Particle morphological features at different scale levels hold the key to understanding the geological origin and mechanical behaviour of natural sands. In this context, it is necessary to characterise and quantify these morphological features by defining a series of reasonable descriptors. In this study, based on X-ray micro-computed tomographic (μCT) images collected from a series of image-processing techniques, the authors first introduced spherical harmonic analysis to reconstruct a three-dimensional (3D) realistic surface of the sand particles. Then 3D sphericity, roundness and fractal dimension were introduced to define the global form, local features and surface textures of the particle morphology. Based on the spherical harmonic-reconstructed surface, a novel framework was established to measure the descriptors of 3D sphericity, roundness and fractal dimension of sand particles. The 3D fractal dimension was an original descriptor used to characterise the fractal nature of the surface textures of real sand particle morphology. By using the proposed methods, these morphological descriptors were measured for two types of natural sand particle. The statistical results show clear correlations between different descriptors at different characteristic scales. The correlation relies heavily upon the distance between the characteristic scales of the morphological descriptors.

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This paper proposes a robust model for the accurate behavioral modeling and digital predistortion (DPD) of wideband radio-frequency power amplifiers (PAs). It is constructed using a complexity-reduced generalized memory polynomial (MP) (GMP) (CR-GMP) connected with a nonlinear memory effect (NME) subblock in parallel. The CR-GMP is a complexity-reduced but accuracy-degraded version of the conventional GMP, and its performance is augmented by the extra NME subblock. Hence, the proposed model is termed as augmented CR-GMP (ACR-GMP). The resultant ACR-GMP model can achieve comparable performance as the GMP model, but with much fewer coefficients and lower complexity. Its performance is experimentally assessed both in forward modeling and DPD linearization. Comparisons are conducted between the ACR-GMP model and some state-of-the-art models, such as the MP, the PLUME, and the GMP. Experimental results have been given for a 1.9-GHz 35-W peak-power GaN Class-AB PA driven by two signal scenarios: a 15-MHz bandwidth long-term-evolution signal and a 20-MHz bandwidth widebandcode-division-multiple-access 1001 signal (with the middle two carriers OFF). All the results show that the ACR-GMP model outperforms both the MP and the PLUME models in terms of performances and the GMP model in terms of complexity (at comparable performances).Index Terms-Behavioral modeling, digital predistortion (DPD), generalized memory polynomial (MP) (GMP), memory effect, MP, power amplifiers (PAs).

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