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Practical Considerations in Frequency Diverse Array Radar Signal Processing
No full textA Master of Science thesis in Electrical Engineering by Abeer Nasir Chaudhry entitled, “Practical Considerations in Frequency Diverse Array Radar Signal Processing”, submitted in April 2021. Thesis advisor is Dr. Hasan Mir. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).The frequency diverse array (FDA) has been shown to produce a range and angle dependent beampattern, unlike a conventional phased array which yields only a direction dependent pattern. The FDA generates a range-selective projection on to the far-field region that migrates spatially along the extent of the functional range. A recent FDA architecture known as equivalent transmit beamforming has been demonstrated to exhibit a time-independent behavior that can potentially simplify the receiver signal processing. This thesis studies lapses in practical considerations of equivalent transmit beamforming system operations. The influence of target motion on equivalent transmit beamforming is elaborated upon, and a metric is constructed to quantify the adverse consequences of a non-stationary target on system detection capabilities. The equivalent transmit beamforming scheme is then translated onto 2-dimensional arrays that have more flexibility in control across dimensions (range, azimuth, elevation). The unique array factors of the planar configurations are formulated from which the radiation characteristics are gauged in order to assess the relative performance of each configuration. Finally, the effect of unknown mutual coupling in both transmit and receive mode is considered. An algorithm is proposed which performs online joint estimation of the mutual coupling coefficients and the target parameters, along with a mutual coupling compensation method. Detailed signal model formulations and simulation results are included to confirm the results of this work.College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
Stress Management Using Physiological Signals and Audio Stimulation
No full textA Master of Science thesis in Biomedical Engineering by Rateb Majd Katmah entitled, “Stress Management Using Physiological Signals and Audio Stimulation”, submitted in November 2021. Thesis advisor is Dr. Hasan Al-Nashash and thesis co-advisors are Dr. Usman Tariq and Dr. Fares Yahya. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Stress has a significant role in the development of a wide variety of mental, psychological, emotional, behavioral, and physical illnesses. Additionally, there is substantial evidence in the literature that stress impairs vigilance. Thus, early stress detection, vigilance enhancement, and stress mitigation may aid in the prevention of a wide range of diseases and improve human health. The purpose of this thesis is to examine the effects of binaural beat stimulation (BBs) on increasing alertness and reducing mental stress in the workplace. We devised an experiment in which participants were subjected to time pressure and negative feedback while completing the Stroop Color-Word Task (SCWT). Then, we used 16 Hz BBs to improve vigilance and reduce stress levels. Functional Near-Infrared Spectroscopy (fNIRS), salivary alpha-amylase, behavioral data, and subjective reactions were used to determine the levels of stress. We quantified the level of stress using statistical analysis, functional connectivity based on Partial Directed Coherence (PDC), Graph Theory Analysis (GTA) and Convolution Neural Network (CNN). We discovered that BBs substantially increased target detection accuracy by 11.05% (p<0.001), decreased effort and temporal demand, boosted performance, and decreased cortisol levels. The deep learning results indicated that the CNN technique combined with PDC features is capable of discriminating between four distinct mental states (vigilance, enhancement, stress, and mitigation) with an average accuracy of 70.62%, a sensitivity of 68.39%, and a specificity of 90.76%.College of EngineeringMultidisciplinary ProgramsMaster of Science in Biomedical Engineering (MSBME
Attitudes towards climate change and energy sources in oil exporters
No full textSwitching to energy mixes that use more non-fossil fuels is critical to reduce greenhouse gas emissions to tackle climate change. Climate change poses a major challenge to oil exporting Gulf countries, like the rest of the world, but research on human views on energy and climate change is limited. We aim to fill this gap by focusing on the UAE, a nation with a peculiar demographic composition that includes an overwhelming proportion of expatriates and transitions towards green and nuclear resources. We examine whether transiency of residence and life satisfaction play a role in influencing perceptions about climate change and energy sources. We also analyze how expatriates' opinions differ from UAE citizens who have significantly higher income and welfare benefits.Economic and Social Research CouncilAmerican University of Sharja
Assessment and Performance Analysis of Machine Learning Techniques for Gas Sensing E-nose Systems
