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Senast publicerade
- Nonlinear Bayesian Filtering for Road Geometry Estimation Using Multi-Source Observations(2026) Elster, Filip; Henrysson, CarlA nonlinear Bayesian filtering framework is proposed for robust road geometry estimation in Advanced Driver Assistance Systems (ADAS). The method is designed for challenging driving conditions in which conventional road feature detection is unreliable, such as snow-covered roads and degraded or occluded lane markings. The road ahead is represented as a sequence of connected clothoid segments, and recursive state estimation is performed using an Extended Kalman Filter (EKF) for prediction and a Cubature Kalman Filter (CKF) for measurement updates. The framework combines information from multiple onboard sensors and digital map data. The considered measurement sources include lane markings, road edges, barriers, map data, and surrounding vehicle trajectories. Vehicle trails are incorporated through a clothoid-fitting procedure, and statistical gating based on the Normalized Innovation Squared (NIS) is used to reject inconsistent observations. In addition, an adaptive segmentation strategy based on map data information is proposed to improve the alignment between the road representation and the underlying road geometry. The proposed framework is evaluated using recorded vehicle data from highway and snow-covered rural driving scenarios. The results show that the inclusion of map data and surrounding vehicle observations improves estimation accuracy, particularly at longer look-ahead distances. The results further show that the framework maintains a stable estimate of the road centerline even when primary road feature detections are weak or unavailable. Overall, the proposed method demonstrates robust road geometry estimation performance across varying driving environments and highlights the value of combining onboard sensing with map data information.
- Krypterad men spårbar: Hur WiFi-trafik kan avslöja användarbeteenden(2026) Alkebro, Amos; Rahman Alkhatib, Abd; Nguyen, Helena; Toft, Marcella; Wallström, Frida; Åkerström, WilliamThis study investigates what information about devices and user behavior can be extracted through passive analysis of wireless network traffic. By developing a pro totype system for data collection and traffic analysis, the feasibility of identifying device type, operating system, currently active application, user activities, and be havioral patterns over time is examined. The results indicate that it is possible, to some extent, to identify device type and operating system based on observable traffic patterns. However, the identification of user activities and specific applica tions remains uncertain and is affected by traffic variability and limited access to representative datasets. Furthermore, general behavioral patterns, such as recurring periods of activity, can be inferred, albeit with limited accuracy. Several factors in fluencing the reliability of the analysis are identified, including variations in signal strength and limitations inherent in passive traffic collection. These factors compli cate the consistent association of observed traffic with individual devices over time. Overall, the study demonstrates that network traffic analysis can provide insights into device characteristics and user behavior, even without access to the content of communication. At the same time, the accuracy is limited, and the results should be interpreted with caution, particularly when generalizing to more complex environ ments. The study thus highlights both the possibilities and limitations of this type of analysis, as well as the associated privacy implications.
- Beyond the Data Center: Distributed Computing on a Raspberry Pi 5 Cluster(2026) Borg, Livia; Fredriksson, Mathias; Burman, Emil; Tiberg, Emily; Forsberg, Axel; Westman, FilipDistributed 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 de signed 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 in ference for distributed memory capacity. The results show that the cluster achieved significant performance improvements compared to single-node execution, particu larly 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.
- Probabilistically Robust Continuous-time Multi-Agent Path Finding Dynamic Risk Allocation and Chance-Constrained Planning under Execution Uncertainty(2026) Johannesson, Moa; Mazen, MajedAutomated logistics and multi-robot systems use Multi-Agent Path Finding (MAPF) algorithms to coordinate collision-free routes. In real-world industrial deployments, execution uncertainties such as mechanical variations and friction inevitably cause delays. Current robust MAPF algorithms handle this either through conservative worst-case deterministic bounds (in both discrete and continuous time) or through probabilistic models limited to discrete-time grids. This thesis bridges that gap by introducing a probabilistically robust framework for continuous-time MAPF, which is essential for maintaining predictable throughput by enabling proactive scheduling instead of reactive halting. To accurately model execution uncertainty, the proposed approach uses Gaussian distributions bounded by strict kinematic hardware limits. Instead of bounding maximum delays, the proposed approach models execution uncertainty using chance constrained programming, evaluating the overlapping probability density of agent trajectories. The framework uses a modified Continuous Conflict-Based Search (CCBS) paired with Safe Interval Path Planning (SIPP), and evaluates several heuristic risk allocation strategies to dynamically distribute a user-defined global risk budget across local interactions. The framework was evaluated on a standard benchmark environment, assessing metrics such as scalability, computational cost, risk utilization, and plan quality. Furthermore, Monte Carlo simulations were conducted to verify the robustness of the generated plans. The results show that the empirical execution success rate accurately matches the theoretical global risk calculated via the Union Bound. Ultimately, this confirms that the proposed framework successfully guarantees the collision risk of the system to be within the user-defined global risk budget, enabling a practical balance between schedule efficiency and system safety.
- Aqueous Supercapacitors Based on Polymer-Biomass Composites(2026) Zalem, YohanThe development of sustainable aqueous supercapacitors requires the integration of high-performance conductive materials and abundant, environmentally friendly resources. This thesis investigates the electrochemical performance of composite electrodes comprising of conjugated polymers and lignosulfonate, a low-cost biomass derivative theoretically capable of providing additive pseudocapacitance through quinone-based reversible redox reactions. A comprehensive comparative analysis was conducted between established p-type polymers (PEDOT:F and PEDOT:PSS) and a novel, ultra-highly conductive n-type polymer, poly(benzodifurandione) (PBFDO). The active materials were deposited onto plasma-treated carbon paper substrates via a controlled sequential drop-casting method and evaluated in symmetrical two electrode Swagelok cells utilizing an aqueous perchloric acid electrolyte. Baseline electrochemical characterization via Cyclic Voltammetry (CV), Galvanos tatic Charge-Discharge (GCD), and Electrochemical Impedance Spectroscopy (EIS) revealed that pristine PBFDO vastly outperformed both PEDOT derivatives. Eval uated at a low comparative current density of 0.25 A/g, PBFDO exhibited superior specific capacitance, exceptional structural resilience, and minimal Equivalent Series Resistance (ESR). Contrary to the central hypothesis, the incorporation of unmodified lignosulfonate severely degraded the performance of all tested polymers. Rather than acting as a synergistic redox contributor, the water-soluble and electrically insulating ligno sulfonate acted as an electrochemically inactive dead weight. While the p-type PEDOT composites suffered catastrophic electrochemical failure at a 1:1 polymer to-lignin mass ratio, the self-doped n-type PBFDO matrix demonstrated remarkable structural resilience. Although the specific capacitance of PBFDO systematically declined as the lignin concentration increased across 3:1, 1:1, and 1:3 mass ratios, it maintained its fundamental charge-storage mechanisms without the massive internal resistance spikes observed in the p-type cells. Ultimately, while highlighting the lim itations of physically blending raw lignosulfonate in aqueous electrolytes, this study unequivocally establishes the novel n-type PBFDO network as a premier, highly robust conjugated polymer for next-generation energy storage applications