Ashraf Khalil

University of Benghazi, English, Faculty Member

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Papers by Ashraf Khalil

Simulation and experimental verification of PID controlled DC/DC buck converter in wind energy conversion system

2017 4th IEEE International Conference on Engineering Technologies and Applied Sciences (ICETAS), 2017

DC/DC converters are the key components in small scale wind energy conversion systems. In this pa... more DC/DC converters are the key components in small scale wind energy conversion systems. In this paper we investigate the performance of Buck DC/DC converter in wind energy conversion system. The output voltage of the system is regulated using buck PID controller. The voltage is stabilized against the variations in the wind speed. The system has been implemented practically. A simulation has been carried out using Matlab/Simulink. A comparison between the simulation and practical results is presented in the paper. The system shows stable operational performance under different wind speeds.

Investigating Financial Literacy Among Emiratis

Financial literacy is one of the key factors needed in making informed financial decisions. As bu... more Financial literacy is one of the key factors needed in making informed financial decisions. As businesses continue to be more profit driven, more financial and economic intrigues arise that continue to put individuals at the risk of spending more and more without considering the short term and long term effects. We conducted a study to assess financial literacy and financial decision making among Emiratis. Our results show that financial literacy is lacking among Emiratis. Also, almost half of respondents owe loans to other peoples and 1/5 of them have bank loans. We expect that the outcome of this research will be useful for designing educational programs and policies to promote financial planning and security among Emiratis. We also posit that deeper and more informed understanding of this problem is a precursor for developing effective financial education programs with the aim of improving financial decision- making among Emiratis.

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Load frequency control system with smart meter and controllable loads

2017 8th International Renewable Energy Congress (IREC), 2017

The advances in the network technology and the emergence of the renewable energy as primary sourc... more The advances in the network technology and the emergence of the renewable energy as primary sources have changed the current view of the power system control. Smart meters will become a very important component in future power systems. In the event of an emergency the demand will play vital role in the stability of the power system. Conventionally, when a sudden generation loss occurs the generators have the responsibility to restore the system frequency. This process is expensive, comparably lengthy and increases the CO2 emissions. The smart meters can control the loads and hence secure the system stability, faster than the conventional generators. One of the important issues is to understand the operation of these emerging devices. In this paper, we present a Matlab/Simulink model for load frequency control system with demand-side control through the smart meter. From the simulation the demand-side control can improve the performance of the load frequency control system.

Web 2.0 and E-Governments: An Exploration of

The emergence of Web 2.0 applications, or social media, is changing the social life of people and... more The emergence of Web 2.0 applications, or social media, is changing the social life of people and beginning to infiltrate and impact governments in the Western World. There, the emergence of a new government paradigm is challenging the traditional governments and governance. A shift is at this point required for governments to stay in line with their citizens, who are becoming more computer savvy and being raised and fed on the internet and mobile devises. We identify for specific areas in which Web 2.0 applications are being used innovatively in Western governments. The paper then turns its focus to the Arab World and puts forward thoughts on the potential, opportunities and challenges for both citizens and governments for embracing this new wave of web-based services. We conclude that Web 2.0 applications have great potential to leverage the mission of Arab e-governments through connecting and collaborating with businesses, educational institutions, private and non-government init...

Detecting and Localizing Prostate Cancer from Diffusion-Weighted Magnetic Resonance Imaging

2019 IEEE International Conference on Image Processing (ICIP), 2019

The purpose of this work is to develop a computer-aided diagnosis (CAD) system for detecting and ... more The purpose of this work is to develop a computer-aided diagnosis (CAD) system for detecting and localizing prostate cancer from diffusion-weighted magnetic resonance imaging (DWI) acquired at five distinct b-values. The first step in the proposed system depends on nonnegative matrix factorization (NMF) to fuse intensity features of prostate voxels, spatial features of neighboring voxels, and shape prior features to guide the evolution of a level set function for accurate prostate segmentation. The second step in the proposed system involves calculating the apparent diffusion coefficient (ADC) maps of the segmented prostate regions as a discriminating feature between malignant and healthy cases. These ADC maps are used in the last step of the CAD system to train a convolutional neural network (CNN)-based model to identify the ADC maps with malignant tumors. To evaluate the accuracy of the system, 50% of the ADC maps are randomly chosen to train the CNN-model while the second 50% of the ADC maps are used to evaluate the accuracy of the trained model. The proposed CAD system resulted in an average area under the receiver operating characteristic curve (AUC) of 0.93 at the five b-values.

