This paper considers calculation methods for the electric field intensity and magnetic flux density in the vicinity of the overhead transmission lines, as well as the calculation of alternating current (AC) corona onset electric field intensity. Calculations within this paper are made using the 2D algorithms of Charge Simulation Method (CSM) and Biot-Savart (BS) law based method. In order to obtain more accurate results, calculations are made by representing each overhead transmission line conductor with a large number of electric and magnetic field sources. By applying this approach, bundle conductors can be represented in a more realistic way and also singularity problems can be avoided when calculating electric field intensity. The presented methods are applied to a real overhead transmission line configuration, and obtained results are compared with field measurement results over the lateral profile. For considered overhead transmission line, AC corona onset electric field intensity is calculated and compared with calculated electric field intensity on the conductor’s surface. A comparison of calculated and measured results shows that considered calculation methods give satisfactory results.

In the fields of computer vision and digital image processing, image segmentation denotes a process whereby an image is segmented into multiple regions. Image segmentation based on multilevel thresholding has received significant attention in recent literature. In this paper, a multilevel thresholding approach based on three different Rao algorithms and Kapur’s entropy is investigated. The performance of the considered thresholding methods is evaluated on a dataset of 10 standard benchmark images using the mean of objective function values, the standard deviation of objective function values, and the best objective function value obtained over a fixed number of independent runs. The experimental results demonstrate the effectiveness of the multilevel thresholding approach based on Rao algorithms and Kapur’s entropy.

This paper considers the method for the calculation of magnetic flux density in the vicinity of overhead distribution lines which takes into account the higher current harmonics. This method is based on the Biot–Savart law and the complex image method. The considered method calculates the values of the magnetic flux density for each harmonic component of the current separately at all points of interest (usually lateral profile). In this way, it is possible to determine the contributions of individual harmonic components of the current intensity to the total value of magnetic flux density. Based on the contributions of individual harmonic components, the total (resultant) value of the magnetic flux density at points of interest is determined. Validation of the computational method is carried out by comparison of the results obtained by the considered calculation method with measurement results. Furthermore, the application of the calculation method was demonstrated by calculating magnetic flux density harmonics in the vicinity of two overhead distribution lines of typical phase conductor arrangements.

In this paper, a novel method for the magnetic flux density estimation in the vicinity of multi-circuit overhead transmission lines is proposed. The proposed method is based on a fully connected feed-forward artificial neural network model that is trained to estimate the magnetic flux density vector components for a range of single-circuit overhead transmission lines. The proposed algorithm is able to simplify estimation process in instances when there are two or more geometrically identical circuits present in the multi-circuit overhead transmission line. In such instances, artificial neural network model is employed to estimate the magnetic flux density distribution over a considered lateral profile for only one of such circuits. The magnetic flux density estimates of the other geometrically identical circuits are derived from these results. The proposed methodology defines the resultant magnetic flux density for the multi-circuit overhead transmission line in terms of the contributions made by individual circuits. The application of the proposed magnetic flux density estimation method is demonstrated on several multi-circuit configurations of overhead transmission lines. The performance of the proposed method is compared with the Biot-Savart law based method calculation results as well as with field measurement results.

This paper considers the application of machine learning models to electric field intensity and magnetic flux density estimation in the proximity of the overhead transmission lines. The machine learning models are applied on two horizontal overhead transmission line configurations at different rated voltages, at height 1 m above ground surface. The obtained results are compared with the results obtained by charge simulation method and Biot-Savart law based method as well as with the field measurement results.

In this paper, a novel method for electric field intensity and magnetic flux density estimation in the vicinity of the high voltage overhead transmission lines is proposed. The proposed method is based on two fully connected feed-forward neural networks to independently estimate electric field intensity and magnetic flux density. The artificial neural networks are trained using the scaled conjugate gradient algorithm. Training datasets corresponds to different overhead transmission line configurations that are generated using an algorithm that is especially developed for this purpose. The target values for the electric field intensity and magnetic flux density datasets are calculated using the charge simulation method and Biot-Savart law based method, respectively. This data is generated for fixed applied voltage and current intensity values. In instances when the applied voltage and current intensity values differ from those used in the artificial neural network training, the electric field intensity and magnetic flux density results are appropriately scaled. In order to verify the validity of the proposed method, a comparative analysis of the proposed method with the charge simulation method for electric field intensity calculation and Biot-Savart law-based method for magnetic flux density calculation is presented. Furthermore, the results of the proposed method are compared to measurement results obtained in the vicinity of two 400 kV transmission lines. The performance analysis results showed that proposed method can produce accurate electric field intensity and magnetic flux density estimation results for different overhead transmission line configurations.

