Visualization and Sound Measurements of Vibration Plate in a Boiling Bubble Resonator

We developed a boiling bubble resonator (BBR) as a new heat transfer enhancement method aided by boiling bubbles. The BBR is a passive device that operates under its own bubble pressure and therefore does not require an electrical source. In the present study, high-speed visualization of the flow motion of the microbubbles spouted from a vibration plate and the plate motion in the BBR was carried out using high-speed LED lighting and high-speed cameras; the sounds in the boiling chamber were simultaneously captured using a hydrophone. The peak point in the spectrum of the motion of the vibration plate and the peak point in the spectrum of the boiling sound were found to be matched near a critical heat-flux state. Therefore, we found that it is important to match the BBR vibration frequency to the condensation cycle of the boiling bubble as its own design specification for the BBR.

A Hybrid Metaheuristic Based on Neurocomputing for Analysis of Unipolar Electrohydrodynamic Pump Flow

A unipolar electrohydrodynamic (UP-EHD) pump flow is studied with known electric potential at the emitter and zero electric potential at the collector. The model is designed for electric potential, charge density, and electric field. The dimensionless parameters, namely the electrical source number (Es), the electrical Reynolds number (ReE), and electrical slip number (Esl), are considered with wide ranges of variation to analyze the UP-EHD pump flow. To interpret the pump flow of the UP-EHD model, a hybrid metaheuristic solver is designed, consisting of the recently developed technique sine–cosine algorithm (SCA) and sequential quadratic programming (SQP) under the influence of an artificial neural network. The method is abbreviated as ANN-SCA-SQP. The superiority of the technique is shown by comparing the solution with reference solutions. For a large data set, the technique is executed for one hundred independent experiments. The performance is evaluated through performance operators and convergence plots.

An effective control algorithm for dynamic voltage restorer under symmetrical and asymmetrical grid voltage conditions

Introduction. Voltage sag, which is associated to a transitory drop in the root mean square voltage characterizing an electrical source network. During these perturbations, the corresponding electronic customers and devices will suffer from serious operating troubles causing dangerous damages. Purpose. In order to attenuate this disturbance effects, the Controlled Dynamic Voltage Restorer constitutes a very interesting solution among many others that have been proposed. The novelty of the proposed work consists in presenting an enhanced algorithm to control efficiently the dynamic voltage restorer when voltage sag is suddenly occurred. Methods. The proposed algorithm is based on an instantaneous phase locked loop using a multi variable filter to synthesize unitary signals involved in compensation voltages computation relative to the sag apparition. Practical value. A detailed study concerning typical voltage sag, which is consolidated by simulation and experimental results, is conducted to show the used algorithm’s effectiveness to cancel the corresponding voltage sag.

Advances in Electrical Source Imaging: A Review of the Current Approaches, Applications and Challenges

Brain source localization has been consistently implemented over the recent years to elucidate complex brain operations, pairing the high temporal resolution of the EEG with the high spatial estimation of the estimated sources. This review paper aims to present the basic principles of Electrical source imaging (ESI) in the context of the recent progress for solving the forward and the inverse problems, and highlight the advantages and limitations of the different approaches. As such, a synthesis of the current state-of-the-art methodological aspects is provided, offering a complete overview of the present advances with regard to the ESI solutions. Moreover, the new dimensions for the analysis of the brain processes are indicated in terms of clinical and cognitive ESI applications, while the prevailing challenges and limitations are thoroughly discussed, providing insights for future approaches that could help to alleviate methodological and technical shortcomings.

An ANN-Based Adaptive Predistorter for LED Nonlinearity in Indoor Visible Light Communications

By modulating the optical power of the light-emitting diode (LED) in accordance with the electrical source and using a photodetector to convert the corresponding optical variation back into electrical signals, visible light communication (VLC) has been developed to achieve lighting and communications simultaneously, and is now considered one of the promising technologies for handling the continuing increases in data demands, especially indoors, for next generation wireless broadband systems. During the process of electrical-to-optical conversion using LEDs in VLC, however, signal distortion occurs due to LED nonlinearity, resulting in VLC system performance degradation. Artificial neural networks (ANNs) are thought to be capable of achieving universal function approximation, which was the motivation for introducing ANN predistortion to compensate for LED nonlinearity in this paper. Without using additional training sequences, the related parameters in the proposed ANN predistorter can be adaptively updated, using a feedback replica of the original electrical source, to track the LED time-variant characteristics due to temperature variation and aging. Computer simulations and experimental implementation were carried out to evaluate and validate the performance of the proposed ANN predistorter against existing adaptive predistorter schemes, such as the normalized least mean square predistorter and the Chebyshev polynomial predistorter.

Role of Electrical Source Imaging in Pediatric Epilepsy and Pre-Surgical Evaluation

Children with drug-resistant epilepsy undergo an extensive pre-surgical evaluation to determine the part of the brain thought to be the cause of seizures. The employment of non-invasive diagnostic imaging tools plays an important role in establishing surgical candidacy, preventing the need for invasive procedures. Electrical source imaging (ESI) has been explored as a modern alternative to traditional diagnostic techniques in pre-surgical workup. Through computational analysis of recorded electric potentials and individualized head scans, ESI provides a non-invasive method of obtaining more accurate localizations. However, its use within the clinical setting is limited. The following review looks to examine the literature surrounding ESI and advocates for its inclusion within the pre-surgical workup of children.

Electrical Source Imaging in Freely Moving Rats: Evaluation of a 12-Electrode Cortical Electroencephalography System

This work presents and evaluates a 12-electrode intracranial electroencephalography system developed at the National Institute of Mental Health (Klecany, Czech Republic) in terms of an electrical source imaging (ESI) technique in rats. The electrode system was originally designed for translational research purposes. This study demonstrates that it is also possible to use this well-established system for ESI, and estimates its precision, accuracy, and limitations. Furthermore, this paper sets a methodological basis for future implants. Source localization quality is evaluated using three approaches based on surrogate data, physical phantom measurements, and in vivo experiments. The forward model for source localization is obtained from the FieldTrip-SimBio pipeline using the finite-element method. Rat brain tissue extracted from a magnetic resonance imaging template is approximated by a single-compartment homogeneous tetrahedral head model. Four inverse solvers were tested: standardized low-resolution brain electromagnetic tomography, exact low-resolution brain electromagnetic tomography (eLORETA), linear constrained minimum variance (LCMV), and dynamic imaging of coherent sources. Based on surrogate data, this paper evaluates the accuracy and precision of all solvers within the brain volume using error distance and reliability maps. The mean error distance over the whole brain was found to be the lowest in the eLORETA solution through signal to noise ratios (SNRs) (0.2 mm for 25 dB SNR). The LCMV outperformed eLORETA under higher SNR conditions, and exhibiting higher spatial precision. Both of these inverse solvers provided accurate results in a phantom experiment (1.6 mm mean error distance across shallow and 2.6 mm across subcortical testing dipoles). Utilizing the developed technique in freely moving rats, an auditory steady-state response experiment provided results in line with previously reported findings. The obtained results support the idea of utilizing a 12-electrode system for ESI and using it as a solid basis for the development of future ESI dedicated implants.