Buildings are becoming smarter through the deployment of intelligent and energy-efficient technologies that reduce energy consumption while making buildings easier to manage and operate. Demand for lighting systems, efficient heating, ventilation and air conditioning (HVAC), as well as other types of hardware such as controls and sub-meters, is growing rapidly that needs more attention in terms of the energy efficiency. Moreover, the energy management of micro-generation or poly-generation system has always been very complex. Keeping the balance between demand and production is a challenging task for researchers. This paper investigates a simple yet very effective and intelligent demand response algorithm to mitigate the intermittency problem, providing an uninterrupted power supply to the users. The developed algorithm is fully capable of balancing the consumption and the production by controlling the user pre-defined load patterns that is based on the state of charge (SOC) of the storage system. The algorithm is tested on a microgrid system that consists of different loads, six photovoltaic (PV) solar panels, a small wind turbine system, and a set of lead-acid battery banks. The experimental result shows the maximum use of renewable energy while reducing the peak demand, cost of end-users and low or no carbon emissions. To achieve a significant reduction in energy consumption as well as to keep the balance between production and demand, this algorithm can be implemented for the efficient use of intermittent renewable energy sources.

A single-phase Cascaded H-Bridge (CHB) grid-tied multilevel inverter is introduced with a detailed discussion of the proposed novel neural controller for better efficiency and power quality in the integration of renewable sources. An LCL (inductor-capacitor-inductor) filter is used in the multilevel inverter system to achieve better harmonic attenuation. The proposed Neural Network (NN) controller performs the inner current control and tracks the references generated from the outer loop to satisfy the requirements of voltage or power control. Two multicarrier-based Pulse Width Modulation (PWM) techniques (phase-shifted modulation and level-shifted modulation) are adopted in the development of the simulation model to drive the multilevel inverter system for the evaluation of the neural control technique. Simulations are carried out to demonstrate the effectiveness and efficient outcomes of the proposed neural network controller for grid-tied multilevel inverters. The advantages of the proposed neural control include a faster response speed and fewer oscillations compared with the conventional Proportional Integral (PI) controller based vector control strategy. In particular, the neural network control technique provides better harmonics reduction ability.

This study aims to assess the performance of Gallium Nitride (GaN)-based inverters in high-power applications through simulations and a hardware-in-the-loop experiment. The focus is on the efficacy of GaN-based inverters for multilevel topologies, a five-level GaN-based cascaded H-bridge inverter controlled by a Texas Instruments (TI) C2000 microcontroller was used to conduct a hardware-in-the-loop experiment. Prior to the experiment, simulations were conducted to showcase the setup of the half-bridge, full-bridge, and five-level cascaded H-bridge inverter, as well as the generation of Pulse Width Modulation (PWM). To test the performance of the multilevel converter topology, specifically the five-level cascaded H-bridge, an evaluation board of a GaN-based half-bridge from Efficient Power Conversion Inc. and a daughter board from GaN Systems Inc. were utilized as the non-linear components in the hardware-in-loop experiments. PWM pulses were generated by the TI microcontroller for most of the setups and phase-shifted modulation was implemented for the five-level cascaded H-bridge inverter. Experimental results are demonstrated with five-level voltages which further indicates a promising future for GaN-based inverters in high-power applications.

This research presents an independent stand-alone graphical computational tool which functions as a neurological disease prediction framework for diagnosis of neurological disorders to assist neurologists or researchers in the field to perform automatic segmentation of gray and white matter regions in brain MRI images. The tool was built in collaboration with neurologists and neurosurgeons and many of the features are based on their feedback. This tool provides the user automatized functionality to perform automatic segmentation and extract the gray and white matter regions of patient brain image data using an algorithm called adapted fuzzy c-means (FCM) membership-based clustering with preprocessing using the elliptical Hough transform and postprocessing using connected region analysis. Dice coefficients for several patient brain MRI images were calculated to measure the similarity between the manual tracings by experts and automatic segmentations obtained in this research. The average Dice coefficients are 0.86 for gray matter, 0.88 for white matter, and 0.87 for total cortical matter. Dice coefficients of the proposed algorithm were also the highest when compared with previously published standard state-of-the-art brain MRI segmentation algorithms in terms of accuracy in segmenting the gray matter, white matter, and total cortical matter.

Unmanned Aerial Vehicles (UAVs) have seen increased use in indoor operation with applications to disaster recovery, inspection in manufacturing industry, and in health care system. For this reason, it is necessary to develop an obstacle avoidance scheme for UAVs flying under limited altitude. This work is focused on multi obstacle avoidance for quadcopter type UAV. A collision-free margin was modeled into the geometry of the obstacles before a navigation path was generated. The strategy, namely Simple Navigation Algorithm used Artificial Navigation Potential to generate a new way point for the flying robot. The approach was verified using MATLAB.