On Detecting Faces And Classifying Facial Races With Partial Occlusions And Pose Variations

In this dissertation, we present our contributions in face detection and facial race classification. Face detection in unconstrained images is a traditional problem in computer vision community. Challenges still remain. In particular, the detection of partially occluded faces with pose variations has not been well addressed. In the first part of this dissertation, our contributions are three-fold. First, we introduce our four image datasets consisting of large-scale labeled face dataset, noisy large-scale labeled non-face dataset, CrowdFaces dataset, and CrowdNonFaces dataset intended to be used for face detection training. Second, we improve Viola-Jones (VJ) face detection results by first training a Convolutional Neural Network (CNN) model on our noisy datasets. We show our improvement over the VJ face detector on AFW face detection benchmark dataset. However, existing partial occluded face detection methods require training several models, computing hand-crafted features, or both. Hence, we thirdly propose our Large-Scale Deep Learning (LSDL), a method that does not require training several CNN models or hand-crafted features computations to detect faces. Our LSDL face detector is trained on a single CNN model to detect unconstrained multi-view partially occluded and non-partially occluded faces. The model is trained with a large number of face training examples that cover most partial occlusions and non-partial occlusions facial appearances. The LSDL face detection method is achieved by selecting detection windows with the highest confidence scores using a threshold. Our evaluation results show that our LSDL method achieves the best performance on AFW dataset and a comparable performance on FDDB dataset among state-of-the-art face detection methods without manually extending or adjusting the square detection bounding boxes. Many biometrics and security systems use facial information to obtain an individual identification and recognition. Classifying a race from a face image can provide a strong hint to search for facial identity and criminal identification. Current facial race classification methods are confined only to constrained non-partially occluded frontal faces. Challenges remain under unconstrained environments such as partial occlusions and pose variations, low illuminations, and small scales. In the second part of the dissertation, we propose a CNN model to classify facial races with partial occlusions and pose variations. The proposed model is trained using a broad and balanced racial distributed face image dataset. The model is trained on four major human races, Caucasian, Indian, Mongolian, and Negroid. Our model is evaluated against the state-of-the-art methods on a constrained face test dataset. Also, an evaluation of the proposed model and human performance is conducted and compared on our new unconstrained facial race benchmark (CIMN) dataset. Our results show that our model achieves 95.1% of race classification accuracy in the constrained environment. Furthermore, the model achieves a comparable accuracy of race classification compared to human performance on the current challenges in the unconstrained environment

Intensification of toxic chlorophenolic compounds degradation over efficient microwave-dried silica-doped tetragonal zirconia nanocatalysts

The work aims to evaluate the efficient microwave (MW) drying method of silica-doped tetragonal zirconia nanocatalysts (SZN-M) for intensification of the degradation of toxic chlorophenolic compounds. The catalyst dried under a conventional oven (SZN-O) was also conducted for comparison. The MW drying time was reduced six times and three times less energy was used than the conventional oven drying. The catalysts were characterized by Fourier-transform infrared, X-ray diffraction, electron spin resonance, nitrogen adsorption-desorption analyses, zeta potential, ultraviolet–visible diffuse reflectance spectroscopy and photoluminescence analyses. Compared with SZN-O, the SZN-M possessed a higher number of Si-O-Zr bonds that led to a greater amount of oxygen vacancies, metal defect sites, larger pore size as well as surface area, and hence displayed excellent performance toward the degradation of toxic 2-chlorophenol, 2-CP (92%), while only 67% for the former. The SZN-M achieved to reduce the total organic carbon and biological oxygen demand up to 88% and 89%, respectively, while for SZN-O, the reduction was up to 82% and 84%. The catalysts still remained active after five cycles and are highly capable of degrading various chlorophenolic compounds that could be very beneficial for the wastewater treatment

