Jordi Riera-Babures

About

Jordi Riera-Babures is a researcher specializing in real-time image processing and embedded vision systems, with a particular focus on Cellular Neural Networks (CNNs) and their FPGA implementations. His work bridges the gap between high-performance computer vision and low-power hardware design, making significant contributions to mobile robotics and autonomous navigation. His most influential paper, "Real time vision by FPGA implemented CNNs" (2006, 7 citations), demonstrated a discrete-time CNN architecture on Altera FPGAs that achieved ninefold faster processing than competing emulations—a breakthrough for real-time robot vision. He further advanced this field with his 2011 study on low-power DT-CNN camera devices using Actel IGLOO FPGAs (5 citations), which balanced performance and energy efficiency without costly components. Riera-Babures also explored practical applications, such as obstacle avoidance algorithms for uncertain environments (2010, 2 citations), integrating both high-speed CMOS cameras and ultrasonic sensors. His work on optimized CNN universal machine emulation (2007, 3 citations) provided a scalable framework for parallel convolution operations. While his citation counts reflect a niche but impactful research area, his contributions are foundational for engineers developing efficient, real-time vision systems for resource-constrained platforms.

Research Focus

Key Achievements

Top Papers

1. 1
   Real time vision by FPGA implemented CNNs

   7 citations · 2006
2. 2
   High performance DT-CNN camera device design on ACTEL IGLOO low power FPGA

   5 citations · 2011
3. 3
   Optimized cellular neural network universal machine emulation on FPGA

   3 citations · 2007
4. 4
   Robot guiding with obstacle avoidance algorithm for uncertain enviroments based on DTCNN

   2 citations · 2010

Key Collaborators

Contact & Links