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List of results

• Graph Neural Networks for Traffic Flow Forecasting  + (The main goal of this project is to explore GNN for traffic flow forecasting)
• Graph Neural Networks for cardiovascular disease  + (The main goal of this project is to explore GNN for cardiovascular disease)
• Positioning of the user at the HINT  + (The positioning of the user at the Halmstad intelligent home (HINT).)
• Multi-robot coordination in a warehouse  + (The project aims to coordinate motion of multiple robots to accomplish a single task)
• Gait analysis using wearable sensors in Parkinson's disease  + (The project aims to develop a machine learning tool for the assessment of Parkinsonian gait in a natural environment)
• Transfer Learning by Selection of Invariant Features  + (The project aims to develop novel methods to identify invariant features to transfer across multiple domains.)
• Convolutional Neural Network (CNN) features behaviour in the context of textures  + (The project aims to quantify the behaviour of Convolutional Neural Network (CNN) features in the context of textures.)
• Digit recognition by lip-movements and time recursive Neural Networks  + (The project aims to recognize digits by lip movements and neural networks)
• Project with Atos  + (The project involves the development of software for the TrueDepth technology of the iPhone.)
• Machine Learning-based optimization of physical activity  + (The project should be able to detect the difference between the current exercise and a reference version of the exercise.)
• The CatFish project  + (The project within Innovation Lab called CatFish has the aim of collecting data from water bodies through a system of drones)
• Non-contact estimation of blood pressure using photoplethysmography  + (The proposal aims to develop a camera-based system for estimating blood pressure using machine learning and photoplethysmography)
• Deep neural network optimization for path prediction in vessels!  + (The purpose of this thesis is analyzing a ferry dataset to identify the most optimal path using deep-net.)
• Evaluating the Effects of Social Media on Educational Sustainability in Sweden  + (The research analyzes sentiment in social media data related to educational sustainability practices and outcomes in Sweden.)
• Multitask Learning in Autonomous Driving  + (The student should annotate 3D point cloud data for semantic segmentation and object detection. A new deep learning model needs to be implemented to learn both tasks.)
• Uncertainty quantification for data driven clinical decision making  + (The student will build upon the field of evidential deep learning to identify and understand when the model says 'I don't know')
• Multivariate Time Series Analysis with Irregularly Sampled Data  + (The student will devise methods for handling irregularly sampled multivariate time series data, addressing missing data and modeling temporal relationships for applications in healthcare)
• Leveraging LLMs for Clinical Note Annotation and Uncertainty Estimation  + (The student will investigate the potential of LLMs to simplify clinical note annotation along with uncertainty estimation, contributing to improved healthcare data management.)
• Virtual reality to support traffic safety  + (The thesis is part of an ongoing project to develop drone-based lighting solutions for improving traffic safety and for encouraging travels to take bicycles.)
• Object Tracking and Anticipation  + (The thesis presents an experimental study of different object-tracking and trajectory anticipation algorithms in the context of autonomous driving.)
• Forecast energy consumption in buildings to help Mestro customers save energy  + (The thesis will be focused on forecasting the energy consumption in buildings (e.g. electricity consumption), with some optional “add-ons” where student will also develop...)
• Generative Approach for Multivariate Signals  + (The topic focuses on generative models (VAE) for CAN-bus data and investigating the representation learning capabilities of such techniques)
• Comparative study of an automated testing coverage for a TCP/IP stack implementation  + (The topic of the project is the comparative study of the coverage of the tests generated by the QuickCheck tool against real coverage requirements)
• The effect of contextual information on fuel consumption using Explainable AI!  + (There are many factors that can minimize pollutions and maximize energy efficiency and fuel consumption in vessels.)
• Thermal Detection of Subtle Human Cues for a Robot Magic Performance  + (Thermal Detection of Subtle Human Cues for a Robot Magic Performance (NOT AVAILABLE HT22/VT23))

• Project(s) at Volvo Cars Corporation  + (Thesis topics at Volvo Car Corporation)

