Physical Sciences and Mathematics Commons^™

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 …

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, …

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 …

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 …

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 …

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 …

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 …

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 …

A. Vs. I In Ai: Is There A Threshold To "Engineered" Intelligence?, Joshit Mohanty

Engineering Management & Systems Engineering Faculty Publications

Despite artificial intelligence reshaping the world, its development generates uncertainties regarding future capabilities. AI simultaneously exists as an artifact of engineering design and as autonomous intelligence, creating an observer-participant feedback loop. This paper proposes that embodied AI faces a bandwidth-limited intelligence threshold T_h that it arises from B = min(C_sens,C_Act). However, Shannon capacity measures bits while intelligence operates on concepts, necessitating a dual-channel model separating physical bandwidth B_io from representational capacity B_rep. Intelligence emerges as multi-dimensional rather than scalar, with components exhibiting different bandwidth dependencies. Surpassing T_h requires either new sensing methods expanding B, enhanced representational frameworks, or reconceptualization within …

Safety Aware Continual Reinforcement Learning-Based Output Tracking Control Of Nonlinear Continuous-Time Systems, Irfan Ganie, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

An output feedback (OF)-based control scheme utilizing both a scalable multilayer neural network (MNN) observer and actor–critic MNN via integral reinforcement learning (IRL)/adaptive dynamics programming (ADP) approach for a class of nonlinear systems with output constraints is introduced. The proposed observer, critic, and actor MNN weight updates are derived using a singular value decomposition (SVD) of MNN activation function gradient along with output error, Bellman and control input errors, respectively. Next, the approach incorporates continual learning (CL), utilizing a penalty function in the weight update laws for both actor–critic MNNs to consolidate knowledge from previous tasks and enhance learning 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 …

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 …

Cybersecurity Center For Offshore Wind Energy (Final Project Round), Sachin Shetty

Center for Secure and Intelligent Critical Systems (CSICS) Publications

This project establishes a Cybersecurity Center for Offshore Wind Energy with the objective of designing and operating a cyber-physical testbed for wind energy farms (WEFs) that enables comprehensive cybersecurity research. The testbed incorporates a Supervisory Control and Data Acquisition (SCADA) system connected to turbine models via industrial-grade programmable logic controllers (PLCs) and remote terminal units (RTUs). It supports side-channel data acquisition, implementation and analysis of various cyberattack scenarios, and development of attack detection, mitigation, and best-practice guidance tailored to wind energy systems. During the project, the team expanded the number and fidelity of mathematical turbine models (MTMs), integrated these models …

Guitar Amplifier Directivity, Rachel C. Edelman, Brian E. Anderson, Samuel D. Bellows, Timothy W. Leishman

Directivity

No abstract provided.

Agricultural Productivity Under Energy Development: Insights From California, Wai Yan Siu, Sherzod B. Akhundjanov

ODU Articles

This paper examines how agricultural productivity patterns in Kern County, California, a leading region for both agricultural production and oil and gas development, co-vary with the spatial and temporal expansion of hydraulic fracturing and associated energy infrastructure. Using parcel- and county-level analyses, we characterize how agricultural productivity differs across proximity to energy development and across spatial scales. The results reveal spatially heterogeneous and scale-dependent patterns: parcel-level evidence indicates lower Enhanced Vegetation Index-based vegetation productivity within the 20-mile proximity zone around fracking wells, while county-level results show heterogeneous crop-specific yield changes during the post-expansion period. Together, these findings highlight the importance …

Solving High-Dimensional Differential Equations Using Recurrent And Residual Neural Network Architectures, Hind Khaled Kolaib

Knowledge Engineering and Data Science

High-dimensional Partial Differential Equations (PDEs) form the foundation of complex process modeling in various scientific and engineering applications, including finance, physics, and optimal control. However, classical numerical methods are adversely affected by the curse of dimensionality, making them inapplicable for large-scale problems. Recently, however, deep learning-based approaches have provided a new toolbox for these high-dimensional PDEs, including methods such as the Deep Backward Stochastic Differential Equation (Deep BSDE) method. Our approach draws on a more sophisticated deep learning backbone, using neural networks (in our case, a Residual Neural Network and a Long Short-Term Memory network (LSTM) integrated into the Deep …

Multi-Objective Optimization Of Energy Costs And Ev Battery Health In V2g Enabled Homes, Dzifa M. Hodey

