Chalmers Open Digital Repository

Välkommen till Chalmers öppna digitala arkiv!

Här hittar du:

Studentarbeten utgivna på lärosätet, såväl kandidatarbeten som examensarbeten på grund- och masternivå
Digitala specialsamlingar, som t ex Chalmers modellkammare
Utvalda projektrapporter

Forskningspublikationer, rapporter och avhandlingar hittar du i research.chalmers.se

Enheter i Chalmers ODR

Välj en enhet för att se alla samlingar.

Visar 1 - 2 av 2

Senast publicerade

Precipitation estimation from the Arctic Weather Satellite: An initial study using probabilistic deep learning
(2026) Bergstrand, Ludvig
Accurate precipitation information is important for understanding the hydrological cycle, improving weather forecasts and supporting hydrological applications. How ever, precipitation is difficult to observe with sufficient spatial and temporal coverage. Rain gauges provide only local measurements and ground-based radars are limited to regions where they are available. Satellite observations can complement these sys tems by providing precipitation information over larger and sparsely instrumented domains. Passive microwave observations are widely used for precipitation estimation, and the newly launched Arctic Weather Satellite (AWS) offers a novel extension of this capability. AWS is a compact satellite carrying a 19-channel microwave radiometer and the first mission to include sub-millimetre channels around 325 GHz for this purpose. This thesis primarily investigates whether AWS observations can be used to retrieve surface precipitation rates using supervised machine learning. A secondary aim is to determine if these novel sub-millimetre channels have a positive effect on the retrieval performance. To achieve this, a dataset is constructed by matching AWS antenna temperatures with Multi-Radar Multi-Sensor (MRMS) radar precipitation estimates over the contiguous United States. Quantile regression neural networks are trained to predict conditional quantiles of the surface precipitation rate, providing both deterministic estimates and probabilistic estimates of retrieval uncertainty. Results demonstrate that AWS antenna temperatures contain valuable information for surface precipitation retrieval. The trained model captures broad precipitation structures and achieves near-zero overall bias on the independent test set. Although the predictions do not fully capture the intensity of the precipitation the spatial mapping is consistent with the radar reference. An additional model trained with out the sub-millimetre channels evaluates the specific impact of these sensors. Com parisons indicate that the sub-millimetre channels provide a modest but consistent improvement across the overall evaluation metrics. These findings serve as an initial assessment of AWS-based precipitation retrieval. This investigation is particularly relevant due to the upcoming launch of EPS-Sterna, a constellation of AWS satellites which could significantly enhance the temporal sampling of global precipitation ob servations. Further efforts could explore using other reference precipitation products ii to expand the geographical domain, as well as incorporating longer data records as newer AWS observations become available.
Deep Learning-Based Multimodal Satellite Precipitation Retrieval with HPC-Optimized Inference
(2026) Huang, Guanhua
Accurate precipitation retrieval is important for weather monitoring and hydrological applications, especially where radar coverage is limited or affected by terrain. This thesis adapts a deep-learning precipitation retrieval framework to geostationary Meteosat Third Generation Flexible Combined Imager (FCI) observations and extends it with passive microwave (PMW) observations from the Advanced Technology Microwave Sounder (ATMS) and Arctic Weather Satellite (AWS). The main aim is to evaluate whether sparse PMW observations improve FCI-only precipitation retrieval over the MetCoOp Ensemble Prediction System (MEPS) domain, and to improve the efficiency of inference on a GPU-based system. The retrieval model estimates near-surface precipitation from recent satellite observa tions using a 3D encoder-decoder convolutional neural network. FCI provides dense and temporally continuous geostationary input, while ATMS and AWS provide sparse PMWinput, available only when polar-orbiting swaths pass over the domain. Missing PMW observations are handled with masks, allowing full-domain retrievals even when microwave coverage is absent. The models are evaluated against BALTRAD radar-derived precipitation for selected periods in 2025. The FCI-only model captures the general precipitation structure, but its precipitation amount is not stably calibrated across the evaluation periods. The main result is that adding PMW improves several metrics: mean absolute error decreases from 2.70 to 2.14 mm/day, R2 increases from-0.19 to 0.17, and the Matthews correlation coefficient at the 0.1 mm/day threshold increases from 0.22 to 0.35. The benefit is strongest when PMW observations are available and weaker when microwave coverage is missing. A separate zenith-angle experiment shows that viewing geometry can strongly affect the retrieved precipitation distribution, but did not give a balanced improvement. Profiling shows that the original inference pipeline was limited mainly by data loading, small tile processing, CPU-side operations, and NetCDF writing, rather than by the 3D convolutional network alone. Batched tile inference, asynchronous I/O, and multi-GPU execution improved throughput. The optimized two-GPU pipeline achieved a 1.58× speedup for near-real-time processing, while temporal sharding achieved a 2.27× speedup for offline processing. Overall, the thesis shows that dense FCI input can be combined with sparse PMW observations, and that system-level optimizations can substantially speed up inference without changing the trained model.
