Jukka Heikkonen
Doctor of technology
jukhei@utu.fi ORCID identifier: https://orcid.org/0000-0002-2468-5708 |
Data analysis; machine learning; sensor fusion data processing; autonomous systems; computer vision; pattern recognition; GIS data processing; remote sensing
Jukka Heikkonen has been a professor of computer science of University of Turku, Finland, since 2009. His research as the head of the Algorithms and Computational Intelligent (ACI) research group is related to data analytics, machine learning and autonomous systems. Currently he is focusing on machine learning based data/sensor fusion applied for pattern recognition, situational awareness modelling of autonomous systems, and GIS data analysis in geographical applications. He has worked at top level research laboratories and Center of Excellences in Finland and international organizations (European Commission, Japan) and has led many international and national research projects. He has authored more than 150 scientific articles.
Some examples of current research activities/projects:
Artificial Intelligence based Virtual Control Room for the Arctic (AI-ARC)
1.9.2021 – 31.8.2024, H2020 project
The main objective of the AI-ARC proposal is to create an innovative, robust, efficient, and user-friendly artificial intelligence (AI) based- platform for coast and border guards, which allows traditional and VR-based interfaces to adapt to users’ preferences in terms of information management, anomaly detection, risk analyses and interoperability; in order to realize a comprehensive surveillance system that delivers powerful sensor fusion based situational awareness for decision making, and safety for all maritime actors.
Exploration Information System (EIS)
1.4.2022-31.3.2025, Horizon Europe project
EIS will develop new data analysis methods by applying artificial intelligence, machine learning, deep learning into mineral prospectivity mapping together with new geomodels and mineral systems modelling. Methods developed reduce the current high exploration costs and improve the accuracy of the targeting of the early phase exploration.
Trafficability Prediction and Route Planning for Forest Machines
1.9.2020-31.7.2024, Academy of Finland
The objective of the project is to develop novel machine learning (ML) approach for terrain trafficability prediction for forest machines based on model-data fusion and to develop efficient route planning approaches based on the trafficability forecasts.
Smart terminals (Sea3value SMARTER)
1.1.2021-28.2.2023, Business Finland
UTU is developing data analytics solutions for smart harbour operations. The objective is to add value to stakeholders by improved sharing of information, and by redesigning processes for operational optimisation, improved safety, and optimise people and cargo flow. The ship turnaround use case focuses on optimising ships visits in harbour. The truck traffic use case focuses on developing a predictive real-time traffic situational awareness model to optimise the cargo logistics to and from the port. The passenger flow use case improves the passenger flow on ropax-ports by developing models and solutions to support collective transportation, ride sharing and other mechanisms to relieve the congestion at ports.
MAATI – Soil type estimation
1.8.2021-31.7.2022, Ministry of Agriculture
This project develops machine learning methods for soil type classification for whole Finland. This includes the use of remote sensing datasets (such as Sentinel 1 and 2 satellites), up-to-date open spatial databases for peatlands and nutrient classes (swamp types), land use (forest / field / wetland / peat production) and drainage. The methods are first developed and tested in three pilot areas in Finland: 1) Keminmaa, 2) Etelä-Pohjanmaa, and 3) Itä-Suomi. The research includes the use of classical pattern recognition approaches based on predefined features and feature selection, and also deep learning approaches and their comparisons. The research is done in collaboration with Natural Resource Institute and Geological Survey of Finland.
Compressive Sensing and Machine Learning Techniques for Radar Applications
1.1.2021-31.12.2022, Finnish Defence Forces
The project investigates integration of Compressive Sensing(CS) and machine learning for radar applications improving the speed and applicability of CS techniques using deep net architectures with learned signal priors, while supporting learning in data starved regimes with CS based models. The work at the UTU is related to the detection and classification of targets in Gaussian and Non-Gaussian clutter.
Currently responsible teacher in the following courses:
TKO 3120 Machine Learning and Pattern Recognition
TKO 5328 Erikoistyö
Supervisor in graduation theses (MSc, BSc).
