Digital Commons Network^™

Automated Software Size Measurement Using Multilingual Domain-Adapted Language Models, Samet Tenekeci̇, Hüseyi̇n Ünlü, Burak Keçeci̇, Muhammed Efe İnci̇r, Onur Demi̇rörs

Turkish Journal of Electrical Engineering and Computer Sciences

Software Size Measurement (SSM) is crucial for estimating required project effort as well as budget and schedule. However, many small and medium-sized companies struggle to apply objective SSM due to limited resources and lack of expertise. This often leads to inaccurate estimates and project overruns. There is a need for practical, low-resource solutions that support these tasks without requiring expert involvement. Motivated by this challenge, this study proposes an automated software size measurement approach that formulates the measurement task as supervised regression over natural language requirements, using domain-adapted transformer models. We construct large-scale Turkish and English software engineering corpora to …

Availability Model To Evaluate Ai Data Centers’ Role In Grid Stability, Troy Mcsimov, Trevor S. Kunz, Jeffrey Billo

SMU Data Science Review

The United States has made it clear; it is imperative that the US wins the global AI race. This paper focuses on one of the most challenging puzzle pieces surfaced at the POWER Data Center conference (San Antonio, Sept. 30.); for Electric Reliability Council of Texas (ERCOT) the limiting factor is not generation alone but the need to balance generation and load to preserve grid reliability.

The regulatory landscape fundamentally changed with the passage of Texas Senate Bill 6 in June 2025, which mandates new large loads must "contribute to the recovery of the interconnecting electric utility’s costs" (Texas Legislature, …

Reducing Range Anxiety Through Predictive Modeling Of Ev Battery Degradation, Caleb Thornsbury, Christian Castro, Bivin Sadler

SMU Data Science Review

Electric Vehicles (EV) range anxiety remains one of the top barriers for broader adoption. Range anxiety can be attributed to battery pack age and degradation over time. This paper plans to explore how to address this issue by creating a machine learning model that can predict degradation based on usage, temperature, battery chemistry, charging habits and exploring whether other factors tie into range degradation. This research will be using real world charging data along with lab tested chemistry data to build a model that can be chemistry specific for degradation. This paper will help perspective used-EV buyers learn about battery …

Sensing Negative-Cone Rotational Diffusion Of Dipole-Like Emitters, Yuanxin Qiu, Kaizhi A. Nie, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

Fluorescence anisotropy and single-molecule orientation-localization microscopy (SMOLM) are powerful techniques that quantify the rotational diffusion of dipole-like emitters, which is important for sensing molecular interactions and chemical environments at the nanoscale. Numerous theoretical and experimental studies have thoroughly characterized single-molecule rotations even when those rotations are much faster than the detector integration time. Here, we extend the theory of measuring rotational diffusion to situations where a single dipole rotates uniformly everywhere outside of an isotropic cone of a certain size, termed a negative cone. This scenario corresponds to negative fluorescence anisotropy 𝑟 and has been observed in emitters exhibiting strong …

Dynamic Deep Prompt Optimization For Defending Against Jailbreak Attacks On Llms, Doniyorkhon Obidov, Honggang Yu, Xiaolong Guo, Kaichen Yang

Michigan Tech Publications

Large Language Models (LLMs) demonstrate impressive capabilities across many applications but remain vulnerable to jailbreak attacks, which elicit harmful or unintended content. While model fine-tuning is an option for safety alignment, it is costly and prone to catastrophic forgetting. Prompt optimization has emerged as a promising alternative, yet existing prompt-based defenses typically rely on static modifications (e.g., fixed prefixes or suffixes) that cannot adapt to diverse and evolving attacks.

We propose Dynamic Deep Prompt Optimization (DDPO), the first jailbreak defense based on deep prompt optimization. DDPO uses the target LLM’s own intermediate layers as feature extractors to dynamically generate defensive …

Review Of Modern Methods For Identification, Forecasting, And Intelligent Control Of Wastewater Biological Treatment Processes, Mirkhalil Agzamovich Ismailov, Boburbek Zokirjon O'G'Li Mannobjonov

Chemical Technology, Control and Management

This article presents a comprehensive review of contemporary approaches to the automation and intelligent control of wastewater biological treatment processes. Particular emphasis is placed on the digitalisation of wastewater treatment plants, ranging from the implementation of automated process control systems (APCS/SCADA-based solutions) to the application of predictive algorithms and the development of digital twins of bioreactors.

