Power line signal are normally subjected to disturbance and noise which degrade the overall performance of power quality monitoring system. This paper proposes a modified approach for denoising by combining an algorithm known as Stein’s Unbiased Risk Estimator (SURE) algorithm incorporated with PHASELET TRANSFORM (PT). This algorithm is chosen particularly, as it not only performs denoising but also tends to optimally smooth the noisy signal. Since the algorithm is incorporated with PHASELET, this leads to increase the coefficients obtained. Hence this method is found to be effective for application for denoising of power line signals. The different types of power quality signals simulated using the proposed method is found to be efficient and provide better results with increased Signal-to-Noise ratio (SNR) when compared with the results obtained by applying wavelet TRANSFORM (WT).

Introduction: Clinical performance of light cured resin composites is related to their degree of polymerization. The purpose of this study was to compare the degree of conversion of packable and hybrid composites by FTIR (Fourier TRANSFORM Infrared Spectroscopy).Materials & Methods: In this experimental study, 40 composite disks were prepared in two groups from Z250 and P60 composites. Each group was divided into four subgroups (2mm thickness cured with QTH unit, 2mm thickness cured with LED unit, 5mm thickness cured with QTH unit, and 5mm thickness cured with LED unit). Then samples were evaluated by FTIR to determine the degree of conversion (DC). Data were analyzed by Kolmogorov-Smirnov and three way ANOVA.Results: There was not a significant difference among the DC of the materials tested. LED significantly increased the degree of conversion of materials tested (P<0.001). DC values were significantly greater in 2mm diameter samples vs 5mm ones (P<0.001).Conclusion: The use of incremental technique in deep cavities restored with these composites is suggested. Also using LED is advised for curing composites because of a better result.

In this paper, a new method for the detection, location, and classification of faults in MTDC solar microgrid has been presented. Some issues such as the expansion of renewable energy sources and DC loads as well as the efforts to increase power quality and to reduce the environmental impact of electricity generation have led to the expansion of solar networks. Identifying the types and locations of faults is important to ensure that the service continues and further breakdowns are prevented and the protection’s selectivity characteristic is increased. In this method, an orbital kit is connected to the network. At time of fault occurrence in the network, the fault is detected by passing a current through the connected kits, measuring the traveling waves derived from the fault current, and applying it to a mathematical morphological filter. The location of the fault is determined using orbital equations and flow calculations. Mathematical morphology filter and signal energy analysis have been used to determine the type of faults. This method was tested with many faults. The results have indicated the accuracy of the proposed method. This method is resistant to changes in arcs resistance (up to 100 ohms) and has a very good performance in high impedance fault conditions (up to 1000 ohms).

In this paper, a new method for fault detection, location, and classification in multi-terminal DC microgrid (MTDC) is proposed. MTDC grids have expanded due to some issues such as the expansion of DC resources, loads, and aims to increase power quality. Diagnosing the types and location of faults is important to continue the service and prevent further outages. In this method, a circuit kit is connected to the grid. In fault time, the fault in the network is detected by passing the current through the connected kits and measuring the traveling waves derived from the fault current as well as applying it to a mathematical morphological filter. Determining the location of the fault is done using circuit equations and current calculations. PHASELET output and fuzzy inference systems are used to determine the type of faults. The presented method was tested in an MTDC microgrid connected to renewable and energy storages with many faults. The results show the ability of the proposed method. The error of the proposed fault location method is less than 7%. This method is resistant towards the change in sampling frequency (between 500 Hz and 50 kHz), fault resistance (up to 125 ohms), and loading (up to 120% of the nominal load); it acts very well in high impedance faults.

Background: A fair amount of important objects in natural images have circular and elliptical shapes. For example, the nucleus of most of the biological cells is circular, and a number of parasites such as Oxyuris have elliptical shapes in microscopic images. Hence, atomic representations by two‑ dimensional (2D) basis functions based on circle and ellipse can be useful for processing these images. The first researches have been done in this domain by introducing circlet TRANSFORM. Methods: The main goal of this article is expanding the circlet to a new one with elliptical basis functions. Results: In this article, we first introduce a new TRANSFORM called ellipselet and then compare it with other X‑ let TRANSFORMs including 2D‑ discrete wavelet TRANSFORM, dual‑ tree complex wavelet, curvelet, contourlet, steerable pyramid, and circlet TRANSFORM in the application of image denoising. Conclusion: Experimental results show that for noises under 30, the ellipselet is better than other geometrical X-lets in terms of Peak Signal to Noise Ratio, especially for Lena which contains more circular structures. However, for Barbara which has fine structures in its texture, it has worse results than dual‑ tree complex wavelet and steerable pyramid.