Engineering Commons^™

Estimating Electric Power Consumption Of Grid-Enabled Residential Heat Pump Water Heaters, Dana Paresa, Robert Bass

Electrical and Computer Engineering Faculty Publications and Presentations

We present two methods for estimating compressor electric power consumption of residential heat pump water heaters. Power is calculated using data from voltage and current instrumentation, which add costs and potential failure points to the unit. The two methods presented herein use thermal instrumentation or derived data that are typically included within modern residential heat pump water heaters. One method uses data from lower and upper tank temperature sensors. The other method uses an attribute that is commonly available within grid-enabled flexible load heat pump water heaters. For both methods, fit equations were derived, for which coefficients are tuned to …

Identification Of Fiducial Points In Seismocardiographic Cycles Using Manual And Automated Annotation Methods, Jasmine-Vy T. Truong

Honors Undergraduate Theses

There is currently a need for complementary methods for non-invasive cardiac monitoring. Seismocardiography (SCG), the measurement of cardiac-induced vibrations at the chest surface, has shown potential clinical utility. Improving the reliability of detecting fiducial points of electrocardiography (ECG) and SCG, which collectively capture the electro-mechanical cardiac activities, could expand ECG/SCG utility as a low-cost, accessible tool for clinical assessment. This study identifies commonly accepted criteria for fiducial point detection in SCG and ECG through an extensive literature review and signal processing techniques. The previous criteria were evaluated to identify their strengths and weaknesses. Based on the findings, an improved set …

Flame Boundary Effects Of Hydrogen-Air Premixing, Alecson L. De Lima Junior

Honors Undergraduate Theses

The objective of this research was to investigate the reaction of hydrogen and heated air premixing when injected into a combusting ethylene-air crossflow, and to study the flame stabilization location with varying premixing levels. The main parameter of this investigation was the flame stabilization diagnosed through chemiluminescence, based on recorded equivalence ratio, temperature, and flame boundaries, and maintaining a constant air temperature, hydrogen and air mass flow, and momentum flux ratios. Testing was conducted at different ratios of hydrogen and air premixing achieved through alternating the distance between the point where hydrogen and air are mixed and the combusting crossflow. …

A Comprehensive Exploration Of 6g Wireless Communication Technologies, Md Nurul Absar Siddiky, Muhammad Enayetur Rahman, Md. Shahriar Uzzal, H. M. Dipu Kabir

Electrical & Computer Engineering Faculty Publications

As the telecommunications landscape braces for the post-5G era, this paper embarks on delineating the foundational pillars and pioneering visions that define the trajectory toward 6G wireless communication systems. Recognizing the insatiable demand for higher data rates, enhanced connectivity, and broader network coverage, we unravel the evolution from the existing 5G infrastructure to the nascent 6G framework, setting the stage for transformative advancements anticipated in the 2030s. Our discourse navigates through the intricate architecture of 6G, highlighting the paradigm shifts toward superconvergence, non-IP-based networking protocols, and information-centric networks, all underpinned by a robust 360-degree cybersecurity and privacy-by-engineering design. Delving into …

Optimizing Ai Language Models: A Study Of Chatgpt-4 Vs. Chatgpt-4o, Md Nurul Absar Siddiky, Muhammad Enayetur Rahman, Md Fayaz Bin Hossen, Muhammad Rezaur Rahman, Md. Shahadat Jaman

Electrical & Computer Engineering Faculty Publications

This paper presents a comparative analysis of OpenAI's GPT-4 and its optimized variant, GPT-4o, focusing on their architectural differences, performance, and real-world applications. GPT-4, built upon the Transformer architecture, has set new standards in natural language processing (NLP) with its capacity to generate coherent and contextually relevant text across a wide range of tasks. However, its computational demands, requiring substantial hardware resources, make it less accessible for smaller organizations and real-time applications. In contrast, GPT-4o addresses these challenges by incorporating optimizations such as model compression, parameter pruning, and memory-efficient computation, allowing it to deliver similar performance with significantly lower computational …

