Electrical and Computer Engineering Commons^™

A Data-Driven Approach To Time-Domain Electromagnetic Modeling Based On Dynamic Mode Decomposition, Yanming Zhang, Steven Gao, Lijun Jiang

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

This paper presents a data-driven methodology that utilizes Dynamic Mode Decomposition (DMD) for the time-domain (TD) electromagnetic (EM) modeling of microwave devices. As an unsupervised machine learning technique, DMD leverages a limited set of unlabeled spatio-temporal electromagnetic (EM) data to determine DMD eigenvalues and eigenmodes. Then, the obtained DMD model reconstructs the dynamics as a series of exponential terms based on linear assumptions. The effectiveness of this approach is demonstrated through the TD EM modeling of photonic crystal waveguides. Comparative analysis with the finite-difference time-domain (FDTD) method shows that the DMD model not only achieves precise modeling but also facilitates …

An Unsupervised Learning Framework For Determining The Excitation Coefficients Using Near-Field Antenna Measurements, Yanming Zhang, Peifeng Ma, Steven Gao, Lijun Jiang

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

This article presents a novel unsupervised learning framework based on multiscale dynamic mode decomposition for determining the excitation coefficients of antennas using time-domain near-field measurements. The proposed framework integrates temporal multiscale analysis to extract a joint distribution of frequency, damping factors, and spatial modes, enabling precise extraction of excitation frequencies, rising/falling edges, and phases without labeled data. We validate the effectiveness of the proposed approach through two examples involving on-off keying modulation and a phase-shift dipole antenna. It is found that the proposed method performs well in handling nonstationary excitation signals and proves particularly advantageous for calibrating tunable antenna systems. …

Enabling Hybrid Optical-Microwave Space Communications With Optically Transparent Rf Components Using Indium Tin Oxide (Ito), John T. O'Keefe

Doctoral Dissertations and Master's Theses

Over the last decade, spacecraft have increasingly integrated both microwave and optical communication systems, driving the need for electrically conductive and optically transparent materials. This study presents a comprehensive examination of advanced electromagnetic characterization techniques and the design, fabrication, and performance of optically transparent antennas using Indium Tin Oxide (ITO) films on alkaline earth boro-aluminosilicate glass for transparent radio-frequency (RF) applications in space communication systems. Addressing the limitations of traditional metal-based antennas—particularly their shadowing effect on satellite solar cells—this study investigates ITO as a pivotal material for creating antennas that are both efficient in communication and non-intrusive to solar efficiency. …

Power And Waiting Time Efficiency At Automatic Toll Gates With The Contactless Card Payment Implementation, Ujang Wiharja, Sri Hartanto

ASEAN Journal on Science and Technology for Development

One method of Electronic Toll Collection (ETC) in Automatic Toll Gate (ATG) currently uses contactless transactions using Radio Frequency IDentification (RFID) technology. Tracking and monitoring objects (the car) with RFID is carried out in real-time and is required to keep up with the speed of an object (the car). The On-Board Unit (OBU) transponder installed on the car's windshield and the Road Side Unit (RSU) installed on the ATG are the main components of the Dedicated Short-Range Communication (DSRC) system, which allows the car and ATG to communicate with each other and carry out transactions, including online toll payments, without …

Analysis, Optimization, And Design Of Small-Scale Hybrid-Core Inductor Designs That Achieve High Energy Densities And Low Loss, Andrew B. Nadler

Dartmouth College Ph.D Dissertations

As the size of power converters and other electronics has shrunk over time, miniaturization of passive magnetic components, namely inductors and transformers, has fallen behind. Much of this is due to magnetic scaling laws, which degrade inductor performance at reduced sizes, in contrast to other common power converter components, such as transistors and capacitors, which are conducive to construction from smaller parallel cells. This does not mean high performance and small sizes are unachievable with magnetic components, but rather that intelligent and creative magnetics design is increasingly critical.

