Electrical and Electronic Engineering - Research Publications

Permanent URI for this collection

Search Results

Now showing 1 - 10 of 193
  • Item
    Thumbnail Image
    Display of Native Antigen on cDC1 That Have Spatial Access to Both T and B Cells Underlies Efficient Humoral Vaccination.
    Kato, Y ; Steiner, TM ; Park, H-Y ; Hitchcock, RO ; Zaid, A ; Hor, JL ; Devi, S ; Davey, GM ; Vremec, D ; Tullett, KM ; Tan, PS ; Ahmet, F ; Mueller, SN ; Alonso, S ; Tarlinton, DM ; Ploegh, HL ; Kaisho, T ; Beattie, L ; Manton, JH ; Fernandez-Ruiz, D ; Shortman, K ; Lahoud, MH ; Heath, WR ; Caminschi, I (American Association of Immunologists, 2020-10-01)
    Follicular dendritic cells and macrophages have been strongly implicated in presentation of native Ag to B cells. This property has also occasionally been attributed to conventional dendritic cells (cDC) but is generally masked by their essential role in T cell priming. cDC can be divided into two main subsets, cDC1 and cDC2, with recent evidence suggesting that cDC2 are primarily responsible for initiating B cell and T follicular helper responses. This conclusion is, however, at odds with evidence that targeting Ag to Clec9A (DNGR1), expressed by cDC1, induces strong humoral responses. In this study, we reveal that murine cDC1 interact extensively with B cells at the border of B cell follicles and, when Ag is targeted to Clec9A, can display native Ag for B cell activation. This leads to efficient induction of humoral immunity. Our findings indicate that surface display of native Ag on cDC with access to both T and B cells is key to efficient humoral vaccination.
  • Item
    No Preview Available
    Investigation of Gallium–Boron Spin‐On Codoping for poly-Si/SiOx Passivating Contacts
    Truong, TN ; Le, TT ; Yan, D ; Phang, SP ; Tebyetekerwa, M ; Young, M ; Al-Jassim, M ; Cuevas, A ; Macdonald, D ; Stuckelberger, J ; Nguyen, HT (Wiley, 2021-12)
  • Item
    Thumbnail Image
    Investigation of Gallium–Boron Spin-On Codoping for poly-Si/SiOx Passivating Contacts
    Truong, TN ; Le, TT ; Yan, D ; Phang, SP ; Tebyetekerwa, M ; Young, M ; Al-Jassim, M ; Cuevas, A ; Macdonald, D ; Stuckelberger, J ; Nguyen, HT (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.
  • Item
    Thumbnail Image
    Bottom-Up Synthesis of Single Crystal Diamond Pyramids Containing Germanium Vacancy Centers
    Nonahal, M ; White, SJU ; Regan, B ; Li, C ; Trycz, A ; Kim, S ; Aharonovich, I ; Kianinia, M (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.
  • Item
    Thumbnail Image
    Progress in Brain Computer Interface: Challenges and Opportunities.
    Saha, S ; Mamun, KA ; Ahmed, K ; Mostafa, R ; Naik, GR ; Darvishi, S ; Khandoker, AH ; Baumert, M (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.
  • Item
    Thumbnail Image
    Model based estimation of QT intervals in non-invasive fetal ECG signals.
    Widatalla, N ; Kasahara, Y ; Kimura, Y ; Khandoker, A ; Aalto-Setala, K (Public Library of Science (PLoS), 2020)
    The end timing of T waves in fetal electrocardiogram (fECG) is important for the evaluation of ST and QT intervals which are vital markers to assess cardiac repolarization patterns. Monitoring malignant fetal arrhythmias in utero is fundamental to care in congenital heart anomalies preventing perinatal death. Currently, reliable detection of end of T waves is possible only by using fetal scalp ECG (fsECG) and fetal magnetocardiography (fMCG). fMCG is expensive and less accessible and fsECG is an invasive technique available only during intrapartum period. Another safer and affordable alternative is the non-invasive fECG (nfECG) which can provide similar assessment provided by fsECG and fMECG but with less accuracy (not beat by beat). Detection of T waves using nfECG is challenging because of their low amplitudes and high noise. In this study, a novel model-based method that estimates the end of T waves in nfECG signals is proposed. The repolarization phase has been modeled as the discharging phase of a capacitor. To test the model, fECG signals were collected from 58 pregnant women (age: (34 ± 6) years old) bearing normal and abnormal fetuses with gestational age (GA) 20-41 weeks. QT and QTc intervals have been calculated to test the level of agreement between the model-based and reference values (fsECG and Doppler Ultrasound (DUS) signals) in normal subjects. The results of the test showed high agreement between model-based and reference values (difference < 5%), which implies that the proposed model could be an alternative method to detect the end of T waves in nfECG signals.
  • Item
    Thumbnail Image
    K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations.
    Park, CY ; Cha, N ; Kang, S ; Kim, A ; Khandoker, AH ; Hadjileontiadis, L ; Oh, A ; Jeong, Y ; Lee, U (Springer Science and Business Media LLC, 2020-09-08)
    Recognizing emotions during social interactions has many potential applications with the popularization of low-cost mobile sensors, but a challenge remains with the lack of naturalistic affective interaction data. Most existing emotion datasets do not support studying idiosyncratic emotions arising in the wild as they were collected in constrained environments. Therefore, studying emotions in the context of social interactions requires a novel dataset, and K-EmoCon is such a multimodal dataset with comprehensive annotations of continuous emotions during naturalistic conversations. The dataset contains multimodal measurements, including audiovisual recordings, EEG, and peripheral physiological signals, acquired with off-the-shelf devices from 16 sessions of approximately 10-minute long paired debates on a social issue. Distinct from previous datasets, it includes emotion annotations from all three available perspectives: self, debate partner, and external observers. Raters annotated emotional displays at intervals of every 5 seconds while viewing the debate footage, in terms of arousal-valence and 18 additional categorical emotions. The resulting K-EmoCon is the first publicly available emotion dataset accommodating the multiperspective assessment of emotions during social interactions.
  • Item
    Thumbnail Image
    Assessment Methods of Post-stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis.
    Mohan, DM ; Khandoker, AH ; Wasti, SA ; Ismail Ibrahim Ismail Alali, S ; Jelinek, HF ; Khalaf, K (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.
  • Item
    Thumbnail Image
    Genetic Variants and Their Associations to Type 2 Diabetes Mellitus Complications in the United Arab Emirates.
    ElHajj Chehadeh, S ; Sayed, NS ; Abdelsamad, HS ; Almahmeed, W ; Khandoker, AH ; Jelinek, HF ; Alsafar, HS (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.
  • Item
    Thumbnail Image
    Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles.
    Alkhodari, M ; Jelinek, HF ; Karlas, A ; Soulaidopoulos, S ; Arsenos, P ; Doundoulakis, I ; Gatzoulis, KA ; Tsioufis, K ; Hadjileontiadis, LJ ; Khandoker, AH (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.