A new technique for predicting anomalies in the near future of an observed signal is being presented. Before any data analysis can be performed on an observed signal, the signal's underlying pattern must be cleared. A wavelet de-noising scheme is used because it provides a better result compared to other de-noising algorithms and it is simple from a computational standpoint. Robust peak-finding...

Functional Near-Infrared Spectroscopy (fNIRS) is a promising method to study functional organization of the prefrontal cortex. However, in order to realize the high potential of fNIRS, effective discrimination between physiological noise originating from forehead skin haemodynamic and cerebral signals is required. Main sources of physiological noise are global and local blood flow regulation pr...

SUMMARY Pyrosequencing of 16S rDNA is widely used to study microbial communities, and a rich set of software tools support this analysis. Pyrosequencing of protein-coding genes, which can help elucidate functional differences among microbial communities, significantly lags behind 16S rDNA in availability of sequence analysis software. In both settings, frequent homopolymer read errors inflate t...

Most common method of clearing image noise is through median filtration. Median smoothers discard outliers (or impulses) quite effectively at lower noise densities but, fail to provide adequate smoothing for heavily noised images for the simple reason that, in the conventional median filter approaches, each and every pixel of an image is filtered without concern about healthy pixels. To suppres...

Active infrared thermography is an attractive and highly reliable technique used for the non-destructive evaluation of test objects. In this paper, defect detection on subsurface STS304 metal specimen was performed by applying line-scanning method to induction thermography. general, camera are fixed in thermography, but can excite a uniform heat source because relative movement occurs. After th...

The fundamental problem of de-noising and de-blurring images is addressed in this study. The great difficulty in this task is due to the ill-posedness of the problem. We suggest to analyze multi-channel images to gain robustness and to regularize it by the Polyakov action which provides an anisotropic smoothing term that use intra-channel information. Blind de-convolution is then solved by addi...

Medical imaging technology is becoming an important component of large number of applications such as diagnosis, research, and treatment. Medical images like X-Ray, CT, MRI, PET and SPECT have minute information about heart brain and nerves. These images need to be accurate and free from noise. Noise reduction plays an important role in medical imaging. Various methods of noise removal such as:...

Data noising is an effective technique for regularizing neural network models. While noising is widely adopted in application domains such as vision and speech, commonly used noising primitives have not been developed for discrete sequencelevel settings such as language modeling. In this paper, we derive a connection between input noising in neural network language models and smoothing in ngram...