Digital Commons Network^™

Proposed Methodology For Correcting Fourier-Transform Infrared Spectroscopy Field-Of-View Scene-Change Artifacts, Kody A. Wilson, Michael L. Dexter, Benjamin F. Akers, Anthony L. Franz

Faculty Publications

Fourier-transform spectrometers are widely used for spectral measurements. Changes in the field of view during measurement introduce oscillations into the measured spectra known as scene-change artifacts. Field-of-view changes also introduce uncertainty about which target the measured spectrum represents. Though scene-change artifacts are often present in dynamic data, their significance is disputed in the current literature. This work presents a theoretical framework and experimental validation for scene-change artifacts. Field-of-view changes introduce variable interferogram offsets, which standard processing techniques assume are constant. The error between the interferogram offset and its estimate is Fourier-transformed, yielding scene-change artifacts, often confused with noise, in the …

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Turkish Journal of Electrical Engineering and Computer Sciences

No abstract provided.

Exploitation Prioritization And Residual Risk Assessment Based On Hybrid Mcdm Model, Zibo Wang, Yaofang Zhang, Sicai Lv, Yingzhou Wang, Hongri Liu, Bailing Wang

Turkish Journal of Electrical Engineering and Computer Sciences

Exploitation is one of the most significant ways to launch attacks using vulnerabilities. The increasing number of vulnerabilities and limited allocation of security resources make it impossible to eliminate all exploitations. Because not every vulnerability can be fixed, it is necessary to rank exploitations and subsequently assess the residual risk, which is defined as the remaining threat potential after each elimination. In this paper, a structured and flexible decision support framework based on a hybrid multicriteria decision-making model is proposed for prioritizing exploitations and assessing residual risk. Metrics are treated as criteria in the model. The hybrid model is developed …

A Deep Learning-Based Real-Time No-Reference Image Decolorization Network With Perceptual Preservation, Mengjuan Zhao, Yitao Liang, Weiya Shi, Juan Xia

Turkish Journal of Electrical Engineering and Computer Sciences

Currently, grayscale images are preferred as input data for some specific vision tasks. Decolorization is the transformation of a color image into a grayscale image. Efficient decolorization algorithms can improve the overall task efficiency, while perceptual preservation in decolorization can provide more information for further processing. In recent research, traditional methods focus on preserving contrast or detail information with little attention to perceptual features. Deep-learning methods are beginning to consider perceptual preservation, but they run inefficiently. In addition, the decolorization methods lack the optimal target grayscale images for reference. Therefore, we propose a new deep learning-based real-time no-reference decolorization network …

Noninvasive Condition Monitoring For Eccentricity Fault Detection In Large Hydro Generators, Atena Tazikeh Lemeski, Di̇dem Tekgün, Ozan Keysan, Kemal Leblebi̇ci̇oğlu, Murat Göl

Turkish Journal of Electrical Engineering and Computer Sciences

Eccentricity faults in electric machines remain a critical concern, as they generate uneven magnetic forces that increase vibration and noise, ultimately raising the risk of premature motor failure. This study proposes a method for the early detection of dynamic eccentricity (DE) faults in hydropower plants through an advanced optimization-based parameter identification technique integrated with finite element analysis (FEA). Finite element modeling (FEM) is first used to analyze an existing salient-pole synchronous generator (SPSG) from a hydroelectric power plant in Türkiye. The effects of DE faults on the SPSG’s magnetic equivalent circuit parameters are then examined under various fault severities. A …

A Deep Learning Model For Accurate Tomato Leaf Disease Identification, Maheen Shahzad, Muhammad Abdullah Javed, Erum Ashraf, Hafiz Ishfaq Ahmad, Sabeen Masood

Turkish Journal of Electrical Engineering and Computer Sciences

Recent advances in machine learning and deep learning have greatly improved how we detect plant diseases, making diagnoses more accurate, faster, and easier to scale. However, many existing solutions depend on large, pretrained models that need powerful hardware, which limits their use in the field, especially in areas with limited resources. To tackle this, we designed a custom lightweight convolutional neural network (CNN) built from scratch using 20,000 carefully selected images from the PlantVillage tomato dataset. Our model uses Squeeze-and-Excitation (SE) blocks and Swish activation functions to boost performance, reaching an accuracy of 97.7% while using far fewer computing resources …

