Recent Submissions

  • Control and Optimization of Chemical Reactors with Model-free Deep Reinforcement Learning

    Alhazmi, Khalid (2020-07) [Thesis]
    Advisor: Sarathy, Mani
    Committee members: Shamma, Jeff S.; Pinnau, Ingo
    Abstract: Model-based control and optimization is the predominant paradigm in process systems engineering. The performance of model-based methods, however, rely heavily on the accuracy of the process model, which declines over the operation cycle due to various causes, such as catalyst deactivation, equipment aging, feedstock variability, and others. This work aims to tackle this challenge by considering two alternative approaches. The first approach replaces existing control and optimization methods with model-free reinforcement learning (RL). We apply a state-of-the-art reinforcement learning algorithm to a network of reactions, evaluate the performance of the RL controller in terms of setpoint tracking, disturbance rejection, and robustness to parameter uncertainties, and optimize the reward function to achieve the desired control and optimization performance. The second approach presents a novel framework for integrating Economic Model Predictive Control (EMPC) and RL for online model parameters estimation. In this framework, EMPC optimally operates the closed-loop system while maintaining closed-loop stability and recursive feasibility. At the same time, the RL agent continuously compares the measured state of the process with the model’s predictions, and modifies the model parameters accordingly to optimize the process. The performance of the proposed framework is illustrated on a network of reactions with challenging dynamics and practical significance.
  • Synthesis and Characterization of Electroactive Vinylidene Fluoride Based Block Copolymers via Iodine Transfer Polymerization

    Alsubhi, Abdulaziz (2020-07) [Thesis]
    Advisor: Hadjichristidis, Nikos
    Committee members: Pinnau, Ingo; Nunes, Suzana Pereira
    Abstract: Poly (vinylidene fluoride) (PVDF), thanks to its versatile properties, finds many applications ranging from water purification membranes (thermal and chemical stability) to electronic devices (piezoelectric, pyroelectric and ferroelectric properties). Block copolymers of PVDF with other polymers further expand its properties and, consequently, its applications. Toward this line, my thesis investigates the synthesis, molecular characterization and properties of novel PVDF-based copolymers mainly with poly(tert-butyl acrylate) (PtBuA), poly(methyl methacrylate) (PMMA) and polystyrene (PSt). To prepare the block copolymers a living polymerization is needed, which is compatible with the VDF and the comonomer (tBuA, MMA, St). For this purpose, we used iodine transfer polymerization (ITP) with the difunctional chain transfer agent (CTA) C4F8I2. Difunctional macroinitiator (I-PVDF-I) was first obtained by ITP of VDF monomer with C4F8I2, followed by addition of the comonomer tBuA, MMA or St to afford the triblock copolymers poly(tert-butyl acrylate)-block-poly(vinylidene fluoride)-block-poly(tert-butyl acrylate) (PtBuA-b-PVDF-b-PtBuA), poly(methyl methacrylate)-block-poly(vinylidene fluoride)-block-poly(methyl methacrylate) (PMMA-b-PVDF-b-PMMA) and polystyrene-block-poly(vinylidene fluoride)-block-polystyrene (PSt-b-PVDF-b-PSt). The structure of all intermediates and final products were characterized by Nuclear Magnetic Resonance (NMR) and Gel Permeation Chromatography (GPC). The microstructure and polymorphism of all triblock copolymers, characterized by XRD, shown that the PVDF in the first two copolymers exhibits the electroactive β-phase, while in the third copolymer there is the coexistence of α- and γ-phases. Linear PVDF homopolymers, using the free radical and IT polymerizations, were prepared for comparison purposes. All linear polymers possess the α-phase. The thesis is divided into the following five chapters: 1. Introduction, where the scope of this thesis is given with a brief background on PVDF; 2. Literature Review, where a summary of previously published works on PVDF synthesis and polymorphism is presented; 3. Experimental Section, where detailed procedures and characterization methods are given; 4. Results and Discussion, where outcomes of successful experiments are discussed; and 5. Conclusion and Perspective, where the outcomes of this work are summarized and perspective are discussed.
  • Blind Estimation of Central Blood Pressure Waveforms from Peripheral Pressure Signals

