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Senast publicerade

BRICK BY BRICK
(2026) Khan, Marilyn; Wigren, Elsa; Wendel Mueller, Emma; Ellwein, Anna
The project site is a small primary healthcare clinic located in Melusi, an informal settlement in Pretoria West, South Africa. Funded by the University of Pretoria, the clinic operates two days per week and provides general healthcare services including family planning and maternal health. Supported by fieldworkers, caretakers, and one doctor, the clinic serves approximately 25–35 patients per day and plays an important role in the local community. For many residents, alternative healthcare facilities are located too far away and require transportation costs, making the clinic an essential and accessible healthcare resource. During the rainy season, large parts of the clinic grounds become muddy and difficult to navigate, limiting access for both patients and staff. In response, a small-scale paving prototype was developed through a process of co-design and co-building with local stakeholders. Workshops, site observations, and continuous discussions with clinic users informed the design and helped identify priority areas for improvement. Following a frugal design approach, the intervention was constructed using low-cost, locally available, and reused paving materials to improve accessibility, safety, and functionality. Rather than being a finished solution, the paving intervention is intended as a prototype and a starting point for future development. Designed to be easily extended and adapted, it aims to support long-term community ownership and inspire further improvements to the clinic environment. The project demonstrates how small, collaborative interventions can create meaningful local impact while contributing to broader discussions on health, sustainability, and community-led development.
Cadence and Stride Length Measurement Using a Foot-Mounted IMU
(2026) Bromander, Gustav; Carlsson, Anton; Ellerstedt, Melker; Månsson Lundberg, William; Stenson, Olle; Svensson, Aron
The purpose of this project was to develop a prototype for measuring cadence (step frequency) and stride length directly from the foot, enabling the collected data to be visualized and presented to the user in a mobile application. These metrics are relevant because they can provide runners with insight into their running technique and help users identify patterns that may contribute to more efficient training. Two different prototypes were developed and evaluated. The first prototype was a lace-mounted design using an Inertial Measurement Unit (IMU)-based step detection algorithm. The second prototype was a sole-based prototype, where the IMU was placed in a cutout on the insole and pressure sensors were used for step detection. For stride length estimation, both systems employed an Extended Kalman Filter (EKF) on the IMU data. The systems communicated with a mobile application via Bluetooth Low Energy (BLE), enabling the collected data to be presented to the user in a graphical interface. The results showed that both designs performed similarly in terms of step detection, achieving more accurate measurements during running and jogging compared to walking. However, regarding stride length estimation, the sole-based prototype outperformed the lace-mounted prototype across all tests, achieving Mean Absolute Relative Error (MARE) values between 5.57% and 15.15%, compared to 27.57% to 35.32%. Overall, the results indicated that the sole-based prototype provides more reliable performance than the lace-mounted prototype. However, this came with a trade-off between usability and performance, which was a recurring challenge throughout the project. The developed prototypes demonstrated the potential of foot-mounted sensing as an alternative to smartwatch-based measurements and highlighted promising opportunities for future development in running analysis applications.
Beyond the Data Center: Distributed Computing on a Raspberry Pi 5 Cluster
(2026) Borg, Livia; Burman, Emil; Forsberg, Axel; Fredriksson, Mathias; Tiberg, Emily; Westman, Filip
Distributed computing clusters are commonly used to provide scalable computation and large memory capacity for demanding workloads. In recent years, single-board computers have become increasingly capable and power-efficient, making them an attractive low-cost alternative for building small-scale distributed systems. How ever, creating such clusters in a way that is scalable, practical, and user-friendly remains challenging due to limited hardware resources and the need for lightweight management and monitoring solutions. This thesis investigates how a distributed computing cluster built from single-board computers can be made practical through lightweight orchestration and purpose-built observability tooling. A central contribution is a custom telemetry system designed for resource-constrained nodes, where existing monitoring solutions impose unnecessary I/O on storage-limited hardware and offer limited control over which metrics are collected and how frequently they are reported. The system collects, transmits and visualizes hardware and performance metrics in real time through a custom web based interface while imposing no measurable impact on workload performance. To evaluate the system, a Raspberry Pi 5 cluster was constructed using Kubernetes for orchestration. Three workloads were deployed to stress different dimensions of the cluster: matrix multiplication for parallel compute throughput, distributed pass word recovery for CPU-intensive data parallelism, and split large language model inference for distributed memory capacity. The results show that the cluster achieved significant performance improvements compared to single-node execution, particularly for highly parallelizable workloads. The system also demonstrated good power efficiency and highlighted the advantages of distributed memory for running larger LLMs. However, the limited computational performance of individual Raspberry Pi nodes means that many devices are required to approach the performance of a conventional high-performance machine. Overall, the work demonstrates that single-board computer clusters can provide a flexible and energy-efficient platform for distributed computing, especially when combined with lightweight orchestration and observability tools.
A Monitoring System for Laboratory Workstations
(2026) Gralén, Nils; Karlsteen, Albin; Menor Löwegren, Nicholas; Edvin, Palmqvist; Paulsson, Elof
University computer laboratories rely on a large number of shared workstations that must remain operational and available to students. However, hardware-related incidents, such as component theft, unexpected shutdowns, and network disconnections, can be difficult to detect using IT monitoring solutions. The proposed monitoring system was implemented as a centralised client-server architecture consisting of lightweight monitoring clients, a central server, a persistent database, and a graphical user interface for administrative oversight. The system combines heartbeat monitoring, hardware validation, chassis intrusion detection, and automated recovery mechanisms to provide centralised situational awareness and alarm handling. The system was evaluated through passive testing and active threat simulations performed on laboratory computers at Chalmers University of Technology. The evaluation demonstrated successful detection of simulated tampering events, stable heartbeat communication, low resource utilization, and reliable alarm handling with minimal interference with normal laboratory usage. Although the system remains a proof of concept prototype, the project demonstrates that event-driven hardware monitoring can provide an effective foundation for improving hardware security and operational awareness in shared university computer laboratory environments.
A Monitoring and Alert System for Shared Computer Laboratories
(2026) Akar, Sukaina; Hagberg, Angelika; Kadamani, Houmam; Moberg, Joel; Tariq, Sufian
Shared computer laboratories at universities provide essential resources to students, but they are also vulnerable to theft of internal hardware components, such as graphics cards, memory modules, and storage devices. Detecting such thefts is challenging because many benign events, including maintenance, configuration changes, and temporary network issues, can produce symptoms similar to physical tampering. This thesis presents the design, implementation, and evaluation of a prototype software-based monitoring and alert system for shared computer workstations. The system uses a client-server architecture in which lightweight clients installed on lab oratory computers send periodic heartbeats and provide information to the server. The server applies context-aware alarm logic to classify events and reduce false alarms. The system was evaluated through scenario-based testing in a laboratory environment. The results show that the system reliably detects hardware component changes and that the context-aware alarm logic reduces false alarms compared to an approach in which every unavailability event triggers an alert. The system was able to distinguish between benign and suspicious events in evaluated scenarios where contextual evidence was available. However, certain situations, such as a single machine losing power, remain inherently ambiguous, as they produce system-level signals identical to those caused by physical tampering.