Electrical and Computer Engineering Commons^™

Competitions Among Stations In Ieee 802.11be Networks, Jun Peng

Electrical and Computer Engineering Faculty Publications

The IEEE 802.11be standard aims for the next-generation applications that demand connections of extremely high bandwidth and low latency. It is the basis for Wi-Fi 7. An IEEE 802.11be device can operate in the 2.4, 5, and 6 GHz frequency bands simultaneously under the multi-link operation (MLO) mode. In the 6 GHz band, it can use a channel width up to 320 MHz. Its QAM-constellation can have up to 4096 states. Some of its other features include multiple resource units (MRU), preamble puncturing, and enhanced security. This paper shows the competitions among the stations in an IEEE 802.11be network with …

Introduction To The Special Issue On Computer Modeling For Future Communications And Networks, Wenbing Zhao, Pan Wang

Electrical and Computer Engineering Faculty Publications

No abstract provided.

Comparative Numerical Analysis Of Lead-Free Perovskite Solar Cells Using Scaps-1d, Md Shazarul Islam, Md Abdul Kuddus Sheikh, Hasina Huq

Electrical and Computer Engineering Faculty Publications

A comprehensive numerical study of lead-free perovskite solar cells was conducted using the SCAPS-1D simulation framework with the device architecture ITO/SnO2/Perovskites/NiOx/Au. The work investigates the replacement of the central Pb cation with Sn, Ge, and Bi, followed by absorber-layer thickness optimization to enhance device performance. The impact of systematic Pb substitution on key photovoltaic parameters was first evaluated. Among the candidates, FASnI3-based devices exhibited the most promising performance, achieving a power conversion efficiency (PCE) of 26.48%, with a short-circuit current density (Jsc) of 19.31 mAcm-2, an open -circuit voltage (Voc) of 1.57 V, and a fill factor (FF) of 87.29%. …

Sonochemically Synthesized Al-Doped Zno Nanorods-Based Flexible Piezoelectric Nanogenerators For Durable Energy Harvesting, Tiham Fayaz, Ahmed Hasnain Jalal, Fahmida Alam

Electrical and Computer Engineering Faculty Publications

This study presents a high-performance piezoelectric nanogenerator (PENG) based on aluminum-doped zinc oxide (Al:ZnO) nanorods synthesized via a novel sonochemical method. This rapid, cost-effective, and reproducible approach enables the synthesis of ZnO nanorods (ZnO NRs) under ambient conditions. The PENG, fabricated on a 177 µm flexible Indium Tin Oxide (ITO) coated polyethylene terephthalate (PET) substrate, benefits from optimized aluminum doping, enhancing the output voltage. Structural analyses conducted using atomic force microscopy (AFM), scanning electron microscopy (SEM), and X-ray diffraction (XRD) demonstrated that the synthesized ZnO nanorods possessed a high degree of crystallinity. The synthesis process was carefully fine-tuned to avoid …

Numerical Investigation Of Highly Efficient Chlorine-Doped Perovskite Solar Cells, Md Abdul K Sheikh, Md Shazarul Islam, Hasina Huq

Electrical and Computer Engineering Faculty Publications

In this study, we present a comprehensive numerical investigation of chlorine-doped perovskite solar cells using the SCAPS-1D simulation framework, with the device structure ITO/ZnO/CH3NH3PbI3−xClx/NiOx/Au. This work focuses on optimizing active-layer properties and compositional engineering to enhance photovoltaic performance. Initially, the influence of absorber thickness on device parameters was investigated, revealing that CH3NH3PbI3 achieves optimal performance at 800 nm thickness, delivering a power conversion efficiency (PCE) of 24.17%, along with a short circuit current density (Jsc) of 25.31 mA cm−2, an open circuit voltage (Voc) of 1.15 V, and a fill factor (FF) of 82.75%. Subsequently, chlorine incorporation was systematically varied …

Study Of Clustering Technique And Communication Topologies For Cooperative Control-Based Volt-Var Optimization, Gaurav Yadav, Yuan Liao, Dan M. Ionel