Evaluating Flinders Technology Associates card for transporting bacterial isolates and retrieval of bacterial DNA after various storage conditions

October-2020, 2020

Background and Aim: Flinders Technology Associates (FTA) cards simplify sample storage, transport... more Background and Aim: Flinders Technology Associates (FTA) cards simplify sample storage, transport, and extraction by reducing cost and time for diagnosis. This study evaluated the FTA suitability for safe transport and storage of Gram-positive and Gram-negative bacterial cells of animal origin on its liquid culture form and from organ impression smears (tissues) under the same routine condition of microbiological laboratory along with detecting their nucleic acid over different storage conditions. Materials and Methods: Increase in bacterial count from 104 to 107 (colony-forming units/mL) of 78 isolates representing seven bacterial species was applied onto cards. FTA cards were grouped and inoculated by these bacteria and then stored at different conditions of 24-27°C, 4°C, and –20°C for 24 h, for 2 weeks, for 1 and 3 month storage, respectively. Bacteriological examination was done, after which bacterial DNA was identified using specific primers for each bacterial type and detected...

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Computer-Aided Diagnosis of Acute Myocardial Infarction using Time-Dependent Plasma Metabolites

2019 IEEE International Conference on Imaging Systems and Techniques (IST), 2019

Acute myocardial infarction (MI) is complicated, and multiple etiologies can result in this clini... more Acute myocardial infarction (MI) is complicated, and multiple etiologies can result in this clinical condition. Guidelines recognize two categories of MI: Thrombotic (Type 1) and non-thrombotic (Type 2), that have quite same prevalence but require unlike treatment. Unfortunately, diagnostic criteria to differentiate between Type 1 and Type 2 require invasive procedures. This results in inefficient and sub-optimal care of patients suspected of MI. This paper presents a novel machine-learning system that detects biomarkers of thrombus formation by analyzing the association between plasma metabolites with the formation of thrombosis in cohort of MI patients at multiple time-points. Study data are collected by a newly introduced non-targeted technique that evaluates the quantities of both known and unknown metabolites from blood samples. Our system uses recursive feature elimination (RFE) and multi-layer perceptron (MLP) neural network to detect associated metabolites at each time-point followed by weighted-voting algorithm using ensemble learning. Our experiment achieves an accuracy of 91%, sensitivity of 89%, and specificity of 94% for MI diagnosis.

Autism Spectrum Disorder Diagnosis framework using Diffusion Tensor Imaging

2019 IEEE International Conference on Imaging Systems and Techniques (IST), 2019

Autism is a complex neurological disorder which affects behavioral and communication skills. Nume... more Autism is a complex neurological disorder which affects behavioral and communication skills. Numerous studies were presented suggesting abnormal development of neural networks in the brain in shape, functionality, and/or connectivity. While conventional diagnosis of autism is subjective and requires long time before confirmation, neuro-imaging techniques provide a promising alternative. This paper introduces an automated autism computer-aided diagnosis system based on the connectivity information of the WM tracts. In this CAD system, two consecutive levels of analysis are implemented: Local analysis utilizing diffusion tensor imaging (DTI) data, then getting global decision. Johns Hopkins WM areas' atlas is employed for DTI-volumes segmentation. Correlations of DTI-derived features between different areas in the brain, demonstrating linkage between WM areas were exploited. Then, feature selection extracting the most prominent features among those associations are made. Lastly, a...