Influence Of Sinr And Noise Variance On Outage Probability For Mimo-Noma System In 5g And Beyond, Sadiq Ur Rehman, Jawwad Ahmed, Muhammad Zubair, Syed Sajjad Hussain Rizvi

Turkish Journal of Electrical Engineering and Computer Sciences

Nonorthogonal multiple access (NOMA) communication presents a promising solution to the limitations of traditional orthogonal multiple access techniques, offering potential improvements in achievable rates. Multiple-input multiple-output (MIMO), when combined with NOMA (MIMO-NOMA), further enhances these benefits by leveraging the diversity advantages of multiple antennas. Looking ahead, the future of wireless communication hinges on deploying heterogeneous networks (HetNets), facilitating the coexistence of various wireless access networks in a hierarchical fashion. However, the advent of 5G and 6G communications brings shorter channel coherence times, rendering channel reciprocity unreliable. Consequently, conventional channel estimation methods relying on uplink (UL) pilots for downlink (DL) transmission …

A Novel Approach To Maximum Weighted Traffic Flow Method For Effective Signal Control, Zülal Hi̇lal Yildiz Budak, Seyi̇t Alperen Çeltek, Aki̇f Durdu

Turkish Journal of Electrical Engineering and Computer Sciences

Traffic signal management is a critical challenge due to its environmental, economic, and public health impacts. The maximum weighted flow method (MaxWeightedFlow) was developed to optimize traffic flow at isolated and coordinated urban intersections. This study proposes a new method, the novel MaxWeightedFlow, which includes two key strategies to enhance the classical approach. The first strategy reduces computational burden by estimating vehicle approach times based on instantaneous speeds, improving real-time performance. The second employs regression analysis to optimize the alpha parameter, representing the vehicle waiting coefficient. The proposed approach, the novel MaxWeightedFlow, was evaluated using real-world traffic data from Kilis, …

Improving Rail System Signaling Efficiency Through Ai-Based Driving Profile Generation: A Comparative Performance Analysis, Mehmet Taci̇ddi̇n Akçay, Abdurrahi̇m Akgündoğdu

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, a dataset comprising 3600 discrete operational snapshots (rather than continuous time-series data) derived from real-field operations is used to obtain a high-accuracy driving profile equation using a second-degree Polynomial Regression method. This equation demonstrates the model’s interpretability. The performance metrics obtained with the second-degree polynomial regression model’s equation are as follows: a coefficient of determination (R2) of 0.84, a Pearson Correlation Coefficient of 0.91, and an RMSE of 11.13. These results indicate the effectiveness of artificial intelligence-based approaches in improving the efficiency of the railway signaling system. The same dataset is also utilized with other machine learning …

Distribution System Reliability Evaluation Considering Protection Coordination Using Petri Nets, Rani Kumari, Bhukya Krishna Naick

Turkish Journal of Electrical Engineering and Computer Sciences

Maintaining reliable and high-quality power delivery becomes increasingly complex with expanding power grids. The lack of protection coordination poses a significant threat, compromising overall system reliability. This research addresses this challenge by proposing a method for coordinating protective devices within the distribution system, specifically during network faults. The proposed approach utilizes a stochastic timed Petri net (STPN) based methodology to model protective device coordination across various fault scenarios. This technique effectively captures the dynamic behavior and interactions of protective equipment, allowing for the anticipation of potential disturbances. This proactive insight facilitates preventative measures to address prewarning situations, thereby preventing cascading …

Exploring Runtime Sparsification Of Yolo Model Weights During Inference, Tanzeel-Ur-Rehman Khan, Sanghamitra Roy, Koushik Chakraborty

Electrical and Computer Engineering Student Research

In the pursuit of real-time object detection with constrained computational resources, the optimization of neural network architectures is paramount. We introduce novel sparsity induction methods within the YOLOv4-Tiny framework to significantly improve computational efficiency while maintaining high accuracy in pedestrian detection. We present three sparsification approaches: Homogeneous, Progressive, and Layer-Adaptive, each methodically reducing the model’s complexity without compromising its detection capability. Additionally, we refine the model’s output with a memory-efficient sliding window approach and a Bounding Box Sorting Algorithm, ensuring precise Intersection over Union (IoU) calculations. Our results demonstrate a substantial reduction in computational load by zeroing out over 50% …

