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Previous     Results 51 – 100    Next        (20 | 50 | 100 | 250 | 500)
  Supervisors OneLineSummary
Fault detection using acoustic signals through anomaly detection Elena Haller
Peyman Mashhadi
Fault detection using acoustic signals through anomaly detection
Federated Learning Aggregation Strategies by Weight Exploration Jens Lundström
Amira Soliman
Sadi Alawadi
Investigation of aggregation strategies for federated learning
Federated learning in automotive industry Zahra Taghiyarrenani
Sławomir Nowaczyk
Sepideh Pashami
Federated learning in automotive industry
Forecasting Industrial IoT Time Series @AlfaLaval Hadi Fanaee Forecasting industrial IoT Time Series
Foundation Models for Time Series Analysis Zahra Taghiyarrenani
Yuantao Fan
Ali amirahmadi
Explore the use of large pre-trained time-series foundation models (TSFM) and design fine-tuning strategies on tasks of interest
Generating synthetic time series data in case of data scarcity Alexander Galozy
Peyman Mashhadi
Generating synthetic time series data in case of data scarcity
Generative Approach for Multivariate Signals Kunru Chen
Thorsteinn Rögnvaldsson
Abdallah Alabdallah
The topic focuses on generative models (VAE) for CAN-bus data and investigating the representation learning capabilities of such techniques
Graph Neural Networks for Multivariate Time Series Data Analysis Prayag Tiwari
Guojun Liang
Our project aims to analyze multivariate time series through the innovative application of graph neural networks.
Graph Neural Networks for Traffic Flow Forecasting Prayag Tiwari
Sławomir Nowaczyk
The main goal of this project is to explore GNN for traffic flow forecasting
Graph Neural Networks for cardiovascular disease Prayag Tiwari The main goal of this project is to explore GNN for cardiovascular disease
Hardware Security Enhancement in Cyber-Physical Systems using Deep Learning-based Anomaly Detection Mahdi Fazeli In this project, we intend to employ a deep learning approach to detect anomalies in cyber-physical systems using data flow monitoring.
Human ground robot interaction Cristofer Englund
Martin Cooney
Fernando Alonso-Fernandez
External communication from mobile robots to minimize conflicts with pedestrians
Human-in-the-loop Discovery of Interpretable Concepts in Deep Learning Models Ece Calikus Interactive discovery of disentangled and interpretable concepts in Deep Learning Models
Hydro Power Station TBD (contact Slawomir Nowaczyk if interested) Collaboration with Ålberga Bruk 1919
Ideology and Power Identification in Parliamentary Debates Pablo Picazo-Sanchez Ideology and Power Identification in Parliamentary Debates
Improving Time-series Generative Adversarial Networks (GANs) for Generating Electronic Health Records (EHRs) Amira Soliman
Atiye Sadat Hashemi
Synthetic Electronic Health Records
Incorporate behaviour modelling into AGV safety performance stack Björn Åstrand Incorporate behaviour modelling into AGV safety performance stack
Intrusion detection and prevention for IIoT using Ensemble Deep Network Reza Khoshkangini
Mohamed Eldefrawy
In this project students will work on network data and try to detect malicious behavior using ensemble deep network.
Investigating depression signs among older adults using Swedish National Registry Data Mahmoud Rahat Investigating depression signs among older adults using Swedish National Registry Data
Investigation of spread spectrum techniques to reduce the electromagnetic interference in switch mode power supply Maria De Lauretis
Elena Haller
The goal of the project is to investigate spread-spectrum-based PWM techniques to reduce the EMI in motor drivers caused by the SMPS
IoT Forensics Mohamed Eldefrawy and Hazem Ali To achieve a systematic approach for data extraction (i.e., imaging), forensically sound, from the hardware level
Joint Analysis of Multimodal data on climate change for the educational purpose Sławomir Nowaczyk
Zeinab Shahbazi
Carlos Silla
Exploring climate change data using machine learning and deep learning
Knowledge graphs in healthcare Grzegorz J. Nalepa Investigae the use of KG in healthcare applications
Leveraging LLMs for Clinical Note Annotation and Uncertainty Estimation Awais Ashfaq
Prayag Tiwari
The student will investigate the potential of LLMs to simplify clinical note annotation along with uncertainty estimation, contributing to improved healthcare data management.
LiDAR Denoising Eren Erdal Aksoy In this project, the candidate is supposed to implement various filtering algorithm to denoise 3D LiDAR point cloud data.
