Ali Tehrani Safa
Amirkabir University of Technology
29 Papers
18 Citations
Ali Tehrani Safa is an academic researcher from Amirkabir University of Technology. The author has contributed to research in topics: Computer science & Gait (human). The author has an hindex of 4, co-authored 12 publications. Previous affiliations of Ali Tehrani Safa include Sharif University of Technology.
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Papers
Passive dynamic of the simplest walking model: Replacing ramps with stairs
TL;DR: The similarities and differences between these two kinds of passive walking are shown to specify the role of surface profile in walking stability.
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How local slopes stabilize passive bipedal locomotion
TL;DR: In this article, a simple model of passive dynamic walking mechanism was employed to extend the boundaries of the maximum stable speed of these autonomous robots by changing their terrain, which is recognized as a general form of a ramp-stair surface.
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A different switching surface stabilizing an existing unstable periodic gait: an analysis based on perturbation theory
TL;DR: In this article, the authors analytically prove that for this type of nonlinear impulsive system, there exist different switching surfaces leading to the same equilibrium points (periodic solutions) with different stabilities.
16
The role of walking surface in enhancing the stability of the simplest passive dynamic biped
Ali Tehrani Safa,Mahyar Naraghi +1 more
TL;DR: Employing passive dynamics of the simplest point-foot walker, it is shown that the walking surface could have a great role in promoting the gait stability, and stabilization of those unstable limit cycles by this technique makes obvious the key role of walking surface on bipedal gait.
13
Open the box of digital neuromorphic processor: Towards effective algorithm-hardware co-design
Guangzhi Tang,Ali Tehrani Safa,K. Shidqi,Paul Detterer,Stefano Traferro,Mario Konijnenburg,Manolis Sifalakis,Gert-Jan van Schaik,Amirreza Yousefzadeh +8 more
- 27 Mar 2023
TL;DR: In this paper , the authors presented the neuron processing instruction set and detailed energy consumption of the SENeCA neuromorphic architecture for benchmarking and optimization of spiking neural network (SNN) algorithms.
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