Robust And High-Efficiency Demodulation Of Ultra-Weak Fbg Arrays In Ofdr-Based Distributed Sensing, Zhaopeng Zhang, Yuxuan Cao, Xu Liu, Dingcheng Wang, Bo Liu, Chen Zhu

Electrical and Computer Engineering Faculty Research & Creative Works

A robust and high-efficiency demodulation scheme for optical frequency domain reflectometry (OFDR) based ultra-weak fiber Bragg grating (UWFBG) array detection system, originating from the Buneman frequency estimation (BFE) algorithm, is proposed and experimentally demonstrated. Due to the current limitations and imperfections of FBG inscription technology, the quasi-continuous inscription approach, along with its less-than-ideal outcomes, gives rise to problems of grating spectrum splitting and spectral distortion during the grating demodulation process. This renders the traditional approach of directly applying the BFE algorithm for grating demodulation ineffective, despite its significant enhancement of demodulation efficiency. To address this issue, we propose utilizing the …

Temperature Dependent High Frequency Performance Of A 62% Algan Channel Hemt, Jiahao Chen, Abdullah Al Mamun Mazumder, Parthasarathy Seshadri, Dheekshinn Nandakumar, Ruixin Bai, Rafael Andrew Choudhury, M. Asif Khan, Chirag Gupta

Faculty Publications

This article reports on the temperature dependent performance of a HEMT with an Al_0.62Ga_0.38N channel layer and an Al_0.84Ga_0.16N barrier layer grown by metal–organic chemical vapor deposition. The device in this report was measured at room temperature and elevated temperatures of 100–150 °C. The sheet resistance increased from 3.5 kΩ/sq (25 °C) to 5.4 kΩ/sq (150 °C), while the contact resistance remained nominally similar. For a device with 160 nm gate length and 2 µm source-to-drain length, excellent electrical characteristics have been achieved when the device was operated at 150 °C with …

Optimized Resnet-18 Architecture For Multi-Class Oral Diseases Classification, Ahmed Ahmed

Karbala International Journal of Modern Science

In recent years, the classification of oral diseases has gained significant attention due to its influence on public health and the necessity for early and accurate diagnosis. Traditional diagnosis depends on manual clinical assessment, which can be slow and subjective. An optimized and subsequently quantized model is required to provide a faster and more consistent diagnostic support tool. This paper proposes an optimized ResNet-18 architecture for the classification of six oral diseases. The optimization process is based on removing the Rectified Linear Unit (ReLU), Batch Normalization (BN), and convolutional layers from the base ResNet-18 blocks that contain 128 filters. This …

Model-Based Investigation Of The Influence Of Environmental Conditions On The Energy Supply Of Multirotor Uavs, Morten Roßberg, Hanna Dibbern, Claudia Werner

Journal of Aviation Technology and Engineering

Flight time of unmanned aerial vehicles (UAVs) is limited by available energy, which is affected by the mission profile and external factors, such as environmental conditions. Two main environmental conditions that need to be considered are the impact of wind and ambient temperature on the UAV and its energy supply. The purpose of this study is to examine the effects of wind and ambient temperature to conduct a preliminary evaluation of flight performance and flight limitations. For this reason, three locations in the United States—New York City, Miami, and Fairbanks—are selected and the impact of the considered environmental conditions of …

Research Progress On Generating Perfect Vortex Beams Based On Metasurfaces, Xiujuan Liu, Manna Gu, Ying Tian, Mingfeng Zheng, Bo Fang, Zhi Hong, Chee Leong Tan, Xufeng Jing

Electrical and Computer Engineering Faculty Publications and Presentations

This article reviews the latest advances in the generation and control of perfect vector beams using metasurfaces. In recent years, metasurfaces have garnered increasing interest due to their simple fabrication and easy integration. Perfect vortex beams (PVBs), as a type of vector beams, exhibit complex polarization states that require the superposition of multiple phases for their generation. The use of metasurfaces provides a compact platform for the generation of perfect vortex beams and enables more complex vortex beam control tasks, which are quite challenging for traditional optics. This paper begins by introducing the principle of perfect vortex beam generation using …