Electrical and Computer Engineering Faculty Publications

Introducing renewable distributed generation (DG) in the power distribution system causes rapid voltage fluctuations due to its intermittency. This intermittency renders conventional voltage regulation devices such as on-load tap changers (OLTCs) and capacitor banks (CBs) inefficient to regulate rapid voltage changes and leads to reduced equipment lifetime and high operation and maintenance costs. Hence, this calls for non-conventional methods to mitigate such voltage fluctuations. This paper presents a cooperative control-based method aimed to optimally control the reactive power of DG inverters to mitigate the voltage deviations by establishing communication among the DG nodes, and between DG and non-DG nodes. This …

Heat Input Control And Deep Learning-Based Indirect Measure Of Process And Deposition Stability In Wire Arc Additive Manufacturing, Alessandra Caggiano, Giulio Mattera, Yuming Zhang, Roberto Teti

Electrical and Computer Engineering Faculty Publications

A process qualification-oriented data-driven framework for Wire Arc Additive Manufacturing (WAAM) integrating qualification data, process monitoring and feedback control, is presented. A proportional control strategy regulating heat input by varying the Contact Tip–to–Workpiece Distance (CTWD) is developed to enhance process stability, ensure consistent layer geometry and maintain the qualified heat-input conditions for process qualification. To assess the control strategy stability, deep learning-based CTWD soft sensing from high-frequency welding signals is combined with an uncertainty-aware process quality index. The framework is validated on Invar 36 alloy, but it supports extension to other alloys and arc welding-based additive processes.

Structured Laser Vision-Based Measurement Of Gta-Weld Pool, Gang Zhang, Jianbo Wang, Yu Shi, Ding Fan, Yuming Zhang

Electrical and Computer Engineering Faculty Publications

The current study of weld pool fluid dynamics in arc welding focuses on the numerical model establishment and simulation, and the x-ray combined with particle trace imaging observations, there is no real-time monitor and quantitatively characterize the weld pool flow behavior in welding process for controlling the weld quality. This study develops an innovative structured laser vision-based sensing system for three-dimensional (3D) reconstruction and quantitative analysis of weld pool surface topographies in gas tungsten arc welding (GTAW). Through characterization of dynamic weld pool morphologies, two novel parameters are proposed: the surface convexity variation rate (R_h) and fluid …

Ev Charging And V2g Operation For Distribution System Vpp Including Model Predictive Control, Rosemary E. Alden, Simone Silvestri, Malcolm D. Mcculloch, Dan M. Ionel

Electrical and Computer Engineering Faculty Publications

Future smart grid virtual power plants (VPPs) are considered for development based on industry communication standards for electric vehicle (EV) chargers such as Open Charge Point Protocol (OCPP), IEC 15118, and IEC 61851. To support research and development of computationally intelligent controls for distributed EV batteries, a python-based API OpenDSS VPP framework is utilized with thousands of experimental smart meter profiles, the IEEE 123 node test feeder, and hundreds of national survey-based EV modules for conventional and optimal charging and vehicle-to-grid (V2G) control development to mitigate any voltage violations and reduce peak load. A methodology is proposed for model-predictive control …

Vision‑Based Online Quality Tracking In Wire Arc Additive Manufacturing Via Hybrid Unsupervised Deep Learning–Statistical Process Monitoring, Giulio Mattera, Yue Cao, Yuming Zhang, Luigi Nele

Electrical and Computer Engineering Faculty Publications

Vision-based monitoring of Wire Arc Additive Manufacturing (WAAM) using supervised deep learning represents the state of the art in anomaly detection, but such approaches require large labeled datasets that are costly to obtain and typically limited to laboratory conditions. To address these limitations, this work proposes a hybrid deep learning–statistical process monitoring (SPM) framework tailored to the stochastic nature of conventional arc welding processes such as GMAW-based additive manufacturing, where existing methods often overfit. The framework integrates a residual convolutional autoencoder (Res-CAE) with skip connections, which jointly analyzes video frames to generate refined latent-space features that are subsequently monitored using …

Accurate Prediction Of Geometrical Parameters Of An Ultra-Broadband Metamaterial Absorber Using Machine Learning, Md. Rezwan Ahmed, Oishi Jyoti, Pritu Parna Sarkar, Mohammod Abdul Motin, Md. Selim Habib, Md. Samiul Habib

