in SSRS rescue mission