- Automated Testing and Resilience of Microservice’s Network-link using Istio Service Mesh (2022)
- Proceedings of Conference of Open Innovations Association FRUCT
(Refereed article in conference proceedings (A4)) - Long-Term Autonomy in Forest Environment Using Self-Corrective SLAM (2022) New Developments and Environmental Applications of Drones Nevalainen Paavo, Movahedi Parisa, Peña Queralta Jorge, Westerlund Tomi, Heikkonen Jukka
(Refereed article in compilation book (A3)) - Meta-Learning, Fast Adaptation, and Latent Representation for Head Pose Estimation (2022)
- Proceedings of Conference of Open Innovations Association FRUCT
(Refereed article in conference proceedings (A4)) - A Comparative Study of Deep Learning-based RGB-depth Fusion Methods for Object Detection (2021) 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA) Farahnakian Fahimeh, Heikkonen Jukka
(Refereed article in conference proceedings (A4)) - Alive in Smart Countryside (2021)
- Smart innovation, systems and technologies
(Refereed article in conference proceedings (A4)) - Applications of UWB Networks and Positioning to Autonomous Robots and Industrial Systems (2021) 2021 10th Mediterranean Conference on Embedded Computing (MECO) Yu Xianjia, Li Qingqing, Peña Queralta Jorge, Heikkonen Jukka, Westerlund Tomi
(Refereed article in conference proceedings (A4)) - Cooperative UWB-Based Localization for Outdoors Positioning and Navigation of UAVs aided by Ground Robots (2021) 2021 IEEE International Conference on Autonomous Systems (ICAS) Proceedings Yu Xianjia, Li Qingqing, Peña Queralta Jorge, Heikkonen Jukka, Westerlund Tomi
(Refereed article in conference proceedings (A4)) - DBA-Filter: A Dynamic Background Activity Noise Filtering Algorithm for Event cameras (2021)
- Lecture notes in networks and systems
(Refereed article in conference proceedings (A4)) - Dynamic Formation Reshaping Based on Point Set Registration in a Swarm of Drones (2021)
- Advances in Intelligent Systems and Computing
(Refereed article in conference proceedings (A4)) - Dynamic Resource-Aware Corner Detection for Bio-Inspired Vision Sensors (2021)
- International Conference on Pattern Recognition
(Refereed article in conference proceedings (A4)) - Federated Learning in Robotic and Autonomous Systems (2021)
- Procedia Computer Science
(Refereed article in conference proceedings (A4)) - Human Presence Detection with Thermal Sensor using Multilayer Perceptron Algorithm (2021)
- IEEE World Forum on Internet of Things
(Refereed article in conference proceedings (A4)) - Low-cost ultrasonic based object detection and collision avoidance method for autonomous robots (2021)
- International Journal of Information Technology
(Refereed journal article or data article (A1)) - Multi-pig Pose Estimation Using DeepLabCut (2021) 2021 11th International Conference on Intelligent Control and Information Processing (ICICIP) Farahnakian Fahimeh, Heikkonen Jukka, Björkman Stefan
(Refereed article in conference proceedings (A4)) - Performance Analysis of Back Surface Field (BSF) Effects in Multijunction Photovoltaic Cell (2021)
- Conference Record IEEE Photovoltaic Specialists Conference
(Refereed article in conference proceedings (A4)) - Pose Estimation of Hurdles Athletes using OpenPose (2021) 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) Jafarzadeh Pouya, Virjonen Petra, Nevalainen Paavo, Farahnakian Fahimeh, Heikkonen Jukka
(Refereed article in conference proceedings (A4)) - RGB and Depth Image Fusion for Object Detection Using Deep Learning (2021) Deep Learning Applications, Volume 3 Farahnakian Fahimeh, Heikkonen Jukka
(Refereed article in compilation book (A3)) - Towards Autonomous Industrial Warehouse Inspection (2021) 2021 26th International Conference on Automation and Computing (ICAC) Farahnakian Fahimeh, Koivunen Lauri, Mäkilä Tuomas, Heikkonen Jukka
(Refereed article in conference proceedings (A4)) - Transfer Learning for Maritime Vessel Detection using Deep Neural Networks (2021)
- Proceedings of the IEEE international conference on intelligent transportation systems
(Refereed article in conference proceedings (A4)) - Actors in society’s hybrid information environment: Grounded theory analysis (2020)
- Proceedings of the European conference on cyber warfare and security
(Refereed article in conference proceedings (A4))