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where J φ i 1 = ( I + ∂ φ i ) − 1, i = 1, 2, is the resolvent operator, ρ, η > 0, α n ∈ [ 0, 1 ] and β n ∈ [ 0, 1 ] for all n ≥ 0. As reported in [12], one of the attractive features of Algorithm 2.1 is that it is suitable for implementing on two different processor computers.

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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.

The heterogeneous ideal adsorption solution (HIAS) model implemented on two patches qualitatively predicts these highly unusual behavior.

The learning framework is applied to the hyperspectral image classification, and some experiments are implemented on two hyperspectral image databases.

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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