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Although inspired by the "universal jamming gripper" created in the lab of Hod Lipson, associate professor of mechanical engineering and computer science, the new method is "hardware agnostic," the researchers said, and will work with any type of robot gripper.
This method is hardware method.
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This method is suitable for hardware implementation to be used in a fast and online drowsiness detection system.
The method is inspired by hardware-oriented approaches with their component libraries, but is more general.
The pairing can be computed very efficiently on hyperelliptic curves of genus 2. In this paper it is demonstrated that the ηT method is ideally suited for hardware implementation since much of the more intensive arithmetic can be performed in parallel in hardware.
The same method is used to synchronize hardware and software blocks within the PTOLEMY-based co-simulation environment.
Considering the nonlinear character of IRFPA, the calibration-based polynomial NUC method is proposed in the hardware system.
The core of the proposed adaptive method is the implementation in hardware of an adaptive behavioral reference model used by the controller, able to track the sensed changes in dynamics.
On average, the DRR reconstruction efficiency using the hardware method is improved by a factor of 67 over the software method.
The simplest method is the use of hardware divider but they are computationally expensive and consume a considerable amount of resources.
With video simulations it was furthermore shown that the limitations of the new method were mainly hardware-related and could be overcome by implementing more advanced camera technology in SDF imaging (that is, higher spatial and temporal resolution).
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