Typical inductors utilize a conductive winding wrapped around a magnetic core, with …

V2i-Based Adaptive Collision Avoidance For Safety And Traffic Efficiency, Vaishnavi Balambeed

Dissertations, Master's Theses and Master's Reports

To leverage the growing communication and connectivity among modern vehicles, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) systems are increasingly being used to implement active safety applications. However, current research often overlooks the impact of algorithms such as collision avoidance on traffic flow efficiency. This work investigates the adaptation of a collision avoidance algorithm implemented in V2I to incorporate a variable time headway and spacing control strategy. The proposed approach aims to maintain higher average speeds among vehicles, lower individual vehicle’s waiting, and travel times in the vicinity of the infrastructural unit while simultaneously avoiding collisions; thereby enhancing both safety and traffic …

Pulse-Width Modulation Spray System Performance And Development Of A Novel System To Optimize Spray Use Efficacy, Prashanta Pokharel

Theses and Dissertations--Biosystems and Agricultural Engineering

The overarching objective of this research was to enhance our understanding of pulse width modulation (PWM) based nozzle control system and develop an intelligent nozzle control system for agricultural spray applications. This dissertation focused on measuring and analyzing the transient nozzle pressure characteristics and performance under PWM control. Additionally, it explored the development of an innovative multi-degree-of-freedom (DOF) nozzle control system for precise chemical applications in specialty crop systems.

The pressure dynamics of a PWM system were studied by developing an instrumentation system capable of operating a solenoid valve and recording pressure signals. A nozzle body and two manifolds of …

Blockchain Based Framework For Secure And Decentralized Energy Trading - Transforming Local Energy Markets For Sustainable Communities, Saurabh Sachdeva

Dissertations and Theses

The emergence of prosumers provides an opportunity for the setup of a local energy market (LEM) where individual households with distributed energy resources (DERs) can produce, store, and trade energy. Peer-to-peer(P2P) and decentralized energy trading (ET) can be implemented between the participants within or across microgrids. Several solutions based on the existing technologies have been proposed worldwide for the integration of prosumers in the existing energy setup and to enable and support ET, but these solutions present the issues of centralization, data integrity, and confidentiality, user and anonymity, and transparency. The applications of blockchain technology have recently become fascinating for …

Uncertainty Quantification For Peec Based On Wasserstein Generative Adversarial Network, Yuan Ping, Yanming Zhang, Lijun Jiang

Electrical and Computer Engineering Faculty Research & Creative Works

This article proposes a modified generative adversarial network (GAN)-based approach, namely Wasserstein GAN (WGAN), for the uncertainty quantification (UQ) in partial equivalent element circuit (PEEC) models. Initially, the stochastic PEEC is constructed to obtain the sample data of the quantities of interest (QoI). This sample data, along with the fake data from the generator, serves as input for the discriminator in WGAN. The loss function of the generator in WGAN is constructed using the Wasserstein distance to provide a more usable gradient than that in the traditional GAN. By estimating the distribution of sample data using the fake data in …

Adaptive Critic Optimal Control Of An Uncertain Robot Manipulator With Applications, Ravi Prakash, Laxmidhar Behera, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Realistic manipulation tasks involve a prolonged sequence of motor skills in varying control environments consisting of uncertain robot dynamic models and end-effector payloads. To address these challenges, this article proposes an adaptive critic (AC)-based basis function neural network (BFNN) optimal controller. Using a single neural network (NN) with a basis function, the proposed optimal controller simultaneously learns task-related optimal cost function, robot internal dynamics, and optimal control law. This is achieved through the development of a novel BFNN tuning law using closed-loop system stability. Therefore, the proposed optimal controller provides real-time, implementable, cost-effective control solutions for practical robotic tasks. The …

Modelling Weave Effect In Pcbs Using 2d Cross-Sectional Analysis, Victor Khilkevich, Scott Hinaga

Electrical and Computer Engineering Faculty Research & Creative Works

Printed circuit board dielectric substrates are composite materials produced by embedding fiber glass fabrics into epoxy resin. Because of this the medium in the PCB transmission lines is inhomogeneous which often leads to degradation of the signal integrity performance of the lines, particularly due to the differential skew. The detrimental effect of the fiber weave can be modeled relatively accurately using full-wave analysis, but at a high computational cost. Alternative modelling techniques are less demanding but often lack accuracy. This article investigates a possibility of using the 2D cross-sectional analysis for the fiber weave effect modeling, which considerably decreases the …

Towards Explainability Of Dimension Reduction Plots Of Unsupervised Learning Model Outcomes, Tony E.Astuhuaman Davila, Daniel B. Hier, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

Dimension reduction methods are used to visualize the output of unsupervised learning models when applied to complex data. These techniques improve interpretability by transforming a high-dimension space to a lower-dimension space (usually 2D or 3D). The results are typically viewed as 2D scatter plots, and class centroids may be added to increase interpretability. Although useful, the relationship of these class centroids to the underlying feature space remains opaque. The innovative aspect of this work is to create a strong link between the dimension-reduced space and the underlying high-dimension feature space by adding selected feature centroids to the 2D scatter plots. …