Lighting up the bicycle roads with drones Cristofer Englund
Lei Chen
Lighting up the bicycle roads with drones
Machine Learning for Segmentation of Lensed Galaxies: Distinguishing Source Galaxies from Gravitational Lenses Tiago Cortinhal
Idriss Gouigah
Eren Erdal Aksoy
Margherita Grespan
Hareesh Thuruthipilly
Machine Learning for Segmentation of Lensed Galaxies: Distinguishing Source Galaxies from Gravitational Lenses
Machine Learning-based optimization of physical activity Cristofer Englund
Kevin Hernandez Diaz
The project should be able to detect the difference between the current exercise and a reference version of the exercise.
Mapping SFO mitigation/Linearization algorithms, trade-off between memory and computation on GPU Hazem Ali (HH)
Håkan Johansson (LiU)
investigating the parallelisation and mapping of Sampling Frequency Offset algorithms
Meta-learning for Multivariate Signals Kunru Chen
Anna Vettoruzzo
Mohamed-Rafik Bouguelia
Apply meta-learning algorithms to unlabelled time-series data to solve machine activity recognition problems.
Model Heterogeneity in Federated Learning Amira Soliman
Sławomir Nowaczyk
Group users within a federated learning environment into different learning overlays according to their behavioural similarities
Model-Based Testing of Zero-Copy Protocols Wojciech Mostowski Challenges in Model-Based Testing of Zero-Copy Protocols
Modeling patient trajectories using different representation learning techniques Stefan Byttner
Kobra Etminani
Amira Soliman
Modeling Electronic Health Record (EHR) data and predict future events for specific patients
Multi-Sensor Fusion for Semantic Scene Understanding Eren Erdal Aksoy Multi-Sensor Fusion for Semantic Scene Understanding
Multi-modal Weather type Classifier Eren Erdal Aksoy Camera, LiDAR, and Radar based weather type classification using deep neural networks on the K-RADAR dataset
Multi-modal risk prediction models on osteoporotic fracture, myocaridal infarction and stroke Kobra Etminani (Farzaneh)
TBD
Peder Wiklund (from Region Halland)
Markus Lingman (from Region Halland)
develop and evaluate how well a multimodal model derived from the regional healthcare information platform, with or without the CT-derived measures, can predict the risk of subsequent osteoporotic fracture, myocardial infarction and stroke
Multiband RF Rectifier for Self-Powered IoT Devices Amjad Iqbal In this project, Mutiband RF rectifiers will be designed for self-powered IoT devices
Multitask Learning in Autonomous Driving Eren Erdal Aksoy 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.
Multivariate Time Series Analysis with Irregularly Sampled Data Awais Ashfaq
Prayag Tiwari
The student will devise methods for handling irregularly sampled multivariate time series data, addressing missing data and modeling temporal relationships for applications in healthcare
Music style transfer Peyman Mashhadi
Yuantao Fan
Develop a system that receives a piece of music in one genre and changes/transfers its style into another genre, using machine learning algorithms.
Network-assisted positioning in confined spaces using 802.11 Access Layer information Oscar Amador Molina
Elena Haller
Proof of Concept of a Position-and-Time entity based on 802.11 to enable embedded beaconing systems (e.g., a “connected reflective vest”) for workers in confined spaces where satellite-based positioning is not possible.
Object Tracking and Anticipation Eren Erdal Aksoy The thesis presents an experimental study of different object-tracking and trajectory anticipation algorithms in the context of autonomous driving.
On the explainability of Graph Neural Networks: an application in credit scoring Atiye Sadat Hashemi
Peyman Mashhadi
On the explainability of Graph Neural Networks: an application in credit scoring
Open and Realistic smart City Activities Simulator (ORCAS) Sławomir Nowaczyk
Richard Bunk
Create a simulation platform, loosely inspired by gamification, that is in principle capable of capturing the complexity of a complete city.
Optimising Energy Consumption for Ferries in Collaboration with Cetasol Yuantao Fan
Peyman Mashhadi
This project aims at developing data-driven methods to understand ferry operations and optimise enegery consumption
Optimization of a 5G algorithm by parallelization Hazem Ali Optimization of a 5G algorithm by parallelization
Predicting Energy Consumption for Heavy-Duty Vehicles via Time Series Embeddings (in collaboration with Volvo) Yuantao Fan & TBD Develop deep learning based methods for time series forecasting; explore self-supervised learning methods for multi-variate time series embeddings
Predicting electricity generation capacity in solar and wind power plants based on meteorological data using machine learning algorithms Reza Khoshkangini
Ramin Sahba
Amin Sahba
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)).
Privacy-Preserved Generator for Generating Synthetic EHR data Atiye Sadat Hashemi
Jens Lundström
Farzaneh Etminani
Time-series GAN and generation of synthetic electrical health records
Project opportunities at ElectronicArts TBD Each year there are several project opportunities at Electronic Arts
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