Electrical and Computer Engineering Faculty Publications

In this paper, we systematically demonstrate the design and analysis of a new type of ultra-broadband tunable metamaterial perfect absorber (MPA) comprising a top vanadium dioxide (VO2) based patterned resonating patch, a continuous metallic film at the bottom, and an intermediate dielectric substrate having a thickness of only 0.18 at the center working frequency. The simulation results reveal that the absorber achieves a bandwidth of 7.26 THz, ranging from 5.40 THz to 12.66 THz, with more than 90% absorptance and an average absorption of 98.21% under normal incidence of the incoming THz wave. Furthermore, absorptance exceeding 99% is achieved between …

Auction Consensus Algorithm With Loss Mechanism For Decentralized Task Allocation, Jose Rodriguez, Wenjie Dong, Constantine Tarawneh, Qi Lu

Electrical and Computer Engineering Faculty Publications

This paper presents an Auction-Consensus Algorithm with a Loss Mechanism (ACALM), a decentralized task allocation method for multi-robot systems that enhances the existing Consensus-Based Auction Algorithm (CBAA) by incorporating a novel loss propagation mechanism. In contrast to purely greedy bidding strategies, it enables agents to dynamically update task priorities based on the accumulated loss from previously unsuccessful bids. This extended work reduces globally inefficient allocations caused by early suboptimal decisions. The proposed approach is evaluated through large-scale simulations in thousands of randomized scenarios and swarm sizes ranging from 5 to 120 robots. Compared to existing CBAA and GCAA algorithms, ACALM …

Bridging Cybersecurity Practice And Law: A Hands-On, Scenario-Based Curriculum Using The Nice Framework To Foster Skill Development, Colman Mcguan, Aadithyan Vijaya Raghavan, Komala M. Mandapati, Chansu Yu, Brian Ray, Debbie Jackson, Sathish Kumar

Electrical and Computer Engineering Faculty Publications

In an increasingly interconnected world, cybersecurity professionals play a pivotal role in safeguarding organizations from cyber threats. To secure their cyberspace, organizations are forced to adopt a cybersecurity framework such as the NIST National Initiative for Cybersecurity Education Workforce Framework for Cybersecurity (NICE Framework). Although these frameworks are a good starting point for businesses and offer critical information to identify, prevent, and respond to cyber incidents, they can be difficult to navigate and implement, particularly for small-medium businesses (SMBs). To help overcome this issue, this paper identifies the most frequent attack vectors to SMBs (Objective 1) and proposes a practical …

Trustworthy Navigation With Variational Policy In Deep Reinforcement Learning, Karla Bockrath, Liam Ernst, Rohaan Nadeem, Bryan Joseph Pedraza, Dimah Dera

Electrical and Computer Engineering Faculty Publications

Introduction: Developing a reliable and trustworthy navigation policy in deep reinforcement learning (DRL) for mobile robots is extremely challenging, particularly in real-world, highly dynamic environments. Particularly, exploring and navigating unknown environments without prior knowledge, while avoiding obstacles and collisions, is very cumbersome for mobile robots.

Methods: This study introduces a novel trustworthy navigation framework that utilizes variational policy learning to quantify uncertainty in the estimation of the robot’s action, localization, and map representation. Trust-Nav employs the Bayesian variational approximation of the posterior distribution over the policy-based neural network’s parameters. Policy-based and value-based learning are combined to guide the robot’s actions …

Underwater Acoustic Integrated Sensing And Communication: A Spatio-Temporal Freshness For Intelligent Resource Prioritization, Ananya Hazarika, Mehdi Rahmati

Electrical and Computer Engineering Faculty Publications

Underwater acoustic communication faces significant challenges including limited bandwidth, high propagation delays, severe multipath fading, and stringent energy constraints. While integrated sensing and communication (ISAC) has shown promise in radio frequency systems, its adaptation to underwater environments remains challenging due to the unique acoustic channel characteristics and the inadequacy of traditional delay-based performance metrics that fail to capture the spatio-temporal value of information in dynamic underwater scenarios. This paper presents a comprehensive underwater ISAC framework centered on a novel Spatio-Temporal Information-Theoretic Freshness metric that fundamentally transforms resource allocation from delay minimization to value maximization. Unlike conventional approaches that treat all …