Statistical Analysis Of Electromagnetic Coupling To Printed Circuit Boards, Shengxuan Xia, Victor Khilkevich, Daryl Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

Determining electromagnetic (EM) coupling to printed circuit boards (PCBs) is essential to finding potential EM susceptibilities early in the design process. For realistic PCB structures, analysis usually relies heavily on time-consuming full-wave simulations because of the complexity of the geometries and the lack of analytical solutions. In this paper, we adopt a segmentation approach based on far-field reciprocity which allows for rapid estimation of the voltage induced at trace terminations over frequency, and which is then used to estimate statistical characteristic of coupling across trace geometries. Frequency-domain results can then be used to estimate time-domain responses with appropriate transformations. Super-position …

An Equivalent Coil Model Of A Wireless Power Transfer System Including Eddy Loss, Hanyu Zhang, Daryl G. Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

A wireless power transfer (WPT) system suffers from eddy loss if a conductive object is placed near the coupling coil. In this paper, a 3-coil equivalent circuit model for the coupling coil in a WPT system is proposed for analyzing the eddy loss due to nearby conductors. This model uses a third coil with inductive coupling to the original transmitting and receiving coils to model the eddy loss. The proposed model was validated by comparing the Z-parameters with a full-wave simulation and showing good correlation over the frequency of interest, where the traditional 2-coil model fails. The 3-coil model is …

K-Perm: Personalized Response Generation Using Dynamic Knowledge Retrieval And Persona-Adaptive Queries, Kanak Raj, Kaushik Roy, Vamshi Bonagiri, Priyanshul Govil, Krishnaprasad Thirunarayan, Raxit Goswami, Manas Gaur

Publications

Personalizing conversational agents can enhance the quality of conversations and increase user engagement. However, they often lack external knowledge to tend to a user’s persona appropriately. This is particularly crucial for practical applications like mental health support, nutrition planning, culturally sensitive conversations, or reducing toxic behavior in conversational agents. To enhance the relevance and comprehensiveness of personalized responses, we propose using a two-step approach that involves (1) selectively integrating user personas and (2) contextualizing the response with supplementing information from a background knowledge source. We develop K-PERM (Knowledge-guided PErsonalization with Reward Modulation), a dynamic conversational agent that combines these elements. …

Tutorial: Knowledge-Infused Artificial Intelligence For Mental Healthcare, Kaushik Roy

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

Personalized Bayesian Inference For Explainable Healthcare Management And Intervention, Utkarshani Jaimini, Krishnaprasad Thirunaravan, Maninder Kalra, Robin Dawson, Amit Sheth

Publications

Chronic healthcare conditions such as Asthma re- quires constant monitoring and managing of symptoms and their triggers for better quality of life. Each asthma patient reacts very differently to potential triggers. Hence, there is a need to develop a explainable personalized framework for each patient to capture susceptibility to asthma triggers. We developed a personalized knowledge-based probabilistic model to predict asthma exacerbation for different environmental factors utilizing patient generated health data from pediatric asthma patients. Further, the personalized model provides a metric, called Health Coefficient, to quantify the health of a patient for varying environmental factors. We demonstrate the predictive …

Causal Neuro-Symbolic Ai: A Synergy Between Causality And Neuro-Symbolic Methods, Utkarshani Jaimini, Cory Henson, Amit Sheth

Publications

Causal Neuro-Symbolic AI combines the benefits of causality with Neuro-Symbolic Artificial Intelligence (NeSyAI). More specifically, it (1) enriches NeSyAI systems with explicit representations of causality, (2) integrates causal knowledge with domain knowledge, and (3) enables the use of NeSyAI techniques for causal AI tasks. The explicit causal representation yields insights that predictive models may fail to analyze from observational data. It can also assist people in decision-making scenarios where discerning the cause of an outcome is necessary to choose among various interventions.

Ontolog Summit 2024 Talk Report: Healthcare Assistance Challenges-Driven Neurosymbolic Ai, Kaushik Roy

Publications

Although Artificial Intelligence technology has proven effective in providing healthcare assistance by analyzing health data, it still falls short in supporting decision-making. This deficiency largely stems from the predominance of opaque neural networks, particularly in mental health care AI applications, which raise concerns about their unpredictable and unverifiable nature. This skepticism hinders the transition from information support to decision support. This presentation will explore neurosymbolic approaches that combine neural networks with symbolic control and verification mechanisms. These approaches aim to unlock AI’s full potential by enhancing information analysis and decision-making support for healthcare assistance1.

