Mahdi Hasanzadeh
Papers
1
Total Citations
3
H-Index
1
About
Mahdi Hasanzadeh is a researcher at the intersection of tactile sensing, deep learning, and intelligent systems. His work centers on advancing texture classification—a critical capability for applications in robotics, product design, and autonomous surface exploration. Hasanzadeh’s most notable contribution is his development of a semisupervised approach using contextually guided convolutional neural networks (CNNs) for tactile texture recognition. This method leverages accelerometer-based sensors and deep learning to identify key surface features without requiring precise replication of human touch, addressing a longstanding challenge in haptic perception. His 2023 paper on this topic has already garnered citations, reflecting its relevance to the growing field of tactile sensing. By reducing the need for large labeled datasets, his approach makes texture classification more practical for real-world deployment. Hasanzadeh’s work bridges the gap between sensor technology and machine learning, offering scalable solutions for robots and automated systems that must interact with diverse surfaces. His research continues to influence how machines perceive and interpret physical environments.
Research Focus
Key Achievements
Top Papers
- 1