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Accessible Card Presentation in Board Games: Developing an Application for Individuals with Visual Impairment

(2023) Al Allaf, Noor; Gustafsson, Kristoffer; Hansson, Stina; Nyberg, Cecilia; Papa, Nadia; Qwinth, Lisa

Board games are a popular form of entertainment played in many cultures and countries in similar and different forms around the world. However, board games usually present barriers to people with visual impairments. One of these barriers is the card element in board games since these cards can be a hassle to read for individuals with visual impairment. This dissertation focuses on how cards in board games can be presented in a more accessible way through an application. Three interviews with individuals who either are visually impaired or have experience working with people with visual impairment were conducted to guide the development of the application. The application works by identifying a card with the camera of a phone or tablet. Then, the card is presented and stored in the application in a more accessible format, using higher contrasts, color-blindness-friendly colors, and a screen readercompatible format. Cards are identified through a combination of image recognition and text recognition. One challenge throughout the report was the broad range of visual impairments, where different solutions work for different individuals.

Exterior Design of an Urban Air Mobility Vehicle Optimizing for Medical Emergencies and Public Acceptance

(2025) Hua, Yu-Han; Gustafsson, David

Technological advancements have enabled the development of previously unfeasible transportation solutions, with Urban Air Mobility (UAM) vehicles emerging as a promising approach to addressing growing urban congestion challenges. This project focuses on designing an emergency UAM vehicle, developing its exterior to support critical rescue missions comparable to those performed by ambulance helicopters, while aiming to design it to be accepted and trusted by the public and society. An iterative design methodology guided the project, beginning with comprehensive research, including literature reviews, market analysis, stakeholder interviews, and field observations, to establish key design requirements. Subsequent phases used various methods and tools, progressing from conceptual sketches to 3D-printed prototypes, physical mockups, and final Class-A surface modeling. The project resulted in a proposal for a concept vehicle: the exterior design of a medical emergency UAM, demonstrated through CAD models and realistic renderings, with requirements and dimensions tailored to emergency medical missions. While the initial focus was on designing a UAM, prioritizing both medical emergencies and public acceptance, the project later shifted towards medical operations, with public acceptance becoming a secondary consideration. This adjustment was due to the project’s scope; findings suggest that emergency UAM design must first fulfill mission-critical needs before addressing semiotic and aesthetic considerations.

Nya applikationer för kooperativ perception

(2025) Niklund, Elias; Bodin, Emma; Carlsson, Hugo; Hall, Isabella; Persson, Jesper; Sunnerstam, Jonathan

As connected and autonomous vehicles become more prevalent, cooperative perception is increasingly vital for ensuring traffic safety and efficiency in urban environments. This project investigates how cooperative perception can enhance intelligent transportation systems by reducing collision risks and improving traffic efficiency through real-time data exchange between vehicles and infrastructure. The system is developed in a simulation framework based on SUMO called ms-van3t. Which also integrates decentralized communication, collision avoidance, and route planning. We introduce a new concept called ITS-fairy, which uses a Server Local Dynamic Map to support real-time data sharing and hazard detection. In addition, a safety mode based on Time-to-Collision and Space-to-Collision estimations provides proactive warnings to prevent crashes. Additionally, a centralized planner is introduced to reduce congestion by assigning routes based on global traffic conditions. The implementations were evaluated through simulations of urban traffic scenarios such as intersections and roundabouts. The results demonstrate ensured safety in urban scenarios and more coordinated traffic behavior under high-density conditions. We conclude that the developed platform offers a robust foundation for continued research and development in cooperative vehicular systems.

Biometric Authentication for the Web: A Face Recognition System

(2025) Gälldin, Erik; Lindström, Linus; Logren, Maria; Motin, Mikael; Svensjö, Emil; Tengblad, Tabita

This bachelor thesis presents the design and implementation of a proof of concept web application for biometric authentication using face recognition. The goal was to investigate if face recognition could function as an accurate, user-friendly and secure alternative to password based login systems on web platforms. The project included developing a face recognition pipeline using existing open-source models, with added features such as anti-spoofing and encryption for data protection. The system was implemented as a web application and was evaluated through a set of user tests and performance tests on datasets. The results show that the system achieves a high accuracy and usability, even though spoofing remains as an issue. Future work includes improving the spoofing detection, fine-tuning the models for better generalization and developing the system into a scalable authentication API.

Edge vs Cloud Models in App-Based AI for Orthodontic Assessment: An Analysis of Architecture, Performance Metrics, and Clinical Effectiveness in AI-driven Malocclusion Detection on Edge and Cloud Platforms

(2025) Andrén, Carl; Awada, Ali; Genberg, Isak; Meyer, Oskar; Norlin, Kevin; Varenhorst, Bertil

This thesis investigates the trade-offs between edge and cloud deployment of CNN models for AI-driven malocclusion detection in a mobile application. The study explores the CNNs VGG-19, YOLOv8 and ResNet-50 and their latency, accuracy and efficiency in Cloud and Edge environments. A mobile prototype was developed to capture dental images, preprocess them using ARKit-based alignment and validation mechanisms, and classify the severity of malocclusion according to the Skåneindex scale. Each model was evaluated using performance metrics such as F1-score, specificity, and inference latency, while also considering computational resource usage. The results indicate that edge deployment reduces latency, improving user responsiveness, while cloud deployment offers marginally higher classification accuracy, particularly with VGG-19. YOLOv8 demonstrated strong overall performance and robustness across environments. Additionally, expert stakeholder validation confirmed the clinical potential of the application in streamlining orthodontic screening and referral processes. These findings highlight key considerations in selecting an appropriate deployment strategy for mobile AI applications in healthcare.