Energy-Aware Sensor Fusion Architecture For Autonomous Channel Robot Navigation In Constrained Environments, Mohamed Shili, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

Navigating autonomous robots in confined channels is inherently challenging due to limited space, dynamic obstacles, and energy constraints. Existing sensor fusion strategies often consume excessive power because all sensors remain active regardless of environmental conditions. This paper presents an energy-aware adaptive sensor fusion framework for channel robots that deploys RGB cameras, laser range finders, and IMU sensors according to environmental complexity. Sensor data are fused using an adaptive Extended Kalman Filter (EKF), which selectively integrates multi-sensor information to maintain high navigation accuracy while minimizing energy consumption. An energy management module dynamically adjusts sensor activation and computational load, enabling significant reductions …

Quantifying Trapped Magnetic Vortex Losses In Niobium Resonators At Mk Temperatures, D. Bafia, B. Abdisatarov, R. Pilipenko, Y. Lu, G. Eremeev, A. Romanenko, A. Grassellino

Electrical & Computer Engineering Faculty Publications

Trapped magnetic vortices in niobium introduce microwave losses that degrade the performance of superconducting resonators. While such losses have been extensively studied above 1 K, we report here their direct quantification in the millikelvin and low-photon regime relevant to quantum devices. Using a high-quality factor 3D niobium cavity cooled through its superconducting transition in controlled magnetic fields, we isolate vortex-induced losses and find the resistive component of the sensitivity to trapped flux S to be approximately 2 n Ω/mG at 10 mK and 6 GHz. The decay rate is initially dominated by two-level system (TLS) losses from the native niobium …

Investigation Of Under-Frequency Load Shedding Prevention Of Isolated Medium-Sized Power Systems Using Energy Storage, Dilan Indoopa Manamperi

Theses: Doctorates and Masters

Due to the rapid uptake of distributed photovoltaic generation, the visible load to transmission operators in distribution feeders is reducing. Traditional load-shedding schemes are facing challenges due to this low-load situation. The required amount of load reduction cannot be achieved with the same load-shedding scheme as before. In some situations, with reverse power flows, load shedding can further increase the generation reduction. On the other hand, increased Rate of Change of Frequency (RoCoF) due to low inertia can result in false activation of under-frequency load shedding (UFLS) relays. These challenges can cause a serious problem with the power system security. …

An Iterative Shifting Disaggregation Algorithm For Multi-Source, Irregularly Sampled, And Overlapped Time Series, Colin O. Quinn, Ronald H. Brown, George F. Corliss, Richard J. Povinelli

Electrical and Computer Engineering Faculty Research and Publications

Accurate time series forecasting often requires higher temporal resolution than that provided by available data, such as when daily forecasts are needed from monthly data. Existing temporal disaggregation techniques, which typically handle only single, uniformly sampled time series, have limited applicability in real-world, multi-source scenarios. This paper introduces the Iterative Shifting Disaggregation (ISD) algorithm, designed to process and disaggregate time series derived from sensor-sourced low-frequency measurements, transforming multiple, nonuniformly sampled sensor data streams into a single, coherent high-frequency signal. ISD operates in an iterative, two-phase process: a prediction phase that uses multiple linear regression to generate high-frequency series from low-frequency …

Computer Vision-Based Framework For Data Extraction From Heterogeneous Financial Tables: A Comprehensive Approach To Unlocking Financial Insights, Iftakhar Ali Khandokar, Priya Deshpande

Electrical and Computer Engineering Faculty Research and Publications

Information extraction from financial document images is crucial in computer vision and NLP, as financial data often exists in image or PDF format, enabling organizations to analyze and make informed business decisions using OCR advancements. The table contents of financial document images are one of the prominent structures to confine important portions of data of the document and many Deep learning-based methods have been proposed to detect Table regions inside document images. The shortcomings of the current approach are that it is bounded within the detection of the table region and struggles in cases such as handling different layouts and …

Power Utilization In Open Ran: Key Findings From A Usa Testbed, Saish Urumkar, Byrav Ramamurthy, Seshu Tirupathi, Sachin Sharma

