Vladimir Risojević

Professor Vladimir Risojević is a full professor at the Department of General Electrical Engineering, Faculty of Electrical Engineering, University of Banja Luka, Republic of Srpska, Bosnia and Herzegovina. With a prolific research career spanning over two decades, he has established himself as a leading expert in remote sensing, machine learning, and biohybrid systems. His work bridges theoretical advancements with practical applications in environmental monitoring, energy systems, and security technologies, with numerous publications in high-impact journals and conferences.

Professor Risojević's research focuses primarily on remote sensing image classification, where he has made significant contributions to understanding the role of pre-training in specialized applications. His work in machine learning spans self-supervised learning, contrastive multiview coding, and efficient neural network architectures. Most notably, his pioneering research in biohybrid systems has developed innovative methods using honeybees as biosensors for landmine and explosive detection, creating a unique intersection between biology and engineering that has received international recognition. His research consistently demonstrates a commitment to solving real-world problems with practical engineering solutions.

Analysis of his recent publications reveals a strategic expansion from his core expertise in remote sensing into complementary domains including energy systems (solar irradiance modeling and Li-ion battery monitoring), 3D human body modeling, and novel neural network architectures. A unifying theme throughout his work is the pursuit of computational efficiency, with multiple publications focusing on approximate computing techniques to make AI systems more energy-efficient for deployment in resource-constrained environments. His research demonstrates both depth in specialized areas and breadth across multiple engineering disciplines.

Professor Risojević is actively involved in numerous significant research projects including:

• NATO Science for Peace and Security project valued at €300,415 on 'Biological Methods (Bees) for Explosive Detection'
• 'Obrada signala primjenom ugradjenih racunarskih sistema i masinskog ucenja' (Signal Processing using Embedded Computer Systems and Machine Learning)
• 'Masinsko ucenje u rubnom racunarstvu' (Machine Learning in Edge Computing)
• 'Elektronski sistem za daljinsko pracenje i analizu uticaja parametara zivotne sredine na aktivnost pcela' (Electronic System for Remote Monitoring of Environmental Parameters on Bee Activity)

His laboratory has developed specialized video analysis systems for bee monitoring, with multiple publications detailing techniques for tracking bee activity, detecting pollen-bearing bees, and creating integrated sensor platforms for remote bee yard monitoring. This work has positioned him as a leader in applying computer vision techniques to biological monitoring systems with applications in both environmental science and security technologies.