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Designing a Collaboration and Communication Process for a Garment Renewal Service
(2026) Gunnelius, Lovisa
The textile industry has a significant environmental impact and is one of the largest contributors to land and water use, greenhouse gas emissions, and raw material consumption. Transitioning the industry toward a circular economy is therefore essential, and one way to support this shift is by extending the lifespan of garments through repair and refresh. RecoMended is a company that provides industrial scale garment renewal services for other companies, enabling the resale or continued use of existing products. However, as industrial scale garment renewal services are new to the market, established collaboration and communication processes for developing service specifications are limited. As a result, the collaboration and communication process currently used by RecoMended is time-consuming, resource-intensive, and highly customerspecific, making it difficult to scale. This study aims to investigate how the collaboration and communication process between RecoMended and its customers can be improved to support more efficient development of service specifications for garment renewal and allow RecoMended to scale its production. The research was conducted through interviews, workshops, and contextual inquiries with RecoMended and several of its customer companies. The collected data were analyzed and synthesized, resulting in the design of a proposed future collaboration and communication process together with a service toolkit. The proposed design outlines a structured, step-based collaboration and communication process that is intended to support RecoMended and its customers in developing service specifications in a more informed and efficient way. The process is envisioned to be supported by a service toolkit consisting of standardized service packages intended to enable scalability in the production workshop, a reference library with visual and material examples of available renewal procedures, and a set of guiding questions designed to ensure that key decisions are addressed. While further development and testing would be required, the proposed process and service toolkit are intended to provide a foundation for clearer communication, more informed decision-making, and improved collaboration, thereby supporting RecoMended’s ability to scale its garment renewal services.
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Exploring Image-to-Text Visual Search Using Open Source Models
(2026) Liu, Tommy
Visual searching refers to the use of visual data, typically images, in order to perform a search rather than textual input. Most visual search implementations rely on performing similarity searching over image features, in which a user-submitted query image is compared against all searchable entries’ features before returning sufficiently similar results. This thesis explores a different method which utilizes image descriptions generated by vision-language models instead of image features, where the descriptions are converted into embeddings in order to match with other search entries. Evaluation data indicate that the method can provide satisfactory retrieval performance in addition to maintaining a low search query execution time, provided that an adequate vision-language model is employed and sufficient server capacity is available.
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PRED-RAG: a Predictive Radial Grid for Automotive Radar Multipath - Identification Identification of objects created by the radar multipath phenomenon, with focus on low computational complexity.
(2025) Kindlund, Erik; Karlsson, Andreas
Automotive radar sensors are crucial for advanced driver assistance systems but are susceptible to the multipath phenomenon, where radio waves reflect multiple times between surfaces, creating false "ghost" objects that can trigger unnecessary safety interventions. Previous work relies on restrictive assumptions about reflection surfaces and environmental conditions, yielding solutions that perform well in specific scenarios but demonstrate limited generalization capabilities in the complex, diverse situations encountered during real-world driving. This thesis addresses the challenge of identifying radar multipath objects in real-time environments, focusing on developing an algorithm that maintains low computational complexity while achieving high accuracy. We established a development and evaluation pipeline using synthetic data together with a simulation framework, enabling data driven development of our algorithm. We propose the PRED-RAG algorithm, a novel approach that utilizes a radial grid structure combined with host motion prediction of static detections for enhanced high-level environment mapping. The algorithm identifies triplets consisting of a ghost object, reflection point and true object, then evaluates them using velocity-based criteria. When compared to a state-of-the-art algorithm, our approach demonstrates superior performance in both accuracy and computational efficiency across various driving scenarios. The PRED-RAG algorithm achieves 94.43% accuracy for high-priority objects compared to 39.26% for the baseline, with significantly better generalization capabilities, particularly in complex environments. The geometric properties employed in the grid-based approach effectively separate ghost objects from true objects while maintaining runtime performance suitable for real-time automotive applications. This work contributes to safer autonomous driving systems by reducing false objects that could lead to unnecessary emergency interventions.