Antibacterial Activity of Delafloxacin and Polymyxin B Against Planktonic and Biofilm-Associated Bacteria
(2026) Priya, Ankita
Abstract Biofilm-associated infections are difficult to treat because bacteria growing in biofilms show increased tolerance to antibiotics compared with planktonic cells. In chronic wound infections, Staphylococcus aureus and Pseudomonas aeruginosa are com monly detected and can contribute to persistent infection. This thesis investigated the antibacterial activity of delafloxacin and polymyxin B against S. aureus and P. aeruginosa under planktonic and biofilm-associated conditions. The antibiotics were tested alone and in combination for MIC determination, time-kill assays, biofilm treatment assays, and scanning electron microscopy. The results showed that both antibiotics reduced bacterial viability, with stronger effects generally observed at higher concentrations. Planktonic cells were more affected than biofilm-associated cells, supporting the increased tolerance of bacteria growing in biofilms. Combina tion treatment reduced bacterial survival in several conditions and showed stronger effects than single-antibiotic treatments. SEM analysis showed dense bacterial at tachment in untreated biofilms, while treated S. aureus and P. aeruginosa biofilms showed reduced bacterial coverage and altered biofilm structure. Overall, the find ings suggest that delafloxacin and polymyxin B have antibacterial effects against both planktonic and biofilm-associated bacteria, but complete biofilm eradication was not achieved under the tested conditions.
MRI Compatible Retention System for a Bone Conduction Device: An evaluation of required design changes to Sentio Ti™ Implant for compliance with 3 T MRI scans
(2026) Alexandersson, Matilda; Luttu, Ebba
Magnetic resonance imaging (MRI) compatibility is an important requirement for implantable hearing devices, as increasing numbers of patients are expected to undergo MRI examinations during their lifetime. This study evaluates the mechanical response of the Sentio Ti™ transcutaneous bone conduction implant under magnetic torque corresponding to a 3 T MRI environment, with the aim of assessing whether the current design meets established performance criteria defined at 1.5 T. A computational approach was conducted using an existing, experimentally validated finite element model developed in LS-DYNA. The model was used to simulate implant displacement and contact pressure on surrounding soft tissue under worst-case magnetic torque conditions. The torque at 3 T was estimated based on proportional scaling from 1.5 T. Parametric studies were conducted to investigate the influence of reinforcement wire properties and silicone stiffness on implant behaviour. In addition, alternative retention magnet concepts were explored through a concept generation process. The results show that increasing the magnetic field strength from 1.5 T to 3 T leads to a significant increase in mechanical response, with displacement rising from 3.12 mm to 5.65 mm and average contact pressure increasing by a factor of approximately 2–3. Among the investigated parameters, reinforcement wire diameter was found to have the greatest influence on reducing both displacement and contact pressure. However, achieving equivalent performance to 1.5 T through structural modifications alone requires design changes that may be impractical within current geometric constraints. Combined modifications of wire diameter and silicone stiffness provided more feasible solutions, although they did not fully replicate baseline pressure levels. The findings indicate that while structural optimization can significantly improve performance, it may not be sufficient to ensure MRI compatibility at 3 T without compromising design constraints. Modifications to the retention magnet system, such as enabling rotational alignment or controlled movement, are therefore identified as promising strategies. This work provides quantitative insight into implant behaviour at higher magnetic field strengths and supports the development of next-generation MRI-compatible bone conduction devices.
Evaluating Motion Model Hypotheses for Automotive Radar Tracking
(2026) Andersson, Noel; Lundberg, Hannes
Automotive radar is a core ADAS sensor due to weather robustness and direct Doppler velocity measurements, but radar multi-object tracking is challenged by heterogeneous and maneuvering target dynamics. This thesis evaluates a white-noise jerk model (CCA) and a curvilinear motion model (CTCA), each implemented in an EKF, and assesses whether combining them in an Interacting Multiple Model (IMM) filter improves robustness across scenarios. To enable an unbiased comparison between CCA, CTCA, and IMM, process-noise parameters and IMM transition/interactions are tuned automatically by formulating tracking as a black-box optimization problem. Performance is optimized using the probabilistic GOSPA (P-GOSPA) metric, which penalizes localization error as well as missed and false tracks under multi-Bernoulli set representations. CMA-ES is used to search the nonconvex parameter space without gradients. Evaluation is performed in a controlled MATLAB simulation with a four-corner radar configuration and known ground truth, fusing radar range-rate detections with object-level pseudo-measurements of position and orientation. Results show strong scenario dependence for single-model tracking and indicate that automated tuning is necessary to avoid biased motion-model conclusions, the IMM provides more consistent performance across diverse driving scenarios than either single model.