A Pilot Study On Tissue Deformation Using An Integrated Sensor–Actuator Blood Collection Setup In Aquaculture (Salmo Salar), Ishrak Siddiquee, Md Ebne Al Ashad, Ahmed Hasnain Jalal

Electrical and Computer Engineering Faculty Publications

This pilot study presents a sensor–actuator setup designed to evaluate tissue deformation in Atlantic Salmon (Salmo salar) during needle insertion. The system integrates three types of low-cost, commercially available force sensors to capture force profiles and identify biomechanical events associated with tissue layer transitions. Controlled insertions were performed on a deceased specimen, and the resulting force data were analyzed to quantify insertion dynamics and estimate tissue deformation. A simulation model based on the recorded force values was developed to calculate stress distribution and deformation, which ranged from 0.001 µm to 8.4 µm and from 0.3 N/m2 to 4.9 N/m2, respectively. …

Multi-Layered Framework For Llm Hallucination Mitigation In High-Stakes Applications: A Tutorial, Sachin Hiriyanna, Wenbing Zhao

Electrical and Computer Engineering Faculty Publications

Large language models (LLMs) now match or exceed human performance on many open-ended language tasks, yet they continue to produce fluent but incorrect statements, which is a failure mode widely referred to as hallucination. In low-stakes settings this may be tolerable; in regulated or safety-critical domains such as financial services, compliance review, and client decision support, it is not. Motivated by these realities, we develop an integrated mitigation framework that layers complementary controls rather than relying on any single technique. The framework combines structured prompt design, retrieval-augmented generation (RAG) with verifiable evidence sources, and targeted fine-tuning aligned with domain truth …

Report On The First Year Of Operation Of Kentucky’S First Utility Wind Turbine, Lawrence E. Holloway, Sophia A. Hahn, Aron Patrick

Electrical and Computer Engineering Faculty Publications

This report examines the first year’s operational data from Kentucky’s first utility wind turbine, the 37 m hub-height 90-kilowatt NPS100C-27, operated by the PPL Corporation Research and Development at their Renewable Integration Research Facility in Mercer County, Kentucky. During that year, the turbine was available 95% of the time, spinning 85% of the time, and generating power 78% of the time, and had a net capacity factor of 11%. This report analyzes the turbine performance and uses the collected wind data to project the performance of an example turbine more typical of larger commercial turbines recently installed elsewhere in the …

Distributed Tracking Control Of Multiple High-Order Uncertain Nonlinear Systems With Guaranteed Performance, Eduardo Alvarez, Wenjie Dong

Electrical and Computer Engineering Faculty Publications

This paper addresses the distributed tracking control of multiple uncertain high-order nonlinear systems with prescribed performance requirements. By introducing a performance function and a nonlinear transformation, the prescribed fixed-time performance tracking control problem is reformulated as a distributed tracking control problem for multiple special nonlinear systems. With the aid of the universal approximation theorem for continuous functions and algebraic graph theory, distributed robust adaptive controllers are designed using the backstepping technique. Simulation results are presented to demonstrate the effectiveness of the proposed algorithms.

A Ka-Band Omnidirectional Metamaterial-Inspired Antenna For Sensing Applications, Khan Md. Zobayer Hassan, Nantakan Wongkasem, Heinrich D. Foltz

Electrical and Computer Engineering Faculty Publications

A Ka-Band, 26.5–40 GHz, omnidirectional metamaterial-inspired antenna was designed, built, and tested to develop a simple printed compact (10.3 mm × 10.3 mm × 0.0787 mm) multiple-point sensor for air pollution monitoring. This Ka-band antenna generated a dual band at 27.49–29.74 GHz and 33.0–34.34 GHz. The VSWR values within the two bands are less than 1.5. The radiation and total efficiency are 97% and 92% in the first band and they are both 96% in the second band. The maximum gain is between 3.26 and 5.50 dBi and between 5.09 and 6.52 dBi in the first and second bands, respectively. …

