RETRACTED: Priority Algorithm Based Coordinated Voltage Control for Distribution System with Distributed Wind Generators

This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy).This article has been retracted at the request of scientific committee of SMART GRID Technologies 2015.The authors have plagiarized part of a paper that had already appeared in PRZEGLĄD ELEKTROTECHNICZNY (Electrical Review), 6 (2012) 304 - 312, http://pe.org.pl/abstract_pl.php?nid=6091. One of the conditions of submission of a paper for publication is that authors declare explicitly that their work is original and has not appeared in a publication elsewhere. Re-use of any data should be appropriately cited. As such this article represents a severe abuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process

A Modular Single-phase Multistring Multilevel Inverter Topology for Distributed Energy Resources

AbstractThis Paper presents simulation analysis of single phase multilevel inverter for distributed energy resources(DER) system are small power generation tools, in order to reduce conversion losses, complexity of the circuit and to improve the size and cost of the system. The system involves a high step up converter is used to set up the voltage coming from the various DER's such as Fuel cell module and Photovoltaic module, this high voltage acts as input to the inverter. This system requires less number of switches as compare to conventional cascade H-bridge (CCHB) inverter. There are some advantages of this multilevel inverter such as improved output waveform, and lower Electromagnetic interference, lower switching power loss and Total Harmonic Distortion (THD)

Speech Communication

Contains table of contents for Part IV, table of contents for Section 1, an introduction, reports on seven research projects and a list of publications.C.J. Lebel FellowshipDennis Klatt Memorial FundNational Institutes of Health Grant T32-DC00005National Institutes of Health Grant R01-DC00075National Institutes of Health Grant F32-DC00015National Institutes of Health Grant R01-DC00266National Institutes of Health Grant P01-DC00361National Institutes of Health Grant R01-DC00776National Science Foundation Grant IRI 89-10561National Science Foundation Grant IRI 88-05680National Science Foundation Grant INT 90-2471

Fundamental frequency estimation of low-quality electroglottographic signals

Fundamental frequency (fo) is often estimated based on electroglottographic (EGG) signals. Due to the nature of the method, the quality of EGG signals may be impaired by certain features like amplitude or baseline drifts, mains hum or noise. The potential adverse effects of these factors on fo estimation has to date not been investigated. Here, the performance of thirteen algorithms for estimating fo was tested, based on 147 synthesized EGG signals with varying degrees of signal quality deterioration. Algorithm performance was assessed through the standard deviation σfo of the difference between known and estimated fo data, expressed in octaves. With very few exceptions, simulated mains hum, and amplitude and baseline drifts did not influence fo results, even though some algorithms consistently outperformed others. When increasing either cycle-to-cycle fo variation or the degree of subharmonics, the SIGMA algorithm had the best performance (max. σfo = 0.04). That algorithm was however more easily disturbed by typical EGG equipment noise, whereas the NDF and Praat's auto-correlation algorithms performed best in this category (σfo = 0.01). These results suggest that the algorithm for fo estimation of EGG signals needs to be selected specifically for each particular data set. Overall, estimated fo data should be interpreted with care

Composite signal decomposition by digital inverse filtering

Inverse filters are conventionally used for resolving overlapping signals of identical waveshape. However, the inverse filtering approach is shown to be useful for resolving overlapping signals, identical or otherwise, of unknown waveshapes. Digital inverse filter design based on autocorrelation formulation of linear prediction is known to perform optimum spectral flattening of the input signal for which the filter is designed. This property of the inverse filter is used to accomplish composite signal decomposition. The theory has been presented assuming constituent signals to be responses of all-pole filters. However, the approach may be used for a general situation