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A four-parameter Weibull probability distribution model whose structural form is developed using a quadratic transformation of linear random variables is presented.
Using a quadratic transformation in conjunction with a nine point Gauss quadrature method, a minimum of 4th order accuracy is achieved for fully general solutions and shock shapes.
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By means of the Finsler's Lemma or a Tustin-like transformation, the progressively complex observer gains and the Lyapunov function are decoupled, thus providing the flexibility of using a quadratic Lyapunov functions while preserving the non-quadratic nature of the observer.
The quality of estimation is validated using a quadratic criterion.
The final estimates are obtained using a quadratic interpolation.
The results were analyzed using a quadratic loglog curve fit.
The quadratic trends of each value across sleep duration categories were tested using a quadratic regression analysis.
The possibility of a local maximum was tested using a quadratic term for year.
We adjusted for maternal age as a continuous variable using a quadratic polynomial function.
The results were analysed using a quadratic or 4PL algorithm curve fit.
The registration between the consecutive IR frames can be achieved using a quadratic motion model.
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