Physical Sciences and Mathematics Commons^™

Constructing Orthonormal Bases With The Residuals Of Successive Approximations, An Introduction To Multiresolution Analysis, Elijah J. Guptill

Master's Theses

Wavelets and wavelet analysis are used in the study of signal processing, quantum field theory, functional analysis, multifractal analysis, and various other areas of mathematics. Multiresolution analysis provides a framework for building a wavelet basis of $\mathcal{L}^{2}(\mathbb{R})$ from a scaling function $\phi$, whose dyadic dilations and translations, $\{2^{j /2}\phi(2^{j}x-k):j,k\in \mathbb{Z}\}$, approximate $\mathcal{L}^{2}(\mathbb{R})$. One of the key properties of  $\phi$ is that it must satisfy $\phi(x)=\sum_{k\in \mathbb{Z}}{p_{k}2^{j /2}\phi(2^{j}x-k)}$ with respect to the norm on $\mathcal{L}^{2}(\mathbb{R})$. This equation is called a two-scale difference equation. Such equations enforce a regularity on the ordinary generating function $2^{-1 /2}\sum_{k\in \mathbb{Z}}{p_{k}z^{k}}$, known as the quadrature condition. …

Pso-Style Social Influence In An Ant Colony Algorithm For Continuous-Domain Optimization, Ashraf M. Abdelbar, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

ACOR is a well-established Ant Colony Optimization (ACO) algorithm for continuous-domain optimization. In this paper, we propose an extension (which we call ACOR∗) in which several fundamental modifications are made to ACOR's solution construction process, including the incorporation of a social influence mechanism borrowed from Particle Swarm Optimization (PSO). Our modifications to the ACOR algorithm are intended to promote search diversity and combat premature convergence. We experimentally evaluate our proposal in the context of training feedforward neural networks for classification using 65 widely used datasets from the University of California Irvine (UCI) repository, as well as the optimization of several …

Rc-Nf: Robot-Conditioned Normalizing Flow For Real-Time Anomaly Detection In Robotic Manipulation, Shijie Zhou, Bin Zhu, Jiarui Yang, Xiangyu Zhao, Jingjing Chen, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Recent advances in Vision-Language-Action (VLA) models have enabled robots to execute increasingly complex tasks. However, VLA models trained through imitation learning struggle to operate reliably in dynamic environments and often fail under Out-of-Distribution (OOD) conditions. To address this issue, we propose Robot-Conditioned Normalizing Flow(RC-NF), a real-time monitoring model for robotic anomaly detection and intervention that ensures the robot's state and the object's motion trajectory align with the task. RC-NF decouples the processing of task-aware robot and object states within the normalizing flow. It requires only positive samples for unsupervised training and calculates accurate robotic anomaly scores during inference through the …

Holistic Dram Enhancements: From Intrinsic In-Memory Operations To Robust Security Mechanisms, Ranyang Zhou

Dissertations

Dynamic Random-Access Memory (DRAM) is both the performance bottleneck and a critical security boundary of modern computing systems. Its physical properties make it an attractive substrate for near-data computation—yet those same properties expose it to disturbance-based hardware attacks. This dissertation argues that these two dimensions are not independent: the architectural choices that make DRAM efficient also reshape its threat landscape. Addressing both requires a unified approach to memory architecture and security co-design.

The first part of this dissertation attacks the memory wall through four processing-in-DRAM (PIM) frameworks. ReD-LUT and LT-PIM unify lookup-table arithmetic with charge-sharing logic, achieving up to 37.8x …

Developing A Framework For Mooc Dropout: The Role Of Utilitarian And Hedonic Values In Student Retention, Bipllab Roy, Mave D'Souza, Madhura Laghane

Northeast Journal of Complex Systems (NEJCS)

Massive Open Online Courses (MOOCs) have expanded access to higher education but continue to face persistently high dropout rates, raising concerns about their long‑term effectiveness and sustainability. This study develops and empirically tests a structural framework that links utilitarian values (perceived usefulness, certificate value, time flexibility), hedonic values (enjoyment, variety and novelty, personal interest alignment), and individual characteristics (goal orientation, self‑efficacy, motivation type) to MOOC student retention. Data were collected through a structured questionnaire administered to 200 MOOC learners from Christ University, Lavasa Campus, and analyzed using Structural Equation Modelling (SEM) in AMOS. The results show that goal orientation, self‑efficacy …