Special attention is devoted to mathematical models that underpin the control of bioprocesses. The evolution of the most widely used activated sludge models—ASM1, ASM2d, and ASM3—is examined, as these models describe key processes such as microbial community growth, nitrification, denitrification, and phosphorus removal. It …

Research Of Logic Elements Based On Complementary Bipolar Transistors, Nodira Batirdjanovna Alimova, Nilufar Baxtiyorovna Gulyamova

Chemical Technology, Control and Management

Important parameters for any type of inverter – a NOT logic gate – are the power consumption during switching and the supply voltage. The proposed connection of complementary (two different types) bipolar transistors reduces the current consumption and supply voltage by simultaneously using the cutoff and saturation modes of the bipolar transistors. It has been theoretically and experimentally established that an inverter using complementary bipolar transistors operates at low supply voltages, approximately 0.7 V. Power consumption is virtually independent of the inverter's static state. A method for calculating the transfer characteristic of an inverter using complementary bipolar transistors is developed, …

Hybrid Cnn–Gru-Based Demand–Supply Forecasting To Enhance Sustainability In Renewable-Integrated Smart Grids, Süleyman Emre Eyimaya, Necmi Altin

Faculty Publications

The rapid integration of renewable energy sources in smart grids has introduced significant uncertainty in both power generation and consumption patterns, posing challenges to environmental, economic, and operational sustainability. Accurate short-term forecasting of energy demand and supply is essential for achieving optimal scheduling, grid stability, and resilient operation in renewable-integrated power systems. This study proposes a hybrid deep learning framework combining Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) for intelligent joint demand–supply forecasting in smart grids. The model was developed and implemented in MATLAB using real-world datasets comprising electricity consumption, photovoltaic (PV) generation, temperature, and irradiance variables. Comparative …

Orthogonal Time Frequency Space Modulation For Underwater Acoustic Communication Systems: A Review, Bevek Subba, Quoc Viet Phung, Stefan Lachowicz, Walid K. Hasan, Muhammad Haziq, Daryoush Habibi, Iftekhar Ahmad

Research outputs 2022 to 2026

Underwater Acoustic Communication (UAC) has garnered significant attention due to its applications in marine science, defence, and exploration. With the vast majority of the Earth's surface covered by water, there is a growing demand for reliable and effective communication techniques in underwater environments. However, the underwater acoustic channel is the most challenging channel due to its harsh characteristics, which significantly hinder the reliability and performance of UAC systems. Orthogonal Time Frequency Space (OTFS) modulation has emerged as a promising solution for next-generation UAC systems to address high Doppler and high mobility scenarios. It has demonstrated tolerance to fast time-varying channel …

Design And Control Of A Multi-Modal Electromagnetic Floor Array For Foot-Based Human Locomotion And Stabilization In Microgravity, Aryan Anand

Electrical Engineering Theses

Long-duration living and working in microgravity creates everyday mobility problems such as drifting, loss of stable footing, higher effort to move, and difficulty doing routine tasks safely. Many solutions have been proposed in literature, including handrails, restraint systems, and concepts for artificial gravity using rotation. Artificial gravity could improve comfort, but it is complex to build and operate for large spacecraft, especially when future missions may include not only trained astronauts but also common people. With companies like SpaceX pushing toward large-scale travel and long-term settlement goals, there is a need for simpler mobility support technologies that can work inside …

Ainet: Integrating Mamba And Cbam For Enhanced Camouflage Object Detection, Henry O. Velesaca, P. Andrea Mero, Abel A. Reyes-Angulo, Angel D. Sappa

Michigan Tech Publications

This paper introduces AINet, a novel deep learning architecture designed for detecting camouflaged objects in complex and diverse environments. The objective of this work is to design an end-to-end camouflaged object detection architecture that simultaneously captures long-range dependencies and refines subtle camouflage cues, improving segmentation accuracy and boundary delineation across both standard COD benchmarks and real-world agricultural scenarios. AINet leverages the strengths of Mamba, an efficient sequential state model for capturing long-range dependencies, and the Convolutional Block Attention Module (CBAM) for feature refinement through attention mechanisms. Detecting camouflaged objects is a significant challenge across a wide range of real-world applications, …

Uas Path Planning With Dynamic Rerouting Using A Space-Time Graph, Kimoy Williams

Electronic Theses and Dissertations 2020 - Present

UAS systems have emerged as multifaceted technologies with applications across a wide range of sectors. Their ability to access areas that are difficult or unsafe for manned systems has made them invaluable tools in various domains. As a result, UAS have transformed numerous industries, including infrastructure inspection, delivery and logistics, military and defense, as well as precision agriculture and environmental monitoring.