Social, health and lifestyle predictors of sleep during pregnancy

The aim of this thesis was to strengthen understanding of self-reported sleep in pregnancy by drawing together evidence from: previously published research; de novo analyses of existing and novel datasets; and the lived experience of pregnant women themselves. This mixed- methods approach found that: a lack of standardisation and potential flaws in the design of previous studies do not yet permit a formal meta-analysis to be performed; and previous findings remain vulnerable to error and publication bias. The three de novo quantitative analyses of self-reported sleep conducted for this thesis sought to address many of the flaws in previous research. These analyses indicate that: several pre- existing/pre-pregnant sociodemographic and health characteristics contribute to the less favourable sleep commonly reported by pregnant women (as compared to age-matched non- pregnant women); and that variation in these and (un)related lifestyle and behavioural factors during pregnancy also contribute to variation in self-reported sleep amongst pregnant women. However, the last of these analyses provides evidence that variation in a commonly experienced phenomenon (glucose intolerance and, at its extreme, gestational diabetes) is associated with less favourable sleep in what appears to be a dose-response relationship. Analysis of posts to web-based forums by women with first-hand experience of sleep in pregnancy confirm that pregnancy-specific somatic changes were experienced/understood to be the principal causes of less favourable sleep; although the advice offered to others facing similar problems tended to focus on behavioural and situational factors as suitable avenues for intervention. On the basis of this evidence, it is clear that none of the self-administered sleep instruments/items available, and used, to-date are capable of comprehensively assessing the sleep of pregnant women. Future research must develop/use a dedicated sleep instrument to improve our understanding of the range, prevalence and likely determinants of the less favourable sleep more commonly reported by pregnant women

AIAA Design Build Fly

AIAA-DBF team is developing a remotecontrolled aircraft engineered to fulfill AIAA DBF 2024–2025 competition requirements, including transporting external payloads and releasing an X-1 test vehicle mid-flight

ViT-DeiT: An Ensemble Model for Breast Cancer Histopathological Images Classification

Breast cancer is the most common cancer in the world and the second most common type of cancer that causes death in women. The timely and accurate diagnosis of breast cancer using histopathological images is crucial for patient care and treatment. Pathologists can make more accurate diagnoses with the help of a novel approach based on image processing. This approach is an ensemble model of two types of pre-trained vision transformer models, namely, Vision Transformer and Data-Efficient Image Transformer. The proposed ensemble model classifies breast cancer histopathology images into eight classes, four of which are categorized as benign, whereas the others are categorized as malignant. A public dataset was used to evaluate the proposed model. The experimental results showed 98.17% accuracy, 98.18% precision, 98.08% recall, and a 98.12% F1 score.Comment: 7 pages, 10 figures, 7 table

Carbohydrate Counting/Bolus Calculator Mobile Application Improves Time in Range in Adults with Type 1 Diabetes Subjects

Objective: To evaluate the effectiveness and safety of a mobile application for carbohydrate counting and bolus calculation (CHOC-BC) in adults with type 1 diabetes mellitus (T1DM). Research Design and Methods: A 12-week randomized controlled trial was conducted at King Fahad Medical City, Riyadh, Saudi Arabia. Adults with T1DM on multiple daily insulin injections and using Libre 2 glucose monitors were randomized to either CHOC-BC or conventional treatment. The primary endpoint was time in range (TIR; 70–180 mg/dL). Results: A total of 127 participants (70 females) were included: 64 in the intervention group and 63 in the control group with a mean age of 26.56 ± 4.8 and 26.74 ± 6.52 years, respectively. After 3 months, the intervention group achieved better TIR than the control group (51.20% ± 11.61% vs. 46.17% ± 13.02%; mean difference [MD], 5.03; 95% confidence interval [CI], 0.70–9.36; P = 0.023). Application users showed a significant reduction in level 2 time above range (17.25% ± 11.61% vs. 24.10% ± 15.74%; MD, −6.85; 95% CI, −11.70 to −1.99; P = 0.006). No significant differences were observed in body weight or time below range. Conclusions: The CHOC-BC mobile application empowered users to achieve better glycemic control while maintaining a safe profile that avoids hypoglycemia and weight gain

The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI

Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the genetic diversity and high intrinsic heterogeneity in appearance, shape, histology, and treatment response. Treatments include surgery, radiation, and systemic therapies, with magnetic resonance imaging (MRI) playing a key role in treatment planning and post-treatment longitudinal assessment. The 2024 Brain Tumor Segmentation (BraTS) challenge on post-treatment glioma MRI will provide a community standard and benchmark for state-of-the-art automated segmentation models based on the largest expert-annotated post-treatment glioma MRI dataset. Challenge competitors will develop automated segmentation models to predict four distinct tumor sub-regions consisting of enhancing tissue (ET), surrounding non-enhancing T2/fluid-attenuated inversion recovery (FLAIR) hyperintensity (SNFH), non-enhancing tumor core (NETC), and resection cavity (RC). Models will be evaluated on separate validation and test datasets using standardized performance metrics utilized across the BraTS 2024 cluster of challenges, including lesion-wise Dice Similarity Coefficient and Hausdorff Distance. Models developed during this challenge will advance the field of automated MRI segmentation and contribute to their integration into clinical practice, ultimately enhancing patient care.Comment: 10 pages, 4 figures, 1 tabl