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List of results

• Deep clustering for vehicle operation type  + (In this project, deep clustering will be used on the logged vehicle data (LVD) to find the best representation of vehicles’ operation to explain the behavior of the vehicles over time.)
• Automatic Generation of Realtime Machine Learning Architectures  + (In this project, it is required to build a tool to generate a dataflow model and construct architectures for such algorithms, while minimizing latency or meeting a specific deadline under area and power constraints.)
• LiDAR Denoising  + (In this project, the candidate is supposed to implement various filtering algorithm to denoise 3D LiDAR point cloud data.)
• Deep Graph Networks for Future Graph Prediction  + (In this project, the candidate is supposed to implement a deep graph network that receives a set of graphs as input and returns the predicted next upcoming graph(s).)
• Hardware Security Enhancement in Cyber-Physical Systems using Deep Learning-based Anomaly Detection  + (In this project, we intend to employ a deep learning approach to detect anomalies in cyber-physical systems using data flow monitoring.)
• Effecient implementation of DL models on embedded platforms  + (In this project, we optimize DL models to run efficiently on resource-bounded embedded platforms.)
• A Reliable IoT Messaging Protocol Based on MQTT Standard  + (In this project, we will modify the well-known IoT protocol, i.e., MQTT to consider a topic-based reliability strategy between the broker and subscribers.)
• Incorporate behaviour modelling into AGV safety performance stack  + (Incorporate behaviour modelling into AGV safety performance stack)
• Increase Data Rate over Error-prone networks  + (Increase Data Rate over Error-prone networks (AIRBUS))
• HUMAI - Test and Demonstration of HUMan-centered AI  + (Increased engagement of industral operators through human-centered AI; the project is done in collaboration with local rubber factory)
• Indoor localization for ground vehicles  + (Indoor localization for ground vehicles)
• Simulating Crowds for Traffic Safety Research  + (Integrate crowd simulation into a mixed-reality platform for development and testing of advanced automotive safety systems.)
• Integrating a new rigid-body dynamics model library with an existing whole-body controller  + (Integrating a new rigid-body dynamics model library with an existing whole-body controller)
• Intelligent claim Process with Volvia  + (Intelligent claim Process)
• Human-in-the-loop Discovery of Interpretable Concepts in Deep Learning Models  + (Interactive discovery of disentangled and interpretable concepts in Deep Learning Models)
• Knowledge graphs in healthcare  + (Investigae the use of KG in healthcare applications)
• Federatad Learning (FL) improving security and privacy in tactical networks  + (Investigate and prototype how FL can improve security and privacy in tactical networks, with a focus on intrusion detection or anomaly detection. The project will combine a literature review with a simulation-based implementation to assess feasibility.)
• Driver Prediction for Automative Industry  + (Investigate if and how it is possible to predict the drivers actions and inentions in a predefined limited number of scenarios)
• Investigating depression signs among older adults using Swedish National Registry Data  + (Investigating depression signs among older adults using Swedish National Registry Data)
• Fuzz testing of network protocols  + (Investigation how fuzz testing of network protocols could be implemented and provide rapid robustness testing)
• Federated Learning Aggregation Strategies by Weight Exploration  + (Investigation of aggregation strategies for federated learning)
• The effect of a mixed-capability vehicular fleet on Vulnerable Road User safety  + (Investigation of the effect of different levels of connection, cooperation, and automation (e.g., local awareness, collective perception, statistics) on road safety and traffic efficiency for future mobility scenarios including pedestrians and cyclists.)
• Comparative Study on Data Abstraction Methodologies for Interoperable V2X Roadside Units (RSU)  + (It is a collaboration with MittLogik on development of Interoperable RSU Prototype focused on Vulnerable Road User (VRU) safety.)
• Value of BIG DATA for Large Building Owners  + (It is an explanatory project with a company called Mutual Benefits Engineering AB)
• Thesis in connection with KEEPER project  + (KEEPER – knowledge creation for efficient and predictable industrial operations)

• Knowledge graphs 4 XAI in Healthcare  + (Knowledge graphs in Healthcare)

• Collaboration with Bankomat 3  + (LLM Feedback Loops for Autonomous Knowledge Updating RQ: What mechanisms allow LLMs to autonomously refine their knowledge based on user feedback?)
• MMultitask Learning on Vehicle Data  + (Learning shared representation using multitask learning on a vehicle-related data)
• Multitask learning on vehicle data  + (Learning shared representation using multitask learning on a vehicle-related data)
• Building a Knowledge-based AI Framework for Mobility  + (Leveraging new knowledge to improve the productivity of mobility services)
• Protein Language Models for drug discovery  + (Leveraging the sequence-based transformer protein language model for improving potential drug targets identification)
• Lighting up the bicycle roads with drones  + (Lighting up the bicycle roads with drones)
• Lightweight foundation model for time series classification  + (Lightweight foundation model for time series classification)
• Adapt LoCoMotif to forklift data  + (LoCoMotif is a novel TSMD method able to discover motifs that have different lengths (variable-length motifs), exhibit slight temporal differences (time-warped motifs), and span multiple dimensions (multivariate motifs))
• Predicting electricity generation capacity in solar and wind power plants based on meteorological data using machine learning algorithms  + (ML algorithms will be used to analyze meteorological data to predict the electricity generation capacity of solar and wind power plants. This project is a collaboration between Halmstad and Sam Houston University (USA)).)
• Machine Learning and LeadTime Prediction  + (Machine Learning and LeadTime Prediction)
• Analysis of Ambient Sound in HINT  + (Machine Learning applied to sound classification. Creation (recoding and annotation) of new a sound database and a baseline system for sound event detection and/or localization)
• Machine Learning for Segmentation of Lensed Galaxies: Distinguishing Source Galaxies from Gravitational Lenses  + (Machine Learning for Segmentation of Lensed Galaxies: Distinguishing Source Galaxies from Gravitational Lenses)
• MultiScale Microscopy Detailed  + (Master Thesis Project)
• Modeling patient trajectories using different representation learning techniques  + (Modeling Electronic Health Record (EHR) data and predict future events for specific patients)
• Behaviour modeling and classification of vehicles at a roundabout  + (Modeling of behaviour, classification based on behaviour, and detection of anomalous behaviour in traffic at a roundabout.)
• Modelling vehicles'/drivers' behaviour using LVD  + (Modeling the bahevaior of the vehicles/drivers exploiting the vehicles usage in different context.)
• Modelling behavior and interaction of road users in transportation systems  + (Modelling behavior and interaction of road users in transportation systems)
• Model behaviour of agents in a warehouse setting  + (Modelling the behaviour of agents (manual driven forklift trucks, other robots, humans etc.) in a warehouse environment)
• Detecting changes in causal relations  + (Monitoring the operation of bus fleet by tracking the changes in causal network)
• Multi-Sensor Fusion for Semantic Scene Understanding  + (Multi-Sensor Fusion for Semantic Scene Understanding)
• Electrical stimulator design and development  + (Multi-pattern electrical stimulator design and development)
• NOMAD  + (Nonlinear methods for accurate deviation detection (NOMAD))
• Feature-wise normalization for 3D medical images  + (Normalization of 3D medical imaging either as a data pre-processing or as feature-wise batch normalization during CNN model training)
• Obstacle Identification from 3D Data for AGVs in a Warehouse Environment  + (Obstacle Identification from 3D Data for AGVs in a Warehouse Environment)
• On the explainability of Graph Neural Networks: an application in credit scoring  + (On the explainability of Graph Neural Networks: an application in credit scoring)