Digital Commons Network^™

Extended S-Parameter Model Of The Power Distribution Network For Rapid Coupling Predictions, Cody Goins, Aaron Harmon, Mckennan Starkey, Kristen Donnell, Victor Khilkevich, Daryl Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

Power and return planes are part of the power delivery network of almost all modern high frequency printed circuit boards. These power and return planes can form the basis of unintended radiated emissions from, or radiated coupling to, these boards. Predicting coupling to complex systems is a difficult problem and typically reserved for full wave simulations. Recent works have introduced segmentation approaches that are able to predict coupling to complex printed circuit board designs by using pre-rendered segments and cascading these segments through a circuit solver approach. The extended S-parameter models used by the segmentation approach currently do not include …

Optimized Modeling Of Pcb Vias With Nonfunctional Pads And High-Frequency Behavior Up To 150 Ghz, Mehdi Mousavi, Kevin Cai, Chaofeng Li, Sathvika Bandi, Manish Mathew, Mehdi Khaleghi, Shameem Ahmed, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents an enhanced closed-form approach for modeling and optimizing high-frequency PCB vias, implemented in Python and validated against industry standard tools such as ADS and HFSS. The model incorporates resistance alongside inductance and capacitance to capture frequency-dependent losses and integrates non-functional pads (NFPs), demonstrating significant improvements in signal integrity by reducing reflections and enhancing return loss, particularly at 100 GHz. The methodology extends the frequency range of previous models from 100 GHz to 150 GHz, ensuring compatibility with next-generation standards like PCIe Gen 6. Validation results show insertion loss deviations under 3 dB and consistent return loss across …

Data Representation And Preprocessing Effects On S-Parameter Modeling Of High-Speed Channels Using Machine Learning, Hyunwook Park, Davit Kharshiladze, Yifan Ding, Ling Zhang, Natalia Bondarenko, Hanqin Ye, Kaushal Sanjay Mhalgi, Brice Achkir, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, the effects of data representations and preprocessing on machine learning based S-parameter modeling of high-speed channels are investigated. Using a transformer network as a base model, two S-parameter representations in real/imaginary and magnitude/phase are compared and studied. Considering S-parameter data distributions, various preprocessing techniques including MinMax normalization, standardization, robust scaling, power transformation, and quantile transformation are compared and analyzed to improve accuracy. Moreover, the accuracy results are compared depending on the electrical length of target channels.

Topology And Parameter Joint Identification In Imbalanced Low-Voltage Distribution Networks Based On Load Characteristic Propagation, Yanan Zhang, Gan Zhou, Huan Mao, Wei Gu, Yanjun Feng, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Low-voltage distribution networks often suffer from incomplete or outdated network records, making it challenging to obtain the topology and line parameters under actual operating conditions. To address this issue, a joint identification method is proposed based on the propagation of load transient characteristics. First, the principle of load characteristic propagation is elaborated, and the concept of coupling impedance is introduced. Second, a set of linear regression equations is established based on the changes in current and voltage of the terminal measurements before and after load switching, and then these equations are solved using the least squares method to form the …

Structural Analysis Of Multi-Domain Dynamic Systems Modeled Via Bond Graphs, Arnold A. Fernandes, Jonathan W. Kimball

Electrical and Computer Engineering Faculty Research & Creative Works

Structural Observability (SO) and Structural Monitorability (SM) are structural properties utilized to determine the state and fault-free operation of components, respectively, in a bond graph (BG) model. BGs enable qualitative system analysis, evaluating whether existing sets of sensors and actuators ensure Structural Observability (SO) and Structural Controllability (SC) without knowledge of parametric values. Furthermore, the analysis determines whether there are sufficient sensors available to identify component faults accurately. This work provides a framework for automated sensor placement in a multi-domain physical system while analyzing the SO and SM properties. The MATLAB Structural Analysis Toolbox (MATSAT) conducts sensor placement in a …

Centralized And Federated Heart Disease Classification Using Uci Dataset: A Benchmark With Interpretability Analysis, Mario Padilla Rodriguez, Eyiara Oladipo, Mohamed Nafea

