Personalized Quantification of Facial Normality using Artificial Intelligence

While congenital facial deformities are not rare, and surgeons typically perform operations to improve these deformities, currently the success of the surgical reconstruction operations can only be “measured” subjectively by surgeons and specialists. No efficient objective mechanisms of comparing the outcomes of plastic reconstruction surgeries or the progress of different surgery techniques exist presently. The aim of this research project is to develop an efficient software application that can be used by plastic surgeons as an objective measurement tool for the success of an operation. The long-term vision is to develop a software application that is user-friendly and can be downloaded on a regular laptop and used by doctors and patients to assess the progress of their surgical reconstruction procedures. The application would work by first scanning a face before and after an operation and providing the surgeon with a normality score of the face from 0 to 3 where 3 represents normal and 0 represents extreme abnormality. A score will be given when the face is scanned before and after surgery. The difference between those scores is what we will call the delta. A high delta value would point to a high improvement in the normality of a face post-surgery, and a low delta value would indicate a small improvement. The first chapter of the thesis represents the introduction which describes the general aspects of the project. The second chapter presents the methodology employed for building the application and the existing solutions and proposed functional model structure. The results chapter presents the process behind collecting and labeling the image database and analyzes the scores produced by the program when fed with new images from the database. Finally, the last chapter of this thesis presents the conclusions. The list of references completes this work

Modelling, analysis, and implementation of a switched-inductor based DC/DC converter with reduced switch current stress

This paper proposes a technique for switch current stress reduction in a Switched Inductor DC-DC Boost Converter (SIBC). The proposed technique comes up with a low-cost design, high voltage conversion ratio with a less duty cycle value, and lower current stress without increasing the component count. This topology is basically a transformer-less design where one diode of the traditional switched inductor configuration has been replaced with a switch, which is in parallel with the existing switch, resulting in a design that can incorporate active switches with a low current rating, since the total input current is equally shared by them. The detailed modes of operation in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) and steady-state analysis, the non-idealities' effect on voltage gain, design approach, and a comparative study with other DC-DC converters for some significant performance characteristics are provided. The experimental validations for the performance and working of the 500 W designed prototype are presented.This publication was made possible by Qatar University-Marubeni Concept to Prototype Development Research grant no. M-CTP-CENG-2020-2 from the Qatar University.Scopu

Machine learning screening of COVID-19 patients based on X-ray images for unbalanced classes

Background: COVID-19 is a pandemic that had already infected more than forty-six million people and caused more than a million deaths by 1st of November 2020. The virus pandemic appears to have had a catastrophic effect on the global population's safety. Therefore, efficient detection of infected patients is a key phase in the battle against COVID-19. One of the main screening methods is radiological testing. The goal of this study is using chest X-ray images to detect COVID-19 pneumonia patients while optimizing detection efficiency. Methods: As shown in Figure 1, we combined three methods to detect COVID-19 namely: convolutional neural network, transfer learning, and the focal loss 1 function which are used for unbalanced classes, to build three binary classifiers which are COVID-19 versus normal, COVID-19 versus pneumonia, and COVID-19 versus normal pneumonia (normal and pneumonia). The database used 2 includes a mixture of 400 COVID-19, 1,340 viral pneumonia, 2,560 bacterial pneumonia, and 1,340 normal chest X-ray images for training, validation, and testing of four pre-trained deep convolutional neural networks. Then, the pre-trained model that gives the best results was chosen to improve its performances by two enhancement techniques which are image augmentation, allowing us to reach approximately 2,500 images per class, and the adjustment of focal loss hyperparameters. Results: A comparative study was conducted of our proposed classifiers with well-known classifiers and obtained much better results in terms of accuracy, specificity, sensitivity and precision, as illustrated in Table 1. Conclusion: The high performance of this computer-aided diagnostic technique may greatly increase the screening speed and reliability of COVID-19 diagnostic cases. Particularly, at the crowded emergency services, it will be particularly helpful in this pandemic when the risk of infection and the necessity for prevention initiatives run contrary to the available resources.qscienc

Differential Flatness-Based Performance Enhancement of a Vector Controlled VSC with an LCL-Filter for Weak Grids

In this paper, a novel single-loop flatness-based controller (FBC) is proposed to control the grid-side current in a shunt converter connected to a weak grid through an LCL-filter. After its mathematical description, the paper reports controller implementation and some performance comparisons with two distinct implementations of the widely diffused vector current control approach, during balanced and unbalanced grid voltages, and weak grid conditions. Obtained results highlight higher tracking capability and better dynamic response of the proposed FBC. Moreover, because of its reduced negative conductance region, unstable behaviors that can be observed in weak grids appear significantly improved due to a reduced influence of the phase-locked loop system

A Novel Deep Learning Technique for Morphology Preserved Fetal ECG Extraction from Mother ECG using 1D-CycleGAN

