Electrical and Electronic Engineering - Research Publications
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ItemInvestigation of Gallium–Boron Spin-On Codoping for poly-Si/SiOx Passivating Contacts(Wiley, 2021-12-01)A doping technique for p‐type poly‐Si/SiOx passivating contacts using a spin‐on method for different mixtures of Ga and B glass solutions is presented. Effects of solution mixing ratios on the contact performance (implied open circuit voltage iVoc, contact resistivity ρc) are investigated. For all as‐annealed samples at different drive‐in temperatures, increasing the percentage of Ga in the solution shows a decrement in iVoc (from ∼680 to ∼610 mV) and increment in ρc (from ∼3 to ∼800 mΩ cm2). After a hydrogenation treatment by depositing a SiNx/AlOx stack followed by forming gas annealing, all samples show improved iVoc (∼700 mV with Ga‐B co‐doped, and ∼720 mV with all Ga). Interestingly, when co‐doping Ga with B, even a small amount of B in the mixing solution shows negative effects on the surface passivation. Active and total dopant profiles obtained by electrical capacitance voltage and secondary‐ion mass spectrometry measurements, respectively, reveal a relatively low percentage of electrically‐active Ga and B in the poly‐Si and Si layers. These results help understand the different features of the two dopants: a low ρc with B, a good passivation with Ga, their degree of activation inside the poly‐Si and Si layers, and the annealing effects.
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ItemBottom-Up Synthesis of Single Crystal Diamond Pyramids Containing Germanium Vacancy Centers(WILEY, 2021-07)Abstract Diamond resonators containing color‐centers are highly sought after for application in quantum technologies. Bottom‐up approaches are promising for the generation of single‐crystal diamond structures with purposely introduced color centers. Here the possibility of using a polycrystalline diamond to grow single‐crystal diamond structures by employing a pattern growth method is demonstrated. For, the possible mechanism of growing a single‐crystal structure with predefined shape and size from a polycrystalline substrate by controlling the growth condition is clarified. Then, by introducing germanium impurities during the growth, localized and enhanced emission from fabricated pyramid shaped single‐crystal diamonds containing germanium vacancy (GeV) color centers is demonstrated. Finally, linewidth of ∼500 MHz at 4 K from a single GeV center in the pyramid shaped diamonds is measured. The method is an important step toward fabrication of 3D structures for integrated diamond photonics.
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ItemProgress in Brain Computer Interface: Challenges and Opportunities.(Frontiers Media SA, 2021)Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.
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ItemAssessment Methods of Post-stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis.(Frontiers Media SA, 2021)Background: Gait dysfunction or impairment is considered one of the most common and devastating physiological consequences of stroke, and achieving optimal gait is a key goal for stroke victims with gait disability along with their clinical teams. Many researchers have explored post stroke gait, including assessment tools and techniques, key gait parameters and significance on functional recovery, as well as data mining, modeling and analyses methods. Research Question: This study aimed to review and summarize research efforts applicable to quantification and analyses of post-stroke gait with focus on recent technology-driven gait characterization and analysis approaches, including the integration of smart low cost wearables and Artificial Intelligence (AI), as well as feasibility and potential value in clinical settings. Methods: A comprehensive literature search was conducted within Google Scholar, PubMed, and ScienceDirect using a set of keywords, including lower extremity, walking, post-stroke, and kinematics. Original articles that met the selection criteria were included. Results and Significance: This scoping review aimed to shed light on tools and technologies employed in post stroke gait assessment toward bridging the existing gap between the research and clinical communities. Conventional qualitative gait analysis, typically used in clinics is mainly based on observational gait and is hence subjective and largely impacted by the observer's experience. Quantitative gait analysis, however, provides measured parameters, with good accuracy and repeatability for the diagnosis and comparative assessment throughout rehabilitation. Rapidly emerging smart wearable technology and AI, including Machine Learning, Support Vector Machine, and Neural Network approaches, are increasingly commanding greater attention in gait research. Although their use in clinical settings are not yet well leveraged, these tools promise a paradigm shift in stroke gait quantification, as they provide means for acquiring, storing and analyzing multifactorial complex gait data, while capturing its non-linear dynamic variability and offering the invaluable benefits of predictive analytics.