Electrical and Computer Engineering Faculty Research & Creative Works

Cardiovascular disease (CVD) is a leading cause of global mortality, highlighting the need for accurate diagnostic methods. This study benchmarks centralized and federated learning (FL) algorithms for heart disease binary classification using the UCI dataset, which includes 920 patient records from four hospitals in the USA, Hungary, and Switzerland. Our benchmark is supported by Shapley-value as well as Local Interpretable Model-agnostic Explanations (LIME) interpretability analyses to quantify feature importance for classification. In the centralized setup, various classification algorithms are trained on pooled data, with the Naive Bayes classifier achieving the highest test accuracy of 81.1%. Further, FL algorithms with four …

Characterizing The Transient Emission Of Particles And Gases From A Single Puff Of Electronic Cigarette Smoke, Kashala Fabrice Kapiamba, Steven Achterberg, Ta Chun Lin, Philip D. Whitefield, Yue-Wern Huang, Yang Wang

Chemistry Faculty Research & Creative Works

This study employed high-time-resolution systems to examine the transient properties of aerosols and gases emitted from electronic cigarette (EC) puffs. Using a fast aerosol sizer, we measured particle size distributions (PSDs) across various EC brands (JUUL, VUSE, VOOPOO), revealing sizes ranging from 5 to 1000 nm at concentrations of 107 to 1010 cm-3. Most aerosols were found to be in the ultrafine range (below 100 nm), with JUUL-, VUSE-, and VOOPOO-producing aerosols with geometric mean sizes of 19.9, 47.3, and 29.4 nm, respectively. Applying the International Commission on Radiological Protection (ICRP) deposition model and assuming no further evolution of aerosols …

Identifying Potential Risk Areas Close To The Oil And Gas Pipelines Due To Subsidence In Qazvin Plain, Mahmoud Rajabi, Sanaz Vajedian, Ryan Smith, Neil Grigg, Hossein Nahavandchi, Mehdi Goli

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Surface displacement caused by natural and anthropogenic activities poses a significant risk to subsurface pipelines, particularly in areas experiencing subsidence. Stress and strain induced by deformation can lead to pipeline buckling and potential vulnerability. This study applies a multidisciplinary approach integrating interferometric synthetic aperture radar (InSAR) deformation analysis, well data, and geological context to assess the risk to oil and gas pipelines in Iran's Qazvin plain. The focus is on evaluating the impact of an unconfined aquifer, which has a lower risk of subsidence compared to a confined aquifer, on pipeline infrastructure. We analyzed multitemporal Sentinel-1 data collected between 2014 …

Reflections On Linear B: An Egyptian Hieroglyphic Component, (Draft #2), Gerald Leonard Cohen

Arts, Languages and Philosophy Faculty Research & Creative Works

Most Linear B signs are presently regarded as being of unknown origin, with a general consensus that Egyptian hieroglyphs are at best of minimal help in solving the mysteries. But the present article revisits this subject and suggests that the Egyptian hieroglyphic component goes beyond a borrowing or two. Along the way I noticed that for some Linear B signs a familiarity with Egyptian religious mythology is essential in this effort.

For example, the mysterious Linear B sign 75 is pronounced /we/ and looks like a backwards letter S. I suggest it can be explained as the cobra goddess Wadjet …

Managing Digital Transformation With Blockchain: Distributed Ledger Innovation Platforms, Nobuyuki Fukawa, Gregory J. Fisher

Business and Information Technology Faculty Research & Creative Works

Realizing its overarching potential in achieving digital transformation, leading organizations, such as Air New Zealand, have recently employed blockchain technology to manage 3D printing innovation. Despite the potential benefits, including the key role of blockchain as a supporting technology in Industry 5.0, these applications of blockchain technology in 3D printing are still at a nascent stage in practice and mostly limited to producer innovation. Additionally, 3D printing and blockchain technologies are often independently revolutionizing value capture and value creation, whereas the theoretical implications of their combined impacts on innovation management remain an underexplored domain. Limited digital transformation efforts have been …

