Unravelling the atomic structure of cross-linked (1 x 2) TiO2(110)

Pieper HH, Venkataramani K, Torbrügge S, et al. Unravelling the atomic structure of cross-linked (1 x 2) TiO2(110). Physical Chemistry Chemical Physics. 2010;12(39):12436-12441.The cross-linked (1 x 2) reconstruction of TiO2(110) is a frequently observed phase reflecting the surface structure of titania in a significantly reduced state. Here we resolve the atomic scale structure of the cross-linked (1 x 2) phase with dynamic scanning force microscopy operated in the non-contact mode (NC-AFM). From an analysis of the atomic-scale contrast patterns of the titanium and oxygen sub-structures obtained by imaging the surface with AFM tips having different tip apex termination, we infer the hitherto most accurate model of the atomic structure of the cross-linked (1 x 2) phase. Our findings suggest that the reconstruction is based on added rows in [001] direction built up of Ti3O6 units with an uninterrupted central string of oxygen atoms accompanied by a regular sequence of cross-links consisting of linear triples of additional oxygen atoms in between the rows. The new insight obtained from NC-AFM solves previous controversy about the cross-linked TiO2(110) surface structure, since previously proposed models based on cross-links with a lower O content do not appear to be consistent with the atom-resolved data presented here. Instead, our measurements strongly support the Ti3O6 motif to be the structural base of the cross-linked (1 x 2) reconstruction of TiO2(110)

Soft information fusion of correlation filter output planes using Support Vector Machines for improved fingerprint verification performance

Reliable verification and identification can be achieved by fusing hard and soft information from multiple classifiers. Correlation filter based classifiers have shown good performance in biometric verification applications. In this paper, we develop a method of fusing soft information from multiple correlation filters. Usually, correlation filters are designed to produce a strong peak in the correlation filter output for authentics whereas no such peak should be produced for impostors. Traditionally, the peak-to-sidelobe-ratio (PSR) has been used to characterize the strength of the peak and thresholds are set on the PSR in order to determine whether the test image is an authentic or an impostor. In this paper, we propose to fuse multiple correlation output planes, by appending them for classification by a Support Vector Machine (SVM), to improve the performance over traditional PSR based classification. Multiple Unconstrained Optimal Tradeoff Synthetic Discriminant Function (UOTSDF) filters having varying degrees of discrimination and distortion tolerance are employed here to create a feature vector for classification by a SVM, and this idea is evaluated on the plastic distortion set of the NIST 24 fingerprint database. Results on this database provide an Equal Error Rate (EER) of 1.36% when we fuse correlation planes, in comparison to an average EER of 3.24% using the traditional PSR based classification from a filter, and 2.4% EER on fusion of PSR scores from the same filters using SVM, which demonstrates the advantages of fusing the correlation output planes over the fusion of just the peak-to-sidelobe-ratios (PSRs)