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Exact(14)
We are interested here in the accuracy of this approximation.
The accuracy of this approximation is evaluated in section 3.1 using measurement data.
Note, however, that the accuracy of this approximation is lower than that for the acceleration.
Of course, the accuracy of this approximation will depend on the quality of the camera position estimates.
We will validate the excellent accuracy of this approximation for coexistence performance analysis later in Section 6.
To measure the accuracy of this approximation, we usually say that approximation (2.3) is better as r 1 ( n ) − γ faster converges to zero.
Similar(46)
The discrete integral is then calculated applying Simpson's trapezoidal rule: (8) L M = ∑ i = 2 C 1 2 β i - β i - 1 (U β i + U β i - 1 ) The accuracy of this discrete approximation of (9) depends on the number of classes C that can represent a limiting factor since the computational time increases linearly with the number of categories.
The effectiveness of this strategy depends largely on the accuracy of the approximation model.
Obviously, this does not bode well for the accuracy of the approximation of the distribution and the performance of ensuing algorithms.
This implies the RANC response approximates the length of the interval, with the accuracy of the approximation depending on how close neuron response functions are to being linear.
The aim is to improve the accuracy of the approximation.
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