Mindful Or Mindless? How And Why Virtual Communities Fail To Contain Information Pollution Across Different Disasters, Mahmudul Hasan, Varqa Shamsi Bahar, Cecil Eng Huang Chua, Michael David Myers

Business and Information Technology Faculty Research & Creative Works

People visit virtual communities to find trusted information (i.e. verifiable, accurate and reliable information) during disasters. Virtual communities are open spaces where people interact and share information. However, virtual communities often suffer from information pollution (including too much information and false or contradictory information) which decreases peoples' ability to find trusted information. Oddly, some virtual communities will develop useful practices for combatting information pollution during one disaster but subsequently fail to effectively employ the same practices or adapt them in subsequent disasters. We conducted a longitudinal, exploratory cross-case study to better understand how and why this occurs. Using the lenses …

Leveraging Intrinsic Properties For Classification Of Coal Seams Towards Spontaneous Combustion Proclivity And Predicting Susceptibility Using Machine Learning: Smart And Sustainable Mining Approach, Siddhartha Agarwal, Pradeep K. Gautam, Yuhao Zou, Rishabh Dwivedi, Durga C. Panigrahi, Cihan H. Dagli, A. Singh

Engineering Management and Systems Engineering Faculty Research & Creative Works

Mine fires and other hazards caused by spontaneous coal combustion are a pervasive and longstanding issue in Jharia coalfields, India. This study proposes a novel approach to classify coal seams based on their propensity to spontaneous combustion using the intrinsic properties of 30 coal samples from different seams. This method eliminates the need for expensive and time-consuming experimental determinations of susceptibility indices (SI) such as crossing point temperature (CPT), critical air blast (CAB), and differential thermal analysis (DTA). All clustering models, viz. hierarchical, k-means, and multidimensional scaling, aptly classify coal seams into three categories: highly risky, medium risky, and low …

Tintin: A Unified Hardware Performance Profiling Infrastructure To Uncover And Manage Uncertainty, Ao Li, Marion Sudvarg, Zihan Li, Sanjoy Baruah, Chris Gill, Ning Zhang

Computer Science Faculty Research & Creative Works

Hardware performance counters (HPCs) enable the measurement of microarchitectural events, which are crucial for tracking and predicting program behavior. High-fidelity measurement and precise attribution are essential for accurate profiling. However, existing profiling tools have fundamental challenges in both aspects. In measurement, numerous events compete for limited hardware monitoring resources; while for attribution, applications have diverse requirements, but systems provide limited support. Existing tools mitigate the former limitation through event multiplexing, but this approach introduces non-trivial errors. The latter limitation, however, remains largely unaddressed. This paper introduces Tintin, an HPC profiling infrastructure with a modular three-component design that addresses both challenges. …

Using Preformed Particle Gels To Control Transport In Geothermal Reservoirs: Mathematical Modeling, Philip Winterfeld, Baojun Bai, Yu Shu Wu

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

We are developing swellable Preformed Particle Gels (PPG), which can control preferential fluid and heat flow through fracture networks to increase the performance of EGS reservoirs. Part of this development is a mathematical model and numerical simulator to simulate PPG treatments by considering coupled thermal-hydraulic-mechanical effects, and gel swelling kinetics and plugging efficiency, from which an optimized gel treatment design and operation can be achieved. The starting point for our mathematical model is the TOUGH2-CSM formulation and code. The TOUGH2-CSM fluid and heat flow formulation is based on the TOUGH2 one for multiphase, multicomponent, and multi-porosity systems, with the latter …

Parallel Multi Objective Shortest Path Update Algorithm In Large Dynamic Networks, S. M. Shovan, Arindam Khanda, Sajal K. Das