Modulating Electronic Structure With Linearly Fused Pyrazine Units For High-Voltage And Stable Zinc-Organic Batteries Cathode, Min-Jian Zhao, Li-Bin Zhang, Jin-Tao Wang, Kun Ding, Hai-Mei Liu, Yong-Gang Wang

Journal of Electrochemistry

High-voltage n-type organic cathode materials are critical for constructing zinc-organic batteries (ZOBs) with high energy density and long cycle life. However, the intrinsically unfavorable electronic structures and relatively high LUMO energy levels of most n-type materials often lead to sluggish kinetics, high solubility, and suboptimal discharge voltages (< 0.8 V). Here, we design a small molecule, quinoxalino[2’,3’:5,6]pyrazino[2,3-f][1,10]phenanthroline (DPQP), as a ZOB cathode by introducing locally electron-deficient motifs into the conjugated backbone of aromatic compounds. The linearly fused pyrazine units extending the pyrazine–benzene framework effectively optimize the electronic structure, thereby significantly enhancing the discharge voltage. Meanwhile, the expanded π-conjugated plane suppresses dissolution and accelerates charge-transfer kinetics. Benefiting from these features, the DPQP electrode exhibits an exceptional increase in average operating voltage from 0.61 V to 1.07 V (vs. Zn²⁺/Zn) at 0.1 A·g^–1, with an overpotential of only 140 mV. Notably, no discernible voltage decay occurs as the current density increases, indicating rapid and highly reversible redox kinetics. Furthermore, the DPQP cathode delivers outstanding cycling stability, maintaining over 2000 h of continuous operation at 0.1 A·g^{–1 …}

(Co,Ni,Mn,Cu,Zn)O High-Entropy Oxide Nanotubes As Efficient Bifunctional Electrocatalyst For Oxygen Evolution And Hydrazine Oxidation Reactions, Pan-Yan Chen, Wan-Wan Wu, Heng Bian, Wei-Wei Li, Xin-Sheng Zhao, Lu Wei

Journal of Electrochemistry

High-entropy oxides (HEOs) present significant scientific challenges in both design and synthesis due to their multielement and high-entropy nature, which involves complex combinations of multiple metal cations and oxygen anions, typically arranged in equimolar ratios to achieve structural stability. Herein, one-dimensional (Co,Ni,Mn,Cu,Zn)O high-entropy oxide nanotubes (HEO-NTs) are fabricated by means of a gradient electrospinning strategy with a tailored polyvinyl alcohol (PVA) molecular weight distribution and controlled pyrolysis. Benefiting from the HEO features and the synergistic effect of multicomponent sites, the as-synthesized (Co,Ni,Mn,Cu,Zn)O HEO-NTs exhibit exceptional bifunctional electrocatalytic activity for the oxygen evolution and hydrazine oxidation reactions (OER/HzOR). This study offers …

Exploring Maximal Length Cellular Automata To Generate Primitive Polynomials In Gf(2), Sumit Adak, Subhrajit Deb, Anurag Ghosh, Angshuman Roy, Souvik Roy

Northeast Journal of Complex Systems (NEJCS)

We present a simple method that uses cellular automata (CAs) to find primitive polynomials over GF(2). We used maximal length CAs as tools to generate primitive polynomials. It is usually very difficult to find maximal length CAs or primitive polynomials since they require exponential time, and there is no linear time method. However, in our work, given an n-size specific sequence of CA with reasonable probability, our technique computes a cycle of length at most 2^n-1 (maximal length) in O(n) time. The characteristic polynomials of synthesized maximal length CAs are claimed to be primitive since it was previously established that …

Decision Making At Triage Classification Using Svm With Smote Technique, Mehanas Shahul, Pushpalatha Kp

Northeast Journal of Complex Systems (NEJCS)

The efficient functioning of triage gates in overcrowded emergency departments (EDs) occurs in the context of the complex adaptive system (CAS) framework, where diverse system elements – patients, medical personnel, resources, patients’ inflow patterns, and patients themselves – simultaneously and dynamically influence the decision process. This study addresses the automated incorporation of machine learning triage algorithms as part of the system triage process to support automated classified risk-level recognition based on a limited set of vital signs. Patients are dynamically subsumed under high and low-risk categories enhanced by sensitivity, which enables optimal diagnosis and triage response to the critical clinician …