No full textA Master of Science thesis in Engineering Systems Management by Lubna Syeda Mahmood entitled, “Assessment and Performance Analysis of Machine Learning Techniques for Gas Sensing E-nose Systems”, submitted in November 2021. Thesis advisor is Dr. Zied Bahroun and thesis co-advisor is Dr. Mehdi Ghommem. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).The electronic nose, commonly known as the E-nose that combines gas sensor arrays (GSAs) with machine learning, has gained a strong foothold in gas sensing technology. The E-nose, inspired from the human olfactory system, is used for the detection and identification of various volatile organic compounds (VOCs). GSAs produce a unique signal fingerprint for each gas, providing vital information for machine learning algorithms to detect the gas type using classification and estimate its concentration through regression. The inexpensive, portable and non-invasive characteristics of E-noses have rendered them indispensable within the gas-sensing arena. As a result, E-noses are now widely employed for several applications in food industries, disease diagnosis, and environment management. In this thesis, we first review various sensor fabrication technologies and provide a comprehensive literature review of machine learning in gas sensing. Then, we present a detailed assessment of machine learning models employed for classification and regression using the software tool RapidMiner. The models discussed in this thesis include the Artificial Neural Networks, k-Nearest Neighbors, Decision Tree, Random Forests, Support Vector Machine and other ensembling-based models. The models are tested on three different experimental datasets obtained from MoX gas sensors as reported in the literature, followed by their performance analysis. The obtained results are compared against those reported in previously published studies. Classification accuracies reached 99.38% using Random Forests and Support Vector Machine whereas mean absolute percentage errors (MAPEs) were found as low as 5.98%, 8.89%, 6.35% using the k-Nearest Neighbors, Random Forests, and ensemble methods, respectively. Techniques of feature selection and Principle Component Analysis (PCA) retained significant signal characteristics that improved model performances with MAPEs of 8.15% using k-Nearest Neighbors and 4% using Random Forests. The assessment, thus, highlights factors that play a pivotal role in machine learning for gas sensing and sheds light on the predictive capability of different machine learning approaches applied on experimental GSA datasets.College of EngineeringDepartment of Industrial EngineeringMaster of Science in Engineering Systems Management (MSESM
Adsorptive Membranes for The Removal of Antibiotics from Pharmaceutical Wastewater
No full textA Master of Science thesis in Chemical Engineering by Liyan Omar Qalyoubi entitled, “Adsorptive Membranes for The Removal of Antibiotics from Pharmaceutical Wastewater”, submitted in December 2021. Thesis advisor is Dr. Amani Lutfi Al-Othman and thesis co-advisor is Dr. Sameer Al-Asheh. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Adsorptive membranes are considered among the promising technologies that have shown competence in removing different pollutants from wastewater. They possess the dual advantage of adsorption and filtration. Pharmaceutical compounds including antibiotics are emerging contaminants of major concern because they cannot be fully removed via conventional wastewater treatment methods. Therefore, there is a crucial need for an effective technology such as the adsorptive membrane technology. In this work, an adsorptive membrane composed of Polyethersulfone (PES) with Zirconium Phosphate (ZrP) adsorbent was synthesized for the removal of Ciprofloxacin antibiotic from synthetic water solutions. Batch adsorption experiments using zirconium phosphate were conducted first to determine the optimum conditions for the antibiotic removal. Several factors were studied including the initial concentration of the antibiotic, the adsorbent dosage, contact time, pH, and temperature. The experimental data were best fit by the Temkin isotherm. Based on the adsorption batch results, the PES/ZrP membrane was synthesized by solution spin coating and tested with various adsorbent loadings to investigate the optimum ZrP loading in the membrane. The composite membrane showed a high ciprofloxacin removal reaching up to 99.7% which indicated an enhancement compared to the use of PES membrane alone (68%). Moreover, a significant improvement in the membrane's water flux (100.84 L/m².h) and permeability (97.62 L/m².hr.bar) were noticed as opposed to pure PES membrane's flux and permeability. Several characterization analyses were conducted including SEM, EDS, FTIR, XRD, and BET, which demonstrated the successful ZrP deposition in the membrane’s pores with enhanced hydrophilicity properties and effective surface area. Lastly, the membrane was successfully regenerated and reused up to 5 times which indicates the potential of PES/ZrP adsorptive membrane for the removal of ciprofloxacin and at a high efficiency.College of EngineeringDepartment of Chemical EngineeringMaster of Science in Chemical Engineering (MSChE
Droplet Geometry and Device Configuration Effects on Droplet Actuation in Open Digital Microfluidics
