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PRED-RAG: a Predictive Radial Grid for Automotive Radar Multipath - Identification Identification of objects created by the radar multipath phenomenon, with focus on low computational complexity.
No full textAutomotive radar sensors are crucial for advanced driver assistance systems but
are susceptible to the multipath phenomenon, where radio waves reflect multiple
times between surfaces, creating false "ghost" objects that can trigger unnecessary
safety interventions. Previous work relies on restrictive assumptions about reflection
surfaces and environmental conditions, yielding solutions that perform well in specific
scenarios but demonstrate limited generalization capabilities in the complex, diverse
situations encountered during real-world driving. This thesis addresses the challenge
of identifying radar multipath objects in real-time environments, focusing on developing
an algorithm that maintains low computational complexity while achieving
high accuracy. We established a development and evaluation pipeline using synthetic
data together with a simulation framework, enabling data driven development of our
algorithm. We propose the PRED-RAG algorithm, a novel approach that utilizes
a radial grid structure combined with host motion prediction of static detections
for enhanced high-level environment mapping. The algorithm identifies triplets
consisting of a ghost object, reflection point and true object, then evaluates them
using velocity-based criteria. When compared to a state-of-the-art algorithm, our
approach demonstrates superior performance in both accuracy and computational
efficiency across various driving scenarios. The PRED-RAG algorithm achieves
94.43% accuracy for high-priority objects compared to 39.26% for the baseline, with
significantly better generalization capabilities, particularly in complex environments.
The geometric properties employed in the grid-based approach effectively separate
ghost objects from true objects while maintaining runtime performance suitable
for real-time automotive applications. This work contributes to safer autonomous
driving systems by reducing false objects that could lead to unnecessary emergency
interventions
The Unpacked; enchancing green space design through participatory methodology
No full textThis thesis investigates how participatory design can enhance green space design to create inclusive and sustainable public green spaces in socially and economically challenged areas. While green spaces are often developed through top-down processes, this research examines how co-design with residents can translate lived experience into operational design directions and spatial strategies.
The study is based on a case in Angered, where residents engaged in sensory walks, biodiversity observations, and co-design workshops. Observations revealed gathering around shaded areas, low species diversity, accessibility gaps, and underused lawns. Workshop discussions highlighted desires for cultural representation, opportunities for ecological stewardship, and safer, more inclusive spaces. These insights were synthesised with theoretical foundations- participation, social sustainability, and biophilic design-into six Design Directions: fostering cultural expression, enhancing sensory diversity, strengthening biodiversity and stewardship enabling flexible gathering integrating nature as a shared resource and creating connected micro-spaces.
The design proposal applied these directions through interventions such as a modular hexagonal seating, pergolas, a language board, sensory and meditation cells, native planting, and a community garden. The hexagonal modular system became the core spatial strategy, embodying flexibility, inclusivity, and ecological integration.
The findings confirm site-specific conditions - cultural and linguistic diversity, socioeconomic challenges and low ecological variety - while pointing to broader lessons for participatory design: translating community knowledge into spatial outcomes, reframing biophilia as a social as well as ecological practice, and demonstrating how small interventions can reinforce wider goals of inclusion and resilience. The study concludes that participatory design when systematically connected to theory and translated into design directions, can generate green spaces that are socially inclusive, ecologically resilient, and adaptable to other urban contexts
Evolution of group reproduction in the transition to multicellularity: A bottom- up modeling approach
No full textGeneration of Wikidata Descriptions with Grammatical Framework
No full textWikidata is a collaborative, multilingual knowledge base that serves a wide range of purposes, such as supporting Wikipedia, and plays a significant role in ensuring equitable access to information worldwide. However, the quality and consistency
of entity descriptions in different languages vary greatly, and many entities lack descriptions altogether. This thesis presents a workflow based on the Grammatical Framework (GF) for the automated generation of multilingual Wikidata entity
descriptions. The system integrates property extraction, grammar design, and automatic linearization, enabling the systematic generation of multilingual descriptions while reducing, to some extent, the need for manual intervention and GF-specific
expertise. Manual evaluation shows that, compared to human-written Wikidata descriptions and those generated by large language models, GF-generated descriptions achieve higher cross-linguistic consistency and factual accuracy. The workflow also supports efficient extension to additional languages, as demonstrated by the Bengali case by Mohammad Rakib Imtiaz. These results highlight the potential of GF, the GF Resource Grammar Library, and the approach introduced in this thesis for scalable, verifiable, and reliable multilingual description generation
Digital Radio Twin of Chalmers for 6G Integrated Sensing, Positioning, and Communication
No full textThis thesis presents the development of a Digital Radio Twin of Chalmers University
campus, aimed at enabling advanced simulation and evaluation of 6G wireless
communication systems. As future cellular technologies increasingly demand precise
modeling of radio environments, this project integrates 3D modeling, ray tracing,
and signal processing to create a simulation framework that reflects realistic propagation
conditions. Using Blender for geometric modeling and NVIDIA’s Sionna RT
for ray-tracing-based channel simulations, a virtual replica of the Chalmers campus
was constructed. This digital environment supports configurable transmitter
and receiver setups, allowing systematic analysis of signal behavior under various
parameters. The generated data was then used to compute key performance indicators
(KPIs) such as channel capacity, latency, and positioning accuracy. Despite
time and scope constraints, this approach demonstrates the feasibility and value of
digital radio twins in exploring and designing future 6G networks. The resulting
datasets and simulation tools offer a valuable foundation for further research in integrated
sensing, positioning, and communication. Analyzing the simulation results
provides insight into how different performance metrics affect the transmitted signal
in terms of both positioning and communication. Parameters such as the number of
subcarriers, orthogonal frequency-division multiplexing (OFDM) symbols, transmission
power, and distance, all seem to impact the performance metrics significantly.
