Fatemeh Noroozi received her Ph.D. in Engineering and Technology from the University of Tartu, Estonia, in 2018. Prior to that, she completed her M.Sc. in Mechatronics Engineering at the University of Tehran, Iran, in 2013, and her B.Sc. in Computer Engineering (Software) at Shiraz University, Iran, in 2010. Her doctoral research focused on the application of machine learning and deep learning techniques for emotion recognition and human-robot interaction.
Fatemeh has since been contributing significantly to the field, currently serving as a Postdoc at the Norwegian Institute of Bioeconomy Research (NIBIO) in Ås, Norway. Previously, she held a research position at Kyoto University, Japan, from 2021 to 2022, and was a Research Fellow in Learning Analytics at the University of Tartu, Estonia, in 2020.
She has authored several impactful publications, including articles in IEEE Transactions on Affective Computing and the International Journal of Speech Technology. Her research interests encompass machine learning, affective computing, human-robot interaction, and digital forestry.
Dr. Noroozi's expertise lies in developing systems for multimodal emotion recognition, with a particular focus on facial identification, vocal-based emotion recognition, and gesture-based emotion recognition. Her work has practical applications in fields such as robotics, automation, and environmental technology.
Motion planning algorithms have seen considerable progress and expansion across various domains of science and technology during the last few decades, where rapid advancements in path planning and trajectory optimization approaches have been made possible by the conspicuous enhancements brought, among others, by sampling-based methods and convex optimization strategies. Although they have been investigated from various perspectives in the existing literature, recent developments aimed at integrating robots into social, healthcare, industrial, and educational contexts have attributed greater importance to additional concepts that would allow them to communicate, cooperate, and collaborate with each other, as well as with human beings, in a meaningful and efficient manner. Therefore, in this survey, in addition to a brief overview of some of the essential aspects of motion planning algorithms, a few vital considerations required for assimilating robots into real-world applications, including certain instances of social, urban, and industrial environments, are introduced, followed by a critical discussion of a set of outstanding issues worthy of further investigation and development in future scientific studies.
SFI SmartForest: Bringing Industry 4.0 to the Norwegian forest sector
SmartForest will position the Norwegian forest sector at the forefront of digitalization resulting in large efficiency gains in the forest sector, increased production, reduced environmental impacts, and significant climate benefits. SmartForest will result in a series of innovations and be the catalyst for an internationally competitive forest-tech sector in Norway. The fundamental components for achieving this are in place; a unified and committed forest sector, a leading R&D environment, and a series of progressive data and technology companies.