Uncovering Discrete States From Multimodal Psychophysiological Data Using Gaussian Latent Dirichlet Allocation (Glda), Congyu Wu, Aaron Fisher, David Schnyer

Northeast Journal of Complex Systems (NEJCS)

In this article we explore and validate the utility of an unsupervised probabilistic model, Gaussian Latent Dirichlet Allocation (GLDA), for discovering discrete states from repeated, multimodal psychophysiological samples collected from multiple individuals. Psychology and medical research heavily involves measuring potentially related but individually inconclusive variables from a cohort of participants to derive diagnosis, necessitating clustering analysis for state identification. Traditional probabilistic clustering models such as Gaussian Mixture Model (GMM) assume a global mixture of component distributions, which may not be realistic for observations from different patients. The GLDA model borrows the individual-specific mixture structure from a popular topic model Latent …

Asymmetric Opinion Formation Of Emotional Excitable Agents, Irene Ferri, Emanuele Cozzo, Aleix Nicolás-Olivé, Albert Díaz-Guilera, Luce Prignano

Northeast Journal of Complex Systems (NEJCS)

The bounded confidence model represents a widely adopted framework for modeling opinion dynamics wherein actors have a continuous-valued opinion and interact and approach their positions in the opinion space only if their opinions are within a specified confidence threshold. Here, we propose a novel framework where the confidence bound is determined by a decreasing function of their emotional arousal, an additional independent variable distinct from the opinion value. Additionally, our framework accounts for agents' ability to broadcast messages, with interactions influencing the timing of each other's message emissions. Our findings underscore the significant role of synchronization in shaping consensus formation. …

Quantum Control Protocols For Robust Quantum Computing, Leeseok Kim

Electrical and Computer Engineering ETDs

The fundamental goal of quantum computing is to precisely control quantum systems to perform meaningful tasks, including implementing high-fidelity quantum gates for reliable quantum computation and accurately simulating complex quantum many- body dynamics. In this dissertation, we develop improved quantum control protocols for three distinct objectives, quantum error suppression, quantum optimal control, and analog quantum algorithms, achieving performance beyond standard approaches. First, we introduce new dynamical decoupling protocols, including both determin- istic and randomized constructions, that can substantially outperform conventional deterministic sequences. We then extend the randomized approach to dynamically corrected gates. Second, we propose a randomized quantum optimal control …

A Descriptive Analysis Of Plant Leaf Disease Detection Using Machine Learning And Deep Learning Models: A Systematic Review, Arzoo Chamoli, Anuj Kumar

Turkish Journal of Electrical Engineering and Computer Sciences

Plant leaf disease detection (PLDD) is a growing active research area with burgeoning practical applications across various sectors such as agricultural monitoring, food security, and environmental conservation. Accurate segmentation and classification of plant leaf diseases remains a key challenge in the field of plant leaf disease prediction. The challenge demands automated methods for the plant disease identification because it needs to develop better crop management systems, which will boost agricultural production. In this article, we provide a systematic review of various machine learning (ML) and deep learning (DL) methods extensively used for PLDD. The review strategy follows a formal protocol, …

Classification Of Hif Detection In Nev Profile Using Wavelet Transform And Convolution Neural Network, Abdul Hafiz Kassim, Mohd Abdul Talib Mat Yusoh, Aster Smith Valentinie Wilson Nottelmarc, Ahmad Farid Abidin, Sim Sy Yi, Daw Saleh Sasi Mohammed

Turkish Journal of Electrical Engineering and Computer Sciences

High impedance faults (HIFs) present a critical challenge in power systems due to their subtle signal characteristics, which often remain undetected by conventional protection methods. These faults typically do not produce significant phase disturbances, making reliable detection difficult. However, analysis of the neutral-to-earth voltage (NEV) profile under fault conditions provides a promising alternative for fault identification. Existing approaches for detecting and classifying HIFs using NEV signals remain limited and may result in inaccurate maintenance decisions. This paper proposes a fault classification framework for multiple fault types, including HIF, three-phase fault, three-phase fault to ground, double line, double line to ground, …

Complex-Valued Convolutional Neural Network With Time-Frequency Representation For Electrocardiogram-Based Arrhythmia Detection, Kajeeth Kumar Gurusamy, Muthurajkumar Sannasy

