Cross-correlation Ratio Research Articles

3D Printing and Rapid Replication of Advanced Numerically Generated Rough Surface Topographies in Numerous Polymers

An approach to rapidly produce high‐quality polymer surface topographies from numerically generated surfaces is presented. The approach uses an advanced surface generation tool to flexibly design surfaces with user‐defined topography characteristics over a large range of surface roughness. Roughness instances with root mean square roughness 25, 50, and 100 μm are studied. 3D printing is used to create a master surface and polymer casting and injection molding are employed to enable rapid replication in various polymers. The cross‐correlation ratio (CCR) and a mean difference approach were used to assess replication quality. Injection molding provides high throughput with high replication quality up to CCR ≈ 0.74. While casting in low‐viscosity polymer resins enables slightly improved high‐quality replication (CCR up to ≈0.82) with reduced throughput. Key results include the ability of the 3D‐printed surfaces to replicate tailored variations in surface topography (e.g., amplitude and frequency) and the importance of low viscosity resins in maximizing replication quality in polymer casting. Several interfacial and surface phenomena (both mechanical and biological) are sensitive to surface roughness. The main application lies in providing a valuable tool for research looking at topography influencing phenomena ranging from friction and lubrication to aerodynamic drag, algae growth, and cell growth.

Intercomparison of various SA techniques on wind estimation using multi-receiver phased array radar

In this paper we have applied various spaced antenna (SA) techniques to derive horizontal winds with Middle and Upper (MU) atmospheric radar observations. SA techniques comprise different analysis methods like Full Correlation Analysis (FCA), Cross Correlation Ratio (CCR), Slope at Zero lag (SZL) and Intersection (INT) methods etc. Among these methods, FCA is considered one of the classical approaches. In the present study, the MU radar antenna array is regrouped (in software) into three different antenna segments, each segment consists of 8 channels and forming an equilateral triangle. Grouping of antenna has been done with 4 different orientations by interchanging the channels. FCA wind has been estimated with an equilateral triangle grouping of antenna array in all orientations. With the same grouping in different orientations, winds are estimated with CCR, SZL and INT methods. Winds estimated with different orientation are averaged to obtain the mean winds component. In most of studies reported earlier using CCR, SZL and INT methods, winds have been estimated with pair of antenna having baseline parallel to the antenna array plane. We have retrieved the zonal wind with a pair of antennas having baseline parallel to the antenna array plane in east–west direction and meridional wind is estimated with a pair of antennas having baseline parallel to the antenna array plane in north–south direction. The mean winds obtained with various SA techniques are compared with that observed by the Doppler Beam swinging (DBS) technique and GPS-sonde. It is observed that mean winds obtained with the approach adopted using different spatially distributed array grouping has yielded higher height coverage up to ~20km and in good agreement with the results obtained using DBS observations even with a temporal resolution of ~1.3min in clear air condition. Various statistical analyses like correlation analysis, standard deviation, root mean square error (RMSE) and error analysis have been performed to understand the performance of other techniques in comparison with FCA. The analysis is carried out for an observation of 6h for two different observation periods.

Cross‐correlation ratio method to estimate cross‐beam wind and comparison with a full correlation analysis

Cross‐beam wind is usually estimated using a full correlation analysis (FCA) method applied to signals from spaced antennas. In this paper we present a cross‐correlation ratio (CCR) method for wind measurements. The CCR method is illustrated using theory, and data obtained with the National Center for Atmospheric Research's multiple antenna profiling radar. The standard errors of estimated cross‐beam wind using CCR and a FCA are studied based on a rigorous analysis of the variance of the cross‐correlation estimates. The results of the analysis are compared with previous works. It is shown that the current method is easy to implement and has smaller error for receiving antenna spacing small compared to the transmitting antenna dimensions.