• Project(s) at Volvo Group  + (Thesis topics at Volvo Group)
• Thesis with Jayway  + (Thesis with Jayway)
• Identification and Classification of Automotive Radar Interference using Data-driven Methods  + (This is a collaboration with Radar Reticence. The project investigates how different detection methods handle radar interference, a crucial factor for ensuring accurate perception in automated driving.)
• Investigating Robustness of DNNs  + (This master thesis project aims at characterizing sensitivity to classification of images (based on deep neural networks).)
• Adaptive Obfuscation Techniques for Privacy- Preserving Machine Learning in IoT Edge De- vices  + (This master thesis project focuses on developing an adaptive obfuscation frame- work for protecting multi-modal data in resource-constrained IoT environments.)
• Secure Hardware Accelerators for Machine Learning: Design, Evaluation, and Mitigation of Vulnerabilities  + (This master's project focuses on investigating the security of hardware accelerators designed for machine learning)
• Securing Multi-Processor System-on-Chips: Assessing Vulnerabilities to Hardware Trojans Through Thermal Profiles  + (This master's thesis project is dedicated to conducting a comprehensive security assessment of modern multi-processor System-on-Chips (MPSoCs) equipped with multiple thermal sensors.)
• Optimising Energy Consumption for Ferries in Collaboration with Cetasol  + (This project aims at developing data-driven methods to understand ferry operations and optimise enegery consumption)
• Deep stacked ensemble  + (This project aims at training multiple parallel deep networks in such a way to learn different representation of data which will be suitable to frame these networks in stacked ensemble framework.)
• Concept Re-identification to Explain Online Continual Learning  + (This project aims to apply techniques from recurrent concept drifts to explain the predictions of Online Continual Learning methods.)
• Evolving Kolmogorov-Arnold Networks  + (This project aims to enhance the architecture of Kolmogorov-Arnold Networks (KANs) by optimizing key components such as loss functions, activation functions, initialization methods, and learning processes to improve their performance and interpretability.)
• Dynamic Churning-Based Logic Locking for Enhanced Hardware Security  + (This project aims to explore and implement advanced logic locking techniques to improve the security of integrated circuits (ICs) against modern attacks.)
• Automatic Idea Detection for controlling Healthcare-associated infections  + (This project aims to use advanced NLP tools to automatically detect interesting ideas by processing text available in the medical forums to address the Healthcare-associated infections problem in the hospitals)
• Automatic Idea Detection from social media for Controlling and Preventing Healthcare-Associated Infections (with funding opportunity)  + (This project aims to use advanced NLP tools to automatically detect interesting ideas by processing text available in the medical forums to address the Healthcare-associated infections problem in the hospitals)
• AGENTIC-AI TOWARDS INTERPRETATION OF SERVICE CANVAS AND AUTOMATION OF TRUSTWORTHY ML-PIPELINES  + (This project allows the MSc student(s) to study and experiment on Agentic AI methods for the generation of trustworthy ML-pipelines)
• Vehicle Usage Modeling over Time  + (This project intents to explore the modeling of the usage of vehicles using unsupervised machine learning algorithms in different context which are logged over time.)
• Forklift Trucks Usage Analysis  + (This project is about applying machine learning methods to have a better understanding for the usage of forklifts trucks in industrial application.)
• Deep feature analysis and extraction on Logged Vehicle data for the task of predictive maintenance  + (This project is about applying supervised/unsupervised methods of feature selection on Logged Vehicle data (LVD) from Volvo trucks and investigate the contribution in model construction for different predictive maintenance tasks)
• Exploring, modelling and optimization of home care regions  + (This project is about developing tools and methods for optimization of health care resources using machine learning as the central technology.)
• Detecting different types of machines based on usage  + (This project is about studying how can we distinguish among different types of machines based on their usage.)
• Empowering Adult Learners and Educators through AI  + (This project is part of an international collaborative project LEAD-AI, which aims to create a capacity-building programme designed to enhance AI skills of both educators and adult learners.)
• Reinforcement Learning with Adaptive Representation Learning  + (This project targets finding representations that make the reinforcement learning more efficient in terms of finding an easier state to action mapping.)
• Reliability Analysis and Assessment of Multi- Core System-on-Chip through Transaction Pro- filing and Machine Learning  + (This project will contribute to the develo … This project will contribute to the development of automated reliability as- sessment techniques for multi-core SoCs and demonstrate the potential of in- tegrating transaction profiling with ML-driven analysis to enhance the overall security and robustness of such systems.l security and robustness of such systems.)
• AI-driven Automotive Service Market Logistics  + (This project, in collaboration with Volvo Logistics, focuses on using state-of-the-art methods based on meta-learning to improve demand forecasting, inventory management and spare parts availability at Volvo dealers and warehouses.)
• Workshop Automation together with Volvo Group  + (This project, in collaboration with Volvo Group, investigates how automation can raise workshop throughput, repair quality, and technician experience through data-driven perception and pragmatic use of automation.)