Noise level estimation is a challenging area of digital image processing with a variety of applications, including image enhancement, image segmentation, and feature extraction. In this paper, an adaptive estimation of additive white Gaussian noise level based on the singular value decomposition (SVD) of images is proposed. The proposed algorithm aims to improve the performance of noise level estimation in the SVD domain at low noise levels. An initial noise level estimate is used to adjust the parameters of the algorithm in order to increase the accuracy of noise level estimation. The proposed algorithm exhibits the ability to adapt the number of considered singular values and to accordingly adjust the slope of a linear function that describes how the average value of the singular value tail varies with noise levels. Although, for each image, the proposed algorithm performs the noise level estimation twice in two distinct stages, the singular value decompositions are only performed in the first stage of the algorithm. The experimental results demonstrate that the proposed algorithm improves the noise level estimation at low noise levels without a significant increase in computational complexity. At noise level $\sigma = 15$ , the improvements in the mean square level are about 39% at the expense of slightly higher additional computational time.

This article presents a review of the investigation of the possibility of increasing the efficiency of existing line test solutions for troubleshooting testing for IPTV over xDSL, by the results of experimental research on real system under commercial exploitation. At the beginning of this article the main weaknesses of the existing troubleshooting testing are described. In the continuation of the article the physical layer parameters of xDSL transceiver are listed. In the reset this article provides a few specific examples of xDSL lines with their physical layer parameters of xDSL transceivers followed by analysis how they can be used for the purposes of more efficient measurement of parameters of copper pairs.

Parameterization and synthesis of electrocardiogram (ECG) recordings are some of the most challenging problems in biomedical signal processing owing to the fact that ECG signals commonly exhibit complex temporal morphology and contain numerous artifacts of data collection process. In this paper, we present a fully automatic framework for accurate and robust parameterization and reconstruction of ECG waveforms. The method uses the observed signal to ascertain a nondeterministic model for the ECG signal and employs the Dynamic Time warping (DTW) algorithm to determine a non-linear temporal relationship between the established ECG model and the individual pulses in the ECG signal. The results of parameterization provide a set of data that accurately describe the morphology of the ECG pulses. The proposed signal synthesis algorithm is able to independently account for the temporal and spatial dynamics of consecutive ECG pulses and provide a faithful reconstruction of ECG signals. Performance evaluation experiments are conducted on a database of 135 one-minute ECG recordings. The percentage rootmean-square difference measure is employed to evaluate the quality of signal reconstruction and also validate the results of signal parameterization.

This paper presents a comparative study of the temporal structure of the glottal flow derivative signal associated with laryngeal pathology in relation to an idealized view of voice source realizations as defined by LiljencrantsFant’s model. Specifically, we endeavor to ascertain the extent by which Liljencrants-Fant’s model can be used to represent the glottal flow derivative estimates obtained via closed-phase pitch synchronous inverse filtering of recorded speech. The results obtained on six common voice pathology examples show that due to the limited degrees of freedom, Liljencrants-Fant’s model is only capable of adequately representing the “coarse” glottal pulse structure. Our findings demonstrate that the “fine” structural elements constitute an important aspect of a glottal flow derivative realization and we have presented evidence that they contain information related to voice individuality. A further inadequacy of Liljencrants-Fant’s model is that its parameters do not always accurately portray significant events in the vocal fold dynamics.