Arturs Nikulins has been working at the EDI since 2021. Programming engineer who connects his activity with the research and development of artificial intelligence (AI), including methods of explainable artificial intelligence (XAI). At the beginning of his activity, the work was related to the implementation of algorithms in computer vision systems for crop harvesting purposes, which contributed as good background understanding of AI implementation in computer vision systems. Currently, active research is related to the implementation of AI for computer vision tasks in various digital environments which includes RGB image, hyperspectral image and pointcloud processing environments.
Recent publications
- Vitalijs Komasilovs, Aleksejs Zacepins, Armands Kviesis, Kaspars Ozols, Artūrs Ņikuļins, Kaspars Sudars “Development of an MCTS Model for Hydrogen Production Optimisation”, Processes (2023) (pp.16). https://www.mdpi.com/2227-9717/11/7/1977.
- Sarmīte Strautiņa, Ieva Kalniņa, Edīte Kaufmane, Kaspars Sudars, Ivars Namatēvs, Arturs Ņikuļins, Edgars Edelmers "RaspberrySet: Dataset of Annotated Raspberry Images for Object Detection", Multidisciplinary Digital Publishing Institute (2023) (pp. 5). https://www.mdpi.com/2306-5729/8/5/86.
- Kaspars Sudars, Ivars Namatevs, Arturs Nikulins, Rihards Balass, Astile Peter, Sarmite Strautina, Edite Kaufmane, Ieva Kalnina "Semantic Segmentation Using U-Net Deep Learning Network for Quince Phenotyping on RGB and HyperSpectral Images", 27th International Conference "Electronics" (2023). https://ieeexplore.ieee.org/document/10177638.
- Kaspars Sudars, Ivars Namatēvs, Jānis Judvaitis, Rihards Balašs, Artūrs Ņikuļins, Astile Peter, Sarmīte Strautiņa, Edīte Kaufmane, Ieva Kalniņa. YOLOv5 Deep Neural Network for Quince and Raspberry Detection on RGB Images
- Arturs Nikulins, Kaspars Sudars, Edgars Edelmers, Ivars Namatevs, Kaspars Ozols, Vitalijs Komasilovs, Aleksejs Zacepins, Armands Kviesis, Andreas Reinhardt. "Deep Learning for Wind and Solar Energy Forecasting in Hydrogen Production" Energies 17(5): pp.12. https://www.mdpi.com/1996-1073/17/5/1053