For positioning, it became surprisingly evident that distance was not the only contributing
factor in achieving low positioning bounds; the system’s resolution also
seemed to play a significant role
Neural Networks for Predicting Fluid Filter Remaining Useful Life
No full textThis research addresses the challenge of estimating the Remaining Useful Life (RUL) of oil filters in industrial hydraulic systems using data-driven predictive maintenance.
Focusing on a proprietary dataset characterized by a severely limited number of operational cycles and sparse laboratory measurements, the study evaluates traditional
machine learning and deep neural networks under various feature engineering approaches. Findings reveal that for this constrained dataset, predictive accuracy
is critically dependent on a single, dominant feature representing the filter’s total workload. Consequently, RUL defined by processed oil volume proved to be a more
robust and predictable target than one based on operational time. While complex feature engineering and models struggled with the limited data, the same methodologies demonstrated strong performance on comprehensive benchmark datasets. To overcome data limitations in the target application, a market study for inline particle sensors was conducted, identifying feasible technologies that could provide the high-frequency oil cleanliness data necessary for robust future RUL predictions.
The study underscores the fundamental importance of sufficient, relevant data for successful predictive maintenance implementation
Sjöingenjörers kompetens inom PLC- systems: En studie av STCW-krav och utbildningsstandarder inom sjöfarten
No full textTeknologin till sjöss har utvecklats mycket de senaste åren, alltmer har gått över till att styras via en dator och de mekaniska systemen har börjat bytas ut i större utsträckning. Vilket kräver mer kunskap i PLC-system, för sjöingenjörer. Arbetet syftar till att undersöka kompetensen till sjöss för sjöingenjörer, för att kunna identifiera om utbildningen idag är tillräcklig för att möta de tekniska svårigheterna till sjöss samt att utvärdera vad STCW har för krav av blivande sjöingenjörer. För att samla in data till undersökningen har en enkät skickats ut till tekniska befäl inom flera svenska rederier. Arbetet har använt sig av en kvantitativ metod med frågor i enkäten, där det har funnits färdiga svarsalternativ och dessutom öppna frågor där respondenterna har kunnat utveckla sitt svar. För att analysera svaren valdes en tematisk metod för att kunna identifiera likheter. Resultatet visade att det var många utav sjöingenjörerna som arbeta med PLC-system, men att det fanns en vilja att vidareutveckla sin kunskap. Vad man identifierade i resultatet var att PLC-system är krångliga att hantera, vilket gör att kunskaperna inom dessa system behöver höjas för sjöingenjörer då mycket har utvecklats sig de senaste åren
The Power of WHY – the Key to Excellence
No full textTo do the right things from start and do it all the time might sound like an apparent statement, however, it is easier said than done. The prerequisite, as this dissertation will show, to do the right things from the start and do it all the time is to thoroughly understand what to do, how to do it and most essentially, why do what you do. This MSc dissertation investigates how a deeper understanding of the underlying why behind customer expectations can enhance value creation and customer satisfaction within product development processes. Using Aurobay as a case study and utilising the highly data-driven and fact-based improvement methodology, Six Sigma, the research explores the current state of the quote process and its impact on business operations, the influence of expectation alignment on perceived quality, and potential methods for bridging the gap between current and future states. The findings indicate that while existing processes are standardised, they lack sufficient focus on identifying the root drivers of customer value. This limitation leads to misaligned value propositions, increased rework, and reduced customer satisfaction. To address this, a method was developed, grounded in Sinek’s Golden Circle and the Value Definition Model, to support early-stage identification and prioritisation of customer expectations. The proposed approach promotes cross-functional collaboration and encourages a shift from task-driven to needs-driven thinking.
The author proposes a novel quality definition. The definition is based on the premise that quality should be assessed not solely by measurable attributes, but by the extent to which a product or service meets or exceeds customer expectations. As such, a product may be perceived as superior in quality even if it is objectively outperformed by alternatives, provided it delivers greater alignment with user expectations. This perspective underscores the importance of perception and expectation management. The study concludes that embedding a deeper understanding of the 'why' behind customer expectations into early development stages enables more accurate value alignment, enhances perceived quality, and supports strategic decision-making. By shifting from a task-focused to a purpose-driven approach, organisations like Aurobay can foster stronger customer alignment, reduce inefficiencies, and create sustainable competitive advantage through improved product development practices.
Ultimately, the thesis highlights the importance of understanding not just what and how to deliver, but why—providing strategic insights for improving customer alignment and value delivery in complex development environment
