Property:OneLineSummary
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Study and develop 1D continuous frequency fitting of a predetermined number of (oriented) sinusoids to images which are strongly oriented locally, in particular forensic fingerprints. +
Create a simulation platform, loosely inspired by gamification, that is in principle capable of capturing the complexity of a complete city. +
Implent a high speed, low latency motion estimation technique +
In this project we intend to design an optimisation system using artificial intelligence algorithms in order to select/extract the best features for developing a forecasting system in predictive maintenance. +
In this project we aim to optimise the vehicles setting based on their usage style. Here we focus more on fuel consumption. +
This project aims at developing data-driven methods to understand ferry operations and optimise enegery consumption +
Optimization of a 5G algorithm by parallelization +
Optimizing Energy Consumption in Maritime Transportation with Machine Learning Methods (in collaboration with Cetasol) +
Develop machine learning methods for forecasting fuel consumption, path, and motion planning, with historical data from furries operation. +
P
Pallet Detection and Mapping in a Warehouse Environment +
Development of an identification algorithm for Pallet Rack Cells in a warehouse. Data acquisition is performed by a mobile robot via fisheye cameras and/or 3D sensors. +
Path and Motion Planning for a Ferry +
Finding suitable peer groups to represent district heating and heat pump customers +
Piglets Detection and Counting using Deep Neural Networks +
Pose & distance of toy-vehicles by spirals +
The positioning of the user at the Halmstad intelligent home (HINT). +
Develop and validate a motorcycle rider posture estimation system using multi-IMU sensors and video-based ground truth for accuracy evaluation. +
Develop and compare posture estimation methods for motorcycle simulator riders using camera-based remote sensing and IMU-based systems, focusing on hip movement, body lean angles, and leg positions. +
Develop machine learning methods to forecast energy consumption for heavy-duty vehicles +
Predicting Energy Consumption for Heavy-Duty Vehicles via Time Series Embeddings (in collaboration with Volvo) +
Develop deep learning based methods for time series forecasting; explore self-supervised learning methods for multi-variate time series embeddings +
Develop and evaluate deep learning models capable of predicting future unit behavior and movement trajectories in tactical scenarios, using a combination of historical trajectory data, environmental context, and mission objectives. +