Modelling and Control of Photovoltaic-Based Microgrid

International Journal of Renewable Energy Research, 2015

This paper presents the modelling and control of PV-based Microgrid. Renewable energy sources suc... more This paper presents the modelling and control of PV-based Microgrid. Renewable energy sources such as PV, wind and fuel cells are usually connected through voltage-source inverters. In order to share the same loads these inverters are connected in parallel to form a Microgrid. The system considered in the paper consists of two parallel inverters each fed with PV array. As the PV array is variable nonlinear DC source the control system should achieve good power sharing even with this imperfections. The controller is designed in the dq reference frame where the Space Vector Pulse Width Modulation is used. The first inverter controller contains outer voltage control loop and inner current control loop while the second inverter controller contains only power control loop. The controller is tested through simulation where each inverter is fed through PV array with different radiation level. The simulation results show that the power sharing is achieved even when the DC sources are differ...

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Localized Assistive Scene Understanding using Deep Learning and the IoT

2019 7th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), 2019

In this paper, we propose a system for localized scene understanding to assist sufferers of visua... more In this paper, we propose a system for localized scene understanding to assist sufferers of visual disabilities. Our system determines the user's indoor location using WiFi fingerprinting and synthesizes a real-time description of the surrounding environment. The description is synthesized from prior information about the environment, real-time information obtained from object detection and localization using deep learning, and sensory information collected from IoT sensors. Our system can be activated automatically or on-demand as configured. On-demand activation happens by issuing a voice command to the environment's smart speaker or the user's mobile phone. Alternatively, our system can be set to activate automatically when detecting a change in the user's environment. When triggered, our system initiates a capture of an image using an environment-attached stationary camera and offloads the image to our server for identifying objects and their approximate locations in the environment. The server uses deep learning to localize persons, pets, or furniture. We then use a prior mapping of the environment to change the detected image-domain pixel coordinates to real-world relative locations. We also collect using IoT sensors information about the environment's temperature, humidity level, and light intensity. With the information available, we fuse a fixed description of the environment's permanent features with a dynamic description from the localized objects and sensors data. We finally use text-to-speech to change the textual description into an audio signal played on the user's Bluetooth headset or the environment's smart speaker. Our results show that our system can be an effective tool in helping the visually-challenged navigate unknown environments using increasingly available smart home technologies.

A Novel CAD System for Detecting Acute Rejection of Renal Allografts Based on Integrating Imaging-markers and Laboratory Biomarkers

2018 IEEE International Conference on Imaging Systems and Techniques (IST), 2018

The goal of this paper is to determine the parameters that are correlated with the biopsy diagnos... more The goal of this paper is to determine the parameters that are correlated with the biopsy diagnosis of acute renal rejection (AR) post-transplantation, using laboratory biomarkers and (3D+ -value) diffusion weighted MR (DW-MR) image- markers. 16 patients with non-rejection (NR) and 45 patients with AR renal allografts determined by their renal biopsy as a gold standard were included. All kidneys were evaluated using both laboratory biomarkers (e.g., creatinine clearance (CrCl) and serum creatinine (SCr)) and DW-MR image-markers. To extract the latter, DW-MR kidney images were first segmented using a geometric deformable model, then, DW-MR image-markers known as apparent diffusion coefficients (ADCs), were estimated for segmented kidneys at multiple b-values (i.e. strength and timing, of, the, field, gradients, (b50,b 100,,…,b 1000 s/mm2)). A statistical analysis investigating possible correlations between potential biomarkers of AR and the biopsy diagnosis was firstly performed. Two...

The load Reliant Power Transfer of the Series-to-Series Inductive Resonant Wireless Power Transfer

In this paper, the effect of the output impedance to the power transfer efficiency of the series-... more In this paper, the effect of the output impedance to the power transfer efficiency of the series-to-series inductive resonant wireless power transfer at the resonance frequency is reviewed in details. The analysis is carried out by utilizing the theoretical inductive resonance wireless power transfer model using the MATLAB ® package. In this paper, the experiment is designed to confirm the highest power transfer efficiency is obtained at the resonance frequency for the given value of the coupling coefficient. Besides that, the experiment is also conducted to find the optimum load impedance for all given value of coupling coefficient. The analysis shows that the maximum wireless power transfer efficiency for series-to-series inductive resonant wireless power transfer is at the maximum peak when operational at the resonance frequency. In addition, the power transfer efficiency is improved by working at the optimum load impedance. The experimental set up is presented and the analytical...

