Interfacial surface roughness determination by coherence scanning interferometry using noise compensation.

The capability of coherence scanning interferometry has been extended recently to include the determination of the interfacial surface roughness between a thin film and a substrate when the surface perturbations are less than ∼10 nm in magnitude. The technique relies on introducing a first-order approximation to the helical complex field (HCF) function. This approximation of the HCF function enables a least-squares optimization to be carried out in every pixel of the scanned area to determine the heights of the substrate and/or the film layers in a multilayer stack. The method is fast but its implementation assumes that the noise variance in the frequency domain is statistically the same over the scanned area of the sample. This results in reconstructed surfaces that contain statistical fluctuations. In this paper we present an alternative least-squares optimization method, which takes into account the distribution of the noise variance-covariance in the frequency domain. The method is tested using results from a simulator and these show a significant improvement in the quality of the reconstructed surfaces.

Comparison of salivary hemoglobin measurements for periodontitis screening.

Salivary hemoglobin (Hb) for screening of periodontitis is approved under the pharmaceutical affairs law of Japan. Two reagents are commercially available for the modified fecal occult blood test: Saliva Hemo Plus and OC-AUTO S Latex Reagent. We simultaneously measured split specimens from 561 samples by using these two methods and compared the differences and agreement between both methods. Moreover, saliva samples were collected from 10 subjects at five time points during the day for analysis of circadian variations and fluctuation. The Pearson's correlation coefficient for these two reagents was 0.794. The Bland-Altman plot of differences in salivary Hb levels measured by the two reagents indicated that the difference included fixed errors (0.55 µg/mL). On analysis of circadian variations, no statistically significant differences were observed using the Friedman test. However, fixed errors were observed between wake-up time and before dinner and before lunch and before dinner, and no random errors were observed by Bland-Altman analysis. In conclusion, the salivary Hb levels measured using OC-AUTO S Latex Reagent were lower than those measured using Saliva Hemo Plus, along with a tendency for higher levels in the morning. Thus, when performing salivary tests these observations must be considered.

Kernel Wiener filter and its application to pattern recognition.

The Wiener filter (WF) is widely used for inverse problems. From an observed signal, it provides the best estimated signal with respect to the squared error averaged over the original and the observed signals among linear operators. The kernel WF (KWF), extended directly from WF, has a problem that an additive noise has to be handled by samples. Since the computational complexity of kernel methods depends on the number of samples, a huge computational cost is necessary for the case. By using the first-order approximation of kernel functions, we realize KWF that can handle such a noise not by samples but as a random variable. We also propose the error estimation method for kernel filters by using the approximations. In order to show the advantages of the proposed methods, we conducted the experiments to denoise images and estimate errors. We also apply KWF to classification since KWF can provide an approximated result of the maximum a posteriori classifier that provides the best recognition accuracy. The noise term in the criterion can be used for the classification in the presence of noise or a new regularization to suppress changes in the input space, whereas the ordinary regularization for the kernel method suppresses changes in the feature space. In order to show the advantages of the proposed methods, we conducted experiments of binary and multiclass classifications and classification in the presence of noise.