Universal Sound Separation: Distance-Aware Mixture Simulation, Co-Occurrence Conditioning, And Chain-Of-Inference, Wonjun Park

Computer Science and Engineering Theses - Archive

Universal Sound Separation (USS) -- the task of disentangling arbitrary sound sources from a single-channel acoustic mixture -- remains an open challenge due to the ill-posed nature of the problem and the distributional gap between synthetic training data and real-world recordings. This thesis addresses three distinct bottlenecks in the USS pipeline: training data realism, inference strategy, and conditioning richness. We first present two knowledge-guided approaches to sound source separation. The first is a distance-aware mixing strategy that leverages Large Language Models (LLMs) to assign plausible loudness relationships between audio sources during training data synthesis. By querying an LLM about the …

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.

Ai Data Center Dynamic Load Effects On Current Transformer Saturation, Sergio A. Hernandez

Electrical Engineering Theses

AI data centers can produce rapid changes in electrical demand that may influence current transformer performance during faults. This study evaluates the effect of an AI data center transient on CT saturation during single line-to-ground faults using a 400 V, 60 Hz grid connected inverter model in MATLAB/Simulink. The normal condition transient produced a maximum RMS current rate of approximately 211 A/ms, which was used along with the maximum power condition to define fault inception cases. A MATLAB time-domain CT model then swept the fault current DC offset coefficient to determine the minimum offset required for CT saturation. The calculated …

A Taxonomy-Driven Modular Defense Against Non-Canonical Language In Vision-Language-Action Models, Viraj Samson

Electronic Theses and Dissertations

Vision-Language-Action (VLA) models have recently achieved strong performance across manipulation benchmarks, but these benchmarks rely on highly templated instructions on which the models are typically fine-tuned, leaving their behavior under realistic language-side perturbation unclear. It remains an open question whether the linguistic flexibility inherited from vision-language pretraining survives this fine-tuning, or whether the resulting policies become narrowly tuned to benchmark phrasing and brittle to the intent-preserving language variation that real users naturally produce. We address this gap with a systematic study of VLA robustness under non-canonical instructions, comprising three components: a structured taxonomy of intent-preserving variations spanning linguistic, orthographic, and …

Data-Driven Optimization Of Memory Effects And Critical Components In Cascading Failure Interaction Networks, Md Farhan Tanvir

Electronic Theses and Dissertations

Cascading failures are a major concern for modern power systems‚ where a small failure can cascade through the network to create a large blackout. Because such an event could have catastrophic economic and social costs it is important to understand cascading failures‚ how to model them‚ and the possibility of reducing them. In this thesis‚ we develop a data-driven framework based on actual utility outage data to model and reduce cascading failures. The proposed methodology is based on generation-dependent interaction models and in this study interaction matrices obtained from historical outage events are used to describe the interaction between generations. …

Data For: Optimizing Finite Structures To Suppress The Photonic Density Of States, Prakash Mishra, Sukhad Dnyanesh Joshi, Quintin A. Hatzis, Aditya Bahulikar, M. Cenk Gursoy, Rodrick Kuate Defo

Electrical Engineering and Computer Science - All Scholarship

We propose a topology-optimization framework for optimizing finite structures of arbitrary shape by combining density-based methods with level-set approaches. We first optimize regular polygonal structures to suppress the photonic density of states and find that the best performing polygon is consistent with a tiling of space with hexagonal unit cells. We next show that introducing cavities into hexagonal structures further suppresses the photonic density of states, particularly when the cavity is also hexagonal. Such a result would find application in the design of fiber-optic cables. We then describe an approach for optimizing arbitrary x-simple or y-simple designs that can recover …

Localized Air Pollution Impacts From Data Centers In Northern Virginia, Damian Pitt, Ivan Suen, Ellie Plisko

Institute for Sustainable Energy and Environment Publications and Presentations

This report examins the extent of air pollution emissions resulting from the use of backup generators at data centers in Northern Virginia, including both actual current emissions and potential future emissions, and how those emissions totals compare to other sources of air pollution in the Northern Virginia region. It also examines how exposure to data center emissions correlates with demographic characteristics such as race, income, and education.