Computer Science Faculty Research & Creative Works

The multi objective shortest path (MOSP) problem, crucial in various practical domains, seeks paths that optimize multiple objectives. Due to its high computational complexity, numerous parallel heuristics have been developed for static networks. However, real-world networks are often dynamic where the network topology changes with time. Efficiently updating the shortest path in such networks is challenging, and existing algorithms for static graphs are inadequate for these dynamic conditions, necessitating novel approaches. Here, we first develop a parallel algorithm to efficiently update a single objective shortest path (SOSP) in fully dynamic networks, capable of accommodating both edge insertions and deletions. Building …

J-Necora: A Framework For Optimal Resource Allocation In Cloud-Edge-Things Continuum For Industrial Applications With Mobile Nodes, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi

Computer Science Faculty Research & Creative Works

In the Industrial Internet of Things (IIoT) landscape, where the Cloud-to-Things Continuum (C2TC) paradigm is now a reality, industrial applications need to cope with highly heterogeneous network and computing resources. Moreover, many industrial applications also involve Mobile Nodes (MNs). Efficient allocation of network and computing resources to meet the stringent requirements of such applications is often a very challenging task. In this paper, we propose J-NECORA (Joint NEtwork and COmputing Resource Allocation), a comprehensive analytical framework to derive the optimal joint allocation of network and computing resources in the C2TC, that guarantees the application requirements, even in the presence of …

Correction: Yolo-Based Miner Detection Using Thermal Images In Underground Mines (Mining, Metallurgy & Exploration, (2025), 10.1007/S42461-025-01249-6), Cyrus Addy, Venkata Sriram Siddhardh Nadendla, Kwame Awuah-Offei

Computer Science Faculty Research & Creative Works

In the original published article, Figure 3 appears with the Fig. 1 caption, Figure 1 appears with the Fig. 2 caption, and Figure 2 appears with the Fig. 3 caption. The article has been updated to correct this error.

Safenav: Safe Path Navigation Using Landmark Based Localization In A Gps-Denied Environment, Ganesh Sapkota, Sanjay Madria

Computer Science Faculty Research & Creative Works

In battlefield environments, adversaries frequently disrupt GPS signals, requiring alternative localization and navigation methods. Traditional vision-based approaches like Simultaneous Localization and Mapping (SLAM) and Visual Odometry (VO) involve complex sensor fusion and high computational demand, whereas range-free methods like DV-HOP face accuracy and stability challenges in sparse, dynamic networks. This paper proposes LanBLoc-BMM, a navigation approach using landmark-based localization (LanBLoc) combined with a battlefield-specific motion model (BMM) and Extended Kalman Filter (EKF). Its performance is benchmarked against three state-of-the-art visual localization algorithms integrated with BMM and Bayesian filters, evaluated on synthetic and real-imitated trajectory datasets using metrics including Average Displacement …

V-Usdt: Vision-Based Uav Swarm Detection And Tracking By Leveraging Swarm Formation Constraints, Md Hasibur Rahman, Sanjay Madria

Computer Science Faculty Research & Creative Works

The rapid proliferation of Unmanned Aerial Vehicles (UAVs) and UAV swarm technologies has raised critical concerns about security and safety in low-altitude airspace. In response, we propose a vision-based system for detecting and tracking UAV swarms, which combines a novel UAV detection mechanism with a swarm tracking strategy. Our UAV detector incorporates parallel receptive field blocks alongside an attention mechanism to enhance detection performance. This design effectively captures multiscale features of UAVs while prioritizing salient features, ensuring robust detection under diverse conditions. For swarm tracking, we leverage the inherent formation constraints typically maintained by UAV swarms. These constraints allow us …

Dynamic Resource Allocation In Cloud-To- Things Continuum For Real-Time Iot Applications, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi

Computer Science Faculty Research & Creative Works

The proliferation of loT devices and the growing demand for real-time applications have driven a shift in the computation paradigm, from Cloud computing to Edge computing, creating the Cloud-to-Things Continuum (C2TC). Many real-time loT applications involve Mobile Nodes (MNs), which may dynamically join or leave. In addition, in future reconfigurable loT systems, applications with different requirements will coexist, and will be dynamically introduced or removed. All this asks for dynamic management mechanisms to ensure the requirements of different real-time applications, even when the system configuration changes over time. In this paper, we propose DJ-NECORA, an online algorithm for the joint …