    Magbool, Ahmed (2020-07) [Thesis]
    Advisor: Al-Naffouri, Tareq Y.
    Committee members: Al-Naffouri, Tareq Y.; Laleg-Kirati, Taous-Meriem; Gao, Xin
    The central aortic blood pressure signal is an important source of information that contains cues about the cardiovascular system condition. Measuring this pulse wave clinically is burdensome as it can be only measured invasively with a catheter. As a result, many mathematical tools have been proposed in the past few decades to reconstruct the aortic pressure signal from the peripheral pressure signals that are usually easier to obtain noninvasively. At the distal level, the blood pressure signal is not directly useful since factors, such as length and stiffness of the arteries, play roles in changing the shape of the pressure signal significantly. In this thesis, multi-channel blind system identification techniques are proposed to estimate the central pressure waveform which vary in their accuracy and complex- ity. First, a simple linear method is applied by approximating the nonlinear arterial system as a linear time-invariant system and applying the cross-relation approach. Next, a more complicated nonlinear Wiener system is proposed to model the nonlinear arterial tree. Along with the channel’s coefficients, the nonlinear functions are estimated using cross-relation and kernel methods. Data-driven machine learning methods are tested to estimate the aortic pressure signals. In many cases, they suffer from underfitting problems. As a remedy, a hybrid machine learning and cross-relation approach is also proposed to add more robustness to the machine learning models. This hybrid approach is implemented by combining the cross-relation with any machine learning method, including deep learning approaches. The various methods are tested using pre-validated virtual databases. The results show that the linear method produces root mean squared errors between 3.40 mmHg and 6.24 mmHg depending on the cross-relation constraint and the equalization tech- nique. On the other hand, the root mean squared errors associated with the nonlinear methods are between 3.76 mmHg and 4.22 mmHg and hence more stable. For the hybrid machine learning and cross-relation approach, applying the cross-relation and the dictionary learning reduce the root mean squared errors up o 67% comparing with the pure machine learning models.
  • Structural and Dynamic Profiles of the WT hFEN1 in solution

    Almulhim, Fatimah F. (2020-06) [Thesis]
    Advisor: Jaremko, Mariusz
    Committee members: Falqui, Andrea; Saikaly, Pascal
    Genomic DNA is under constant assault by environmental factors that introduce a variety of DNA lesions. Cells evolved several DNA repair and recombination mechanisms to remove these damages and ensure the integrity of the DNA material. A variety of specific proteins, called nucleases, processes toxic DNA structures that deviate from the heritable duplex DNA as common pathway intermediates. DNA-induced protein ordering is a common feature in all DNA repair nucleases. Still, the conformational requirement of the DNA and the protein and how they control the catalytic selectivity of the nuclease remain largely unknown. This study focus on the bases of catalytic activity of a protein belongs to the 5’ nuclease super-family called the human Flap endonuclease 1 (FEN1); it removes excess 5’ flaps that are generated during DNA replication. hFEN1 mutations and over-expression had been linked to a variety of cancers. This thesis aims to study the structural and dynamic properties of free hFEN1 and the catalytic activity of DNA-bound hFEN1 in solution utilizing the modern high-resolution multidimensional Nuclear Magnetic Resonance (NMR) spectroscopy. It was possible to depict the secondary structure and backbone conformation in solution of wild type (WT) hFEN1 by the usage of the improved list of assigned resonances, derived from the NMR 2D and 3D ¹⁵N-detected experiments and compared to the assignment with the previously published resonance assignment (BMRB id: 27160). I was successfully assigned the new spectrum and enhanced it by assigning seven more residues. Moreover, we tested the interaction of 1:10 ratio of hFEN1-Ca2+ with DNA by the ¹³C-detected 2D CACO experiment. The results indicate hFEN1:DNA interaction. Furthermore, parts of hFEN1 get more ordered/structured once DNA appears, thus we recorded the protein flexibly by 2D ¹H-¹⁵N TROSY-HSQC using the relaxation rate parameters: longitudinal R1, transverse R2 complemented with ¹⁵N-{¹H} NOEs (heteronuclear Overhauser enhancement). It was found that the overall molecular architecture is rigid, and the highest flexibility lies in the α2-α3 loop and arch (α4-α5) regions. Further analysis is needed to understand more profoundly the activity of hFEN1 in an atomic level by inducing mutations and testing the protein in various environmental conditions.
  • Machine Learning to Predict Entropy and Heat Capacity of Hydrocarbons