The advancement in UAS technology is fundamentally reliant upon ongoing research efforts in the specialized area of UAS path planning. Optimal flight planning is essential for a UAV to effectively execute its mission’s task safely, effectively, and in congruence …

A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines

Dissertations

Artificial Intelligence (AI) is transforming Supply Chain Management (SCM), yet many organizations struggle to assess their readiness for AI adoption and to understand how AI capabilities develop across maturity stages. This dissertation addresses this gap by developing a Capability Maturity Model (CMM) for AI integration in SCM, grounded in Organizational Information Processing Theory (OIPT), the Resource-Based View, and related capability frameworks. The model provides a structured approach for evaluating an organization's information-processing requirements, resource configurations, and alignment needed for effective AI-enabled supply chain operations.

Using a design science research approach, the AI-SCM CMM and its associated assessment instrument were derived …

A Holistic Modelling Framework For Functionally Safe Software Architectures In Embedded Control Systems, Thomas Barth

Doctoral

Embedded control systems are integral to most modern electrified products and an essential backbone of ongoing digitalisation [1]. In this context, these systems increasingly perform safety-critical functions where failures can lead to severe personal injury, environmental damage, or significant economic loss [2]. Consequently, they fall more often within the scope of regulation such as IEC 61508 and its derivatives [3]. At the same time, driven by hardware evolution and market demands, embedded control systems continue to grow in both integration density and functional complexity [4]. These demands necessitate structured development methods that balance compliance with cost-efficiency and development agility. A …

Exploring Noise Induced Extreme Events In Neuronal Oscillators Networks And Machine Learning Forecasts, Hariharan S Mr

Theses and Dissertations

This doctoral dissertation comprehensively investigates the underexplored phenomenon of noise-induced extreme events (EE). The word “extreme” is accompanied by an occurring “event” when the deviation is extreme or higher than that of regular occurrences. These extreme occurrences are rare, abrupt, sudden, and irregular, often causing a profound impact on the system and its surroundings. Tsunami, earthquakes, solar flares, and tornadoes are such events that do not occur often but still significantly cause damage to mankind. This thesis particularly focuses on EE in neuronal systems where sudden synchronization can trigger seizures, tremors, and strokes which serve as classic examples of such …

Scalable And Fault-Tolerant Network Architectures For Real-Time Video Transmission In Industrial Networked Control Systems, Moustafa Awad

Theses and Dissertations

This thesis addresses the challenge of transporting supervisory video alongside time-critical control traffic in industrial Networked Control Systems (NCS) without violating stringent real-time constraints. A simple yet scalable network architecture is developed and evaluated for a plant-level deployment comprising three interconnected workcells with sensors, controllers, actuators, and cameras. The design explicitly accommodates bandwidth-intensive video streams while preserving the responsiveness of watchdog/control traffic. Analytical delay modeling decomposes end-to-end latency into transmission, propagation, processing, and queuing components, and Riverbed-based simulations are used to validate the model under realistic mixed-traffic conditions. A traffic-engineering strategy - phase-shifting supervisory camera transmissions - effectively desynchronizes frame …

Flexible Fault-Tolerant Multi-Die Fpga-Based Architectures For Varying Space Environments, Yosof Ali Seif El Din Ali Maklad

Theses and Dissertations

It is well-known fact that spacecraft’s electronic components operate in an extreme harsh and varying space environments, beside changing orbit or passing through Van Allan Belts during orbital course results of radiation levels change. This thesis focuses on SRAM-based FPGA systems on-board of such spacecrafts, that are commonly utilized in space applications’ critical applications due to their capabilities and flexibility to reconfigure, since these systems are vulnerable to frequent negative impacts of ionizing radiation, thus inducing soft and hard errors leading to disastrous failures that could jeopardize the entire spacecraft. The soft errors’ effects are frequent yet can be mitigated, …

