The Kabuki (Niikawa-Kuroki) syndrome was reported in 1981 by Niikawa et al. and Kuroki et al. in a total of ten unrelated Japanese children with a characteristic array of multiple congenital anomalies and mental retardation. The syndrome is characterized by a distinct face, mild to moderate mental retardation, postnatal growth retardation, dermatoglyphic and skeletal abnormalities. In Japan, the syndrome appears to have an incidence of about 1:32,000 newborns. Outside of Japan, a growing number of patients have been recognized. Clinical data are presented on 29 Caucasian patients; the patients were diagnosed over a relatively short period of time, indicating that the incidence outside of Japan is probably not lower than in Japan. A literature review of 89 patients (60 Japanese and 29 non-Japanese) is given. In 66% of the non-Japanese patients serious neurological problems were present, most notably hypotonia and feeding problems (which were not only related to the cleft palate); this was not reported in the Japanese patients. Inheritance is not clear. Most patients are isolated, sex-ratio is equal. The syndrome can be recognized in patients with cleft (lip/)palate, with mild to moderate developmental delay and in young children with hypotonia and/or feeding problems. In counselling parents, the designation "Kabuki" syndrome seems to be more appropriate than "Kabuki make-up" syndrome.

This paper presents a spatial noise reduction technique designed to work on CFA (Color Filtering Array) data acquired by CCD/CMOS image sensors. The overall processing preserves image details using some heuristics related to the HVS (Human Visual System); estimates of local texture degree and noise levels are computed to regulate the filter smoothing capability. Experimental results confirm the effectiveness of the proposed technique. The method is also suitable for implementation in low power mobile devices with imaging capabilities such as camera phones and PDAs.

Accurate noise level estimation is essential to assure good performance of noise reduction filters. Noise contaminating raw images is typically modeled as additive white and Gaussian distributed (AWGN); however raw images are affected by a mixture of noise sources that overlap according to a signal dependent noise model. Hence, the assumption of constant noise level through all the dynamic range represents a simplification that does not allow precise sensor noise characterization and filtering; consequently, local noise standard deviation depends on signal levels measured at each location of the CFA (Color Filter Array) image.This work proposes a method for determining the noise curves that map each CFA signal intensity to its corresponding noise level, without the need of a controlled test environment and specific test patterns. The process consists in analyzing sets of heterogeneous raw CFA images, allowing noise characterization of any image sensor. In addition we show how the estimated noise level curves can be exploited to filter a CFA image, using an adaptive signal dependent Gaussian filter.

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The production process of a wafer in the semiconductor industry consists of several phases such as a diffusion and associated defectivity test, parametric test, electrical wafer sort test, assembly and associated defectivity tests, final test, and burn-in. Among these, the fault detection phase is critical to maintain the low number and the impact of anomalies that eventually result in a yield loss. The understanding and discovery of the causes of yield detractors is a complex procedure of root-cause analysis. Many parameters are tracked for fault detection, including pressure, voltage, power, or valve status. In the majority of the cases, a fault is due to a combination of two or more parameters, whose values apparently stay within the designed and checked control limits. In this work, we propose an ensembled anomaly detector which combines together univariate and multivariate analyses of the fault detection tracked parameters. The ensemble is based on three proposed and compared balancing strategies. The experimental phase is conducted on two real datasets that have been gathered in the semiconductor industry and made publicly available. The experimental validation, also conducted to compare our proposal with other traditional anomaly detection techniques, is promising in detecting anomalies retaining high recall with a low number of false alarms.