Articles

Open Radio Access Networks (Open RAN) provide flexible, scalable, and interoperable solutions to address the growing demands of mobile traffic while also aiming to reduce energy consumption. Most prior research on energy-efficient Open RAN has focused on switching techniques such as dynamic cell on/off strategies and adaptive resource allocation, primarily through simulations. This letter investigates Central Processing Unit (CPU) power utilization at the NodeB (base station) level, focusing on User Equipment (UE) connection states by making use of a USA testbed (i.e., POWDER testbed). Two scenarios are considered for the experimental setup: (1) a simulated virtual environment with a single …

Snake Optimization Algorithm For Fractional Order Controller In Three-Area Load Frequency Regulation, Shubra Goel, Omveer Singh

ASEAN Journal on Science and Technology for Development

The transition toward renewable energy-dominated power systems have accentuated the intricacies of frequency control, necessitating advanced regulatory mechanisms. This investigation articulates a tri-zonal frequency stabilization approach, employing a hybridized controller that synergizes Fuzzy Fractional-Order PI and Tilt-Integral-Derivative methodologies. The uniqueness of this approach is further amplified by the deployment of the Snake Optimization algorithm for precise parameter tuning. Set within a conventional grid topology integrated with assorted renewable energy sources, the study evaluates the controller’s adaptability and resilience through a series of comprehensive scenario-based analyses.

Data-Driven Insights For Optimizing Ev Charging Infrastructure: A Case Study On Efficiency And Utilization, Kazi Zehad Mostofa, Md Fokrul Islam, Mohammad Aminul Islam, Mohammad Khairul Basher, Tarek Abedin, Boon Kar Yap, Mohammad Nur-E-Alam

Research outputs 2022 to 2026

The increasing global adoption of electric vehicles (EVs) has led to a growing demand for a cost-effective and reliable charging infrastructure. This study presents a novel data-driven approach to assessing EV station performance by analyzing power consumption efficiency, station utilization rates, no-power session occurrences, and CO2 reduction metrics. A dataset of 17,500 charging sessions from 305 stations across a regional network was analyzed to identify operational inefficiencies and opportunities for infrastructure optimization. Results indicate a strong correlation between station utilization and energy efficiency, highlighting the importance of strategic station placement. The findings also emphasize the impact of no-power sessions on …

Design Of A Robust Adaptive Cascade Fractional-Order Nonlinear-Based Controller Enhanced Using Grey Wolf Optimization For High-Power Dc/Dc Dual Active Bridge Converter In Electric Vehicles, Seyyed Morteza Ghamari, Daryoush Habibi, Mehrdad Ghahramani, Asma Aziz

Research outputs 2022 to 2026

The dual active bridge (DAB) converter is a key technology in electric vehicles (EVs), offering efficient, bidirectional DC–DC power conversion with galvanic isolation. This paper presents a novel cascade control strategy incorporating a fractional-order PID (FOPID) controller in the outer loop for precise voltage regulation and an adaptive backstepping controller (ABSC) in the inner loop for robust current control. The ABSC is designed using Lyapunov stability theory, ensuring systematic stabilization of nonlinear dynamics and robust performance under disturbances. The FOPID controller introduces superior filtering characteristics, effectively mitigating high-frequency measurement noise and transient disturbances, making it highly suitable for high-frequency power …

Biological Computing From Microscopic To Macroscopic Evolution, Xiaofeng Ding

Masters Theses

A unified theory from microscopic to macroscopic DNA-based biological systems is explained in terms of the rule components used when the system size is increased. Even though the eight female rules in each ruleset are provided with an equal probability of the computation-state outcomes, the initial ensemble of the computation states will exponentially settle into one dominant rule that supports the nutrition needed for growth. The remaining seven rules form into two minority groups to provide the biological characteristics of the system's growth from a micro to macroscopic evolution. The object of this study is to prove that such a …

Computation-Efficient Deep Learning Models For Computer Vision And Multimodal Vision-Language Tasks Via Network Pruning, Abir Mohammad Hadi