A Comprehensive Survey On Rare Event Prediction, Chathurangi Shyalika Jayakody Kankanamalage, Ruwan Wickramarachchi, Amit Sheth

Publications

Rare event prediction involves identifying and forecasting events with a low probability using machine learning (ML) and data analysis. Due to the imbalanced data distributions, where the frequency of common events vastly outweighs that of rare events, it requires using specialized methods within each step of the ML pipeline, i.e., from data processing to algorithms to evaluation protocols. Predicting the occurrences of rare events is important for real-world applications, such as Industry 4.0, and is an active research area in statistics and ML. This paper comprehensively reviews the current approaches for rare event prediction along four dimensions: rare event data, …

Electronic On Planes: Security Measures And Avionics Disruption, Martin Jasek, Ivana Olivková

International Journal of Aviation, Aeronautics, and Aerospace

Airlines have implemented various security measures to address the use of electronics on aircraft, focusing on telecommunication services, Bluetooth technology, and personal electronic devices (PEDs). This study evaluates 50 airlines using data from websites and safety videos to assess these technologies. While the integration of new technologies like in-flight Wi-Fi and Bluetooth offers passenger benefits, concerns persist about the potential electromagnetic interference with aircraft avionics. Early research by NASA and the FCC highlighted the risks, finding that devices like mobile phones could disrupt GPS signals during critical flight phases. Despite conflicting studies, ongoing monitoring is essential to balance passenger connectivity …

Radiation Damage Simulation Using Molecular Dynamics In Ni-Based Alloy, Yanxin Shen, Yue Yang, Xuelian Ou, Peng Wang, Zhenjiang You, Xiaofeng Tian

Research outputs 2022 to 2026

In the present study, we investigated the irradiation-induced induction in Ni80Cr20, Ni80Fe20, Ni50Fe50, Ni40Fe40Cr20 by molecular dynamics (MD) simulation. A previously published modified potential is used to provide a detailed account of the process involved in the production and evolution of defects. Ni50Fe50 and Ni40Fe40Cr20 alloys exhibit comparable damage level and better radiation response compared to Ni80Cr20 and Ni80Fe20. The inhibition effect of interstitial clusters increases with the complexity of alloying elements. The alloying of Cr has resulted in Ni80Fe20 and Ni50Fe50 tend to form 1/3<111> dislocation loops while at the same time making Ni40Fe40Cr20 and Ni80Cr20 more susceptible to …

Dynamic Modeling And Control Of A Solid State Semiconductor-Based Transformer, Microgrid And Storage Systems, Rubén Darío Viñán-Velasco

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Smart Grids are power grid models designed with the idea of including the growing new technologies, from generation to storage devices, and are a response to the growing demands from consumers and the presence of electronic components being commonplace in the modern devices. The design requires a dynamic alternative in order to build an independent grid that can also work in cooperation with other micro-grids and the power grid in an integrated way. Smart-grids present several advantages over the traditional power grid scheme, but the economic costs of the components required to implement smart-grids is currently a great limitation. This …

Designing High-Performance Identity-Based Quantum Signature Protocol With Strong Security, Sunil Prajapat, Pankaj Kumar, Sandeep Kumar, Ashok Kumar Das, Sachin Shetty, M. Shamim Hossain

VMASC Publications

Due to the rapid advancement of quantum computers, there has been a furious race for quantum technologies in academia and industry. Quantum cryptography is an important tool for achieving security services during quantum communication. Designated verifier signature, a variant of quantum cryptography, is very useful in applications like the Internet of Things (IoT) and auctions. An identity-based quantum-designated verifier signature (QDVS) scheme is suggested in this work. Our protocol features security attributes like eavesdropping, non-repudiation, designated verification, and hiding sources attacks. Additionally, it is protected from attacks on forgery, inter-resending, and impersonation. The proposed scheme benefits from the traditional designated …

Design Of An Adaptive Robust Pi Controller For Dc/Dc Boost Converter Using Reinforcement-Learning Technique And Snake Optimization Algorithm, Seyyedmorteza Ghamari, Mojtaba Hajihosseini, Daryoush Habibi, Asma Aziz