Theses and Dissertations--Computer Science

Electric vehicles (EVs) and rooftop solar photovoltaic (PV) systems are increasingly being integrated into residential settings, creating new opportunities for vehicle-to-grid (V2G) and vehicle-to-home (V2H) operations. In these systems, the EV battery functions as a controllable energy storage unit that can charge from the grid or PV and discharge energy to supply household load or export to the grid for a profit. By intelligently scheduling this bidirectional power exchange, households can reduce electricity costs and enhance PV utilization. Realizing these benefits requires optimization strategies that balance cost reduction with EV battery health preservation. However, existing V2G/V2H studies largely emphasize cost …

Self-Calibrating Uav Navigation: Reinforcement Learning Approaches For Horizontal Trajectory Estimation, Shirin Nasr-Esfahani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Accurate unmanned aerial vehicle (UAV) trajectory estimation is essential for autonomous navigation, particularly in GPS-denied environments. Visualodometry and simultaneous localization and mapping (SLAM) approaches require precise camera intrinsic parameters, which are typically obtained through predefined or offline calibration. Instead, in this work, we propose a reinforcement learning (RL)-based self-calibration framework that estimates camera intrinsic parameters directly from monocular video sequences, without requiring prior knowledge of the camera, environment, or calibration targets. This intrinsic parameter estimation is then leveraged to achieve robust UAV trajectory estimation using only video data. We formulate the problem as a sequential decision-making task, where an RL …

Analysis Of Policies And Incentives For The Successful Implementation Of Hydrogen-Fueled Medium-Duty And Heavy-Duty Vehicles In Humboldt County, California, Alka Verma

Cal Poly Humboldt theses and projects

The 21st century has seen a significant rise in global greenhouse gas (GHG) emissions, with the transportation sector contributing 23% of these emissions. Medium-duty and heavy-duty vehicles (MD/HD) are particularly impactful, accounting for over a quarter of transport-related emissions. In Humboldt County, California, transportation represents 53% of total emissions, with MD/HD vehicles being a major contributor. As light-duty vehicles shift to zero-emission alternatives, the MD/HD sector faces unique challenges. Hydrogen fuel cell vehicles offer a promising solution, providing longer range, higher energy density, and quicker refueling compared to battery electric vehicles (BEVs). These features make hydrogen an attractive option for …

Adaptive Boundary-Aware Fact-Checker Placement For Misinformation Suppression In Social Networks, Mostafa Taghizade Firouzjaee, Ghazal Naderi, Ross Gore, Neda Moghim

School of Cybersecurity Faculty Publications

The spread of fake news on online social networks is driven by imitation-based user behavior and network topology, often leading to persistent misinformation clusters and echo chambers. In this study, we develop a spatial evolutionary game-theoretic framework in which agents update their latent opinions through payoff-biased imitation, while external fact-checkers act as non-imitative intervention nodes. Building on this formulation, we propose an adaptive, boundary-aware intervention mechanism that dynamically regulates both the density and spatial allocation of fact-checkers according to real-time system conditions. Competing information clusters are identified through local neighborhood composition, enabling boundary nodes, i.e., interfaces between fake-news and non-fake-news …

A Digital Calibration Source For 21 Cm Cosmology Telescopes, Kalyani Balkrishna Bhopi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Precise calibration of radio telescope beams and gains is a central requirement for 21 cm intensity mapping experiments, which aim to measure large scale cosmological structure through the redshifted emission line of neutral hydrogen. Bright astrophysical foregrounds dominate the sky at these frequencies, and separating them from the cosmological signal demands precise control over instrumental systematics, particularly the telescope beam and its frequency-dependent response. Existing aerial calibration sources are incoherent broadband emitters, detectable only as total power. They provide no direct phase information and suffer from poor sensitivity in low signal-to-noise regimes.

We present the Precision Emitter for 21cm Array …

Modeling Rank Distribution And The Relative Importance Factor Index In Discrete Power-Law Models: Application To Social Resilience Using The Scopus Database, Brian Llinas, Jose Padilla, Humberto Llinas, Erika Frydenlund, Katherine Palacio

VMASC Publications

Prior research on power-law distributions has primarily focused on modeling frequency patterns, with less attention given to rank distributions and how ranked positions reflect relative importance among elements. In discrete power-law distributions, frequency-based metrics often provide limited discrimination in the tail, where elements may exhibit similar counts but differ in relative dominance. These patterns are especially evident, for instance, in academic publishing, where keywords, affiliations, and citations commonly exhibit power-law behavior. To address this limitation, we introduce the Relative Importance Factor (RIF) Index, a statistical measure derived from the estimated discrete power-law rank distribution rather than an additional independent parameter. …

