Single Phase Power Generation System from Fuel Cell

Power conditioning circuits are required for the fuel cell systems due to its nature in energetic state. This paper proposed the small signal average modelling of a duel active bridge (DAB) DC-DC converter with LC filter, to generate the single phase AC power by using the H1000 fuel cell system. The controller is designed for the stable operation of the system. Implemented the controller, which gives the constant output voltage to DC-bus from the DAB DC-DC converter, this DC-bus voltage fed to the inverter, which inverts the DC-bus voltage to single Phase AC power with the LC-filter. The proposed system simulated in the MATLAB/Simulink.

MEA Viewer: a High-performance Interactive Application for Visualizing Electrophysiological Data

Multi-electrode arrays (MEAs) have been used for many years to measure electrical activity in ensembles of many hundreds of neurons, and are used in research areas as diverse as neuronal connectivity and drug discovery. A high sampling frequency is required to adequately capture action potentials, also known as spikes, the primary electrical event associated with neuronal activity, and the resulting raw data files are large and difficult to visualize with traditional plotting tools. Many common approaches to deal with this issue, such as extracting spikes times and solely performing spike train analysis, significantly reduce data dimensionality. Unbiased data exploration benefits from the use of tools that minimize data transforms and such tools enable the development of heuristic perspective from data prior to any subsequent processing. Here we introduce MEA Viewer, a high-performance interactive application for the direct visualization of multi-channel electrophysiological data. MEA Viewer provides many high-performance visualizations of electrophysiological data, including an easily navigable overview of all recorded extracellular signals overlaid with spike timestamp data and an interactive raster plot. Beyond the fundamental data displays, MEA Viewer can signal average and spatially overlay the extent of action potential propagation within single neurons. This view extracts information below the spike detection threshold to directly visualize the propagation of action potentials across the plane of the MEA. This entirely new method of using MEAs opens up new and novel research applications for medium density arrays. MEA Viewer is licensed under the General Public License version 3, GPLv3, and is available at http://github.com/dbridges/mea-tools.

PRIOR FORECASTING OF EPILEPTIC SEIZURE AND LOCALIZATION OF EPILEPTOGENIC REGION

Forecasting of an epileptic seizure and localization of the epileptogenic region is a challenging task. Scalp electroencephalogram (EEG) is the most commonly used signal for studying various brain disorders. This paper presents an algorithm for seizure forecast and detection of epileptogenic region by analyzing EEG signals from frontal, temporal, central and parietal region of the brain. Eight features have been extracted from each EEG signal. Average of features extracted from different regions of brain is computed for each region. An artificial neural network is trained to predict an epileptic seizure by identifying the pre-ictal duration. The trained neural network is tested and found to have an accuracy of 92.3%, sensitivity of 100% and specificity, of 83.3%. Two prominent features, accumulated energy and power in beta band, have been identified to identify the epileptogenic region. The result shows that the region corresponding to temporal lobe has maximum variation in these two features for pre-ictal and inter-ictal duration. The result validates the proposed algorithm to identify the pre-ictal state and predict the seizure in advance and identification of the epileptogenic region.