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Web Based I/O Simulator for Education in Machine-Oriented Programming

(2025) Nilsson, Andreas; Kjellberg, Alexander; Nordén Elgh, Cecilia; Andersson, Niklas; Ahmed, Omar; Ranhage, Oskar

The Machine-Oriented Programming course at Chalmers University of Technology and the University of Gothenburg introduces students to low-level programming through microcontroller-based laboratory exercises. During the course, physical access to hardware is limited to scheduled lab sessions, which restricts opportunities for practice. To address this issue, a simulator is used to simulate the microcontroller unit (MCU) and the connectable Input/Output (I/O) units that are used in the course. This bachelor’s thesis presents the development of a web-based simulator for the I/O units that interfaces with the existing MCU simulator via WebSockets. This I/O simulator is developed separate from the MCU logic, with the goal of making it platform-independent as well as more usable, maintainable and accessible. It supports various I/O units such as switches, bargraphs, 7-segment displays and keypads. The application was evaluated through user testing with students who have previously taken the course. The overall reception was positive in terms of usability, visual design, and its potential as an educational tool for understanding and experimenting with machine-oriented programming.

Utveckling samt evaluering av lokalisering och kooperativa styrsystem

(2025) Nylander, Emil; Malmentun, Tobias; Reinerson, Tobias; Viberud, John; Svensson, Elias; Samuelsson, Elias

This report covers a bachelor’s thesis at Chalmers University of Technology. The purpose of this project was to further develop systems for localization and driving of autonomous vehicles inside of a lab environment building upon previous projects. Improvements were made in three distinct areas. The first being the indoor localization system, called GulliView, which consists of four ceiling-mounted cameras using Apriltags as calibration and detection of vehicles. GulliView was improved by implementing an efficient undistortion algorithm to counteract the camera’s distortion while maintaining accuracy. As well as creating a unified global coordination system using the world position in meters. Secondly, additional general maneuvers were implemented to autonomous robots using GulliView for positioning. The added maneuvers handle common traffic situations such as intersections, highway merging and roundabouts. Lastly, an advancement was made on implementing Sensor Fusion between a vehicle, which has a previously developed internal positioning system, and GulliView. The vehicle integrates the internal position and the external position given from GulliView. GulliView attained a median delay decrease of 86%, going from 102 ms to 14.0 ms in time per execution cycle. Meanwhile, the median frequency increased from 10.5 Hz to 16.6 Hz. Improvements on GulliView’s positioning accuracy were also observed, going from discrepancies of 8-18% to 1-2%. For the autonomous vehicles, the added maneuvers added only an average of 24.32% increased waiting time in traffic scenarios while maintaing safety. Finally, the fusion of internal and external values resulted in a positioning discrepancy of 5%. These results prove promising and may greatly help further development of all three systems in future projects.

MatchThesis - En webbapplikation för matchning mellan studenter och examensarbeten

(2025) Olofsson, Tyra

Denna rapport beskriver utvecklingen av MatchThesis, en webbaserad applikation med syfte att förenkla processen att matcha studenter med företag och examensarbeten. Idag sker matchningen mellan studenter och företag genom manuella och ofta tidskrävande processer. Studenter behöver ofta själva söka och tolka ett stort antal annonser, medan företag ibland kan ha svårt att nå rätt målgrupp. Detta projekt undersöker hur denna process kan effektiviseras med hjälp av en webbaserad matchningsapplikation. Applikationen är utvecklad som en fullstack-lösning där användarupplevelse har varit en central del av arbetet. En matchningsalgoritm sorterar både examensarbeten och studenters profiler utifrån utbildningsprogram och gemensamma intressen mellan studenten och företaget. Resultatet visar att MatchThesis erbjuder ett användarvänligt, och vidareutvecklingsbart stöd för matchning av examensarbeten.

Data-Driven Automated Reporting Solution for External Collaborations - LLM-driven KPI Definition

(2026) Juul, Jakob; Lorentzon, Marcus

This 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.

Reducing downlink reference signal overhead for CSI acquisition in massive MIMO systems

(2026) Führ, Andreas

The 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.