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Towards Personalized Autism Diagnosis: Promising Results

The ultimate goal of this paper is to develop a novel personalized comprehensive computer aided d... more The ultimate goal of this paper is to develop a novel personalized comprehensive computer aided diagnostic (CAD) system for precise diagnosis of autism spectrum disorder (ASD) based on the 3D shape analysis of the cerebral cortex (Cx), To achieve the main goal of the proposed system, we used structural MRI modality (sMRI) to be able to extract the shape features of the brain cortex. After segmenting the brain cortex from sMRI, we used a spherical harmonics analysis to measure the surface complexity, in addition to studying surface curvatures. Finally, a multi-stage deep network based on several autoencoders and softmax classifiers is constructed to provide the final global diagnosis. The presented CAD system was tested on several datasets, achieving an average accuracy of 92.15%. In addition to its global diagnostic accuracy, the local diagnostic accuracies of the most significant areas also demonstrated the ability of the proposed system to construct very promising local maps of AS...

The Impact of Knowledge Sharing on Innovation Capability in United Arab Emirates Organizations

The purpose of this study was to explore the relationship between knowledge sharing and innovatio... more The purpose of this study was to explore the relationship between knowledge sharing and innovation capability, by examining the influence of individual, organizational and technological factors on knowledge sharing. The research is based on a survey of 103 employees from different organizations in the United Arab Emirates. The study is based on a model and a questionnaire that was previously tested by Lin [1]. Thus, the study aims at examining the validity of that model in UAE context. The results of the research show varying degrees of correlation between the different variables, with ICT use having the strongest relationship with the innovation capabilities of organizations. The study also revealed little evidence of knowledge collecting and knowledge sharing among UAE employees.

Evaluating the value of the excitation capacitance of a wind turbine/induction generator system using genetic algorithms

2017 8th International Renewable Energy Congress (IREC), 2017

Due to their robustness, low cost, and low maintenance, induction generators are good candidates ... more Due to their robustness, low cost, and low maintenance, induction generators are good candidates for wind energy systems. In addition, when the wind turbine is isolated from the grid, the induction generator can operate within a wide speed range which utilize maximum power extraction. To ensure that voltage buildup will take place between the machine terminals, the reactive power required by the machine must be supplied. To achieve that, a three phase capacitor bank is connected to the generator terminals. Evaluating the adequate value of the capacitance is very crucial since it affects the value of the generated voltage. In this paper, the desired operating voltage and the corresponding value of the magnetizing inductance are defined from the magnetizing curve. By using genetic algorithms (GAs), the corresponding values of the excitation capacitance and the rotor slip are evaluated. Matlab/Simulink model is then used for validation. It is noticed that there is a good agreement between the values predicted by the GAs and the ones obtained from the simulation.

Control of master-slave microgrid based on CAN bus

2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), 2017

In this paper, the control of parallel voltage-source inverters Microgrid based on Controller Are... more In this paper, the control of parallel voltage-source inverters Microgrid based on Controller Area Network (CAN) is introduced. The design is based on the maximum time delay that guarantees the stability where the system is composed of three-phase DC/AC inverters with master-slave control strategy in the rotational reference frame (dq frame). The reference current signals are sent from the master to the slave converters through Controller Area Network bus (CAN bus). A model for masterslave communication-based Microgrid is presented and the system is modeled as a general time delay system. The maximum time delay that guarantees the stability of the system is calculated using a Lyapunov-Krasovskii based linear matrix inequalities method. The results are tested through Matlab/Simulink and True-Time 2.0 simulation.

A noninvasive approach for the early detection of diabetic retinopathy

Diabetes and Retinopathy, 2020

Abstract This chapter introduces one of the most critical problems in ophthalmology, specifically... more Abstract This chapter introduces one of the most critical problems in ophthalmology, specifically the diagnosis and detection of diabetic retinopathy (DR). Developing a fast, accurate, and reliable method for the early detection of DR is of great clinical importance to prevent blindness in patients. For this reason, various methods for early detection of DR have been investigated and used such as a dilated eye examination, tonometry, fluorescein angiography, optical coherence tomography, and ultrawide-field retinal imaging. With the increased popularity of machine learning, researchers have formulated their own algorithms and methods to detect DR with various rates of success. This chapter overviews past and current diagnostic methods that have been developed for DR. In addition, this chapter addresses new methodologies being developed/researched and some challenges that researchers face in developing fast, accurate, and reliable diagnosis.