Electronic Theses and Dissertations

With the rapid evolution of deep neural networks over the past decade, the demand for efficient, generalizable, and task-adaptable models, especially in computer vision, has increased significantly. To address the computational and deployment challenges posed by overparameterized models, the research community has extensively explored model compression techniques such as pruning, quantization, and distillation. These approaches aim to enhance model efficiency without compromising performance, particularly when adapting to domain-specific tasks under limited resources. This dissertation investigates several underexplored yet critical aspects of task-aware deep learning model compression, spanning both convolutional and vision-language architectures. In the early part of this work, we …

Sensitivity Analysis Of The 197 Mhz Prototype Crab Cavity For Eic, Subashini De Silva, Jean R. Delayen, B. Xiao, E. Drachuk, I. H. Senevirathne, A. Castilla, N. Huque, Z. Li

Physics Faculty Publications

The Electron-Ion Collider at BNL requires several crabbing systems that will be operating at 197 MHz and 394 MHz to compensate for the loss of luminosity due to the large crossing angle of the colliding beams. Two 197 MHz crab cavity cryomodules containing two cavities each will be installed in the Hadron Storage Ring (HSR) at the IP6 interaction region. Due to its large size compared to previously developed crabbing cavities, the 197 MHz crabbing cavity system was identified as one of the critical rf systems in the EIC. Therefore, a cavity has been designed including the ancillaries, and is …

Effect Of Activation Temperature On Quantum Efficiency And Lifetime Of Nea Truncated Nanocone Array Gaas Photocathode, Md. Aziz Arroyo Rahman, Md Abdullah Mamun, Shukui Zhang, Hani E. Elsayed-Ali

Physics Faculty Publications

This study investigates the quantum efficiency (QE) and operational lifetime of a negative electron affinity GaAs truncated nanocone array (TNCA) photocathode benchmarked against a conventional flat GaAs photocathode under varying activation temperatures. The TNCA structure demonstrated a QE of up to 13.6% at 590 nm with room temperature (RT) activation—approximately 1.5 times higher than its flat counterpart. This enhancement is due to Mie resonance effects within the nanostructure, as confirmed by finite-difference time-domain simulations. Moreover, the TNCA photocathode exhibits significantly extended charge lifetime, with enhancement factors of ∼6.1 and ∼19.8 under RT and 50 °C activations, respectively. These gains are …

Modeling Strain And Quantum Confinement In Gaas/GaXIn1-XP Superlattices For Spin-Polarized Electron Sources, A. Kachwala, G. Blume, S. Marsillac, J. Grames, M. Grau

Physics Faculty Publications

In this study, we systematically design and simulate a series of GaAs-based superlattice configurations aimed at enhancing heavy-hole–light-hole band splitting while simultaneously optimizing band alignment to reduce the conduction band barrier, thereby facilitating efficient electron transport. These combined effects are crucial for achieving high electron spin polarization and high quantum efficiency, the two key performance metrics of next-generation spin-polarized electron sources. We investigated three types of superlattice architectures: (1) compressively strained GaAs wells on GaInP barriers, yielding a maximum band splitting of 140 meV, (2) lattice-matched GaAs/GaInP structures, resulting in the maximum band splitting of 75 meV, and (3) tensile …

Non-Line-Of-Sight Classification In Urban Areas Based On Machine Learning Algorithms, Mohamed Ali Ezzelarab, Mohamed Abdelazim Mohamed, Eman Abdelhalim, Ashraf Abosekeen

Mansoura Engineering Journal

Non-Line-Of-Sight (NLOS) and multipath errors are significant causes of poor accuracy in Global Navigation Satellite Systems (GNSS). These errors occur when satellite signals are diffracted or reflected by obstacles instead of traveling directly along the Line-Of-Sight (LOS), particularly in urban environments. Such diffracted or reflected signals can reach GNSS receivers, skewing pseudorange measurements and posing challenges to accurate GNSS positioning. This study introduces an innovative approach using supervised Machine Learning (ML) models to detect and mitigate NLOS error. To develop and validate these models, GNSS data collected during the smartLoc project from Frankfurt and Berlin in Germany were leveraged, representing …