    Aldosari, Mohammed (2020-06) [Thesis]
    Advisor: Sarathy, Mani S.
    Committee members: Farooq, Aamir; Castano, Pedro
    Chemical substances are essential to all aspects of human life, and understanding their properties is essential for effective application. The properties of chemical species are usually measured by experimentation or computational calculation using theoretical methods. In this work, machine learning models (ML) for predicting entropy, S, and heat capacity, cp, were developed for alkanes, alkenes, and alkynes at 298.15 K. The data for entropy and heat capacity were collected from various sources. Commercial software (alvaDesc) then generated the molecular descriptors of all the hydrocarbons in the dataset used as input for the ML models. Support vector regression (SVR), v-support vector regression (v-SVR), and random forest regression (RFR) algorithms were trained with K-fold cross-validation on two levels. The first level assessed the models’ performance and the second level generated the final models. After a performance comparison of the three models, the SVR was chosen. To illustrate the advantage of using the ML approach, the SVR model was compared against Benson’s group additivity. Finally, a sensitivity analysis was performed.
  • An Experimental and Theoretical Investigation of Pressure-Induced Wetting Transitions

    Ahmad, Zain (2020-05) [Thesis]
    Advisor: Mishra, Himanshu
    Committee members: Nunes, Suzana; Farooq, Aamir; Ghaffour, Noreddine
    A number of industries suffer from inefficient use of energy resources due to frictional drag manifesting at solid-liquid interfaces. A simple method to reduce frictional drag under laminar flow conditions is to entrap air at the liquid-solid interface – in wetting state known as Cassie state. Over time, however, the entrapped air can be lost, and the Cassie state transitions to the fully-filled or the Wenzel state, thereby increasing the frictional drag dramatically. In particular, many practical applications expose surfaces to elevated pressures, and it is thus crucial to investigate pressure-induced Cassie-to-Wenzel transitions in gas-entrapping microtextured surfaces. However, there is a dearth of experimental techniques that can provide high-resolution optical images during wetting transitions at elevated pressures. In this thesis, we address this challenge designing and developing an inexpensive and robust pressure device that can act as an accessory for confocal laser scanning microscopy (CLSM). Equipped with this platform, we set out to visualize Cassie-to-Wenzel transitions in FDTS-coated circular doubly reentrant cavities (DRCs) and simple cavities. We demonstrate that on immersion in water, DRCs stabilize water-air interface, such that on the application of the external pressure as water penetrates into the DRCs, the liquid meniscus at the inlet remains pinned. In stark contrast, in SCs the water meniscus does not get pinned at the inlet, and it keeps on advancing with the increasing pressure along the cavity walls. Since localized laser heating in CLSM can influence wetting transitions, we utilized another custom-built pressure cell connected with upright optical microscopy as a complementary platform. We investigated the following wetting transitions: (i) breakthrough pressures (BtPs), defined as the pressure at which the liquid-vapor meniscus touches the cavity floor, by gradually ramping the external pressure, and (ii) wetting transitions at fixed pressures below the BtP. To understand the physical mechanisms underlying our experimental results, we utilized the Fick’s diffusion model and found that the consideration of air diffusion into water under elevated pressures is crucial. To conclude, we hope that the experimental and theoretical results presented here would advance the rational development of robust gas-entrapping microtextured surfaces for a myriad of applications
  • Conservation and Regulation of the Essential Epigenetic Regulator UHRF1 Across Vertebrata Orthologs

