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Detecting Mental Stress using EEG and Deep Learning
No full textA Master of Science thesis in Biomedical Engineering by Yara Badr entitled, “Detecting Mental Stress using EEG and Deep Learning”, submitted in April 2023. Thesis advisor is Dr. Hasan Al-Nashash and thesis co-advisors are Dr. Usman Tariq and Dr. Fares Al-Shargie. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Stress plays a significant role in the development of mental, emotional, behavioral, and physical illnesses, and impairs focus, concentration, and performance. The objective of this thesis is to devise a novel approach to identify and alleviate stress levels using electroencephalogram (EEG) signals combined with deep learning techniques and binaural beats stimulation (BBs). The study involved an experiment under four different mental states: rest, control, stress, and stress mitigation. During the stress state, all participants performed Stroop Color-Word Task (SCWT) under time pressure. Meanwhile, in the stress mitigation, the participants performed the SCWT while listening to 16 Hz BBs. EEG, salivary cortisol, behavioral, and subjective measures were used to quantify stress levels, and a novel approach was proposed by merging Partial Directed Coherence (PDC) with Graph Convolutional Network (GCN). Two scenarios were investigated, one including all 45 participants, estimating them to have the same average baseline and detecting 4 mental states, and another where we divided the participants into two different baseline groups (each with 22 subjects) based on subjective data, thus ending up with 8 mental states. In scenario 1, we found that BBs increased target detection accuracy by 27.08% (p<0.001), while in scenario 2, BBs improved detection accuracy by 31.6% and 22.8% for groups 1 and 2, respectively. The improved detection accuracy could be attributed to the beta state induced by the 16 Hz wave. However, there was no significant change noticed for the Perceived Stress Scale (PSS-10) and cortisol. Nevertheless, using PSD topography, a shift in cortical activity back to the temporal region was observed during mitigation, signifying recovery of participants' mental activity and focus. The deep learning results showed that the GCN-PDC could discriminate between four distinct mental states with average accuracies of 99.59%, 99.40%, 99.26%, and 99.64% in alpha, beta, delta, and theta bands for scenario 1, and could classify between 8 mental states (low rest, high rest, low control, high control, low stress, high stress, low mitigation, and high mitigation) with average accuracies of 98.49%, 98.38%, 98.12%, and 98.49% in alpha, beta, delta, and theta bands for scenario 2.College of EngineeringMultidisciplinary ProgramsMaster of Science in Biomedical Engineering (MSBME
INScription: Department of International Studies (INS) Issue #13 (March 30, 2023, Issue 3)
No full textCollege of Arts and SciencesDepartment of International Studie
Video-Based Recognition of Human Activity Using Novel Feature Extraction Techniques
No full textThis paper proposes a novel approach to activity recognition where videos are compressed using video coding to generate feature vectors based on compression variables. We propose to eliminate the temporal domain of feature vectors by computing the mean and standard deviation of each variable across all video frames. Thus, each video is represented by a single feature vector of 67 variables. As for the motion vectors, we eliminated their temporal domain by projecting their phases using PCA, thus representing each video by a single feature vector with a length equal to the number of frames in a video. Consequently, complex classifiers such as LSTM can be avoided and classical machine learning techniques can be used instead. Experimental results on the JHMDB dataset resulted in average classification accuracies of 68.8% and 74.2% when using the projected phases of motion vectors and video coding feature variables, respectively. The advantage of the proposed solution is the use of FVs with low dimensionality and simple machine learning techniques.American University of Sharja
INScription: Department of International Studies (INS) Issue #18 (November 30, 2023, Issue 4)
No full textCollege of Arts and SciencesDepartment of International Studie
Effect of CFRP Wraps on the Compressive Strength of Normal and Effect of CFRP Wraps on the Compressive Strength of Normal and Structural Lightweight Concrete Structural Lightweight Concrete
No full textConcrete is one of the most prominent building materials in the construction industry. Further, lightweight concrete has recently emerged, contributing to the sustainable values of modern construction. Many researchers have worked on enhancing the concrete's compressive strength by applying externally bonding fiber-reinforced polymer (FRP) composites via epoxy adhesives. Such FRP materials are very promising due to their lightweight and high tensile strength along with high corrosion, impact, and fatigue resistance. This paper aims to investigate the use of carbon fiber reinforced polymer (CFRP) wraps in enhancing concrete's compressive properties. The study is conducted on strengthened and control normal weight (NWC) and structural lightweight concrete (LWC) cylinders (150 mm x 300 mm). Experimental results show that CFRP wrapping increase the compressive strength and stiffness of both types of concrete. The compressive strength of structural LWC increased by 67.9% and 118.1%, compared to an increase of 46.1% and 105.0% for LWC, using one and two layers of CFRP, respectively. Further, the ductility is significantly enhanced with CFRP wrapping, suggesting increased resilience and ability to absorb energy during deformation. In terms of behavior at failure, LWC exhibited a more favourable response than NWC at failure. Results indicate that LWC has a satisfactory performance in withstanding compressive loads. This underscores the potential for lightweight concrete to serve as a viable structural material and highlights the role of CFRP reinforcement in further elevating its structural capabilities
Enhancing Business Students’ Employability Skills Awareness
