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The learning framework is applied to the hyperspectral image classification, and some experiments are implemented on two hyperspectral image databases.
Since the discretizations of the density field and the displacement field are implemented on two independent sets of points, the method is well suited for a topology optimization problem with a design domain containing higher-order elements or non-quadrilateral elements.
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To prove the effectiveness of our method for uneven lighting images with strong noise injection, experimental tests are implemented on six uneven illumination images corrupted by the Gaussian noise.
This algorithm is implemented on two robot systems: one with a fixed base and another with a mobile base.
The proposed algorithm is implemented on two simulation scenarios, one of them involving global knowledge of the environment, and the other based on local knowledge of the environment.
The algorithm was implemented on two different voice coil-based positioning systems: one already presented in the literature recently[5] and one completely new, trying to get optimum speed and performance of the presented approach.
The software for the kart is implemented on two Texas Instruments microcontrollers.
It is implemented on two different applications, namely machine tool monitoring and gearbox monitoring.
The method has been implemented on two images taken by the TM sensor.
These strategies have been implemented on two holonomic mobile platforms designed and built at Stanford in collaboration with Oak Ridge National Laboratories and Nomadic Technologies.
SoPHy+ has been implemented on two different many-core architectures: the Intel Xeon Phi coprocessor and an Epiphany-like NoC virtual prototype.
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