Robust Distributed Learning Against Both Distributional Shifts And Byzantine Attacks, Guanqiang Zhou, Ping Xu, Yue Wang, Zhi Tian

Electrical and Computer Engineering Faculty Publications

In distributed learning systems, robustness threat may arise from two major sources. On the one hand, due to distributional shifts between training data and test data, the trained model could exhibit poor out-of-sample performance. On the other hand, a portion of working nodes might be subject to Byzantine attacks, which could invalidate the learning result. In this article, we propose a new research direction that jointly considers distributional shifts and Byzantine attacks. We illuminate the major challenges in addressing these two issues simultaneously. Accordingly, we design a new algorithm that equips distributed learning with both distributional robustness and Byzantine robustness. …

Sonochemically Synthesized Zno Nanowires (Nws)-Based Sensor For Non-Invasive Glucose Detection In Sweat, G. M. Mehedi Hossain, Ahmed Hasnain Jalal, Hasina Huq, Nazmul Islam, Karen Lozano, Nezih Pala, Fahmida Alam

Electrical and Computer Engineering Faculty Publications

Glucose is a crucial metabolic marker, providing critical insights into energy regulation and insulin sensitivity, making its monitoring essential for managing diabetes and overall metabolic health. In this context, flexible biosensors have gained considerable attention for their potential to enable real-time and non-invasive glucose monitoring. One promising advancement in this domain is using Zinc-Oxide (ZnO) nanostructures through sonochemical synthesis. ZnO has a wide bandgap of ∼3.37 eV and a large binding energy of 60 meV. This research focuses on ZnO-Nanowires' growth for the first time on a flexible polymer substrate. The average length and diameter of the nanowires are 2.5µm …

Machine Learning In Baseball Analytics: Sabermetrics And Beyond, Wenbing Zhao, Vyaghri Seetharamayya Akella, Shunkun Yang, Xiong Luo

Electrical and Computer Engineering Faculty Publications

In this article, we provide a comprehensive review of machine learning-based sports analytics in baseball. This review is primarily guided by the following three research questions: (1) What baseball analytics problems have been studied using machine learning? (2) What data repositories have been used? (3) What and how machine learning techniques have been employed for these studies? The findings of these research questions lead to several research contributions. First, we provide a taxonomy for baseball analytics problems. According to the proposed taxonomy, machine learning has been employed to (1) predict individual game plays; (2) determine player performance; (3) estimate player …

Zno Nanowires For Biosensing Applications, G.M. Mehedi Hossain, Daniel Garza, Emilio Chavez, Ahmed Hasnain Jalal, Fahmida Alam

Electrical and Computer Engineering Faculty Publications

Zinc oxide Nanowires (ZnO-NWs) are promising biosensor materials and hold the key to overcoming challenges in the field. This chapter provides an introductory overview of biosensing technology, focusing on the fundamental principles and comparing ZnO-NWs with other nanostructures regarding the surface area, reactivity, electrical properties, charge transport behavior, optical, magnetic, and piezoelectric properties, and mechanical flexibility. Providing the synthesis and characterization methods, ZnO-NWs’ biosensing processes are also elaborated on surface modification for selectivity, integration with microfluidic systems, enhancing signal transduction, and connecting with biological elements like enzymes, antibodies, and DNA. The chapter also discusses the applications of ZnO-NWs-based biosensors in …

The Rhythm Of Renewables: Minute-By-Minute Insights From Kentucky, David S. Beyerle, Noah M. Stewart, Shaun E. Lavin, Chad C. Alkire, Heather Nikolic, Samuel Kelty, Ezekiel A. Boggs, Declan Boyle, Lawrence E. Holloway, Aron Patrick

Electrical and Computer Engineering Faculty Publications

Kentucky’s renewable energy landscape beats with a distinct rhythm shaped by ever-changing variations in sunlight, wind, rainfall, and the ever-modernizing grid that connects them. This paper analyzes minute-by-minute performance data from seven renewable and storage assets owned and operated by the PPL Corporation in Kentucky, including hydroelectric, solar, wind, and lithium-ion battery systems. Using a full year of synchronized, high-resolution data from multiple sites in the Commonwealth, the study examines daily and seasonal capacity factor trends, explores correlations among generation types, and evaluates their alignment with utility load profiles. Our analysis found 279 hours—about 3.2% of the year—with zero renewable …