Turkish Journal of Electrical Engineering and Computer Sciences

This research proposes an end-to-end procedure for arrhythmia detection based on electrocardiogram (ECG) signals using complex-valued convolutional neural network (CVCNN) incorporated with time-frequency representation. The proposed model leverages complex numbers to capture amplitude and phase information that enhances the ability of the model for detecting time-frequency variation in cardiac signals. First, signal preprocessing techniques---including normalization, wavelet denoising, and R-peak detection---are applied. Subsequently, the model extracts complex features from raw ECG data by employing the Hilbert transform to derive the analytic signal and the short-time Fourier transform (STFT) to generate a time–frequency representation. The proposed CVCNN framework effectively learns spatial-temporal features …

Adapting Independent Large-Scale Pretrained Models For Human Action Recognition, Selen Pehli̇van

Turkish Journal of Electrical Engineering and Computer Sciences

Transferring knowledge from large-scale, independently pretrained image and text models to video understanding requires addressing several challenges, including maintaining generalization capabilities of models, integrating them into multimodal architectures, and fine-tuning with temporal dynamics. This study evaluates the effectiveness of parameter-efficient fine-tuning (PEFT) techniques in transferring pretrained knowledge from two independent models for video action recognition within a simple, streamlined multimodal fusion pipeline. Specifically, we adapt CLIP as the text branch and DINOv2 as the image branch, keeping both backbones frozen to preserve their pretrained robustness, while introducing lightweight, task-specific modules to adapt and fuse the branches with temporal dynamics. A …

Adaptive Backstepping Nonsingular Fast Terminal Sliding Mode Control For Robotic Manipulators Based On Disturbance Observer, Xin Zhang, Xu Wang

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents an adaptive backstepping nonsingular fast terminal sliding mode controller integrated with a nonlinear disturbance observer to achieve precise trajectory tracking of robotic manipulators subject to model uncertainties and unknown time-varying disturbances. A dead-zone–based adaptive gain mechanism is introduced to dynamically adjust the control gain according to the deviation of the sliding surface, thereby enhancing robustness and reducing chattering. The proposed reaching law ensures fast, nonsingular, and adaptive convergence, suppressing high-frequency oscillations without compromising stability and the nonlinear disturbance observer enables real-time estimation and compensation of modeling errors, friction, and external disturbances for superior rejection. The semiglobal uniform …

Chaotic Artificial Bee Colony-Optimized Stacking Ensemble For Robust Multifault Diagnosis Of Wind Turbines, Veilraj Revathi, Solaimalai Jeyadevi, Madasamy Sudalaimani

Turkish Journal of Electrical Engineering and Computer Sciences

The complex electromechanical structure of wind turbines, along with harsh operating conditions, poses significant challenges for precise and robust fault diagnosis. To address this challenge, an ensemble multifault diagnostic framework based on an adaptive chaotic artificial bee colony (C-ABC)-optimized support vector machine (SVM) and gradient boosting machine (GBM) is proposed. In the proposed framework, data redundancy and overfitting are reduced through a two-stage hybrid filter-transformer-based feature reduction approach using ReliefF, followed by Principal Component Analysis. The chaos function of the proposed C-ABC maintains an adaptive balance between the exploration and exploitation phases, thereby preventing premature convergence, which is a common …

Multiobjective Optimization Framework For Renewable Energy Iot Networks Balancing Security, Energy Efficiency, And Communication Reliability, Muhammad Hjouj Btoush, Ashraf S. Mashaleh, Amjad Gawanmeh

Turkish Journal of Electrical Engineering and Computer Sciences

The deployment of Internet of things (IoT) networks powered by renewable energy sources presents unique challenges in balancing security requirements, energy efficiency, and communication reliability. This paper presents a comprehensive multiobjective optimization framework for secure renewable energy IoT nodes that addresses fundamental trade-offs between these competing objectives. We develop a mathematical model incorporating energy harvesting dynamics, security protocols, and communication performance metrics across various environmental scenarios. The proposed framework employs a modified NSGA-II algorithm to identify Pareto-optimal configurations for different deployment contexts. Through extensive simulation analysis, we demonstrate that hybrid energy sources (solar-wind combinations) with lightweight security protocols achieve optimal …

Accurate Diagnosis Of Diseases By A Novel Ai Pipeline Based On Feature Extraction, Feature Ranking, And Feature Selection From Medical Images, Tuğba Nur Bozkurt, Mehmet Emi̇n Yüksel

