Organizer: Professor Faruk Özger, Department of Computer Engineering, Iğdır University, Türkiye
Email: farukozger@gmail.com
Symposium Description
This symposium focuses on mathematical and computational methods—based on approximation operators, Bézier-based geometric modeling, and robust control theory—that aim to improve the reliability and performance of complex engineering and scientific systems. The central goal is to bring together researchers working on techniques that make such systems more accurate, stable, noise-resistant, and interpretable.
We welcome contributions that develop new operator-theoretic models, geometric modeling tools, numerical algorithms, and control strategies with strong theoretical foundations and demonstrated practical value. Submissions addressing real-world challenges such as noise, uncertainty, incomplete data, model mismatch, and computational constraints are particularly encouraged.
Applications may include image and signal processing, biomedical analysis, shape modeling, robotics, autonomous systems, and other areas requiring precise modeling and control.
Key Themes
1. Operator-Based Approximation and Control
- Development and theoretical analysis of generalized approximation operators (Bernstein, Kantorovich, Stancu, Lupaş-type, (p,q)-extensions).
- Operator-based methods for designing noise-tolerant controllers, improving state estimation, and solving ordinary, integral, and fractional differential equations.
- Error bounds, convergence results, and stability analyses enabling reliable deployment in complex systems.
2. Geometric Modeling and Bézier Representations for Data Robustness
- Construction of generalized geometric and Bézier-based frameworks for accurate representation of shapes, boundaries, and surfaces.
- Robust geometric modeling and reconstruction under noise, missing data, and irregular structures, including biomedical images and computational geometry settings.
- Applications in medical imaging, computer graphics, computational anatomy, and any field requiring precise geometric representation.
3. Robust Control Systems and Intelligent Mechatronics
- Design of advanced controllers and observers for robotic manipulators, autonomous systems, and mechatronic platforms.
- Integration of approximation operators into control design to smooth control signals, reduce noise sensitivity, and guarantee stability.
- Low-complexity, real-time implementations for disturbance rejection, trajectory tracking, and nonlinear system stabilization.
4. Interpretable and Lightweight Algorithms
- Development of numerically stable, interpretable, and linear-time algorithms that complement data-driven methods.
- Use of Bézier curves, Bézier surfaces, and operator-based models to create compact and explainable representations of structures and trajectories.
- Hybrid approaches combining classical mathematical modeling with AI methods (CNNs, Vision Transformers) to achieve domain-shift robustness, improved generalization, and reduced computational cost.