Maryam Ranjbar

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About

Maryam Ranjbar is a researcher whose work sits at the intersection of computer vision and energy-efficient hardware design, with a particular focus on edge detection for robotic systems. Her most-cited paper, “Using stochastic architectures for edge detection algorithms” (2015, 11 citations), addresses a critical challenge in autonomous robotics: performing complex image preprocessing under severe power constraints. Ranjbar’s key contribution lies in demonstrating how stochastic computing architectures can implement computationally intensive edge detection algorithms—essential for reliable object detection—while dramatically reducing sensitivity to circuit noise and power consumption. This work is especially relevant for mobile and embedded robotic platforms where traditional deterministic circuits are impractical. By rethinking the hardware-software interface for low-level vision tasks, Ranjbar has opened a pathway toward more robust, energy-efficient perception systems. Though her citation count is modest, the precision of her contribution—solving a specific bottleneck in real-world robotic vision—marks her as a thoughtful engineer focused on practical, deployable solutions. Her research continues to inspire those working at the crossroads of algorithm design and hardware implementation.