Feature Fusion Transferability Aware Transformer For Unsupervised Domain Adaptation, Xiaowei Yu, Zhe Huang, Zao Zhang

Computer Science Faculty Research & Creative Works

Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from labeled source domains to improve performance on the unlabeled target domains. While Convolutional Neural Networks (CNNs) have been dominant in previous UDA methods, recent research has shown promise in applying Vision Transformers (ViTs) to this task. In this study, we propose a novel Feature Fusion Transferability Aware Transformer (FFTAT) to enhance ViT performance in UDA tasks. Our method introduces two key innovations: First, we introduce a patch discriminator to evaluate the transferability of patches, generating a transferability matrix. We integrate this matrix into self-attention, directing the model to focus …

Ca-Vqvae: Cortical Folding Aware Numerical Representation Of White-Matter Structure, Yanjun Lyu, Jing Zhang, Lu Zhang, Tong Chen, Xiaowei Yu, Minheng Chen, Yan Zhuang, Chao Cao, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

White matter (WM) serves as a fundamental component of the brain providing essential structural support and facilitating the brain cognitive processes. Thus, an accurate and efficient description of the brain's white matter structure is essential for understanding brain function connectivity and development. In this work we used the deep model to combine the information of the WM fiber bundle shape and its related cortical folding patterns together representing the WM fiber bundle from diffusion MRI tractography into a pre-defined low-dimensional space and generate the numerical representation vector. This cortical-aware vector-quantized variational encoder (CA-VQVAE) framework leverages cortical locations and folding patterns …

Classiffication Of Mild Cognitive Impairment Based On Dynamic Functional Connectivity Using Spatio-Temporal Transformer, Jing Zhang, Yanjun Lyu, Xiaowei Yu, Lu Zhang, Chao Cao, Tong Chen, Minheng Chen, Yan Zhuang, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Dynamic functional connectivity (dFC) using resting-state functional magnetic resonance imaging (rs-fMRI) is an advanced technique for capturing the dynamic changes of neural activities and can be very useful in the studies of brain diseases such as Alzheimer's disease (AD). Yet, existing studies have not fully leveraged the sequential information embedded within dFC that can potentially provide valuable information when identifying brain conditions. In this paper, we propose a novel framework that jointly learns the embedding of both spatial and temporal information within dFC based on the transformer architecture. Specifically, we first construct dFC networks from rs-fMRI data through a sliding …

Secure Data Relay In Federated Digital Twins Of Iot-Enabled Smart Interconnected Factories, Anusha Vangala, Jack Wyeth, Ashok Kumar Das, Sajal K. Das

Computer Science Faculty Research & Creative Works

Smart interconnected factories allow manufacturing units from physically distanced factory sites to communicate classified information needed for additive manufacturing. Each factory has interconnected digital twins of their equipment autonomously managed by a Point-of-Contact digital twin creating a hierarchical system with federated digital twins. The data sharing between the factories must be directed through an edge node responsible for managing multiple factories. We proposed a novel lightweight protocol to prevent the leakage of classified information at any nodes other than the origin and destination digital twins. It leverages elliptic curve cryptography to design a proxy re-encryption scheme with the edge node …

When Federated Learning Meets Quantum Computing: Survey And Research Opportunities, Aakar Mathur, Ashish Gupta, Sajal K. Das

Computer Science Faculty Research & Creative Works

Quantum Federated Learning (QFL) is an emerging field that harnesses advances in Quantum Computing (QC) to improve the scalability and efficiency of decentralized Federated Learning (FL) models. This paper provides a systematic and comprehensive survey of the emerging problems and solutions when FL meets QC, from research protocol to a novel taxonomy, particularly focusing on both quantum and federated limitations, such as their architectures, Noisy Intermediate Scale Quantum (NISQ) devices, and privacy preservation, so on. With the introduction of two novel metrics, qubit utilization efficiency and quantum model training strategy, we present a thorough analysis of the current status of …