Turkish Journal of Electrical Engineering and Computer Sciences

The rapid growth of the global population has led to a substantial increase in the number of patients, while the availability of healthcare professionals has not expanded at a comparable rate. This imbalance highlights the urgent need for efficient and reliable computer-aided decision support systems that can reduce clinical workload while maintaining high diagnostic accuracy. In this study, a novel and systematically integrated artificial intelligence-based pipeline is proposed for medical image classification, combining statistical significance-driven feature ranking with evolutionary feature selection in a unified framework. The proposed pipeline consists of four sequential stages: feature extraction, ranking, selection, and classification. Features …

Predictive Current Control Approach For Grid-Integrated Multifunctional Converter Under Source And Load Disturbances, Ravi Kumar Majji, Tirumalasetty Chiranjeevi, Chilukoti Varaha Narasimha Raja, Nagulapati Kiran

Turkish Journal of Electrical Engineering and Computer Sciences

This paper discusses and presents a model predictive control (MPC)-based predictive current control technique for a solar photovoltaic (PV)-integrated grid system during dynamic operation. This control technique employs extension pq (EPQ) theory to estimate reference currents and utilizes an MPC framework for tracking reference currents. Various MATLAB/Simulink simulations were conducted for solar PV generation (source disturbances) and dynamic loading. The results of the OPAL-RT OP4510 real-time simulation are also presented. A multifunctional grid-integrated converter (MFGC) integrates solar active power into the utility grid while achieving unity power factor, reactive power compensation, current balancing, and harmonic suppression. EPQ optimizes mathematical calculations, …

Visual Interpretability Of Multimodal Tissue Perfusion Classification Using Grad-Cam And Saliency Maps, Metehan Zorluoglu

UNLV Theses, Dissertations, Professional Papers, and Capstones

Accurate identification of the tissue perfusion phase from hand images can aid doctors in decision-making with non-invasive techniques. The present study proposes a multimodal deep learning model for classifying the tissue perfusion phase using infrared, thermal, and visible spectrum images of the human hand. The proposed model consists of various preprocessing techniques such as manipulation, homography alignments, and masking. The significant contribution of this thesis is the interpretability analysis of deep learning models, achieved through the analysis of saliency maps and the Gradient-weighted Class Activation Mapping (Grad-CAM) methods. The purpose of this method is to find out how the convolutional …

Testing And Qualification Of Low-Voltage Power Supplies For The Atlas Tile Hadronic Calorimeter Phase-Ii Upgrade, Justice A. Jones

2026 Spring Honors Capstones Projects

The High Luminosity upgrade of the Large Hadron Collider (HL-LHC) places increased thermal and operational demands on detector electronics, requiring highly reliable power systems. The ATLAS Tile Hadronic Calorimeter (TileCal) uses low-voltage power supply (LVPS) bricks to power front-end electronics, but these units operate in inaccessible regions, making failures difficult to repair. Therefore, rigorous qualification procedures are essential. This work focuses on improving LVPS reliability through a structured burn-in process. Each unit undergoes pre-burn-in electrical verification using a Single Test Stand (STS), followed by sustained operation under load and elevated temperature, and post-burn-in requalification. Standard cooling conditions limit temperatures to …

Riki&Dolphin: Real Time Data Transmission From The Bottom Of A Cave To A Website, Luca Tringali, Giacomo Canciani Dr., Tecla Tripari, Alexander Debenjak, Caterina Bearzotti

International Journal of Speleology

Coming from over 10 years of experience in cave monitoring in northeast Italy, Gruppo Speleologico Talpe del Carso, has designed Riki and Dolphin: customizable, low cost, and easy to assemble tools for getting real time data transmission from the bottom of a cave, even underwater, to a webserver. Their use has been tested to monitor air temperature inside the Abisso Bonetti Cave (Classical Karst, Italy), proving for the first time that a cave in Gorizian Karst can systematically be colder than the outdoor temperature even in winter, recording an internal temperature even lower than 0°C. The Dolphin device can be …