    Aljahani, Abrar (2020-05) [Thesis]
    Advisor: Fischle, Wolfgang
    Committee members: Arold, Stefan T.; Aranda, Manuel
    UHRF1 is a critical epigenetic regulator which serves as a molecular model for understanding the crosstalk between histone modification and DNA methylation. It is integrated in the process of DNA maintenance methylation through its histone ubiquitylation activity, ultimately functioning as a recruiter of DNA methyltransferase 1 (DNMT1). As the faithful propagation of DNA methylation patterns during cell division is a common molecular phenomenon among vertebrates, understanding the underlying conserved mechanism of UHRF1 for executing such a key process is important. Here, I present a broad-range evolutionary comparison of UHRF1 binding behavior and enzymatic activity of six species spanning across the vertebrata subphylum. According to their distinct binding modes to differentially methylated histone H3, a pattern is emerging which separates between mammalian and nonmammalian orthologs. H. sapiens, P. troglodytes and M. musculus UHRF1 orthologs utilize the functionality of both TTD and PHD domains to interact with histone H3 peptides, while G. gallus, X. laevis, and D. rerio employ either TTD or PHD. Further, UHRF1 allosteric regulation by 16:0 PI5P is a unique case to primate orthologs where H3K9me3 peptide binding is enhanced upon hUHRF1 and pUHRF1 interacting with 16:0 PI5P. This is due to their closed and autoinhibited conformation wherein TTD is blocked by the PBR region in linker 4. 16:0 PI5P outcompetes TTD for PBR binding resulting in a release of TTD blockage, hence, enhanced H3K9me3 binding. However, owing to the lack of phosphatidylinositol binding specificity and reduced sequence conservation of linker 4, the regulatory impact of 16:0 PI5P in avian and lower vertebrate orthologs could not be detected. Additionally, all UHRF1 orthologs exert their ubiquitylation enzymatic activity on histone H3 substrates, supporting the notion that the overall functionality of UHRF1 orthologs is conserved, despite their divergent molecular approaches. Taken together, my findings suggest that UHRF1 orthologs adopt distinct conformational states with a differential response to the allosteric regulators 16:0 PI5P and hemi-methylated DNA.
  • Assessing sharks and rays in shallow coastal habitats using baited underwater video and aerial surveys in the Red Sea

    Mcivor, Ashlie (2020-05) [Thesis]
    Advisor: Berumen, Michael Lee
    Committee members: Jones, Burton; Coker, Darren; Spaet , Julia
    Years of unregulated fishing activity have resulted in low abundances of elasmobranch species in the Saudi Arabian Red Sea. Coastal populations of sharks and rays in the region remain largely understudied and may be at risk from large-scale coastal development projects. Here we aim to address this pressing need for information by using fish market, unmanned aerial vehicle and baited remote underwater video surveys to quantify the abundance and diversity of sharks and rays in coastal habitats in the Saudi Arabian central Red Sea. Our analysis showed that the majority of observed individuals were batoids, specifically blue-spotted ribbontail stingrays (Taeniura lymma) and reticulate whiprays (Himantura sp.). Aerial surveys observed a catch per unit effort two orders of magnitude greater than underwater video surveys, yet did not detect any shark species. In contrast, baited camera surveys observed both lemon sharks (Negaprion acutidens) and tawny nurse sharks (Nebrius ferrugineus), but in very low quantities (one individual of each species). The combination of survey techniques revealed a higher diversity of elasmobranch presence than using either method alone, however many species of elasmobranch known to exist in the Red Sea were not detected. Our results suggest that aerial surveys are a more accurate tool for elasmobranch abundance estimates in low densities over mangrove-associated habitats. The importance of inshore habitats, particularly for batoids, calls for a deeper understanding of habitat use in order to protect these environments in the face of unregulated fishing, mangrove removal, and anticipated developments along the coastline of the Saudi Arabian Red Sea.
  • A Computational Study of Ammonia Combustion

    Khamedov, Ruslan (2020-05) [Thesis]
    Advisor: Im, Hong G.
    Committee members: Roberts, William Lafayette; Knio, Omar; Parsani, Matteo
    The utilization of ammonia as a fuel is a pragmatic approach to pave the way towards a low-carbon economy. Ammonia compromises almost 18 % of hydrogen by mass and accepted as one of the hydrogen combustion enablers with existing infrastructure for transportation and storage. From an environmental and sustainability standpoint, ammonia combustion is an attractive energy source with zero carbon dioxide emissions. However, from a practical point of view, the direct combustion of ammonia is not feasible due to the low reactive nature of ammonia. Due to the low combustion intensity, and the higher nitrogen oxide emission, ammonia was not fully investigated and there is still a lack of fundamental knowledge of ammonia combustion. In this thesis, the computational study of ammonia premixed flame characteristics under various hydrogen addition ratios and moderate or intense low oxygen dilution (MILD) conditions were investigated. Particularly, the heat release characteristics and dominant reaction pathways were analyzed. The analysis revealed that the peak of heat release for ammonia flame occurs near burned gas, which raises a question regarding the physics of this. Further analysis identified the dominant reaction pathways and the intermediate species (NH2 and OH), which are mainly produced in the downstream and back diffused to the leading edge and produce some heat in the low-temperature zone. To overcome low reactivity and poor combustion performance of pure ammonia mixture, the onboard ammonia decomposition to hydrogen and nitrogen followed by blending ammonia with hydrogen is a feasible approach to improve ammonia combustion intensity. With increasing hydrogen amount in the mixture, the enhancement of heat release occurs due to both transport and chemical effect of hydrogen. Another approach to mitigate the low reactive nature of ammonia may be eliminated by applying the promising combustion concept known as MILD combustion. The heat release characteristics and flame marker of ammonia turbulent premixed MILD combustion were investigated. The high fidelity numerical simulation was performed to answer fundamental questions of ammonia turbulent premixed combustion characteristics.
  • SeedQuant: A Deep Learning-based Census Tool for Seed Germination of Root Parasitic Plants

    Ramazanova, Merey (2020-04-30) [Thesis]
    Advisor: Ghanem,Bernard
    Committee members: Wonka, Peter; Thabet, Ali Kassem
    Witchweeds and broomrapes are root parasitic weeds that represent one of the main threats to global food security. By drastically reducing host crops' yield, the parasites are often responsible for enormous economic losses estimated in billions of dollars annually. Parasitic plants rely on a chemical cue in the rhizosphere, indicating the presence of a host plant in proximity. Using this host dependency, research in parasitic plants focuses on understanding the necessary triggers for parasitic seeds germination, to either reduce their germination in presence of crops or provoke germination without hosts (i.e. suicidal germination). For this purpose, a number of synthetic analogs and inhibitors have been developed and their biological activities studied on parasitic plants around the world using various protocols. Current studies are using germination-based bioassays, where pre-conditioned parasitic seeds are placed in the presence of a chemical or plant root exudates, from which the germination ratio is assessed. Although these protocols are very sensitive at the chemical level, the germination rate recording is time consuming, represents a challenging task for researchers, and could easily be sped up leveraging automated seeds detection algorithms. In order to accelerate such protocols, we propose an automatic seed censing tool using computer vision latest development. We use a deep learning approach for object detection with the algorithm Faster R-CNN to count and discriminate germinated from non-germinated seeds. Our method has shown an accuracy of 95% in counting seeds on completely new images, and reduces the counting time by a signi cant margin, from 5 min to a fraction of second per image. We believe our proposed software \SeedQuant" will be of great help for lab bioassays to perform large scale chemicals screening for parasitic seeds applications.
  • Evaluating the Application of Allele Frequency in the Saudi Population Variant Detection

    Alsaedi, Sakhaa (2020-04-26) [Thesis]
    Advisor: Hoehndorf, Robert
    Committee members: Gao, Xin; Gojobori, Takashi
    Human Mendelian disease in Saudi Arabia is both significant and challenging. Next-generation sequencing (NGS) has resulted in important discoveries of the genetic variants responsible for inherited disease. However, the success of clinical genomics using NGS requires accurate and consistent identification of rare genome variants. Rarity is one very important criterion for pathogenicity. Here we describe a model to detect variants by analyzing allele frequencies of a Saudi population. This work will enhance the opportunity to improve variant calling workflow to gain robust frequency estimates in order to better detect rare and unusual variants which are frequently associated with inherited disease.
  • Applications of Graph Convolutional Networks and DeepGNC's in Point Cloud Part Segmentation and Upsampling

    Abualshour, Abdulellah (2020-04-18) [Thesis]
    Advisor: Ghanem,Bernard
    Committee members: Hadwiger, Markus; Wonka, Peter
    Graph convolutional networks (GCNs) showed promising results in learning from point cloud data. Applications of GCNs include point cloud classi cation, point cloud segmentation, point cloud upsampling, and more. Recently, the introduction of Deep Graph Convolutional Networks (DeepGCNs) allowed GCNs to go deeper, and thus resulted in better graph learning while avoiding the vanishing gradient problem in GCNs. By adapting impactful methods from convolutional neural networks (CNNs) such as residual connections, dense connections, and dilated convolutions, DeepGCNs allowed GCNs to learn better from non-Euclidean data. In addition, deep learning methods proved very e ective in the task of point cloud upsampling. Unlike traditional optimization-based methods, deep learning-based methods to point cloud upsampling does not rely on priors nor hand-crafted features to learn how to upsample point clouds. In this thesis, I discuss the impact and show the performance results of DeepGCNs in the task of point cloud part segmentation on PartNet dataset. I also illustrate the signi cance of using GCNs as upsampling modules in the task of point cloud upsampling by introducing two novel upsampling modules: Multi-branch GCN and Clone GCN. I show quantitatively and qualitatively the performance results of our novel and versatile upsampling modules when evaluated on a new proposed standardized dataset: PU600, which is the largest and most diverse point cloud upsampling dataset currently in the literature.
  • Image Embedding into Generative Adversarial Networks

    Abdal, Rameen (2020-04-14) [Thesis]
    Advisor: Wonka, Peter
    Committee members: Hadwiger, Markus; Ghanem, Bernard
    We propose an e cient algorithm to embed a given image into the latent space of StyleGAN. This embedding enables semantic image editing operations that can be applied to existing photographs. Taking the StyleGAN trained on the FFHQ dataset as an example, we show results for image morphing, style transfer, and expression transfer. Studying the results of the embedding algorithm provides valuable insights into the structure of the StyleGAN latent space. We propose a set of experiments to test what class of images can be embedded, how they are embedded, what latent space is suitable for embedding, and if the embedding is semantically meaningful.
  • Crosstalk Cancellation in Structured Light Free Space Optical Communication

    Briantcev, Dmitrii (2020-04) [Thesis]
    Advisor: Alouini, Mohamed-Slim
    Committee members: Ooi, Boon S.; Park, Ki-Hong
    Free-space optics (FSO) is an unlicensed communication technology that uses the free space as a propagation medium to connect two communicating terminal wire- lessly [1]. It is an attractive solution to the last-mile connectivity problems in commu- nication networks, mainly when installing optical fibers is expensive or unavailable. A possible idea to increase the throughput of wireless optical links in free space is to use spatial multiplexing (SMM) [2]. Optical beam distortion due to propagation through a turbulent channel is one of the main factors limiting performance of such a system. Therefore, overcoming the effect of turbulence is a major problem for structured light optical communication in free space. Usually, this problem is approached by using adaptive optics systems and various methods of digital signal processing (DSP) on the receiver side [3–5]. Recently, an idea of optical channel pre-compensation to mit- igate inter-modal crosstalk was proposed [6] and experimentally validated [7]. Such a method, if implemented, will allow the use of entirely passive receivers or, in the case of full-duplex transmission, increase throughput. Here, the performance of a zero-forcing precoding technique to mitigate the effects of an optical turbulence in a Laguerre Gaussian mode based SMM FSO is investigated. Equally, details on a close to reality simulation of the atmospheric turbulence and beam propagation are provided.
  • Modeling and Assessment of Dynamic Charging for Electric Vehicles in Metropolitan Cities

    Nguyen, Duc Minh (2020-04) [Thesis]
    Advisor: Alouini, Mohamed-Slim
    Committee members: Shihada, Basem; Amin, Osama
    Electric vehicles (EVs) have emerged to be the future of transportation as the world observes its rising demand and usage across continents. However, currently, one of the biggest bottlenecks of EVs is the battery. Small batteries limit the EVs driving range, while big batteries are expensive and not environmentally friendly. One potential solution to this challenge is the deployment of charging roads, i.e., dynamic wireless charging systems installed under the roads that enable EVs to be charged while driving. In this thesis, we establish a framework using stochastic geometry to study the performance of deploying charging roads in metropolitan cities. We first present the course of actions that a driver may take when driving from a random source to a random destination, and then analyze the distribution of the distance to the nearest charging road and the probability that the trip passes through at least one charging road. These probability distributions assist not only urban planners and policy makers in designing deployment plans of dynamic wireless charging systems, but also drivers and automobile manufacturers in choosing the best driving routes given the road conditions and level of energy of EVs.
  • Contributions to the semi-classical signal analysis method: The arterial stiffness assessment case study

    Piliouras, Evangelos (2020-04) [Thesis]
    Advisor: Laleg-Kirati, Taous-Meriem
    Committee members: Feron, Eric; De Wolf, Stefaan; Al Attar, Talal
    Semi-classical signal analysis (SCSA) is a signal representation framework based on quantum mechanics principles and the inverse scattering transform. The signal of interest is decom- posed in a linear combination of the Schrodinger operator squared eigenfunctions, influenced by the semi-classical parameter. The framework has been utilized in several applications, in virtue of the adaptivity and localization of its components. In this thesis, we expand two direc- tions. From the theoretical perspective, up to date, the semi-classical parameter was selected in an error minimization context or a representation sparsity requirement. The framework is reinforced by providing the interval of this parameter, where a proper representation can be obtained. The lower bound is inspired by the semi-classical approximation and the sampling theorem, while the upper bound is based on the quantum perturbation theory. Such an interval defines the sampling theorem of the framework. Based on existing properties, we propose a non-uniform sampling of the semi-classical parameter, which can significantly increase the speed of convergence with minimal accuracy error. An immediate representation is also in- vestigated by providing an alternative convergence criterion drawn from signal features. Such criterion paves the way to a calculus-based parameter definition and extension to a filtering scenario. The semi-classical parameter exerts a strong influence on the SCSA components. Each component can be viewed as a soliton, a wave whose amplitude determines its width and velocity. In parallel, there exist arterial dynamics models where the solitons are solu- tions of the describing equations. We therefore propose that the soliton propagation velocity extracted from the algorithm is correlated with the pulse wave velocity, which is the blood pressure propagation velocity in the systolic phase. The velocity in the carotid-femoral seg- ment is considered the golden-standard to indicate cardiovascular risk. We therefore turn our attention to validate such a model and utilize it for arterial stiffness assessment. The model was validated based on an in-silico database fostering more than 3000 subjects. This SCSA-based model is proposed to be integrated into existing methods, where its calibration can yield single-point continuous velocity measurements.

    Celis Sierra, Sebastian (2020-04) [Thesis]
    Advisor: Salama, Khaled N.
    Committee members: Bagci, Hakan; Shihada, Basem
    Communication systems have remained almost unchanged since the invention of the superheterodyne receiver in 1918 by the US engineer Edwin Armstrong. With the introduction of multiple-input-multiple-output (MIMO) technologies, Index Modulation appears to be the promising technology to revolutionize the traditional radio-frequency (RF) chain. Index modulation is a high-spectrum, energy-efficient, simple digital communication technique that uses the states of the building blocks of a communication system. In this study, we have focused on the use of radiation patterns scattered by antenna arrays or a metasurface as indices that are encoded as data bits. Initially, we explore sets of 𝑁tx transmitting point source antennas located on the XY plane; we assume that every antenna has phase tunability capability. The phase, the position in space, and the size of the array determine the shape of the far-field radiation pattern. Following the antenna excitation, a set of 𝑁rx receiver antennas spread at specific locations of the spherical space measures the incoming power signal, allowing the sampling of the radiation pattern that is demodulated into information bits.This work is focused on the characterization of the measured radiation patterns under different system and channel variables and their direct effect on the Bit Error Rate.
  • A First Principle Investigation of Band Alignment in Emerging III-Nitride Semiconductors

    Al Sulami, Ahmad (2020-04) [Thesis]
    Advisor: Li, Xiaohang
    Committee members: Salama, Khaled N.; Schwingenschoegl, Udo
    For more than seventy years, semiconductor devices have functioned as the cornerstone for technological advancement, and as the defining transition into the information age. The III-Nitride family of semiconductors, in particular, underwent an impressive maturation over the past thirty years, which allowed for efficient light- emitting devices, photo-detectors, and power electronic devices. As researchers try to push the limits of semiconductor devices, and in particular, as they aim to design ultraviolet light emitters and high threshold power devices, the search for new materials with high band gaps, high breakdown voltages, unique optical properties, and variable lattice parameters is becoming a priority. Two interesting candidates that can help in achieving the aforementioned goals are the wurtzite BAlN and BGaN alloy systems, which are currently understudied due to difficulties associated with their growth in epitaxial settings. In our research, we will investigate the band alignment between BAlN and BGaN alloys, and other wurtzite III-Nitride semiconductors from first principle simulations. Through an understanding of band alignment types and a quantification of the band offset values, researchers will be able to foresee the applicability of a particular interface. As an example, a type-I band alignment with a high conduction band offset and a low valence band offset is a potential electron blocking layer to be implemented in standard LED designs. This first principle investigation will be aided by simulations using Density Functional Theory (DFT) as implemented in the Vienna Ab Initio Simulation Package (VASP) environment. In addition, we will detail an experiment from the literature that uses X- ray Photoelectron Spectroscopy on multiple samples to infer and quantify the band alignment between different materials of interest to us. We aim in this study to anticipate the band alignment in interfaces involving materials at the cutting edge of research. Our hope is to set a theoretical ground for future experimental studies on this same matter in parallel to the current efforts to improve the quality and stability of wurtzite BAlN and BGaN alloy crystals.
  • Asymptotic Sum Rate Analysis Over Double Scattering Channels With MMSE Estimation and MRT Precoding

    Ye, Jia (2020-04) [Thesis]
    Advisor: Alouini, Mohamed-Slim
    Committee members: Alouini, Mohamed-Slim; Ooi, Boon S.; Kammoun, Abla
    This thesis investigates the performance of a multi-user multiple-input single- output (MISO) system considering maximum ratio transmission (MRT) downlink precoding. The transmitted signal from the base station (BS) to each user is as- sumed to experience the double scattering channel. We adopt the minimum-mean- square-error (MMSE) channel estimator for the proposed model. Within this setting, we are interested in deriving tight approximations of the ergodic rate assuming the number of BS antennas, users, and scatterers grow large with the same pace. Under the special multi-keyhole channels, these deterministic equivalents are expressed in more simplified closed-form expressions. The simplified expressions reveal that unlike the standard Rayleigh channel in which the SINR grows as as O(N/k), the SINR associated with a multi-keyhole channel scales as O(S/N). This particularly shows that the K reaped gains of the large-scale MIMO over double scattering channels do not linearly increase with the number of antennas and are limited by the number of scatterers. We further show that the derived asymptotic results match the simulation results closely under moderate system dimensions and provide some useful insights into the interplay between N, K and S.
  • Exploring the Role of Glutamate Signaling in the Regulation of the Aiptasia-Symbiodiniaceae Symbiosis

    Konciute, Migle (2020-04) [Thesis]
    Advisor: Aranda, Manuel
    Committee members: Lauersen, Kyle J.; Morán, Xosé Anxelu G.
    The symbiotic relationship between cnidarians and their photosynthetic dinoflagellate symbionts underpins the success of coral reef communities in oligotrophic, tropical seas. Despite several decades of study, the cellular and molecular mechanisms that regulate the symbiotic relationship between the dinoflagellate algae and the coral hosts are still not clear. One of the hypotheses on the metabolic interactions between the host and the symbiont suggests that ammonium assimilation by the host can be the underlying mechanism of this endosymbiosis regulation. An essential intermediate of the ammonium assimilation pathway is glutamate, which is also known for its glutamatergic signaling function. Interestingly, recent transcriptomic level and DNA methylation studies on sea anemone Aiptasia showed differences in metabotropic glutamate signaling components when comparing symbiotic and non-symbiotic animals. The changes in this process on transcriptional and epigenetic levels indicate the importance of glutamate signaling in regard to cnidarian symbiosis. In this study, I tested glutamatergic signaling effect on symbiosis in sea anemone Aiptasia using a broad-spectrum glutamate receptor inhibitor 7- CKA and glutamate. Significantly decreased cell density was observed in animals with inhibitor treatment suggesting a possible correlation between glutamate signaling and the establishment or maintenance of symbiosis. Using RNA-Seq, I was able to obtain transcriptional profiles of the animals under inhibitor and glutamate treatment. Differential gene expression and gene ontology analyses indicated changes in amino acid metabolism, lipid metabolism and such signaling pathways as MAPK, NF-kappa B and phospholipase C. Although amino acid and lipid metabolism could be a result of the reduced symbiotic state of inhibitor treated Aiptasia, the signaling pathways which are related to apoptosis and immune response provide an exciting venue for direct regulatory interaction between symbiosis and glutamatergic signaling. However, as these signaling pathways mainly act via signal transduction through protein phosphorylation, further studies looking at changes on a post-translational level might provide further insight into the mechanisms underlying the observed phenotype.

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