Dynamic Multi-Layered Hardware Obfuscation With Behavioral Drift Locking For Sat-Resistant Designs, Ahmed Yehia Salah Mohamed Emish

Theses and Dissertations

The globalization of the semiconductor supply chain has introduced critical vulnerabilities, including intellectual property (IP) piracy, reverse engineering, and hardware tampering. While traditional logic locking offers a baseline of defense, the emergence of powerful Boolean satisfiability (SAT) solvers has rendered many static obfuscation techniques ineffective. This work proposes a Dynamic Multi- Layered Hardware Obfuscation Framework that utilizes Behavioral Drift Locking (BDL) to provide a robust defense-in-depth against advanced adversarial models. The methodology integrates four synergistic layers: • Dynamic Key Management using a time-dependent rotation mechanism that updates keys every clock cycle to prevent static analysis. • Control Obfuscation through opcode …

Electromagnetic Simulations For Vulnerable Road User Safety In 5g/6g Wireless Systems, Colin Mcnerny

Theses and Dissertations

Protecting vulnerable road users is imperative. As 5G wireless technology matures and 6G standards are established, new operational technology emerges to reduce traffic accidents. There is widespread evidence to support the need for pedestrian awareness in modern traffic ecosystems as a significant number of vulnerable road users are injured or killed every year due to observable traffic failures. Computational electromagnetic (CEM) solvers are used to model, simulate, and analyze the performance of antennas deployed to meet this challenge. Validation studies using the Altair FEKO CEM solver demonstrate scenarios where vulnerable road users are at risk. The simulation data in this …

Silent Sabotage: Internal State Triggered Backdoor Attacks On Llm-Powered Robotic Systems, Doniyorkhon Obidov, Shivayogi Akki, Tan Chen, Kaichen Yang

Michigan Tech Publications

The integration of Large Language Models (LLMs) into robotic control systems is enabling a new generation of autonomous agents capable of complex reasoning and planning. While this paradigm shift accelerates progress, it also introduces novel security risks that remain largely unexplored. Current research into LLM backdoors has focused on attacks triggered by external stimuli, such as specific words, visual objects, or environmental states. These attacks, while potent, overlook a more insidious class of vulnerability where the trigger is internal to the agent’s own operational logic. This paper presents the first comprehensive study of history-based backdoor attacks on LLM-powered robotic systems. …

Biologically-Inspired Multiscale Neuromorphic Architecture, Christian O'Reilly, Ramtin Zand

Publications

This white paper proposes a biologically-inspired multiscale neuromorphic architecture that bridges key gaps between artificial neural networks (ANNs), spiking neural networks (SNNs), and biological neural networks (BNNs). While SNNs offer promising energy efficiency, their broader adoption remains limited by suboptimal performance and the need for novel learning paradigms. To address these challenges, the proposed framework integrates structural and functional principles observed in the brain, including hierarchical organization, sparse and modular connectivity, predictive coding, and diverse neuronal dynamics.

The architecture operates across micro-, meso-, and macro-scales, incorporating neuron-level diversity (e.g., excitatory/inhibitory and principal/support cells), canonical microcircuits (CMCs), and large-scale hierarchical organization. …

The Impact Of A Structured Training Program On The Depth Perception Of Ab Initio Drone Pilots, John Murray, Steven Richardson, Keith Joiner, Graham Wild

Research outputs 2022 to 2026

Highlights: What are the main findings? First empirical assessment of depth-perception improvement from an official RPAS training program. Demonstrates measurable perceptual-skill gains despite no explicit syllabus objective for depth perception. What are the implications of the main findings? Conducted in authentic operational settings under regulated conditions, ensuring strong external validity. Establishes a validated baseline for future comparative and longitudinal studies on perceptual training effectiveness. Flying remotely requires accurate perception of the environment to ensure safe operation. While remotely piloted aircraft (RPA) bring unique opportunities, they also present new challenges for the pilot, including exercising accurate depth perception. The impact of …

Optical Fiber Sensors Using Vernier Effect In Cascaded Fiber Interferometers, Zhouchen Wang