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ItemGenetic Variants and Their Associations to Type 2 Diabetes Mellitus Complications in the United Arab Emirates.(Frontiers Media SA, 2021)AIM: Type 2 Diabetes Mellitus (T2DM) is associated with microvascular complications, including diabetic retinopathy (DR), diabetic nephropathy (DNp), and diabetic peripheral neuropathy (DPN). In this study, we investigated genetic variations and Single Nucleotide Polymorphisms (SNPs) associated with DR, DNp, DPN and their combinations among T2DM patients of Arab origin from the United Arab Emirates, to establish the role of genes in the progression of microvascular diabetes complications. METHODS: A total of 158 Emirati patients with T2DM were recruited in this study. The study population was divided into 8 groups based on the presence of single, dual, or all three complications. SNPs were selected for association analyses through a search of publicly available databases, specifically genome-wide association study (GWAS) catalog, infinome genome interpretation platform, and GWAS Central database. A multivariate logistic regression analysis and association test were performed to evaluate the association between 83 SNPs and DR, DNp, DPN, and their combinations. RESULTS: Eighty-three SNPs were identified as being associated with T2DM and 18 SNPs had significant associations to one or more diabetes complications. The most strongly significant association for DR was rs3024997 SNP in the VEGFA gene. The top-ranked SNP for DPN was rs4496877 in the NOS3 gene. A trend towards association was detected at rs833068 and rs3024998 in the VEGFA gene with DR and rs743507 and rs1808593 in the NOS3 gene with DNp. For dual complications, the rs833061, rs833068 and rs3024997 in the VEGFA gene and the rs4149263 SNP in the ABCA1 gene were also with borderline association with DR/DNp and DPN/DNp, respectively. Diabetic with all of the complications was significantly associated with rs2230806 in the ABCA1 gene. In addition, the highly associated SNPs rs3024997 of the VEGFA gene and rs4496877 of the NOS3 gene were linked to DR and DPN after adjusting for the effects of other associated markers, respectively. CONCLUSIONS: The present study reports associations of different genetic polymorphisms with microvascular complications and their combinations in Emirati T2DM patients, reporting new associations, and corroborating previous findings. Of interest is that some SNPs/genes were only present if multiple comorbidities were present and not associated with any single complication.
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ItemDeep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles.(Frontiers Media SA, 2021)Background: Left ventricular ejection fraction (LVEF) is the gold standard for evaluating heart failure (HF) in coronary artery disease (CAD) patients. It is an essential metric in categorizing HF patients as preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF) ejection fraction but differs, depending on whether the ASE/EACVI or ESC guidelines are used to classify HF. Objectives: We sought to investigate the effectiveness of using deep learning as an automated tool to predict LVEF from patient clinical profiles using regression and classification trained models. We further investigate the effect of utilizing other LVEF-based thresholds to examine the discrimination ability of deep learning between HF categories grouped with narrower ranges. Methods: Data from 303 CAD patients were obtained from American and Greek patient databases and categorized based on the American Society of Echocardiography and the European Association of Cardiovascular Imaging (ASE/EACVI) guidelines into HFpEF (EF > 55%), HFmEF (50% ≤ EF ≤ 55%), and HFrEF (EF < 50%). Clinical profiles included 13 demographical and clinical markers grouped as cardiovascular risk factors, medication, and history. The most significant and important markers were determined using linear regression fitting and Chi-squared test combined with a novel dimensionality reduction algorithm based on arc radial visualization (ArcViz). Two deep learning-based models were then developed and trained using convolutional neural networks (CNN) to estimate LVEF levels from the clinical information and for classification into one of three LVEF-based HF categories. Results: A total of seven clinical markers were found important for discriminating between the three HF categories. Using statistical analysis, diabetes, diuretics medication, and prior myocardial infarction were found statistically significant (p < 0.001). Furthermore, age, body mass index (BMI), anti-arrhythmics medication, and previous ventricular tachycardia were found important after projections on the ArcViz convex hull with an average nearest centroid (NC) accuracy of 94%. The regression model estimated LVEF levels successfully with an overall accuracy of 90%, average root mean square error (RMSE) of 4.13, and correlation coefficient of 0.85. A significant improvement was then obtained with the classification model, which predicted HF categories with an accuracy ≥93%, sensitivity ≥89%, 1-specificity <5%, and average area under the receiver operating characteristics curve (AUROC) of 0.98. Conclusions: Our study suggests the potential of implementing deep learning-based models clinically to ensure faster, yet accurate, automatic prediction of HF based on the ASE/EACVI LVEF guidelines with only clinical profiles and corresponding information as input to the models. Invasive, expensive, and time-consuming clinical testing could thus be avoided, enabling reduced stress in patients and simpler triage for further intervention.
