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Our segmentation method is an extension of standard motion detection methods with adaptive background modeling.
The proposed gradient systems can be implemented as simple analog circuits as well as using standard software for integration of ODEs, and in order to use efficient integration methods with adaptive stepsize selection, the discontinuous terms are smoothed around a neighborhood of the discontinuity surface by means of the boundary layer technique.
Altogether, our results suggest that complementing model fitting methods with adaptive landscape perspectives provides a richer, more informative understanding of clade-wide divergence patterns and their possible underlying evolutionary processes.
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linearized alternating direction method with adaptive penalty.
They also consider orthogonal matching pursuit and an Iterative Method with Adaptive Thresholding for channel estimation.
To solve the VI problem, an extragradient method with adaptive stepsizes is developed.
The resulting nonlinear two-point boundary value problem is solved by Newton's method with adaptive gridding techniques.
Comparisons with results obtained via a sixth-order Runge Kutta method with adaptive time step-sizes are also provided.
For the numerical solution we propose the Rothe method with adaptive time integration performed by Rosenbrock schemes.
We also investigate the performance of the recovery methods such as orthogonal matching pursuit (OMP) and iterative method with adaptive thresholding (IMAT) for estimation of the channel taps.
In this section, we propose an iterative method with adaptive thresholding (IMAT) [10] for the purpose of estimating the sparse CIR.
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