Enhancing Channel Data Savings And Information Transfer Efficiency In Ultrasound Imaging, Sai Konda, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

Ultrasound is a popular imaging technique mainly due to its non-invasive nature. And so, it is being used in a variety of applications. Due to plane wave imaging technique in ultrasound, frame rate of ultrasound imaging has the potential for being very high. Due to which, many channel data frames are being generated within a few seconds. As a result, tasks such as storing data frames and transferring them from front end ultrasonic system to processing computers are presenting significant challenges. Our current research work minimized these issues. We proposed and implemented: (a) Data encoding technique - We combined every …

Mxenes In Biosensing: Enhancing Sensitivity And Flexibility - A Review Of Properties, Applications, And Future Directions, Ali Mohammad Amani, Lobat Tayebi, Ehsan Vafa, Alireza Jahanbin, Milad Abbasi, Ahmed Vaez, Hesam Kamyab, Lalitha Gnanasekaran, Shreeshivadasan Chelliapan

Electrical & Computer Engineering Faculty Publications

MXenes are a novel type of nanostructured material that has received a lot of attention for their potential applications in bioanalysis owing to their unique features. These materials, made from transition metal nitrides, carbides, or carbonitrides, have a number of advantages, including high hydrophilicity, a large surface area, strong metallic conductivity, superior ion transport capabilities, biocompatibility, and low diffusion barriers. Their surfaces are easily manipulated, making them more adaptable for a variety of applications, including biosensing. The outstanding properties of MXenes have attracted researchers of different fields, including renewable energy, fuel cells, supercapacitors, electronics, and catalysis. In the context of …

Autonomous Vehicle Platooning, Tony Abelson

McNair Summer Research Program

This research investigates the construction and performance optimization of two autonomous vehicles with using "Platooning," a strategy aimed at reducing fuel consumption and enhancing transportation efficiency. Platooning allows one vehicle to follow another closely, minimizing aerodynamic drag and improving fuel economy. This study addresses the growing need for sustainable transportation solutions in the context of increasing urbanization and environmental concerns, emphasizing the importance of efficient autonomous vehicle operation. The primary objectives of this research are to assemble autonomous vehicles from scratch and to optimize their performance in both individual and platoon operations. The methodology involves using Traxxas Slash chassis and …

Electro-Vascular Dynamics During Auditory Processing: Variation Along The Schizotypy Continuum, John P. Mclinden

Open Access Dissertations

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are complementary noninvasive neuroimaging modalities that have contributed greatly to our understanding of the mechanisms of auditory processing. As an emerging technology, fNIRS has produced promising insights into the mechanisms of auditory processing, although considerable work remains to characterize hemodynamic responses in auditory tasks. In addition, the complementary nature of EEG and fNIRS and the relationships between the signals they record have been underexplored. In particular, nonlinear interactions between these signals remain under-characterized, despite the potential benefit to future multimodal neuroimaging studies. A challenge in understanding these interactions is the influence of systemic …

A Multiscale Ai Framework For Forest And Agriculture Health Monitoring: Drone-Based Object Recognition And Segmentation For Automated Ecological Assessment, Sruthi Keerthi Valicharla

Graduate Theses, Dissertations, and Problem Reports (ETD)

Forest and agricultural ecosystems are increasingly at risk due to invasive species, pests, and diseases, necessitating scalable, automated, and intelligent monitoring solutions. Traditional field based forest and agriculture health assessments are limited by cost, time, and spatial coverage. This dissertation presents a multiscale deep learning framework that automates forest and agriculture health monitoring using drone imagery and computer vision techniques. The system operates across three spatial levels: forest level, tree level, and leaf level, combining object detection, segmentation, and classification models to support large scale ecological assessment.