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ItemInvestigating myocardial performance in normal and sick fetuses by abdominal Doppler signal derived indices.(Elsevier BV, 2021)INTRODUCTION: Fetal myocardial performance indices are applied to assess aspects of systolic and diastolic function in developing fetal heart. The aim of this study was to determine normal values of Tei Index (TI) and modified TI (KI) for systolic and diastolic performance in early (<30 weeks), Mid (30-35 weeks) and late (36-41 weeks) relating to both normal fetuses as well as fetuses carrying a variety of fetal abnormalities, which do not call for precise anatomic imaging. MATERIAL AND METHODS: Fetal Electrocardiogram Signals (FES) and Doppler Ultrasound Signal (DUS) were simultaneously documented from 55 normal and 25 abnormal fetuses with a variety of abnormalities including Congenital Heart Diseases (CHDs) and a variety of non-CHDs. The isovolumic contraction time (ICT), isovolumic relaxation time (IRT), ventricular ejection time (VET) and ventricular filling time (VFT) were estimated from continuous DUS signals by a hybrid of Hidden Markov and Support Vector Machine based automated model. The TI and the KI were calculated by using the formula (ICT + IRT)/VET and (ICT + IRT)/VFT respectively. RESULTS: The TI was not found to show any significant change from early to late fetuses, nor between normal and abnormal cases. On the other hand, KI was shown to significantly decline in values from early to late normal cases and from normal to abnormal groups. Significant correlation (r = -0.36; p < 0.01) of gestational ages with only KI (not TI) was found in this study. CONCLUSION: Modified TI (KI) may be a useful index to monitor the normal development of fetal myocardial function and identify fetuses with a variety of CHD and non-CHD cases.
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ItemNovel Measures of Similarity and Asymmetry in Upper Limb Activities for Identifying Hemiparetic Severity in Stroke Survivors(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-06)Stroke survivors are often characterized by hemiparesis, i.e., paralysis in one half of the body, severely affecting upper limb movements. Monitoring the progression of hemiparesis requires manual observation of limb movements at regular intervals, and hence is a labour intensive process. In this work, we use wrist-worn accelerometers for automated assessment of hemiparesis in acute stroke. We propose novel measures of similarity and asymmetry in hand activities through bivariate Poincaré analysis between two-hand accelerometer data for quantifying hemiparetic severity. The proposed descriptors characterize the distribution of activity surrogates derived from acceleration of the two hands, on a 2D bivariate Poincaré Plot. Experiments show that while the descriptors CSD1 and CSD2 can identify hemiparetic patients from control subjects, their normalized difference CSDR and the descriptors Complex Cross-Correlation Measure ( C3M) and Activity Asymmetry Index ( AAI) can distinguish between mild, moderate and severe hemiparesis. These measures are compared with traditional measures of cross-correlation and evaluated against the National Institutes of Health Stroke Scale (NIHSS), the clinical gold standard for hemiparetic severity estimation. This study, undertaken on 40 acute stroke patients with varying levels of hemiparesis and 15 healthy controls, validates the use of short length ( 5 minutes) wearable accelerometry data for identifying hemiparesis with greater clinical sensitivity. Results show that the proposed descriptors with a hierarchical classification model outperform state-of-the-art methods with overall accuracy of 0.78 and 0.85 for 4-class and 3-class hemiparesis identification respectively.
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ItemNo Preview AvailableASYMPTOTICS OF EIGENSTRUCTURE OF SAMPLE CORRELATION MATRICES FOR HIGH-DIMENSIONAL SPIKED MODELS(STATISTICA SINICA, 2021-01)Sample correlation matrices are widely used, but for high-dimensional data little is known about their spectral properties beyond "null models", which assume the data have independent coordinates. In the class of spiked models, we apply random matrix theory to derive asymptotic first-order and distributional results for both leading eigenvalues and eigenvectors of sample correlation matrices, assuming a high-dimensional regime in which the ratio p/n, of number of variables p to sample size n, converges to a positive constant. While the first-order spectral properties of sample correlation matrices match those of sample covariance matrices, their asymptotic distributions can differ significantly. Indeed, the correlation-based fluctuations of both sample eigenvalues and eigenvectors are often remarkably smaller than those of their sample covariance counterparts.