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Senast publicerade
- Contextual Factors influencing Smart Environment Control Panel(2026) Ahlberg, Malkolm; Mao, XunanIndoor Internet of Things (IoT) control units are increasingly used to manage connected systems such as lighting, temperature, window blinds, and scene (scenario) settings. However, the same control unit may be used across very different environments, from private rooms to shared or public spaces. In these situations, interaction requirements are shaped not only by the system being controlled, but also by users, roles, tasks, physical conditions, social expectations, and responsibility. This thesis investigates which context-of-use factors affect interaction requirements for indoor IoT control units, how they do so, and how these factors can be translated into control-and-feedback design. The project developed a context-to-interaction framework and applied it to the design of two smart control unit concepts: one framework-driven concept and one business-informed concept. The concepts were evaluated through hotel room and hotel lobby scenarios. The evaluation showed a trade-off between first-use intuitiveness and cross-contextual stability. The business-informed concept was easier to understand at first time use, while the framework-driven concept remained more stable when the user role, context, or task goal changed. The thesis contributes a practical framework for analyzing contextual demands and translating them into design guidance for indoor IoT control units.
- Development of an SDR-Based System for UHF RFID Signal Analysis(2026) Bardun, GustavThis thesis presents the development of a Software-Defined Radio based system for capturing and analyzing Ultra High Frequency (UHF) RFID signals. While UHF RFID has been explored as a sensing modality, commercial readers lack the transparency required to analyze the underlying signal and any interference taking place. A custom Software-Defined Radio based system was implemented to capture, detect, and decode RFID backscatter while providing access to the raw In-phase/Quadrature data. The system utilizes a derivative-based metric to detect RFID backscatter and support tag decoding. The system was used to analyze RFID signals in scenarios involving multiple nearby tags, including cases where one tag was undergoing controlled motion. The results indicate significant interference between nearby tags. Stationary tags were observed to exhibit "motion-like" periodic variations in phase and RSSI when in close proximity to a moving tag. This often appeared as an inverted reflection of the actual motion, creating ambiguity that makes it difficult to interpret tag behavior on a per-tag basis. This suggests that RFID systems designed for motion sensing may require fundamentally different signal processing chains than commercial systems designed for rapid retrieval of tag information.
- Detection of Ongoing GPS Spoofing Using A Convolutional Neural Network(2026) Pettersson, BenjaminDeliberate GPS interference by broadcasting fake signals, so-called spoofing, can compromise critical infrastructure that relies on location and timing services. This work develops a dual-branch convolutional network (CNN) that automatically detects spoofing attacks during the acquisition stage of a GPS receiver. The receiver’s 2-D acquisition map (a single-channel image) is processed both locally (region-ofinterest, ROI) and globally to capture spoofing cues. The CNN was trained and evaluated on MATLAB-simulated recordings as well as on two publicly available datasets, the Oak Ridge Spoofing and Interference Test Battery (OAKBAT) and the Finnish Geospatial Research Institute (FGI) repository, which together comprise 11 GPS recordings containing spoofing scenarios. Results show reliable detection when training and testing on data from the same domain, achieving balanced-accuracy scores of 99 % on targeted scenarios. Cross-domain generalization to datasets with different parameters decreases noticeably, with performance dropping to nearrandom- guessing levels. The study also provides visual illustrations of authentic and spoofed scenarios. The total average inference time of the solution is 22-23 ms per tracked satellite, indicating practical applicability. These findings suggest that a dual-branch CNN operating in the acquisition stage is a viable method for spoofing detection.
- Optimizing Microgrid Energy Scheduling - Comparing performance of Genetic algorithms and Mixed Integer Linear Programming(2026) Strandvik, PontusThis thesis deals with the comparison of Genetic algorithms and Mixed Integer Linear Programming on the microgrid energy scheduling problem. The focus lies on genetic algorithms and improvements to problems experienced by the GA with MILP being used as a baseline for comparison. The GA and MILP are being compared on the unit commitment / economic load dispatch problem within a microgrid setting. The objective is to investigate under which circumstances the individual optimizers are preferable. In the GA approach constraints were handled using penalty terms added in the fitness function encoding physical constraints such as battery storage limits. Additional penalties were used to enforce objectives such as temperature ranges and binary statuses of components. In the MILP approach constraints were handled in the model creation with objectives encoded as terms in the objective function scaled by individual weight factors. The research was carried out on a simulation tool where two scenarios were modeled. One scenario represents a microgrid in a remote setting without external grid connection where energy consumption minimization was of importance to prolong the useful life of components. The second scenario represents a grid connected microgrid where precise temperature management and energy cost minimization through strategic energy market interactions was the main objective. The results suggest that the MILP outperforms the GA on most of the problems considered with the exception of certain cases of uncertainty in the renewable energy generation. Results also show that improvements could be made to the genetic algorithm by seeding the initial population. Furthermore, the gentic algorithm performance and computation time improved on the mountain hut scenario when a timestep of 8-16 hours was used. This result is unexpected as a lower temporal resolution would make the problem harder to handle, however in the case of maximizing up time of components a larger timestep duration seems preferable.
- Probing Aerosol–Weather–Cloud Interactions with Multi-Instrument Measurements at the Onsala Space Observatory(2026) Widéen, VictorOne of the key uncertainties in our understanding of the climate system is the production of aerosols and their interactions with clouds. The Onsala Space Observatory (OSO) provides an ideal site, at the land-sea interface, to address this knowledge gap, offering a comprehensive set of instruments for integrated in situ and remote-sensing observations. This thesis investigates the production mechanisms of aerosols - primarily sea salts - and their influence on clouds from late 2024 to early 2025, while confirming the local climate properties of the OSO site. To supplement existing observatory data, ground measurements at OSO and a drone flight at a nearby site were conducted using a Portable Optical Particle Spectrometer (POPS). Our findings confirm the previously established climatic baselines for the OSO site and highlight several key atmospheric relationships. Specifically, strong winds originating from the ocean correlate with high local aerosol concentrations, particularly sea salts; precipitation events drive aerosol scavenging (washout); and higher aerosol loading is directly associated with increased local cloud cover.