Element-Based Predictive Modeling Of Hydrothermal Liquefaction Bioproducts Derived From Corn Stover, Isamu Umeda, Meicen Liu, Yi Zheng, Jiefu Wang, Zhiwu Wang, Sandeep Kumar

Civil & Environmental Engineering Faculty Publications

The hydrothermal liquefaction (HTL) process offers an energetic advantage over pyrolysis because it does not require prior drying of the biomass feedstock. However, there are significant challenges in simultaneously estimating both the yields and characteristics of products from the HTL of biomass with theoretical support. This study developed a unique element-based kinetic model to predict the yields, higher heating values, and fuel characteristics of solid residue and heavy bio-oil, based on the temperature, residence time, solid loading, and elemental composition (C, H, N, and O) of corn stover. Furthermore, the model predicted the weights of dissolved carbon and nitrogen in …

Generative Ai And Llm Applications In Renewable Energy And Smart Grids: A Systematic Review For The Sustainable Energy Transition, Umit Cali, Ugur Halden, Merlinda Andoni, Ferhat Ozgur Catak, Si Chen, Benoit Couraud, Emre Kantar, Samuel Knapper, Ibrahim Kucukdemiral, Huseyin Kusetogullari, Murat Kuzlu, Yashar Mousavi, Sonam Norbu, Taha Selim Ustun, David Flynn

Engineering Technology Faculty Publications

The global energy transition toward decarbonization and digitalization requires advanced methods to manage decentralized, data-intensive cyber-physical energy systems. This systematic review analyzes 106 research studies on Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) in renewable energy and smart grids, organized into seven application clusters covering forecasting, system design, operation, reliability, data and cybersecurity, and energy markets. The review situates these applications within a Cyber-Physical-Social Systems (CPSS) framework. Results show that GANs dominate current applications (47.2%), followed by LLMs (10.4%) and VAEs (9.4%), with growing adoption of diffusion and score-based models (7.5% each). Selected studies report improved probabilistic forecasting …

Improving Medical Diagnostics With Vision-Language Models: Convex Hull-Based Uncertainty Analysis, Ferhat Ozgur Catak, Murat Kuzlu, Taylor Patrick, Michel Audette

Engineering Technology Faculty Publications

In recent years, vision-language models (VLMs) have been applied to various fields, including healthcare, education, finance, and manufacturing, with remarkable performance. However, concerns remain regarding VLMs’ consistency and uncertainty, particularly in critical applications such as healthcare, which demand a high level of trust and reliability. This paper proposes a novel approach to evaluate uncertainty in VLMs’ responses using a convex hull approach on a healthcare application for visual question answering (VQA). For any VLM, temperature refers to a sampling parameter used in probabilistic generation, which controls the randomness of the model’s output. The LLM-CXR model is selected as the medical …

Adaptive Multi-Grade Deep Learning For Highly Oscillatory Fredholm Integral Equations Of The Second Kind, Jie Jiang, Yuesheng Xu

Mathematics & Statistics Faculty Publications

This paper studies the use of Multi-Grade Deep Learning (MGDL) for solving highly oscillatory Fredholm integral equations of the second kind. We provide rigorous error analyses of continuous and discrete MGDL models, showing that the discrete model retains the convergence and stability of its continuous counterpart under sufficiently small quadrature error. We identify the DNN training error as the primary source of approximation error, motivating a novel adaptive MGDL algorithm that selects the network grade based on training performance. Numerical experiments with highly oscillatory (including wavenumber 500) and singular solutions confirm the accuracy, effectiveness and robustness of the proposed approach.

Qubit Lattice Algorithm Simulations Of The Scattering Of A Bounded Two Dimensional Electromagnetic Pulse From The Infinite Planar Dielectric Interface, Min Soe, George Vahala, Linda Vahala, Efstratios Koukoutsis, Abhay K. Ram, Kyriakos Hizanidis

Electrical & Computer Engineering Faculty Publications

Qubit lattice algorithm (QLA) simulations are performed for a two-dimensional spatially bounded pulse propagating onto a plane interface between two dielectric slabs. QLA is an initial value scheme that consists of a sequence of unitary collision and streaming operators, with appropriate potential operators, that recover Maxwell equations in inhomogeneous dielectric media to the second order in the lattice discreteness. For the case of total internal reflection, there is transient energy transfer into the second medium due to the evanescent fields as the Poynting unit vector of the pulse is rotated from its incident to reflected direction. Because of the finite …