Wavelet transform (WT) has the ability to decompose signals into different frequency bands using multiresolution analysis (MRA). It can be utilized in detecting faults and to estimate the phasors of the voltage and current signals, which are essential for transmission line distance protection. A digital distance-protection scheme for transmission lines based on analyzing the measured voltage and current signals at the relay location using WT with MRA is presented in this paper. The scheme has been tested by both computer simulation and experimentally. The tests presented include solid ground faults, phase faults, high impedance and nonlinear ground faults, and line charging.Index Terms-Distance-protection relaying, multiresolution analysis (MRA), power systems, wavelet transform (WT).

Nine strains isolated from mycetoma patients and received as Streptomyces somaliensis were the subject of a polyphasic taxonomic study. The organisms shared chemical markers consistent with their classification in the genus Streptomyces and formed two distinct monophyletic subclades in the Streptomyces 16S rRNA gene tree. The first subclade contained four organisms, including the type strain of S. somaliensis, and the second clade the remaining five strains which had almost identical 16S rRNA sequences. Members of the two subclades were sharply separated using DNA:DNA relatedness and phenotypic data which also showed that the subclade 1 strains formed an heterogeneous group. In contrast, the subclade 2 strains were assigned to a single genomic species and had identical phenotypic profiles. It is evident from these data that the subclade 2 strains should be recognised as a new species of Streptomyces. The name proposed for this new species is Streptomyces sudanensis sp. nov. The type strain is SD 504(T) (DSM = 41923(T) = NRRL B-24575(T)).

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As the smart city applications are moving from conceptual models to development phase, smart transportation is one of smart cities applications and it is gaining ground nowadays. Electric Vehicles (EVs) are considered one of the major pillars of smart transportation applications. EVs are ever growing in popularity due to their potential contribution in reducing dependency on fossil fuels and greenhouse gas emissions. However, large-scale deployment of EV charging stations poses multiple challenges to the power grid and public infrastructure. To overcome the issue of prolonged charging time, the simple solution of deploying more charging stations to increase charging capacity does not work due to the strain on power grids and physical space limitations. Therefore, researchers have focused on developing smart scheduling algorithms to manage the demand for public charging using modeling and optimization. More recently, there has been a growing interest in data-driven approaches in modeling EV charging. Consequently, researchers are looking to identify consumer charging behavior pattern that can provide insights and predictive analytics capability. The purpose of this paper is to provide a comprehensive review for the use of supervised and unsupervised Machine Learning as well as Deep Neural Networks for charging behavior analysis and prediction. Recommendations and future research directions are also discussed.

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The last two decades have shown an increasing trend in the use of navigation technologies in several applications including land vehicles and automated car navigation. Navigation systems incorporate the global positioning system (GPS) and the inertial navigation system (INS). While GPS provides position information when there is direct line of sight to four or more satellites, INS utilizes the local measurements of angular velocity and linear acceleration to determine both the vehicle's position and attitude. Both systems are integrated together to provide reliable navigation solutions by overcoming each of their respective shortcomings. The present integration schemes, which are predominantly based on Kalman filtering, have several inadequacies related to sensor error models, immunity to noise and observability. This paper aims at introducing a multi-sensor system integration approach for fusing data from an INS and GPS hardware utilizing wavelet multi-resolution analysis (WMRA) and artificial neural networks (ANN). The WMRA is used to compare the INS and GPS position outputs at different resolution levels. The ANN module is then trained to predict the INS position errors in real time and provide accurate positioning of the moving vehicle. The field-test results have demonstrated that substantial improvements in INS/GPS positioning accuracy could be obtained by applying the proposed neuro-wavelet technique.

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This paper suggests the use of wavelet multiresolution analysis (WMRA) as a reliable tool for digital signal processing in structural health monitoring (SHM) systems. A damage occurrence detection algorithm using WMRA augmented with artificial neural networks (ANN) is described. The suggested algorithm allows intelligent monitoring of structures in real time. The probability of damage occurrence is determined by evaluating the wavelet norm index (WNI) representing the energy of a signal describing the change in the system dynamics due to damage. An example application of the proposed algorithm is presented using a finite element simulated structural dynamics of a prestressed concrete bridge. The new algorithm showed very promising results.Key words: structural health monitoring, neural networks, wavelet analysis, signal processing, damage detection.