At the forest level, high-altitude drone imagery is processed using object detection and …

Improving Large Scale Face Recognition With Identity Codes, Mohammad Saeed Ebrahimi Saadabadi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Despite significant advances in deep face recognition, current systems face several practical challenges in real-world scenarios. These include high computational cost of training on large-scale datasets, inefficient use of metric space, and mismatch between training and evaluation frameworks. This dissertation addresses these limitations through three completed studies. The first part presents a research effort aimed at addressing the computational bottlenecks of large-scale FR training. This work proposes a framework that replaces conventional scalar identity labels with structured identity codes, \ie, sequences of tokens optimized to preserve semantic and metric separation. The formulation is designed to reduce the computational cost of …

Neural Network-Based Image Compression, Atefeh Khoshkhahtinat

Graduate Theses, Dissertations, and Problem Reports (ETD)

The rapid advancement of information technology and the exponential growth of digital communication have significantly increased the demand for efficient data compression techniques that reduce storage requirements, minimize bandwidth consumption, and accelerate data transmission—without substantially compromising data quality. This dissertation addresses these challenges by investigating and developing advanced learned image compression (LIC) methods, with a particular focus on lossy compression for both natural images and scientific imagery obtained from NASA’s Solar Dynamics Observatory (SDO) mission. Traditional image compression standards—such as JPEG, JPEG2000, BPG, and HEVC—rely on manually engineered transforms and heuristic rules, which often lack the adaptability required to accommodate …

Ai-Generated Messaging For Life Events Using Structured Prompts: A Comparative Study Of Gpt With Human Experts And Machine Learning, Christopher Lynch, Erik Jensen, Ross Gore, Virginia Zamponi, Kevin O'Brien, Brandon Feldhaus, Katherine Smith, Joseph Martínez, Madison H. Munro, Timur E. Ozkose, Tugce B. Gundogdu, Ann Marie Reinhold, Hamdi Kavak, Barry Ezell

VMASC Publications

Large Language Models (LLMs) play an increasingly integrated and pivotal role in generating diverse types of texts, such as social media messages, emails, narratives, and technical reports, among other textual communication forms. As AI-generated messaging filters into human communication, a systematic exploration of their effectiveness for mimicking human-like communication of life events is needed. In this study, we employ a zero-shot structured narrative prompt to generate 24,000 life event messages for birth, death, hiring, and firing events using OpenAI's GPT-4. From this dataset, we manually classify 2880 messages and evaluate their validity in conveying these life events through the form …

High Voltage Circuit Breaker Arc Modelling State Of Art, Raghdaa I. Elbahy, Ahmed M. Abdelbaset, Ebrahim A. Badran, Mansour H. Abdel-Rahman

Mansoura Engineering Journal

High-voltage (HV) circuit breakers (CBs) are critical for safe and reliable power system operation. However, electric arcs formed during current interruption pose a challenge due to their complex, non-linear behavior. These arcs can hinder CB efficiency and cause equipment damage. To address this, understanding and modeling arc properties are essential. This paper explores existing methods for modeling and simulating electric arcs in HV CBs, including physical, black box, and parameter models. By analyzing these approaches, the paper emphasizes the importance of arc modeling for CB optimization and outlines the physical, mathematical, and software requirements for effective modeling and simulation.

Ai-Driven Dynamic Pilot Placement For 5g Mmwave Massive Mimo: A Random Forest Regression Approach, Mohammad R. Abou Yassin, Soubhi Abo Chahine, Hamza Issa

BAU Journal - Science and Technology

Efficient pilot placement in 5G millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems is critical to enhancing performance, achieving high spectral efficiency (SE), low bit error rate (BER), reduced pilot overhead, and minimized latency. However, this requires pilot symbols transmission, which occupies spectral resources and results in reducing spectral efficiency (SE). This paper proposes a novel dynamic pilot placement (DPP) framework, optimized using a Random Forest Regression (RFR) approach, to enhance system performance. Unlike traditional static and semi-static pilot allocation methods, the DPP approach dynamically adjusts pilot positions based on real-time channel state information (CSI) and system requirements, reducing interference and …