Electroencephalography (EEG) signals were analyzed in many research applications as a channel of communication between humans and computers. EEG signals associated with imagined fists and feet movements were filtered and processed using wavelet transform analysis for feature extraction. The proposed work used Neural Networks (NNs) as a classifier that enables the classification of imagined movements into either fists or feet. Wavelet families such as Daubechies, Symlets, and Coiflets wavelets were used to analyze the extracted events and then different feature extraction measures were calculated for three detail levels of the wavelet coefficients. Intensive NN training and testing experiments were carried out and different network configurations were compared. The optimum classification performance of 89.11% was achieved with a NN classifier of 20 hidden layers while using the Mean Absolute Value (MAV) of the Coiflets wavelet coefficients as inputs to NN. The proposed system showed a good performance that enables controlling computer applications via imagined fists and feet movements.

This paper discusses the work done in the testing and measurement of Differential Nonlinearity (DNL) of Analogto-Digital Converters (ADCs) by applying a novel testing technique of wavelet transform. The results of the new method is compared with conventional testing methods.The wavelet transform technique shows significant advantage over the conventional techniques.

This paper presents a new method of detecting Analog to Digital Converter harmonic distortion. The new method is based on Wavelet multi-resolution process to identify instantaneous harmonic components. In classical testing, Fourier transform algorithm was long adopted to estimate Total Harmonic Distortion by obtaining signal power spectrum. While the conventional method of Fourier transform tend to be complicated and lengthy, the new investigated algorithms of Wavelet transform has shown less computations process and instantaneous testing of ADCs harmonic distortions. By shorten testing times and reduced computation complexity, Wavelet transform can be particularly appropriate for developing ADCs low-cost, and fast testing procedure.

Connecting a single-phase or three-phase inverter to the grid in distributed generation applications requires synchronization with the grid. Synchronization of an inverter-connected distributed generation units in its basic form necessitates accurate information about the frequency and phase angle of the utility grid. Phase Locked Loop (PLL) circuit is usually used for the purpose of synchronization. However, deviation in the grid frequency from nominal value will cause errors in the PLL estimated outputs, and that’s a major drawback. Moreover, if the grid is heavily distorted with low order harmonics the estimation of the grid phase angle deteriorates resulting in higher oscillations (errors) appearing in the synchronization voltage signals. This paper proposes a modified time delay PLL (MTDPLL) technique that continuously updates a variable time delay unit to keep track of the variation in the grid frequency. The MTDPLL is implemented along a Multi-Harmonic Decoupling Cell (MHDC) to overcome the effects of distortion caused by gird lower order harmonics. The performance of the proposed MTDPLL is verified by simulation and compared in terms of performance and accuracy with recent PLL techniques.

This paper presents a control scheme for a photovoltaic (PV) system that uses a single-phase grid-connected inverter with low-voltage ride-through (LVRT) capability. In this scheme, two PI regulators are used to adjust the power angle and voltage modulation index of the inverter; therefore, controlling the inverter’s active and reactive output power, respectively. A fuzzy logic controller (FLC) is also implemented to manage the inverter’s operation during the LVRT operation. The FLC adjusts (or de-rates) the inverter’s reference active and reactive power commands based on the grid voltage sag and the power available from the PV system. Therefore, the inverter operation has been divided into two modes: (i) Maximum power point tracking (MPPT) during the normal operating conditions of the grid, and (ii) LVRT support when the grid is operating under faulty conditions. In the LVRT mode, the de-rating of the inverter active output power allows for injection of some reactive power, hence providing voltage support to the grid and enhancing the utilization factor of the inverter’s capacity. The proposed system was modelled and simulated using MATLAB Simulink. The simulation results showed good system performance in response to changes in reference power command, and in adjusting the amount of active and reactive power injected into the grid.