No full textA Master of Science thesis in Mechanical Engineering by Malik Mamoun Al-Lababidi entitled, “Droplet Geometry and Device Configuration Effects on Droplet Actuation in Open Digital Microfluidics”, submitted in December 2021. Thesis advisor is Dr. Mohamed Abdelgawad. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Digital microfluidics (DMF) is the manipulation of liquid droplets over an array of micro electrodes. It is considered as a new technology that facilitate several applications such as chemical synthesis, biological assays, and electronics cooling. Determination of droplet actuation forces is essential in DMF analysis to ensure fast and reliable droplet motion. This thesis demonstrates the origin of these electrical actuation forces and investigates the parameters that affect these forces. In addition, different approaches to calculate actuation forces are explored. In most previous studies, droplets actuation forces on open digital microfluidic devices were calculated using the capacitive energy approach, whereas this thesis implements numerical modeling using COMSOL Multiphysics to calculate the electrical forces generated on a droplet based on the Maxwell stress tensor. The different investigated parameters are droplet volume, droplet contact angle, electrode shape, and grounding configuration. It has been found that droplets with large volumes and small contact angles experience highest actuation forces. Both droplet volume and contact angle decide the droplet base radius which should be large and far enough from the high electric field intensity region at rear of the actuated electrode to avoid generating any backward forces. The best studied electrode shape for droplet actuation was the pinned electrodes with a pin radius of 0.6 mm for a 28 μL droplet. However, square electrodes with all-ground configuration had the highest actuation force average (39.04 μN) for the 48 μL droplet. In general, having substantial grounding surrounding the droplet enhances actuation forces. A summary of all simulated models with different droplet volumes, contact angles, electrode shape, and grounding configuration was tabulated.College of EngineeringDepartment of Mechanical EngineeringMaster of Science in Mechanical Engineering (MSME
A Low Cost Process for Fabricating FDM Filaments Reinforced with Inorganic Fillers
No full textA Master of Science thesis in Mechanical Engineering by Mohamed Hassanien entitled, “A Low Cost Process for Fabricating FDM Filaments Reinforced with Inorganic Fillers”, submitted in July 2021. Thesis advisor is Dr. Maen Alkhader and thesis co-advisor Dr. Bassam Abu-Nabah. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Low cost desktop-sized fused deposition modelling (FDM) printers have been widely embraced by small to large scale institutions as well as individuals. Their ease of use and low cost qualified them to become an important enabling platform that enhances creativity and transforms design and modelling processes. To further enhance their utility and add another dimension to the range of possible materials that desktop FDM printers can process, multiple efforts originating from within the desktop FDM community have tried to create low cost desktop sized solutions capable of fabricating customized filaments. These attempts utilized short single screw extruders and did not have the mixing abilities provided by industrial multi-stage twin screw extruders. Therefore, low cost solutions were not effective in fabricating filaments customized with particle based fillers. This work aims to propose a process that enables low cost extruders to fabricate filaments with particle based fillers. In the proposed process, particles are heated and deposited on thermoplastic pellets that are subsequently extruded using a low cost desktop single screw extruder. Depositing the reinforcing particles on the pellets allows for minimizing the need for the mixing process that takes place in industrial extruders. To demonstrate the effectiveness of the process, PLA based filaments with two types of fillers were fabricated from commercial PLA pellets. Fillers used were Dune sand and Silicon Carbide. Dune sand was selected for its availability in the UAE and Silicon Carbide for its high stiffness and strength. Filaments with different particle weight fractions were fabricated and experimentally tested. Filaments’ stiffness and strength were measured, and their microstructure along their lateral and longitudinal directions was observed. Improvements in elastic moduli, stiffness and yield strength were recorded for both of the developed reinforced filaments. The effect of aging on tensile strength, elastic moduli and yield strength had been investigated which stems from biodegradability of PLA. Results show that the proposed process can fabricate filaments with multiple types of inorganic fillers. Produced filaments were successfully used to fabricate parts using a commercial Desktop FDM printer.College of EngineeringDepartment of Mechanical EngineeringMaster of Science in Mechanical Engineering (MSME
INScription: Department of International Studies (INS) Issue #3 (November 28, 2021, Issue 3)
No full textCollege of Arts and SciencesDepartment of International Studie
Finite Element Investigation of Pre-Damaged RC Columns Retrofitted with FRCM