Monitoring the electrical pulse of fetal heart through a non-invasive fetal electrocardiogram (fECG) can easily detect abnormalities in the developing heart to significantly reduce the infant mortality rate and post-natal complications. Due to the overlapping of maternal and fetal R-peaks, the low amplitude of the fECG, systematic and ambient noises, typical signal extraction methods, such as adaptive filters, independent component analysis, empirical mode decomposition, etc., are unable to produce satisfactory fECG. While some techniques can produce accurate QRS waves, they often ignore other important aspects of the ECG. Our approach, which is based on 1D CycleGAN, can reconstruct the fECG signal from the mECG signal while maintaining the morphology due to extensive preprocessing and appropriate framework. The performance of our solution was evaluated by combining two available datasets from Physionet, "Abdominal and Direct Fetal ECG Database" and "Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations", where it achieved an average PCC and Spectral-Correlation score of 88.4% and 89.4%, respectively. It detects the fQRS of the signal with accuracy, precision, recall and F1 score of 92.6%, 97.6%, 94.8% and 96.4%, respectively. It can also accurately produce the estimation of fetal heart rate and R-R interval with an error of 0.25% and 0.27%, respectively. The main contribution of our work is that, unlike similar studies, it can retain the morphology of the ECG signal with high fidelity. The accuracy of our solution for fetal heart rate and R-R interval length is comparable to existing state-of-the-art techniques. This makes it a highly effective tool for early diagnosis of fetal heart diseases and regular health checkups of the fetus.Comment: 24 pages, 11 figure

Design, construction and testing of iot based automated indoor vertical hydroponics farming test-bed in qatar

Growing plants in the gulf region can be challenging as it is mostly desert, and the climate is dry. A few species of plants have the capability to grow in such a climate. However, those plants are not suitable as a food source. The aim of this work is to design and construct an indoor automatic vertical hydroponic system that does not depend on the outside climate. The designed system is capable to grow common type of crops that can be used as a food source inside homes without the need of large space. The design of the system was made after studying different types of vertical hydroponic systems in terms of price, power consumption and suitability to be built as an indoor automated system. A microcontroller was working as a brain of the system, which communicates with different types of sensors to control all the system parameters and to minimize the human intervention. An open internet of things (IoT) platform was used to store and display the system parameters and graphical interface for remote access. The designed system is capable of maintaining healthy growing parameters for the plants with minimal input from the user. The functionality of the overall system was confirmed by evaluating the response from individual system components and monitoring them in the IoT platform. The system was consuming 120.59 and 230.59 kWh respectively without and with air conditioning control during peak summer, which is equivalent to the system running cost of 13.26 and 25.36 Qatari Riyal (QAR) respectively. This system was circulating around 104 k gallons of nutrient solution monthly however, only 8–10 L water was consumed by the system. This system offers real-time notifications to alert the hydroponic system user when the conditions are not favorable. So, the user can monitor several parameters without using laboratory instruments, which will allow to control the entire system remotely. Moreover, the system also provides a wide range of information, which could be essential for plant researchers and provides a greater understanding of how the key parameters of hydroponic system correlate with plant growth. The proposed platform can be used both for quantitatively optimizing the setup of the indoor farming and for automating some of the most labor-intensive maintenance activities. Moreover, such a monitoring system can also potentially be used for high-level decision making, once enough data will be collected. This work presents significant opportunities for the people who live in the gulf region to produce food as per their requirements

RISK FACTORS, LIFESTYLE AND HEALTH HABITS OF YOUNG ADULTS IN QATAR

Background & Objectives The state of Qatar has witnessed significant lifestyle changes due to rapid urbanization, the introduction of labour-saving devices and the availability of high-caloric density food. This has impacted on the daily lifestyle and health habits of young adults leading to significant increases in non-communicable diseases (WHO, 2014). This study explored the risk factors associated with such diseases amongst young adults in Qatar. Methods A representative sample of 732 males and females (aged 18-25 years) from Qatar University took part in this cross-sectional, mixed-method design study. Physical Activity (PA) and dietary habits were assessed using a validated questionnaire. Total energy expenditure per week was calculated based on the metabolic equivalent values of each activity reported by the participant (Al-Nakeeb et al., 2012). Body Mass Index (BMI) was calculated according to the International Obesity Task Force criteria and using the age and gender-specific BMI classification established by Cole et al. (2000). Results The percentage of overweight/obesity in males and females was 39.5% and 38.5% respectively. It was evident that there was a significant increase in the percentage of students classified as overweight/obese from year 1 to year 4. Meanwhile, there was a decline in the level of PA and an increase in sedentary time during that period. Whilst health was reported to be the main reason for participation in PA/sport, lack of available time was singled out as the main barrier to engagement in an active lifestyle. Ironically, students reported more than 4 hours of TV/DVD viewing and internet use per day. Conclusions The adoption of healthier lifestyles amongst the Qatari population, including an increase in PA and a reduction in overweight/obesity are major objectives cited in Qatar Vision (2030). This study has revealed a high prevalence of overweight/obesity amongst male and female university students with regressive trends in their lifestyle and health habits. The findings reveal a worrying picture of young people's lifestyle that ought to be a cause for concern for policy makers and health professionals. Undoubtedly, there is an urgent need to seriously consider putting in place intervention strategies concerning behaviour modification and the built environment in order to reverse these trends. Such strategies could have major implications on the health and well-being of young people at this critical age developmentally and on the future welfare of the wider community in the long run.qscienc

A systematic approach to the design and characterization of a smart insole for detecting vertical ground reaction force (vGRF) in gait analysis

Gait analysis is a systematic study of human locomotion, which can be utilized in various applications, such as rehabilitation, clinical diagnostics and sports activities. The various limitations such as cost, non-portability, long setup time, post-processing time etc., of the current gait analysis techniques have made them unfeasible for individual use. This led to an increase in research interest in developing smart insoles where wearable sensors can be employed to detect vertical ground reaction forces (vGRF) and other gait variables. Smart insoles are flexible, portable and comfortable for gait analysis, and can monitor plantar pressure frequently through embedded sensors that convert the applied pressure to an electrical signal that can be displayed and analyzed further. Several research teams are still working to improve the insoles' features such as size, sensitivity of insoles sensors, durability, and the intelligence of insoles to monitor and control subjects' gait by detecting various complications providing recommendation to enhance walking performance. Even though systematic sensor calibration approaches have been followed by different teams to calibrate insoles' sensor, expensive calibration devices were used for calibration such as universal testing machines or infrared motion capture cameras equipped in motion analysis labs. This paper provides a systematic design and characterization procedure for three different pressure sensors: force-sensitive resistors (FSRs), ceramic piezoelectric sensors, and flexible piezoelectric sensors that can be used for detecting vGRF using a smart insole. A simple calibration method based on a load cell is presented as an alternative to the expensive calibration techniques. In addition, to evaluate the performance of the different sensors as a component for the smart insole, the acquired vGRF from different insoles were used to compare them. The results showed that the FSR is the most effective sensor among the three sensors for smart insole applications, whereas the piezoelectric sensors can be utilized in detecting the start and end of the gait cycle. This study will be useful for any research group in replicating the design of a customized smart insole for gait analysis. 2020 by the authors. Licensee MDPI, Basel, Switzerland.This research was partially funded by Qatar National Research Foundation (QNRF), grant number NPRP12S-0227-190164 and Research University Grant DIP-2018-017. The publication of this article was funded by the Qatar National Library. The authors would like to thank Engr. Ayman Ammar, Electrical Engineering, Qatar University for helping in printing the printed circuit boards (PCBs). This research was partially funded by Qatar National Research Foundation (QNRF), grant number NPRP12S-0227-190164 and Research University Grant DIP-2018-017. The publication of this article was funded by the Qatar National Library.Scopu

Real-time smart-digital stethoscope system for heart diseases monitoring

One of the major causes of death all over the world is heart disease or cardiac dysfunction. These diseases could be identified easily with the variations in the sound produced due to the heart activity. These sophisticated auscultations need important clinical experience and concentrated listening skills. Therefore, there is an unmet need for a portable system for the early detection of cardiac illnesses. This paper proposes a prototype model of a smart digital-stethoscope system to monitor patient’s heart sounds and diagnose any abnormality in a real-time manner. This system consists of two subsystems that communicate wirelessly using Bluetooth low energy technology: A portable digital stethoscope subsystem, and a computer-based decision-making subsystem. The portable subsystem captures the heart sounds of the patient, filters and digitizes, and sends the captured heart sounds to a personal computer wirelessly to visualize the heart sounds and for further processing to make a decision if the heart sounds are normal or abnormal. Twenty-seven t-domain, f-domain, and Mel frequency cepstral coefficients (MFCC) features were used to train a public database to identify the best-performing algorithm for classifying abnormal and normal heart sound (HS). The hyper parameter optimization, along with and without a feature reduction method, was tested to improve accuracy. The cost-adjusted optimized ensemble algorithm can produce 97% and 88% accuracy of classifying abnormal and normal HS, respectively.Funding: This research was partially funded by Qatar National Research Foundation (QNRF), grant number UREP19-069-2-031 and UREP23-027-2-012 and Research University Grant AP-2017-008/1. The publication of this article was funded by the Qatar National Library.Scopu

Can AI help in screening Viral and COVID-19 pneumonia?

Coronavirus disease (COVID-19) is a pandemic disease, which has already caused thousands of causalities and infected several millions of people worldwide. Any technological tool enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI) in the rapid and accurate detection of COVID-19 from chest X-ray images. The aim of this paper is to propose a robust technique for automatic detection of COVID-19 pneumonia from digital chest X-ray images applying pre-trained deep-learning algorithms while maximizing the detection accuracy. A public database was created by the authors combining several public databases and also by collecting images from recently published articles. The database contains a mixture of 423 COVID-19, 1485 viral pneumonia, and 1579 normal chest X-ray images. Transfer learning technique was used with the help of image augmentation to train and validate several pre-trained deep Convolutional Neural Networks (CNNs). The networks were trained to classify two different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and COVID-19 pneumonia with and without image augmentation. The classification accuracy, precision, sensitivity, and specificity for both the schemes were 99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%, respectively.Comment: 12 pages, 9 Figure