The differences between the formant trajectories of British and broad Australian English accents are analysed and used for accent conversion. An improved formant model based on linear prediction (LP) feature analysis and a 2-D hidden Markov model (HMM) of formants is employed for estimation of the formant trajectories of vowels and diphthongs. Comparative analysis of the formant values, the formant trajectories and the formant target points of British and broad Australian accents are presented. A method for ranking the contribution of formants to accent identity is proposed whereby formants are ranked according to the normalised distances between formants across accents. The first two formants are considered more sensitive to accents than other formants. Finally a set of experiments on accent conversion is presented to transform the broad Australian accent of a speaker to British Received Pronunciation (RP) accent by formant mapping and prosody modification. Perceptual evaluations of accent conversion results illustrate that besides prosodic correlates such as pitch and duration, formants also play an important role in conveying accents.

This paper, on the basis of experimental research of the system in exploitation, identifies the main disadvantages of the existing troubleshooting scenarios for IPTV over xDSL. Also, this paper shows how the process of troubleshooting can be made more efficient in practice, with the already existing test solutions and other possibilities of test devices and xDSL transceivers.

This research suggests a framework, Digital Humanities Readiness Assessment Framework (DHuRAF), to assess the maturity level of the required infrastructure for Digital Humanities studies (DH) in different communities. We use a similar approach to the Basic Language Resource Kit (BLARK) in developing the suggested framework. DH as a fairly new field, which has emerged at an intersection of digital technologies and humanities, currently has no framework based on which one could assess the status of the essential elements required for conducting research in a specific language or community. DH offers new research opportunities and challenges in the humanities, computer science and its relevant technologies, hence such a framework could provide a starting point for educational strategists, researchers, and software developers to understand the prerequisites for their tasks and to have a statistical base for their decisions and plans. The suggested framework has been applied in the context of Kurdish DH, considering Kurdish as a less-resourced language. We have also applied the method to the Gaelic language in the Scottish community. Although the research has focused on less-resourced and minority languages, it concludes that DHuRAF has the potential to be generalized in a variety of different contexts. Furthermore, despite significant reliance on Natural Language Processing (NLP) and computational utilities, the research showed that DH could also be used as an essential resource pool to leverage the NLP study of less-resourced and minority languages.

The blind additive white Gaussian noise level estimation is an important and a challenging area of digital image processing with numerous applications including image denoising and image segmentation. In this paper, a novel block-based noise level estimation algorithm is proposed. The algorithm relies on the artificial neural network to perform a complex image patch analysis in the singular value decomposition (SVD) domain and to evaluate noise level estimates. The algorithm exhibits the capacity to adjust the effective singular value tail length with respect to the observed noise levels. The results of comparative analysis show that the proposed ANN-based algorithm outperforms the alternative single stage block-based noise level estimating algorithm in the SVD domain in terms of mean square error (MSE) and average error for all considered choices of block size. The most significant improvements in MSE levels are obtained at low noise levels. For some test images, such as “Car” and “Girlface”, at σ = 1 , these improvements can be as high as 99% and 98.5%, respectively. In addition, the proposed algorithm eliminates the error-prone manual parameter fine-tuning and automates the entire noise level estimation process.

Estimation of additive white Gaussian noise levels in images has a variety of image processing applications including image enhancement, segmentation and feature extraction. Designing an algorithm with a consistent performance across a range of noise levels and image contents is a challenging problem; without any prior information, it is difficult to differentiate the noise signal from the underlying image signal. In this paper, an adaptive block-based noise level estimation algorithm in the singular value decomposition domain is proposed. The algorithm has the ability to change the singular value tail length according to the observed noise levels. A number of different choices of block size are considered and, for each choice, a mathematical model is proposed to describe how to adjust the singular value tail length as a function of the initial noise level estimates. In comparison with a seminal fixed singular value tail length algorithm, the proposed algorithm significantly improves the noise level estimation accuracy at low noise levels at the expense of a small increase in computational time; for example, for the block size of 64 × 64 and AWGN level σ = 1 , the MSE is reduced by 65%, whilst the computational time is increased by less than 1.3%.

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.

Multilevel image thresholding is a challenging digital image processing problem with numerous applications, including image segmentation, image analysis and higher level image processing. Although, threshold estimation based on exhaustive search is a relatively straight forward task, it can be computationally very expensive to evaluate optimal thresholds when the number of threshold levels is large. In this paper, a metaheuristic approach to multilevel thresholding of x-ray images has been examined. Specifically, firefly and bat algorithms are used in the conjunction with Kapur's entropy, Tsallis entropy and Otsu's between-class variance criterion to estimate optimal threshold values. The performance of various image segmentation strategies have been evaluated on a dataset of x-ray images. The simulation results show that the bat algorithm in conjunction with Otsu's objective function offers the best X-ray image segmentation strategy. Out of all considered strategies, this multilevel thresholding approach to image segmentation produces the highest PSNR and SSIM values as well as fast execution times.