No full textEmployability skills have become vital in helping recent graduates distinguish themselves in the competitive job market, transcending disciplinary boundaries. Dissatisfaction with graduates' communication abilities has been a long-standing concern in academia and the workplace. The shift from traditional lecture-based instruction to active learning has prompted changes in English for Specific Purposes (ESP) and technical communication courses globally. These changes encompass well-developed professional communication skills, collaborative work practices, effective self-management, and social responsibility. This study addresses the imperative of understanding and addressing the skill prerequisites of the corporate sphere, as employers increasingly seek competencies beyond academic qualifications. The study employs a student-centered approach, allowing students to align these skills with real-world job requirements. The Experiential Learning Theory by Kolb serves as the theoretical foundation for this approach. The results indicate that this teaching method, emphasizing learning by doing, received positive feedback from students. It not only provided insight into the skills required by the labor market but also improved students' awareness of their own strengths and weaknesses. Additionally, students found that this approach prepared them for their future careers. In conclusion, this research emphasizes the importance of equipping students with employability skills through authentic, context-based learning. The findings underscore the need for a shift from teacher-centered practices to more student-centered, collaborative learning environments that foster student autonomy and responsibility. The research closes with limitations and suggestions for further research.American University of Sharja
Introduction to Photography - DES 160
No full textSyllabus for the Department of Art and Design course "Introduction to Photography - DES 160", by Instructor(s) Zinka Bejtic for the Summer 2023 semester.College of Architecture, Art and DesignDepartment of Art and Desig
Antimicrobial Activity of Novel Deep Eutectic Solvents
No full textHerein, we utilized several deep eutectic solvents (DES) that were based on hydrogen donors and hydrogen acceptors for their antibacterial application. These DES were tested for their bactericidal activities against Gram-positive (Streptococcus pyogenes, Bacillus cereus, Streptococcus pneumoniae, and methicillin-resistant Staphylococcus aureus) and Gram-negative (Escherichia coli K1, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Serratia marcescens) bacteria. Using lactate dehydrogenase assays, DES were evaluated for their cytopathic effects towards human cells. Results from antibacterial tests revealed that DES prepared from the combination of methyl-trioctylammonium chloride and glycerol (DES-4) and DES prepared form methyl-trioctylammonium chloride and fructose (DES-11) at a 2 µL dose showed broad-spectrum antibacterial behavior and had the highest bactericidal activity. Moreover, DES-4 showed 40% and 68% antibacterial activity against P. aeruginosa and E. coli K1, respectively. Similarly, DES-11 eliminated 65% and 61% E. coli K1 and P. aeruginosa, respectively. Among Gram-positive bacteria, DES-4 showed important antibacterial activity, inhibiting 75% of B. cereus and 51% of S. pneumoniae. Likewise, DES-11 depicted 70% B. cereus and 50% S. pneumoniae bactericidal effects. Finally, the DES showed limited cytotoxic properties against human cell lines with the exception of the DES prepared from Methyltrioctylammonium chloride and Citric acid (DES-10), which had 88% cytotoxic effects. These findings suggest that DES depict potent antibacterial efficacies and cause minimal damage to human cells. It can be concluded that the selected DES in this study could be utilized as valuable and novel antibacterial drugs against bacterial infections. In future work, the mechanisms for bactericides and the cytotoxicity effects of these DES will be investigated
Deep Neural Networks for Electromagnetic Inverse Scattering Problems in Microwave Imaging
No full textA Master of Science thesis in Electrical Engineering by Mohammed Farook Maricar entitled, “Deep Neural Networks for Electromagnetic Inverse Scattering Problems in Microwave Imaging”, submitted in November 2023. Thesis advisor is Dr. Amer Zakaria and thesis co-advisor is Dr. Nasser Qaddoumi. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Microwave imaging, due to its non-invasive and non-destructive nature of detection, is of interest in various applications such as biomedical imaging, geophysical surveying, and non-destructive testing. The most common technique for microwave imaging is by solving an electromagnetic inverse scattering (EMIS) problem. The high nonlinearity and ill-posedness of these EMIS problems have led researchers to develop various inversion algorithms, such as the distorted Born iteration method (DBIM), the contrast source inversion (CSI) method, and the sub-space optimization method (SOM). Though these conventional nonlinear inversion algorithms provide acceptable results in many applications with moderate size and contrast, their computational costs are usually very high. In addition, for high-contrast targets, the performance is generally degraded. To tackle these issues, various deep-learning techniques have been proposed in recent years. In this thesis, the EMIS problem will be solved using deep neural networks (DNNs), with a focus on solving two-dimensional (2D) microwave imaging problems. Different DNN architectures are tested with three types of complex inputs: the measured scattered field 〖(Eˢ ⃑ᶜᵗₘₑₐₛ〗^; an input image obtained from backpropagating (BP) the measured scattered field; or the novel physics-incorporated input (ITER10) obtained from the output image from the tenth iteration of CSI. For the purpose of training, validation, and testing the networks, 10000 samples from the well-known MNIST dataset are used. In the initial phase, two encoder-decoder-based convolutional neural networks (CNNs) are designed and implemented; the Baseline-AE, and the deep convolutional encoder-decoder network (DCEDnet). These models were tested in the two different input scenarios: (Eˢ ⃑ᶜᵗₘₑₐₛ and BP. The BP-DCEDnet provided improved results than the CSI at a much lesser time when tested within the MNIST database. In the next phase, three additional networks namely the Unet-Lite, Unet and the Attention- Unet (ATTN-Unet) together with DCEDnet were trained with BP and tested with complex profiles under different noise levels and permittivity range. The BP-ATTN- Unet outperformed the rest. However, the real component still needed to improve in comparison with the CSI. Thereby, the ATTN-Unet was trained with the ITER10 input to produce more stronger results. The optimized ITER10-ATTN-Unet was then successfully tested with experimental data and the results obtained were as expected.College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
Optical and Thermal Enhancements of PV Solar Collectors Via Encapsulated Phase Change Material
No full textA Master of Science thesis in Mechanical Engineering by Ahmed Tarek Hamada entitled, “Development and performance analysis of substrates coated with metal-organic framework for gas sensing applications”, submitted in April 2023. Thesis advisor is Dr. Mehmet F. Orhan. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Mechanical EngineeringMaster of Science in Mechanical Engineering (MSME