Digital predistorter design using B-Spline neural network and inverse of De Boor algorithm

This contribution introduces a new digital predistorter to compensate serious distortions caused by memory high power amplifiers (HPAs) which exhibit output saturation characteristics. The proposed design is based on direct learning using a data-driven B-spline Wiener system modeling approach. The nonlinear HPA with memory is first identified based on the B-spline neural network model using the Gauss-Newton algorithm, which incorporates the efficient De Boor algorithm with both B-spline curve and first derivative recursions. The estimated Wiener HPA model is then used to design the Hammerstein predistorter. In particular, the inverse of the amplitude distortion of the HPA's static nonlinearity can be calculated effectively using the Newton-Raphson formula based on the inverse of De Boor algorithm. A major advantage of this approach is that both the Wiener HPA identification and the Hammerstein predistorter inverse can be achieved very efficiently and accurately. Simulation results obtained are presented to demonstrate the effectiveness of this novel digital predistorter design.

The design of UV absorbing systems for horticultural applications

The synthesis and fluorescence behavior of a series of bis(trisilylalkyl)anthracene molecules is described. The photodegradation of these molecules under UV light has been monitored and compared to a commercially available fluorescent optical brightener. There is a relationship between the structure and the rate of photo decay. The materials with more bulky substituents exhibit the greater stability towards UV. For bis(triphenylsilyl)anthracene the photostability appears to be comparable with a commercially available optical brightener, but the molecule may be susceptible to thermal decay.