No full textA Master of Science thesis in Civil Engineering by Muhammad Kyaure entitled, “Finite Element Investigation of Pre-Damaged RC Columns Retrofitted with FRCM”, submitted in June 2021. Thesis advisor is Dr. Farid Abed. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Reinforced concrete (RC) structures in the UAE and globally face adverse deterioration during their lifetime. A novel retrofitting system using poly-paraphenylene-benzobisoxazole (PBO) Fiber Reinforced Cementitious Matrix (FRCM) is investigated in this thesis. FRCM is a noncorrosive two-dimensional high strength FRP mesh saturated with inorganic cement mortar which is compatible with concrete substrates. A three-dimensional (3D) nonlinear finite element (FE) model is developed using ABAQUS to study the behaviour of corrosion damaged RC columns retrofitted with PBO-FRCM systems. A total 180 cases of FE models are developed using a concrete compressive strength of 30 MPa and a longitudinal reinforcement ratio of 2% typical to columns. A comprehensive parametric study is conducted considering the effects of five parameters: (a) cross section type (square vs circular), (b) FRCM layers (1 vs 2 vs 3 vs 4 layers), (c) damage level (mild vs moderate vs severe damage), (d) eccentricity ratio (e/h= 0.0, 0.3, 0.5, 0.75, 1.0, 1.25 and 1.5) (e) column type/length (short/800mm vs slender/1200mm). Displacement controlled loading condition is used and material nonlinearities in concrete, cement mortar and composite are incorporated in the FE model. The FE models are validated against published literature. Results indicated a positive correlation between the number of FRCM layers, axial capacity, and ductility enhancement which is more pronounced in circular columns. Enhancement in axial capacity of 20% was observed in square columns and 35% in circular columns while axial ductility enhancement of 42% was observed in square columns and 164% in circular columns. All strengthened specimens failed by matrix damage indicating effectiveness of the strengthening system irrespective of the cross-section type. Retrofitting corrosion damaged RC columns with PBO-FRCM restored and enhanced the axial capacity and ductility at all damage levels. Increasing the number of FRCM layers increased the axial capacity of eccentrically loaded columns irrespective of damage level and eccentricity ratio. Comparison of column axial capacity, which was computed based on ACI 549.4R-13 provisions, against FEA revealed that the code provisions underestimate the axial capacity of short RC columns retrofitted with PBOFRCM by 20%.College of EngineeringDepartment of Civil EngineeringMaster of Science in Civil Engineering (MSCE
Construction Materials Classification Using Wi-Fi and Convolutional Neural Networks
No full textA Master of Science thesis in Electrical Engineering by Mohamed Ait Gacem entitled, “Construction Materials Classification Using Wi-Fi and Convolutional Neural Networks”, submitted in November 2021. Thesis advisor is Dr. Mahmoud Ibrahim and thesis co-advisors are Dr. Amer Zakaria and Dr. Usman Tariq. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).The process of identifying the properties of a construction material is vital in several industrial and quality assurance applications. Such process normally requires not causing damage to the observed sample and having a high accuracy with low cost of implementation. In this work, a novel non-destructive construction materials classification tool is proposed. The proposed method is based on passing Wi-Fi signals through the observed samples, then analyzing the Channel State Information (CSI) amplitude and phase components. Wi-Fi signals are affected by the channel variations in the form of amplitude attenuation and phase shift. Hence, placing different objects in the channel will yield different CSI responses. The collected CSI data packets are pre-processed by performing an averaging operation. Then the resulting data are divided into training and validation sets to be used to train a Convolutional Neural Network (CNN). The trained CNN models are formulated as classifiers when the data can be sorted into specific classes. Alternatively, the models are formulated as regression models when the data have a continuous nature. The proposed method is used to classify materials through two phases. The main goal of phase 1, is to investigate the proposed method’s potential to perform materials classification using a relatively simple set of samples, which are composed of one type of materials. The experiments are namely, thickness estimation of Plexiglas, estimating the water content value in fine materials and confirming the compaction of individual materials. While in phase 2, the studied samples are heterogeneous, which makes them more challenging since having several materials with different properties in the same mixture generates a more complex response. Nevertheless, such samples are ideal in highlighting the proposed method’s generalization efficacy and possible limits. The experiments in phase 2 include confirming concrete mixtures homogeneity and detecting the presence of a specific construction material within a concrete mixture. The obtained experimental results effectively demonstrate the potential and merits of the proposed method. Overall, the CNN models achieved a 100% validation accuracy and a low validation loss, which confirms that the method is valid and highly accurate.College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE