Electrical and Computer Engineering

Permanent URI for this collection

Browse

Recent Submissions

Now showing 1 - 9 of 9
  • Item
    Innovative Designs For Low Profile Antenna Systems For Mimo 5G/V2X And Gnss Communications
    (2022-11-07) Yacoub, Ahmad; Aloi, Daniel N; Cesmelioglu, Aycil; Li, Jia; Qu, Guangzhi
    The research in this dissertation shows the analysis and development of designing antenna elements and MIMO antenna systems that can be implemented in the automotive industry and have a significant role in an autonomous driving system. The antennas presented in this research cover 5G-sub-6GHz bands which have much more extended bandwidth compared to previous LTE network, in addition to covering Vehicle-to-Everything (V2X) frequency band. The design work presented in this research followed a scientific method that included simulating the antenna element using 3-D electromagnetic solver (HFSS) on 1-meter ground plane. The antenna was then fabricated using properlycut metal sheet, measured on 1-meter rolled-edge ground plane, and measured on a vehicle’s roof inside an anechoic chamber for practical measurements. The design guidelines and measurements are discussed in detail throughout this work. The first two sections of this research begin with presenting a novel wideband low-profile Planar Inverted F-Antenna (PIFA) that covers a wide frequency range (617MHz-6000MHz) that includes cellular 5G bands and V2X band while having reasonable rejection for Global Navigation Satellite System (GNSS) frequency bands. Then, Multiple MIMO configurations based on the novel PIFA design are studied by using spatial diversity, rotational diversity, and orthogonal diversity to increase the total throughput and data rate of the system. The performance of each element in the MIMO system is analyzed and discussed to evaluate the functionality of the system. The third section of this work introduces a 2x2 MIMO antenna system for vehicular application in the sub-6GHz 5G systems that operates in the middle and high frequency bands from 1.71GHz to 5GHz. The design consists of two novel raised printed monopoles on Flame Retardant 4 (FR4) dielectric material with Partial Ground Plane (PGP) structure to improve bandwidth impedance and achieve higher isolation across the operating frequency range. The design is an excellent candidate to be implemented in a shark-fin housing due to its low-profile characteristics and good electrical performance. Eventually, in the fourth section of this work, a fully integrated low-profile antenna systems for MIMO 5G and global navigation satellite system (GNSS) for L1/L5 frequency bands is presented. The designs can be used on the vehicle’s roof inside a low profile housing due to its physical parameters and RF performance.
  • Item
    Robust Non-Linear Lyapunov Deep Learning Control Design For Chaotic Systems
    (2022-11-07) Mahmoud, Amr Salah; Zohdy, Mohamed; Dean, Brian; Schmidt, Darrell; Olawoyin, Richard
    Despite their operational success, machine learning controllers lack theoretical guarantees in terms of system stability. In contrast, classic model-based controller design uses principled approaches such as Linear Quadratic Regulator (LQR) to synthesize stable controllers with verifiable proofs. In addition, deep learning controllers encounter feedback timing bottlenecks that increase exponentially with the system complexity. Deep learning is also dependent on the quality and diversity of the dataset to produce unbiased findings; therefore, the prediction of deep learning is not guaranteed. As a result, in this research, we develop and implement a guaranteed stability solution for safety critical and chaotic systems through the integration of Lyapunov Stability theory and deep machine learning. Three control methods are researched, leading to the development of the Deep Lyapunov-stable controller: the deep learning methodology, the Lyapunov control function, and controller parameters. In this research, we provide a generic method for synthesizing a Deep Lyapunov-stable control and a way to simultaneously confirm its stability. A unique Lyapunov control function is devised and shown to be effective in managing Duffing, Van der Pol, and Zohdy-Harb nonlinear systems, but with restrictions on the system's oscillation frequency, initial conditions and disturbances. Subsequently, Dynamic Lyapunov Deep Learning is introduced to alleviate the Lyapunov control’s shortcomings. Developing a deep learning architecture in combination with a customized Lyapunov control resolves the temporal delay and Lyapunov parameters calibration concern. Different datasets are also presented before establishing the one with the best accuracy. In addition to the dataset, the architecture of the deep learning model has a significant effect on the model's accuracy. A process for relearning is intended to accommodate the introduction of new system dynamics. Based on the correlation study, we also designed an optimization technique to improve the integration of the deep learning layer and controller layer. The proposed integration of Deep Learning and Lyapunov Control, referred to as Lyapunov Deep Learning (LDL) control, is applied in MATLAB / SIMULINK to the magnetic levitation chaotic nonlinear system to demonstrate its effectiveness in addressing sudden changes in system behavior, the environment, and demands in comparison to other methods of control.
  • Item
    Novel Piezoelectric Biosensor Based On Sars-Cov-2 (Covid-19) Spike Antibody For Coronavirus (Covid-19) Detection
    (2022-07-17) Alromithy, Fares Sulaimin; Zohdy, Mohamed A.; Auner, Gregory; Kamel-ElSayed, Suzan; Das, Manohar; Kaur, Amanpreet
    At the end of December 2019, the novel coronavirus SARS_CoV_2 appeared in Wuhan, China. The World Health Organization released a global health emergency declaration based on growing case notification rates in several locations worldwide. Therefore, sensitive, specific, rapid, and deliverable diagnostic monitoring is vital for making proper decisions on treating and isolating infected patients, which will help prevent the spread of infectious diseases. The surface Acoustic Wave (SAW) biosensor provides a unique, highly sensitive electrical approach to biomolecule detection and cell growth. For this study, a novel SAW sensor is developed, and the mass sensitivities are tested to detect the SARS_CoV_2 by attaching the SARS-CoV-2 spike antibody immobilized on the sensor surface. First, a two-dimensional (2D) and a three-dimensional (3D) finite element model were developed based on a realistic device to obtain a complete characterization of the senor. Then, the AlN/Al2O3 fabricated sensor was tested and ultrasonically rinsed in preparation for silanization. After depositions of (APTMS) on the sensor by the Chemical Vapor Deposition method, the antibodies were immobilized on surfaces with the aid of a crosslinker (EDC) and (Sulfo-NHS). Finally, the SARS-CoV-2 was introduced to the sensor, and the attachment of the immobilized antibody was tested and evaluated. The sensor was tested and characterized by Raman spectroscopy and the vector Network Analyzer. Finally, our device was able to detect the virus in real-time time (within two to three minutes), confirming its high sensitivity and selectivity with regard to the SARS-CoV-2 virus.
  • Item
    AN OPTIMAL ASYNCHROPHASOR IN PMU USING SECOND ORDER KALMAN FILTER
    (2021-11-15) Alqahtani, Nayef Mohammed S; Zohdy, Mohamed; Ganesan, Subramaniam; Kobus, Christopher; Elsayed, Suzan
    Phasor Measurement Units (PMU) are very costly according to energy regulator and utility companies. Utility operators work on alternative solutions to reduce the error rate and operation costs of PMU. In this paper, we sought to optimize the PMU to reduce the level of error using Second-Order Kalman Filter (SOKF). Consequently, this optimization is based on minimizing the number of errors when receiving the signal from access points or from the main access point. We derived a simple mathematical model to estimate the phase coming from the PMU.PMUs provide Global Positioning System (GPS) time stamped synchronized measurements of voltage and currents with the phase angle of the system at certain points along the grid system. Those synchronized data measurements were extracted in form of amplitude and phase from various locations of the power grid to monitor and control the power system conditions. A PMU device is a crucial part of the power equipment in terms of the cost and operative point of view. However, such ongoing development and improvement to PMUs' principal work is essential to the network operators to enhance the grid quality and the operating expenses. A MATLAB model was created to implement the proposed method in the presence of Gaussian and non-Gaussian noise. It is based on an Asyncrhophasor technique resulting in a phase error minimization when receiving the signal from access point or from the main access point. The results show the proposed SOKF method outperforming the existing model as tested using Mean Square Error (MSE). The SOKF method was replaced with a synchronization unit into the PMU structure to clarify the significance of the proposed new PMU. This paper's proposed method leads to lower costs and less complex techniques to optimize the performance of PMU using SOKF
  • Item
    OPTIMIZATION APPROACHES FOR OPTIMAL POWER FLOW PROBLEMS IN RENEWABLE ENERGY GRIDS
    (2021-11-15) Alzahrani, Abdullah Mohammed A; Zohdy, Mohamed; Alaweh, Shadi; Monroe, Ryan; Elsayed, Suzan
    In recent years, the global energy crisis confronting humanity is attributable to a deficiency in classical energy sources whereas the energy demands are widespread growth. In overcoming this crisis, increasing the efficiency of energy and sustainability are becoming significantly crucial. Therefore, the integration of various renewable energy resources into the traditional electrical grid is quite a challenging issue due to their intermittent nature. In this dissertation, the main focus is on the operational stage optimization problem considering the optimal power flow (OPF) problem to economically meet time-varying consumer demands. This brings a new level of complexity to the OPF problem that needs to be addressed by new and more performant optimization algorithms. One promising area of research that will be followed in this work is the use of heuristic optimization algorithms. These have been used both alone or in hybrid combinations of two optimization algorithms to compensate for the weaknesses of each.Due to nonlinearity introduced by alternating current power flow equations, this research focuses on improving the non-linear of the OPF problem for an electrical power grid that includes different energy sources along with thermal plants as energy generators. This dissertation illustrates how to improve the numerical stability of the proposed optimization approach by considering different variables to avoid ill-conditioned numerical operations. The proposed optimization approach, on the other hand, is able to deal with non-convex problems with multiple local optima, and also it is able to deal with the additional complexity of the hybrid power grid. This dissertation aimed at improving the use of stand-alone and hybrid AC/DC microgrid systems by minimizing some issues, e.g., global warming emissions and the increasing cost of power systems and losses during the entire time horizon. In this work, some assumptions have been made by considering line constraints on the network to theoretically prove that the performance of the polynomial optimization problem relaxation can satisfy the original power flow equations. Finally, the performance of the proposed algorithm has been examined on different electrical power networks with various power system sizes, such as IEEE 5, 14, and 30 bus. The outcomes of the experiments proved the adaptability of the algorithm when considering large-scale electrical power networks. In addition, this proposed algorithm has been compared with some exiting optimization algorithms given in the literature for similar power systems, where it shows the effectiveness of this proposed method. This achievement will strengthen the use of this convexification approach in its applications on large-scale systems to reduce energy expenses and network system losses.
  • Item
    GNSS PATCH ANTENNA MODELING PASSIVE GAIN OPTIMIZATION USING FEKO, DESIGN OF EXPERIMENTS AND P-TRANSFORM TECHNIQUE
    (2021-11-16) Aghashirin, Gholam D; Abdel-Aty-Zohdy, Hoda S.; Zohdy, Mohamed; Kafafy, Maged; Timmons, Adam; Schmidt, Darrell
    The objective of this work was to design a compact new microstrip patch antenna for applications in support of Global Navigation Satellite System (GNSS), and automotive. My GNSS patch antenna was created and developed in this dissertation to serve and represent the critical component from the system level perspective for the next generation of Automotive Radio Head Units, Navigation Systems, and L3 systems HD maps in autonomous domain. Conducted literature review and published papers studied, however observed a deficiency in the area of modeling, optimization of the design parameters, passive gain of rectangular patch antenna using FEKO, Design of Experiments, and P-Transform algorithm. Furthermore, there is no such antenna related to the Center Frequency of 1.555 [GHz] GNSS antenna, which has not been investigated. The proposed work involved a modeling of New GNSS rectangular patch antenna. The design focus was on the operating frequency range of 1.500 [GHz] to 1.610 [GHz] and FEKO 3D Electromagnetic simulation software package from Altair [4] was used, Design of Experiments (DoE) [5], and as well as applying the P-Transform algorithm [6] optimization method on the presented dual band GNSS (GPS and GLONASS) patch antenna passive v gain. The proposed antenna designed to operate at both bands, GPS (L1=1.57542 [GHz]) and GLONASS (L1=1.602 [GHz]) signal. Moreover, the ground plane length (X1[mm]), ground plane width (X2[mm]), and the substrate dielectric constant design (X3[mm]) parameters were varied at each FEKO simulation run, in order to obtain the simulation of GNSS patch antenna passive gain output results for the purpose of the optimization study by using P-Transform technique within the MATLAB environment. The presented GNSS patch antenna 2D far field and/or average passive gain measurement of GPS and GLONASS at center frequency of 1.555 [GHz] was plotted and analyzed. The computation and analysis of passive gain involved at taking the delta/difference between elevation angle at 30 and 90 degrees from the average passive gain 2D graph and this step was conducted for 120 FEKO simulation iteration runs. For each FEKO simulation run the far field (average passive gain=Y [dBi]) was computed separately unique for that specific design parameters (X1 [mm], X2 [mm], X3 [mm]) and recorded in a lookup matrix table (.csv). This four columns lookup table was called out for the purposed of the P- Transform algorithm utilization and execution within the MATLAB environment.
  • Item
    HEAR DEVICE FOR ELECTRICAL ACTIVITY OF THE HEART: COMPUTATION TOWARDS CARDIOVASCULAR DISEASES USING NOVEL COMMA-Z CLASSIFIER AND GPU FOR AUTOMOTIVE
    (2022-03-21) Sinnapolu, Giribabu; Alawneh, Shadi Dr; Ganesan, Dr. SubramaniamSuzan; Simon R Dixon MD; Saraydar, Dr. Cem U
    The silent heart attack which is also known as silent myocardial infarction occurs in almost 45% of the heart attacks and strikes men more than women. Women have less tendency to get sudden heart attack or heart failure or arrhythmias due to the menstrual cycle hormone released by their bodies until certain age. However, studies also show that higher risk of cardiovascular events are found both in men and women. During the phase of myocardial infarction patients develop Ventricular Fibrillations or rapid Atrial Fibrillations which may lead to the risk of death in a short period of time. Covid-19 also induces arrhythmias and myocardial injury and acute coronary syndrome. The average time spent by a person behind the wheel is approximately 1 hour on an everyday basis for a 30 miles drive. The Driver and Vehicle agency promotes that a driver with heart arrhythmias require approval from medical professionals to drive and based on today’s estimation most of the drivers are likely to get into an emergency or accident or collision due to Cardiac Stress, Hypertension, Cardiomyopathy, or complications after Angioplasty. There is a tremendous increase worldwide for wearables devices ranging from 325 million in 2016 to 929 million by 2021 and now 1 in every 6 Americans are using a wearable device, automakers are trying methods that involve measuring heart data via seat sensors and steering wheel sensors. Finally, my contribution in this research comprises of inventing a circular earlobe device (HEAR) placed to the earlobe that sends enormous amount of heart related photoplethysmography (PPG) data to the in-vehicle GPU device and invented a method to observe, compute, analyze, predict, and study AF and VF arrhythmias along with NSR using novel COMMA-Z filter, Signal Differentiation and DFS filters while driving. Such a sensitive work requires thorough communication and discussion with medical professionals for correctness of final heart computed data so based on the various experiments and clinical trials conducted this research was extensively supported by well-known surgeons from Beaumont hospitals, Michigan. The HEAR device also helps in long term monitoring and study of AF and VF tachycardias and related heart conditions because it’s all about timing for heart conditions and diseases as these tachycardias may lead to occurrence of ischemic stroke and cardiac arrest and complete heart failure and other dangerous conditions.
  • Item
    CELLULAR 5G AND V2X ANTENNAS DESIGN FOR AUTOMOTIVE APPLICATIONS
    (2022-03-14) Khalifa, Mohamed Osman Hussein; Aloi, Daniel N; Kaur, Amanpreet; Cheng, Eddie; Azadegan, Reza
    The work presented in this dissertation involves investigation and development of antennas and antenna systems that can contribute to autonomous vehicles realization. The antennas targeted in this work are namely Fifth Generation (5G) cellular antennas and Vehicle to Everything (V2X) antenna. The studies conceived in this work followed a scientific approach which starts by accurately simulating the antennas using three dimensional Electromagnetics (EM) solver High Frequency Simulator System (HFSS) software on one meter rolled edges GND. Then antenna and antenna systems were measured on one-meter GND inside anechoic chamber and also measured either on the top of vehicle roof or at the vehicle’s windshield. Phase one of this work starts by presenting a multi-wideband branched monopole antenna that covers 5G cellular bands between 617MHz- 5000MHz. This antenna utilizes two arms and L-Shape slot structure to provide coverage for low, mid, and high 5G cellular bands and also to reject Global Navigation Satellite System (GNSS) frequency bands. The antenna has compact size, light weight, low cost, and excellent gain characteristics at the solid angle facing cellular base station communication towers. The design, simulated, and measured results were presented and discussed in detail. Phase two of this work uses the element developed in phase one to construct high order Multiple-Input-Multiple-Output (MIMO) structure in order to boost overall system throughput, capacity, and data rate. Three MIMO systems configurations were studied, the first two are 2x2 5G cellular MIMO systems with similar individual antenna elements and opposite antennas orientations whereas the third configuration is a 4x4 5G cellular MIMO system. The individual antennas performance, diversity radiation pattern, and correlation between antennas were reported and discussed for all three MIMO configurations. Phase three of this work presents a V2X cavity-backed slot antenna that can be mounted in the vehicle’s windshield or rear-view mirror. The antenna is GND independent, and it provides excellent below horizon performance allowing the vehicles to communicate with other objects of less height. The antenna can be used as a building block to implement a full V2X system that provides null-free omnidirectional coverage at V2X solid angle of interest while providing aesthetic look for the vehicle which makes it very attractive in the automotive industry. The antenna was simulated and measured, and its radiation pattern, gain, and efficiency were presented and discussed in detail.
  • Item
    ROBUST IMPLEMENTATION OF THE MODEL PREDICTIVE CONTROL FOR DCDC POWER CONVERTERS AND INDUSTRIAL CONTROL APPLICATIONS
    (2022-03-26) Albira, Mohamed E.; Zohdy, Mohamed; Ganesan, Subramaniam; Barber, Gary; Li, Li
    This dissertation addresses the development of the robustness and strength of the Model Predictive Control algorithm subjected to input constraints for a plant system with and without parameters' uncertainties. In the beginning, the MPC control system was implemented for systems with no types of parameters' uncertainties. The proposed system models were stable and linear and all of its parameters were fully known. They were formulated in model state-space system format. The main objective of this control system design was to maintain a smooth and constant output signal that could easily track theassigned desired output signal. The technical process of this control design was to calculate the optimal solutions for the proposed plant system by optimizing the Quadratic programming problem (QP) subjected to linear inequality constraints. Therefore, the proposed control system successfully forced the outputs of the proposed systems to track the output reference signals in a fast response and with very small steady-state errors, even with the change in prediction horizon values. The second control system approach was for a system model which assumed its parameters’ uncertainties. In the other words, the parameters were not precisely known, but they were bounded in a minimum and maximum range. The parameters' uncertainties and the converter's switching behaviors made it act as a highly nonlinear system. Therefore, the Adaptive Model Predictive controller (AMPC) with the Linear Parameter Varying (LPV) control algorithm was implemented to address these issues and to secure a sustainable output signal with on types of noise or degradations. In this algorithm, the LPV model was created out of a set of Linear Time-Invariant (LTI) models, which are used to update the AMPC controller based on the feedback signals that come from the plant model and the change in the system parameters. Due to the changes in the plant model parameters over time, the AMPC was the perfect control approach due to its capability to update its prediction model and the operating condition over a prediction horizon interval. Since the AMPC is an online optimization-based approach, the QP variables and parameters can be tuned based on changes of the system measurements in real-time, and the LPV scheduling parameters. The proposed AMPC and LPV algorithm was compared against different control system approaches. Also, the proposed AMPC and LPV algorithm was implemented using an Arduino Mega 2560 microcontroller to show its performance in a real-time environment. In summary, from the outputs and the results, the proposed AMPC and LPV control system showed higher levels of the performance interims of the purified output, faster responses, and the computational time in both the simulation and real time results. MATLAB, SIMULINK, and ARDUINO support packages were used for the system design and implementations.