Dissertations

The optical Vernier effect has emerged as a powerful tool to enhance the sensitivity of optical fiber interferometer-based sensors, opening new opportunities for developing highly sensitive fiber sensing systems. Optical fiber interferometric sensors based on the Vernier effect are widely used for various applications due to their ultra-compact size, high sensitivity, immunity to electromagnetic interference, electrical isolation, resistance to harsh environments, flexibility, multiplexing capability, and remote operation. The aim of this doctoral thesis was to gain a deeper fundamental understanding of the Vernier effect in optical fiber structures and to develop and investigate a series of novel Vernier effect optical …

The Risc-V Fpga (Rvfpga) Teaching Package, Daniel Chaver, Sarah Harris, Luis Pinuel, Olof Kindgren, Zubair Kakakhel, Chris Owen, Roy Kravitz, Jose I. Gomez-Perez, Fernando Castro, Katzalin Olcoz, Multiple Additional Authors

Electrical and Computer Engineering Faculty Publications and Presentations

RISC-V is a free and open-standard ISA based on RISC principles, allowing anyone to design, manufacture, and sell RISC-V chips and software. Its flexibility and growing ecosystem have made it popular in research, education, and industry, increasing the need for educational materials. This paper provides an in-depth description of the RVfpga course, which offers a solid introduction to computer architecture using the RISC-V instruction set and FPGA technology. It focuses on providing hands-on experience with real-world RISC-V cores, the VeeR EH1 and EL2 cores, developed by Western Digital and hosted by ChipsAlliance. The course targets students and educators in computing-related …

The Risc-V Fpga (Rvfpga) Teaching Package, Daniel Chaver, Sarah Harris, Luis Pinuel, Olof Kindgren, Zubair Kakakhel, Chris Owen, Jose I. Gomez-Perez, Fernando Castro, Katzalin Olcoz, Julio Villalba-Moreno, Alexander Grinshpun, Freddy Gabbay, Luke Seed, Rui Duarte, Manuel Lopez, Oscar Alonso, Robert Owen

Electrical & Computer Engineering Faculty Research

RISC-V is a free and open-standard ISA based on RISC principles, allowing anyone to design, manufacture, and sell RISC-V chips and software. Its flexibility and growing ecosystem have made it popular in research, education, and industry, increasing the need for educational materials. This paper provides an in-depth description of the RVfpga course, which offers a solid introduction to computer architecture using the RISC-V instruction set and FPGA technology. It focuses on providing hands-on experience with real-world RISC-V cores, the VeeR EH1 and EL2 cores, developed by Western Digital and hosted by ChipsAlliance. The course targets students and educators in computing-related …

Hybrid Quantum-Classical Optimization Of The Resource Scheduling Problem, Tyler Christeson, Md Habib Ullah, Ali Arabnya, Amin Khodaei, Rui Fan

Electrical and Computer Engineering: Faculty Scholarship

Resource scheduling is critical in many industries, especially in power systems where the Unit Commitment (UC) problem determines the on/off status and output levels of generators under physical and economic constraints. Traditional exact methods, such as Branch-and-Bound, Branch-and-Cut, dynamic programming and mixed-integer linear programming (MILP), remain the backbone of UC solution techniques, but they often rely on linear approximations or exhaustive search, leading to high computational burdens as system size grows. Metaheuristic approaches, such as genetic algorithms, particle swarm optimization, and other evolutionary methods, have been explored to mitigate this complexity; however, they typically lack optimality guarantees, exhibit sensitivity to …

Developing A Collaborative Tool To Foster Communication In Sustainability Research, Nina Hunter, Noëlle-Laetitia Perret, Martin Klepal

Electrical & Electronic Engineering

Climate change necessitates urgent responses based on knowledge produced by teams that transcend disciplinary boundaries, and with members whose work focuses on the generation of knowledge and on the application of knowledge, with some integrating both. Almost a third of Europe’s building stock consists of heritage buildings requiring renovation that is ideally sustainable as part of an energy transition response. The European transdisciplinary CALECHE study team with use-cases in France, Italy, Sweden and Switzerland aims to support decision-making on the sustainable renovation of heritage buildings, via research that employs co-design. However, team members are from various disciplines, with different skillsets …

Cover And Contents

Turkish Journal of Electrical Engineering and Computer Sciences

No abstract provided.

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 …