We while the air pollution emissions from individual data centers is minor, the collective impact from the 100+ such facilities in the Northern Virginia region is significant. Notably, the cumulative emissions exposure in …

Ransomware As Organization: A Comparative Analysis Of Corporate And Criminal Structures In Conti, George Urling

Theses, Dissertations and Capstones

Cybercriminal groups continue to pose major threats to global cybersecurity. One of the most common types of cybercriminal groups are, “Ransomware-as-a-Service (RaaS)" groups, who create and sell ransomware. While research is conducted into the development of ransomware, there is limited reporting on the organizational structure and habits of RaaS groups. In 2022, prominent RaaS group Conti had their chat logs leaked, with the logs ranging from 2020 to 2022. This study seeks to provide a deeper understanding of RaaS group structures by utilizing the Conti leaked logs as a case study. The study, entitled “Ransomware as Organization: A Comparative Analysis …

Goal-Driven Shared Control In Eeg-Based Brain Machine Interface For Freewill Reaching And Grasping With Movement Intention Detection And Goal Position Decoding, Bhoj Raj Thapa

Theses and Dissertations--Electrical and Computer Engineering

Upper limb motor impairments can severely limit a person’s ability to perform everyday reaching and grasping tasks. Electroencephalogram (EEG)-based brain machine interfaces (BMIs) offer a non-invasive approach for translating neural activity into control signals for assistive devices such as robotic arms. However, traditional EEG-based BMI studies have generally focused on externally cued paradigms, where both movement timing and target selection are specified by the experimenter rather than freely chosen by the user. In addition, shared control offers a practical framework for assistive BMI operation by dividing responsibility between the user and the intelligent robotic system. However, in many EEG-based shared …

Determining The Electric Field In A 10 Ns Pulsed Plasma In Fuel-Air Mixtures Using Efish, Md Ziaur Rahman, Christopher J. Kliewer, Brian D. Patterson, Chunqi Jiang

Bioelectrics Publications

Transient plasma ignition (TPI) utilizes non-equilibrium plasmas, produced by nanosecond high-voltage pulses, to improve lean-fuel combustion performance and reduce emission. It is known that the relatively high reduced electric field (E/N) in TPI plays an important role in generating energetic electrons and facilitating energy-efficient radical productions, resulting in reliable ignition for lean combustion. Determining the reduced electric field in the discharge is hence important for the understanding of the TPI process and ultimately allowing for the control of the plasma chemistry. This study reports spatiotemporally resolved measurements of the electric field (E) in a 10 ns pulsed plasma that is …

Ammonia Synthesis By Nanosecond Pulsed Atmospheric Pressure Plasma Jets Impinging On Water, Zach Caudell, Lynnet Rich, Olga Pakhomova, Chunqi Jiang

Bioelectrics Publications

Developing energy-efficient technologies for carbon-neutral ammonia (NH₃) synthesis is critical for decentralized fertilizer production and global decarbonization. This study investigates generating NH₃ from water using a nanosecond pulsed atmospheric pressure plasma jet (ns‑APPJ) operating in either N₂ or dry air. The plasma jet reactor employed approximately 250 ns, up-to-22 kV pulses at 500 Hz to sustain a nonequilibrium discharge impinging directly on static liquid water. The kinetics, energy efficiency, and product selectivity of NH3 formation were quantified as functions of the pulse voltage, repetition frequency (PRF), and gas flow rate. NH₃ production increased linearly with treatment time and scaled strongly …

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 …

Federated And Explainable Spiking Neural Networks For Fair And Privacy-Preserving Nail Disease Diagnostics, Ch Pavani Reddy, Krishnanaik Vankdoth

Mansoura Engineering Journal

Automated nail disease diagnostics provide a non-invasive pathway for identifying underlying systemic health conditions; however, conventional centralized deep learning approaches often raise concerns related to privacy, fairness, and interpretability. Although the original NeuroNail-SNN framework demonstrated an energy-efficient and edge-ready diagnostic solution, its broader clinical adoption remained limited by unresolved trust, transparency, and ethical considerations. In this study, we propose the Federated and Explainable NeuroNail-SNN, which extends the original spiking neural architecture by integrating federated learning (FL), explainable artificial intelligence (XAI), fairness evaluation, and uncertainty quantification within a unified framework. Federated learning enables decentralized model training across hospitals and mobile clinics …