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Senast publicerade

Upptäcka dämpningsvariationer av TV signaler som passerar genom skog
(2026) Backlund, Ella; Engström, Alice; Hernqvist Larsson, Carl; Wallberg, Andreas; Wyssbrod, Jonathan
Det är av vikt att undersöka torka i skog för att förebygga bränder och skade- djursangrepp. I det här kandidatarbetet undersöks därför möjligheten att upptäcka vattenstress och torka i skog med hjälp av signaler från TV-master. Vatten i träden dämpar TV-signaler. Eftersom trädens vatteninnehåll förändras över dygnet vari erar också dämpningen av TV-signalerna som passerar genom skogen. Genom att mäta variationer i mottagen signalstyrka från TV-masten kan man därför identifi era förändringar i skogens vatteninnehåll. I projektet skapades två mätsystem vars syfte var att mäta signalstyrkan från Brudaremossenmasten i Göteborg samt samla in väderparametrar under dagtid, från kl 09:00 till 16:00. Utöver detta har data från radartornet BorealScat-2 analyserats för att jämföras med fältmätningen vid Brudaremossenmasten. Fältmätningen genomfördes i Delsjöområdet, där ett mätsystem placerades så att TV-signalen enbart passerar genom luft och det andra så att signalen också passerar genom ett stycke skog. Genom att studera skillnaden i mottagen effekt mellan de två mätsystemen utlästes att signalen dämpades successivt mindre från kl 09:00 till kl 13:00, då det minsta värdet uppnåddes. Den maximala skillnaden i dämpning uppgick till 2 dB. Detta stämmer väl överens med teori, där trädens vatteninnehåll följer samma nedåtgående trend, samt resultatet från BorealScat-2. Resultatet visar på möjligheten att observera dygnsvariationer av vatteninnehåll med signaler från TV-master, men för att komma fram till om det går att upptäcka torka under en längre period krävs vidare studier med mätningar på olika platser, väderförhållanden och under längre tid.
Training end-to-end planners in sensor-level simulation
(2026) Álvarez Guinarte, Miguel
End-to-end autonomous driving planners are commonly trained with imitation learning on offline expert demonstrations, an approach that can achieve strong openloop performance. However, planners following this paradigm usually suffer during closed-loop deployment, since they have not been exposed to the consequences of their own actions during training. Addressing this requires closed-loop training, which has typically involved high-level representations, forgoing the benefits of sensor-level end-to-end planning. This work investigates whether recent advances in efficient 3D Gaussian Splatting can make closed-loop training of sensor-level end-to-end planners feasible. A closedloop simulator is developed by reconstructing driving sequences and integrating them with a trajectory tracker and a kinematic vehicle model capable of executing the planner’s predicted trajectories. This makes it possible to render new views that deviate from the original logged trajectory. The resulting framework is used to fine-tune a pretrained Latent TransFuser planner through reinforcement learning and closed-loop imitation learning strategies. The simulator shows that closed-loop execution reveals failure modes that are not visible from open-loop evaluation, highlighting the importance of closed-loop training and evaluation. The explored training strategies produced mixed but some modest improvements over the baseline, supporting the feasibility of Gaussian Splattingbased sensor-level simulation as a training platform and laying the groundwork for future work on scalable closed-loop learning for end-to-end autonomous driving.
Att följa ideala trajektorier med kinodynamiskt begränsade robotar
(2026) Alkhuzaee, Ahmed Yaman; Tahhan, Eyad
Autonomous mobile robots are becoming increasingly common in production, warehouses, and other automated environments. When several robots move in the same environment, their motions must be coordinated to avoid collisions. Planning collision free motions is a well known and difficult problem, and simplified motion models are often used to make the planning easier. However, these simplifications can make it difficult for real robots to follow the planned paths exactly, which can reintroduce the risk of collisions. This thesis investigates how well robots with more constrained, and therefore possibly more realistic, motion models can follow plans created using simplified motion assumptions. The robots are described using a kinematic bicycle model, where the motion is limited by velocity, acceleration, and steering angle. In contrast, the planning model assumes omnidirectional motion with unlimited acceleration. Four control strategies are compared: Linear Quadratic Regulator (LQR), Model Predictive Control (MPC), LQR with Control Barrier Functions (LQR+CBF), and MPC with Control Barrier Functions (MPC+CBF). The results show that LQR works in simpler scenarios, but has difficulties with sharp turns and when several robots move close to each other. MPC provides better plan tracking and more stable motion. When CBF is used, the number of collisions is reduced, but not all collisions are eliminated in the tested scenarios. Overall, MPC+CBF gives the best balance between plan tracking and collision avoidance.
Advancing 5G Calibration Methods In The Compact Antenna Test Range - A single antenna approach for over the air testing from 18 GHz to 110 GHz
(2026) Zhou, Yuehong; Daniel, Christian
This project presents a study on the calibration methodologies for OTA testing in CATR chamber, focusing on the frequency span of 18 GHz to 110 GHz. We introduce a single-antenna method, proving it more efficient and accurate than traditional multi-antenna setups. The research also delves into the challenges of highfrequency signal testing in 5G and upcoming 6G communications. These challenges include signal propagation issues and the complexity of signal processing techniques. Through a two-phase approach, our research validates the new calibration method and assesses the signal generation and measurement system’s performance.
Transfer Learning for Battery Health Prediction under Varying Operating Conditions
(2026) Xia, Chunqiu
Battery health estimation and lifetime prediction are essential for the safe and reliable operation of Li-ion batteries. In practical applications, predictive models are often required to generalize across different cells or cell groups, where operating conditions, materials, and degradation behaviours may vary. This thesis investigates battery health modelling from a cross-condition perspective under constant-current operating protocols, where charge C-rate, discharge C-rate, and depth of discharge define the domain structure. The study first examines state of health (SOH) estimation using features extracted from incremental capacity (IC) curves. Although SOH estimation provides useful diagnostic information, it shows limited domain divergence in the present dataset, mainly because most available samples are concentrated in the early ageing stage where degradation trajectories across-conditions remain similar. Therefore, while still considering SOH estimation as an important diagnostic task, this thesis also considers remaining useful life (RUL) prediction because it can construct sufficiently large domain divergence to support the transfer learning objective. A transfer learning framework is then developed for cross-condition RUL prediction. The model is first trained on a source cell group and subsequently adapted to a target group through fine-tuning. Optuna with the Tree-structured Parzen Estimator (TPE) is employed to optimize model structure and learning hyperparameters, and a sensitivity analysis is conducted to examine the influence of early-stage data availability on prediction performance. The results show that direct cross-condition prediction is challenging and that models trained without transfer learning have limited generalization capability. Finetuning improves RUL prediction on the target group, demonstrating the practical value of transfer learning for cross-condition battery prognostics. The results also indicate that prediction performance is affected by feature selection, domain definition, and the quantity and selection of cells used for fine-tuning.