Jonas Vlasselaer
Papers
1
Total Citations
8
H-Index
1
About
Jonas Vlasselaer is a researcher focused on energy-efficient embedded systems, particularly at the intersection of sensing, machine learning, and hardware design. His work addresses a critical challenge in portable and always-on applications—how to sustain continuous sensing for tasks like human activity recognition and robot navigation without draining device batteries. Vlasselaer’s major contribution lies in developing dynamic sensor-frontend tuning techniques that adapt sensor configurations in real time, dramatically reducing power consumption while maintaining classification accuracy. His most-cited paper, "Dynamic Sensor-Frontend Tuning for Resource Efficient Embedded Classification" (2018), has garnered 8 citations and lays the groundwork for smarter, more autonomous edge devices. By enabling systems to sense less without compromising performance, Vlasselaer’s research directly impacts the viability of next-generation wearables, IoT nodes, and mobile robots. His work represents a key step toward truly energy-proportional computing, where hardware resources are matched precisely to the task at hand. For students and researchers interested in low-power design, embedded AI, or sensor systems, Vlasselaer offers a compelling model of how algorithmic and hardware co-design can unlock new capabilities in resource-constrained environments.
Research Focus
Key Achievements
Top Papers
- 1