Content Subversion Against 1 Information-Based Systems, Junjie Xiong, Ian Markwood, Dakun Shen, Yao Liu, Zhuo Lu

Computer Science Faculty Research & Creative Works

We present a novel class of content subversion attacks against information-based services, causing documents to appear to humans dissimilar to the underlying content extracted by information-based services. We demonstrate the significant impact of these attacks on real-world systems through five distinct variants. Our first attack allows academic paper writers and reviewers to collude via subverting the automatic reviewer assignment systems in current use by academic conferences including INFOCOM, which we reproduced. Our second attack renders ineffective plagiarism detection software, particularly Turnitin, targeting specific small plagiarism similarity scores to appear natural and evade detection. In our third attack, we place masked …

Grace-Fl: Green Resource-Aware Communication-Efficient Federated Learning, Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy, but its deployment on resource-constrained devices is hindered by high communication overhead, inefficient energy usage, and poor convergence under non-IID data distributions. To address these challenges, we propose GRACE-FL: a Green Resource-Aware Communication-Efficient Federated Learning framework that explicitly incorporates device energy capacity into training. Each client adapts its learning rate, number of local epochs, and gradient quantization bit-width based on its available energy, allowing high-capacity devices to sustain more intensive training while low-capacity devices operate with lighter configurations. A novel energy-weighted aggregation strategy ensures that clients …

Alertble: Alert Workzone Hazards Using Hybrid Filtering And Machine-Learning-Enabled Ble, Samuel Akinyede, Sejun Song

Computer Science Faculty Research & Creative Works

Collision hazard detection in industrial work zones faces challenges from signal instability, mobility-induced fluctuations, and nonline-of-sight (NLOS) conditions. While Bluetooth low energy (BLE) offers cost-effective proximity sensing, its received signal strength indicator (RSSI) variability - fluctuating by ±10 dBm even at fixed distances - limits reliability in safety-critical applications. This article presents AlertBLE, a hybrid BLE-based hazard detection system that combines extended Kalman filter (EKF) and adaptive moving average (AMA) algorithms to achieve up to 94% RSSI variance reduction in static NLOS conditions. The system introduces speed-aware safety thresholds based on reaction time and braking distance models, dynamically expanding hazard …

Electrochemically Active Molybdenum Phosphate With Open-Framework Structure, Sutapa Bhattacharya, Milad Aghayi-Anaraki, Nikolay Gerasimchuk, Steven P. Kelley, Amitava Choudhury

Chemistry Faculty Research & Creative Works

Somewhat less investigated, lithium ion containing molybdenum phosphate compounds are synthesized via simple template free hydrothermal method. Single-crystal X-ray diffraction (SCXRD) study reveals that the compounds Li_0.89Mo₂O_1.89F_0.11(PO₄)2(H₂PO₄)·xH₂O (x ≈1) and Li₃Mo₂O₂(PO₄)₃·xH₂O (x ≈1) crystallize in monoclinic C2/m and C2/c space group, respectively. The structures are built of alternate corner-sharing of MoO₆ octahedra and PO₄ tetrahedra forming layers, which are pillared by phosphate groups creating a 3D framework with channels. These …

Hyperspectral Imaging For Temperature Measurements Of Hot Spots In Shocked Plastic-Bonded Explosives, Dhanalakshmi Sellan, Siva Kumar Valluri, Dana D. Dlott

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Hot spots are formed when energetic microstructures are shocked, and they play a critical role in shock sensitivity. It is important to know both the time-dependent size and temperature of the hot spots, in order to generate a kinetic model to describe reaction growth in plastic-bonded explosives (PBX). Here we use a recently developed technique where PBX is fabricated in the form of a thin wafer, embedded within a transparent polymer binder, and shocked with a laser-launched flyer plate that produces pressures of about 30 GPa range. In this method, every crystal of the cyclotetramethylene-tetranitramine (HMX)-based PBX can be observed …