Effect of wettability and surface roughness on ice-adhesion strength of hydrophilic, hydrophobic and superhydrophobic surfaces

The anti-icing properties of hydrophilic, hydrophobic and superhydrophobic surfaces/coatings were evaluated using a custom-built apparatus based on zero-degree cone test method. The ice-adhesion reduction factor (ARF) of these coatings has been evaluated using bare aluminium alloy as a reference. The wettability of the surfaces was evaluated by measuring water contact angle (WCA) and sliding angle. It was found that the ice-adhesion strength (tau) on silicone based hydrophobic surfaces was similar to 43 times lower than compared to bare polished aluminium alloy indicating excellent anti-icing property of these coatings. Superhydrophobic coatings displayed poor anti-icing property in spite of their high water repellence. Field Emission Scanning Electron Microscope reveal that Silicone based hydrophobic coatings exhibited smooth surface whereas the superhydrophobic coatings had a rough surface consisting of microscale bumps and protrusions superimposed with nanospheres. Both surface roughness and surface energy play a major role on the ice-adhesion strength of the coatings. The 3D surface roughness profiles of the coatings also indicated the same trend of roughness. An attempt is made to correlate the observed ice-adhesion strength of different surfaces with their wettability and surface roughness. (C) 2014 Elsevier B.V. All rights reserved.

A coupled discriminative dictionary and transformation learning approach with applications to cross domain matching

Cross domain and cross-modal matching has many applications in the field of computer vision and pattern recognition. A few examples are heterogeneous face recognition, cross view action recognition, etc. This is a very challenging task since the data in two domains can differ significantly. In this work, we propose a coupled dictionary and transformation learning approach that models the relationship between the data in both domains. The approach learns a pair of transformation matrices that map the data in the two domains in such a manner that they share common sparse representations with respect to their own dictionaries in the transformed space. The dictionaries for the two domains are learnt in a coupled manner with an additional discriminative term to ensure improved recognition performance. The dictionaries and the transformation matrices are jointly updated in an iterative manner. The applicability of the proposed approach is illustrated by evaluating its performance on different challenging tasks: face recognition across pose, illumination and resolution, heterogeneous face recognition and cross view action recognition. Extensive experiments on five datasets namely, CMU-PIE, Multi-PIE, ChokePoint, HFB and IXMAS datasets and comparisons with several state-of-the-art approaches show the effectiveness of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.

A coupled discriminative dictionary and transformation learning approach with applications to cross domain matching

Cross domain and cross-modal matching has many applications in the field of computer vision and pattern recognition. A few examples are heterogeneous face recognition, cross view action recognition, etc. This is a very challenging task since the data in two domains can differ significantly. In this work, we propose a coupled dictionary and transformation learning approach that models the relationship between the data in both domains. The approach learns a pair of transformation matrices that map the data in the two domains in such a manner that they share common sparse representations with respect to their own dictionaries in the transformed space. The dictionaries for the two domains are learnt in a coupled manner with an additional discriminative term to ensure improved recognition performance. The dictionaries and the transformation matrices are jointly updated in an iterative manner. The applicability of the proposed approach is illustrated by evaluating its performance on different challenging tasks: face recognition across pose, illumination and resolution, heterogeneous face recognition and cross view action recognition. Extensive experiments on five datasets namely, CMU-PIE, Multi-PIE, ChokePoint, HFB and IXMAS datasets and comparisons with several state-of-the-art approaches show the effectiveness of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.

Effect of particle size of sand and surface asperities of reinforcement on their interface shear behaviour

This paper investigates the effect of particle size of sand and the surface asperities of reinforcing material on their interlocking mechanism and its influence on the interfacial shear strength under direct sliding condition. Three sands of different sizes with similar morphological characteristics and four different types of reinforcing materials with different surface features were used in this study. Interface direct shear tests on these materials were performed in a specially developed symmetric loading interface direct shear test setup. Morphological characteristics of sand particles were determined from digital image analysis and the surface roughness of the reinforcing materials was measured using an analytical expression developed for this purpose. Interface direct shear tests at three different normal stresses were carried out by shearing the sand on the reinforcing material fixed to a smooth surface. Test results revealed that the peak interfacial friction and dilation angles are hugely dependent upon the interlocking between the sand particles and the asperities of reinforcing material, which in turn depends on the relative size of sand particles and asperities. Asperity ratio (AS/D-50) of interlocking materials, which is defined as the ratio of asperity spacing of the reinforcing material and the mean particle size of sand was found to govern the interfacial shear strength with highest interfacial strength measured when the asperity ratio was equal to one, which represents the closest fitting of sand particles into the asperities. It was also understood that the surface roughness of the reinforcing material influences the shear strength to an extent, the influence being more pronounced in coarser particles. Shear bands in the interface shear tests were analysed through image segmentation technique and it was observed that the ratio of shear band thickness (t) to the median particle size (D-50) was maximum when the AS/D-50 was equal to one. (C) 2015 Elsevier Ltd. All rights reserved.