Research outputs 2022 to 2026

The DC/DC Boost converter exhibits a non-minimum phase system with a right half-plane zero structure, posing significant challenges for the design of effective control approaches. This article presents the design of a robust Proportional-Integral (PI) controller for this converter with an online adaptive mechanism based on the Reinforcement-Learning (RL) strategy. Classical PI controllers are simple and easy to build, but they need to be more robust against a wide range of disturbances and more adaptable to operational parameters. To address these issues, the RL adaptive strategy is used to optimize the performance of the PI controller. Some of the main …

Generation Expansion Planning In Isolated Power Systems: A Robust Approach With Dunkelflaute Assessment, Taraneh Ghanbarzadeh, Daryoush Habibi, Asma Aziz

Research outputs 2022 to 2026

Generation expansion planning is vital for decarbonizing power systems and ensuring a reliable and sustainable energy future. Strategically adding new generation and grid capacity is essential for supporting a seamless transition to renewable energy while reducing greenhouse gas emissions. However, achieving the optimal capacity mix of energy resources to ensure network reliability and economic efficiency presents significant challenges, particularly for isolated electricity grids. These challenges are exacerbated during periods of low renewable generation and due to the inherent intermittency of weather-dependent energy resources. This paper presents a comprehensive approach to optimizing long-term expansion planning for an isolated electricity grid, focusing …

Optimal Operation Of An Islanded Hybrid Energy System Integrating Power And Gas Systems, Mehrdad Ghahramani, Daryoush Habibi, Seyyedmorteza Ghamari, Asma Aziz

Research outputs 2022 to 2026

Remote communities and geographically isolated areas require a secure supply of energy. Isolated hybrid energy systems offer an effective and reliable solution for delivering power to these regions. However, shifting to renewable energy sources introduces uncertainty challenges for low-inertia stand-alone systems. In this paper, we propose a two-stage energy management strategy to address the uncertainties of wind generation and load consumption while minimizing operational expenses. Furthermore, the study explores the integration of multi-carrier energy networks, in this case electricity and gas, to enhance the reliability of hybrid energy systems. Two modeling methods are proposed to tackle the uncertainties. First, a …

Enhancing Water Safety: Exploring Recent Technological Approaches For Drowning Detection, Salman Jalalifar, Andrew Belford, Eila Erfani, Amir Razmjou, Rouzbeh Abbassi, Masoud Mohseni-Dargah, Mohsen Asadnia

Research outputs 2022 to 2026

Drowning poses a significant threat, resulting in unexpected injuries and fatalities. To promote water sports activities, it is crucial to develop surveillance systems that enhance safety around pools and waterways. This paper presents an overview of recent advancements in drowning detection, with a specific focus on image processing and sensor-based methods. Furthermore, the potential of artificial intelligence (AI), machine learning algorithms (MLAs), and robotics technology in this field is explored. The review examines the technological challenges, benefits, and drawbacks associated with these approaches. The findings reveal that image processing and sensor-based technologies are the most effective approaches for drowning detection …

Explore Security And Machine Learning Applications In Next Generation Wireless Networks, Haolin Tang

Theses and Dissertations

Next-generation (NextG) or Beyond-Fifth-Generation (B5G) wireless networks have become a prominent focus in academic and industry circles. This is driven by the increasing demand for cutting-edge applications such as mobile health, self-driving cars, the metaverse, digital twins, virtual reality, and more. These diverse applications typically require high communication network performance, including spectrum utilization, data speed, and latency. New technologies are emerging to meet the communication requirements of various applications. Intelligent Reflecting Surface (IRS) and Artificial Intelligence (AI) are two representatives that have been demonstrated as promising and powerful technologies in NextG communications. While new technologies significantly enhance communication performance, they …

Energy Efficient Spintronic Devices For Non-Volatile Memory And Hardware Ai, Walid Al Misba

Theses and Dissertations

Nanomagnetic devices have emerged as a promising alternative to conventional complementary metal-oxide-semiconductor (CMOS) devices due to their low energy dissipation and inherent non-volatility. However, the widespread adoption of these devices requires high-density, high-speed, reliable, scalable, and energy-efficient technologies. This thesis investigates the use of nanomagnetic memory devices as both conventional Boolean memory and multistate memory for hardware AI applications.

Magnetic tunnel junctions (MTJs) are nanomagnetic memory devices that can be switched reliably and energy-efficiently using stress-mediated switching. However, realistic material inhomogeneity and scalability pose challenges for stress-mediated switching of MTJs scaled to lateral dimensions below 50 nm. We demonstrate that …