Largest 2-Regular Subgraphs In Complete S-Partite Graphs, Yiyang Jiang

McKelvey School of Engineering Graduate Student Theses & Dissertations

In this thesis, we focus on the class of complete $S$-partite graphs, for $S$ an undirected graph possibly with self-loops, and address the problem of finding largest $2$-regular subgraphs of these graphs, which can be formulated as an integer linear program. Roughly speaking, a complete $S$-partite graph is obtained by replacing every single node of $S$ with a number of nodes, preserving the edge/non-edge relations of $S$. Our motivation in studying largest $2$-regular subgraphs is rooted in the structural systems theory, particularly in the problem of finding largest subnetworks that can sustain controllability or asymptotic stability of the corresponding subsystems. …

Leveraging Information Theory And Ecological Network Analysis To Monitor Communication Networks In A Student Aerospace Team, Christine Sessions

Doctoral Dissertations and Master's Theses

Effective communication is a critical component of successful collaboration in group projects and team settings. However, systematically tracking and analyzing team communications can be challenging, especially in complex, multi-member teams such as those found in the aerospace industry. This paper explores an information-theoretic approach that leverages encoding techniques and graph theory to analyze communication networks within a university’s multi-year, student-led cubesat design project (Project COMET). By utilizing Shannon Entropy as a measure of information flow, encoding communication patterns, and analyzing Ecological Network Analysis parameters, this research aims to understand how an Embry-Riddle Aeronautical University student project team evolves over the …

Designing Enhanced Nonlinearity In Plasmonic Devices With Epsilon-Near-Zero Films, Kevin Tran Le

Electrical Engineering and Computer Science (MS) Theses

The growing demand for energy-efficient optical information processing motivates compact nonlinear photonic devices that can operate at low power. Silicon photonics is a mature platform for linear optical functions, but nonlinear operation remains challenging because of its weak Kerr response, two-photon absorption at telecommunication wavelengths, and limited compatibility with deeply subwavelength plasmonic confinement. This thesis computationally investigates epsilon-near-zero thin films integrated into plasmonic waveguide architectures as a route toward stronger light–matter interaction in compact nonlinear devices.

Two waveguide geometries are examined: a hybrid metal-insulator-metal plasmonic slab waveguide incorporating an ultrathin indium tin oxide epsilon-near-zero layer (5–50 nm), and a dielectric-loaded …

​Autonomous Agentic Orchestration For Physics-Aware Scientific Discovery: An Integrative Multimodal Framework For 2d Material Characterization​, Sankalp Pandey

Electrical Engineering and Computer Science Undergraduate Honors Theses

The advancement of next-generation semiconductor and quantum technologies relies on the scalability of the fabrication of two-dimensional (2D) van der Waals heterostructures. However, this process is severely bottlenecked by characterization workflows. Optical microscopy provides high-throughput imaging of 2D material flakes, but lacks the explicit physical priors required for the discernment of sub-nanometer thickness variations, such as distinguishing monolayers from bilayers. The use of computer vision models to automate the localization and characterization process of the flakes was proposed. As a part of this effort, we develop QuantumFlake, an open-source framework to streamline the integration and deployment of computer vision models …

Measurements And Scaling Of Ion Propulsion Impulse During Driven Magnetic Reconnection, Fatima Ebrahimi, Nicholas A. O'Gorman, Kush Maheshwari, Jongsoo Yoo, Alexandre Sainterme, Hantao Ji

Faculty Publications

Impulse scaling during magnetic reconnection, the magnetic energy conversion to kinetic energy, via direct Mach probe measurements in Magnetic Reconnection Experiment is examined. Ion exhaust velocity and impulse scalings with reconnecting magnetic field during the push phase of driven reconnection are presented. The outflows and impulse measurements are compared with global MHD simulations. Both measurements and simulations reveal a favorable scaling, greater than linear, of impulse with reconnecting field. These scaling results establish that magnetic reconnection could be utilized for plasma propulsion.

A Study Of Visualized Diagnostics In Early Stage Digital Twin Implementation Of An Industrial Control System, Michael R. Kinzel

All-Inclusive List of Electronic Theses and Dissertations

Industrial Control Systems (ICS) are used for process control in almost all industries. An ICS combines Operational Technologies (OT) with Information Technologies (IT) to allow human supervision of a process through surveillance of process variables and manipulation of controlling elements such as valves to maintain stable process conditions. ICSs have been in-service for several decades and may remain operational past their technological service life. Organizational personnel interact with the ICS through visual displays that both indicate the process variables and also the controlling elements. The Human Machine Interface (HMI) allows visibility of the process and the ability to manipulate controlling …