Modeling and Control of Supercritical and Ultra-Supercritical Power Plants: A Review

Energies, 2020

This paper presents a critical review of the research conducted for modeling and controlling supe... more This paper presents a critical review of the research conducted for modeling and controlling supercritical power plants. Thermal power plants are classified according to the boiler pressure to supercritical and subcritical. The modeling concepts and control strategies of supercritical generation units are far more complex than those of subcritical. On the other hand, supercritical generation technologies are more efficient and much cleaner than subcritical generation units. From a deep technical analysis of the literature, there is no review that is dedicated to models-based control of supercritical power plants and most previous reviews are found to be too general to modeling-based control of fossil fuelled energy sources. This review reports the advancements on modeling and control of supercritical and ultra-supercritical plants as cleaner generation technologies. The various published achievements for modeling supercritical and ultra-supercritical units have been reviewed. The co...

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Investigating Bias in Facial Analysis Systems: A Systematic Review

IEEE Access, 2020

Recent studies have demonstrated that most commercial facial analysis systems are biased against ... more Recent studies have demonstrated that most commercial facial analysis systems are biased against certain categories of race, ethnicity, culture, age and gender. The bias can be traced in some cases to the algorithms used and in other cases to insufficient training of algorithms, while in still other cases bias can be traced to insufficient databases. To date, no comprehensive literature review exists which systematically investigates bias and discrimination in the currently available facial analysis software. To address the gap, this study conducts a systematic literature review (SLR) in which the context of facial analysis system bias is investigated in detail. The review, involving 24 studies, additionally aims to identify (a) facial analysis databases that were created to alleviate bias, (b) the full range of bias in facial analysis software and (c) algorithms and techniques implemented to mitigate bias in facial analysis. INDEX TERMS Algorithmic discrimination, classification bias, facial analysis, bias, unfairness. ASHRAF KHALIL (Member, IEEE) received the Ph.D. degree in computer science from Indiana University, USA. He is currently the Director of Research and a Professor of computer science and information technology with Abu Dhabi University. His work was featured in public media, including the UAE's the National and the USA's National Public Radio. Specializing in applying and developing usability testing techniques to mobile applications, he is interested in applying diverse technological innovations in addressing pertinent problems. His researches are published in top conferences in the fields, such as CHI, CSCW, and INTERACT. His research interests include ubiquitous computing, social and mobile computing, bioinformatics, persuasive computing, and human-computer interaction. He was a recipient of many awards and research grants, including the Second Place from the National Made in UAE Competition, the Abu Dhabi University Ambassador Award, and the Leadership Award.

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Control strategies in AC microgrid: A brief review

2018 9th International Renewable Energy Congress (IREC), 2018

Increasing energy demand led to shifting the focus to the use of the renewable and sustainable en... more Increasing energy demand led to shifting the focus to the use of the renewable and sustainable energy sources. Microgrids (MGs), mainly inverter based, is gaining more importance as they can accommodate various types of renewable energy effectively. Their control is one of the most challenging research areas. In the last few years, many control strategies have been developed. In this paper, the control strategies for AC Microgrids based on hierarchical structures are introduced in different aspects. The control strategies in AC microgrid can be classified into three layers: firstly inner and outer control layer that controls the output current and manages the output active and reactive power of renewable energy sources (RESs), secondly, power sharing control layer that adjusts the amplitude and frequency of the voltage reference values and provides these reference values to inner and outer loops, finally supervisory control layer that restores voltage and frequency with controls the output active and reactive power between utility grid and MG, this control layer can be classified depending on operating mode into two levels: 1. MG supervisory control level or secondary control level, 2. Grid supervisory control level or tertiary level.

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