A Large Electroencephalogram Database Of Freewill Reaching And Grasping Tasks For Brain Machine Interfaces, Bhoj Raj Thapa, John Boggess, Jihye Bae

Electrical and Computer Engineering Faculty Publications

Brain machine interfaces (BMIs) offer great potential to improve the quality of life for individuals with neurological disorders or severe motor impairments. Among various neural recording modalities, electroencephalogram (EEG) is particularly favorable for BMIs due to its noninvasive nature, portability, and high temporal resolution. Existing EEG datasets for BMIs are often limited to experimental settings that fail to address subjects’ freewill in decision making. We present a large EEG dataset, containing a total of 6808 trials, recorded from 23 healthy young adults (eight females and 15 males with an age range from 18 to 24 years) while performing reaching and …

Ieee 802.11n And Ieee 802.11ax Networks Under Various Propagation Models, Jun Peng

Electrical and Computer Engineering Faculty Publications

IEEE 802.11n networks are the first generation of Wi-Fi networks to reach data rates of hundreds of megabits per second. They are also the first to use MIMO technologies in Wi-Fi networks. They can operate in both 2.4 GHz and 5 GHz frequency bands and use 20 or 40 MHz channel width. Similarly, IEEE 802.11ax networks can operate in both 2.4 GHz and 5.0 GHz frequency bands and use 20, 40, 80, or 160 MHz channel width. For channel access, IEEE 802.11ax networks use CSMA/CA, multi-user MIMO (MU-MIMO), and OFDMA. They can reach data rates of multiple gigabits per second. …

Reevaluating The Opportunity For Wind Energy In Kentucky: Advancing Technology, Changing Economics, And Generation Complementarity, Lawrence E. Holloway, Aron Patrick, Dan M. Ionel

Electrical and Computer Engineering Faculty Publications

Recent developments in wind turbine technology, new wind resource data, and new federal tax credits for renewable energy are increasing the suitability of wind electricity generation in Kentucky. In 2022, Kentucky had 68% of its electricity from coal and in 2023 was one of eight U.S. states with no utility-scale wind generation. In the past, the state has been viewed as being generally unsuitable for wind power generation. While wind is not the most economic resource in Kentucky, all states bordering the Commonwealth have wind power. Wind power generation in Kentucky appears increasingly likely in the coming decades to play …

Interdigitated Gear-Shaped Screen-Printed Electrode Using G-Pani Ink For Sensitive Electrochemical Detection Of Dopamine, Pritu P. Sarkar, Ridma Tabassum, Ahmed Hasnain Jalal, Ali Ashraf, Nazmul Islam

Electrical and Computer Engineering Faculty Publications

In this research, a novel interdigitated gear-shaped, graphene-based electrochemical biosensor was developed for the detection of dopamine (DA). The sensor’s innovative design improves the active surface area by 94.52% and 57% compared to commercially available Metrohm DropSens 110 screen-printed sensors and printed circular sensors, respectively. The screen-printed electrode was fabricated using laser processing and modified with graphene polyaniline conductive ink (G-PANI) to enhance its electrochemical properties. Fourier Transform Infrared (FTIR) Spectroscopy and X-ray diffraction (XRD) were employed to characterize the physiochemical properties of the sensor. Dopamine, a neurotransmitter crucial for several body functions, was detected within a linear range of …

Reinforcement Learning-Based Optimal Control Of Uncertain Nonlinear Systems, Miguel Garcia, Wenjie Dong

Electrical and Computer Engineering Faculty Publications

This paper considers the optimal control of a second-order nonlinear system with unknown dynamics. A new reinforcement learning based approach is proposed with the aid of direct adaptive control. By the new approach actor-critic reinforcement learning algorithms are proposed with three neural network approximation. Simulation results are presented to show the effectiveness of the proposed algorithms.