Electrical and Computer Engineering Commons^™

Experimental Characterization Of Photonic Crystal Based Invisibility Cloak Under Tm-Polarized Microwaves, Muhammad Danyal

Dissertations, Master's Theses and Master's Reports

Electromagnetic invisibility cloaks guide waves around an object so that the transmitted wavefront remains undisturbed. Most experimental microwave cloaking studies have focused on transverse electric (TE) polarization due to established measurement techniques. In this thesis, the experimental realization and characterization of a dielectric photonic crystal cloak operating under transverse magnetic (TM) polarization are presented. A measurement system operating in the X-band was developed to map the electric field distribution of the transmitted waves. Cloaking performance was first evaluated qualitatively by comparing numerical and experimental field distributions, where restoration of a flat wavefront indicated effective cloaking. Quantitative evaluation was performed by …

Recent Advancements Of Applied Robotics In Construction Project Management: A Life Cycle Perspective, Yifan Wang, Xiaoyu Hou, Tan Chen, Bo Xiao

Michigan Tech Publications

Robotic applications in the architecture, engineering, construction, and operation (AEC/O) industry have advanced rapidly, driven by innovations in sensing, artificial intelligence (AI), and automation. Yet, despite notable progress, existing studies are often confined to isolated systems, specific technologies, or individual project phases. This fragmentation has resulted in a diverse but disjointed body of knowledge, underscoring the need for a comprehensive synthesis that connects emerging practices into a holistic perspective. This study presents a systematic review of 315 peer-reviewed publications from 2016 to 2025, examined through a life cycle lens. A mixed-methods approach combining quantitative analysis with qualitative discussion is employed …

Investigating The Impacts Of Agrivoltaic Design Choices On Inter-Row Shading And Electricity Production, Marcus Wu, Anna Stuhlmacher

Infinite Loop

No Abstract available.

Siamese: Stealing Fine-Tuned Visual Foundation Models Via Diversified Prompting, Madhureeta Das, Gaurav Bagwe, Miao Pan, Kaichen Yang, Xiaoyong Yuan, Lan Zhang

Michigan Tech Publications

Visual foundation models, characterized by their robust generalization and adaptability, serve as the basis for a wide array of downstream tasks. When fine-tuned for specific tasks, these models encapsulate confidential and valuable task-specific knowledge, making them prime targets for model stealing (MS) attacks. While recent efforts have exposed MS threats in practical scenarios such as data-free and hard-label contexts, these attacks predominantly target traditional victim models trained from scratch. Fine-tuned visual foundation models, pre-trained on vast and diverse datasets and then fine-tuned on downstream tasks, present significant challenges for traditional MS attacks to extract task-specific knowledge. In this paper, we …

An Audio Data-Driven Roadway Digital Twin And Its Underlying Framework For A Digitized Transportation Construction Environment, Anisha Deria, Pedro J. Chacon Dominguez, Yong-Cheol Lee, Jin W. Choi

Michigan Tech Publications

The increasing need to build and maintain transportation systems has led project managers to manage multiple projects simultaneously. Roadway projects often entail several miles of job site, making it difficult to keep track of progress and maintenance activities. To improve the situation, this study proposes an audio data-driven roadway digital twin framework for real-time and remote monitoring of construction projects. The latent characteristics of a digital twin required for establishing a digitized work environment were investigated. As a primary method of seamlessly linking virtual and physical environments, audio data classified and analyzed by deep neural network (DNN) has been employed …

Investigation Of Insertion Loss In Inkjet-Printed Coplanar Waveguide Based On Drying Temperature, Jun Ho Yu, Sung Min Sim, Jin Woo Choi, Sang Ho Lee, Jung Mu Kim

Michigan Tech Publications

In this study, we propose an optimized inkjet printing process to improve the insertion loss of inkjet-printed coplanar waveguide (CPW) transmission lines. The process involves varying the drying temperature and adjusting the number of printing steps to investigate their effects on the electrical characteristics of the printed CPW. The relationships between surface roughness, surface cavities, morphological changes, and insertion loss are studied by conducting atomic force microscopy analysis and by examining the insertion loss up to 3 GHz. The printed CPW that underwent low-temperature drying after the first printing and high-temperature drying after the second printing before sintering showed improved …

Resilience Oriented Distribution System Service Restoration Considering Overhead Power Lines Affected By Hurricanes, Kehkashan Fatima, Hussain Shareef, Flavio Costa

Michigan Tech Publications

In recent years, there has been an increase in the frequency of severe weather events (like hurricanes). These events are responsible for most power outages in power distribution systems (PDSs). Particularly susceptible to storms are overhead PDSs. In this study, the dynamic Bayesian network (DBN)-based failure model was developed for different hurricane scenarios to predict the line failure of overhead lines. Based on the outcomes of the DBN model, a service restoration model was formulated to maximize restored loads and minimize power losses using Particle Swarm Optimization (PSO)-based distributed generation (DG) integration and system reconfiguration. Three different case studies based …

An Audio Data-Driven Roadway Digital Twin And Its Underlying Framework For A Digitized Transportation Construction Environment, Anisha Deria, Pedro J. Chacon Dominguez, Yong-Cheol Lee, Jin W. Choi

Michigan Tech Publications

The increasing need to build and maintain transportation systems has led project managers to manage multiple projects simultaneously. Roadway projects often entail several miles of job site, making it difficult to keep track of progress and maintenance activities. To improve the situation, this study proposes an audio data-driven roadway digital twin framework for real-time and remote monitoring of construction projects. The latent characteristics of a digital twin required for establishing a digitized work environment were investigated. As a primary method of seamlessly linking virtual and physical environments, audio data classified and analyzed by deep neural network (DNN) has been employed …

Performance Analysis Of Multivariable Control Structures Applied To A Neutral Point Clamped Converter In Pv Systems, Renato Santana Ribeiro Junior, Eubis Pereira Machado, Damásio Fernandes Júnior, Tárcio André Dos Santos Barros, Flavio Bezerra Costa

Michigan Tech Publications

This paper addresses the challenges encountered by grid-connected photovoltaic (PV) systems, including the stochastic behavior of the system, harmonic distortion, and variations in grid impedance. To this end, an in-depth technical and pedagogical analysis of three linear multivariable current control strategies is performed: proportional-integral (PI), proportional-resonant (PR), and deadbeat (DB). The study contributes to theoretical formulations, detailed system modeling, and controller tuning procedures, promoting a comprehensive understanding of their structures and performance. The strategies are investigated and compared in both the rotating ((Formula presented.)) and stationary ((Formula presented.)) reference frames, offering a broad perspective on system behavior under various operating …

Replicating Associative Learning Of Rodents With A Neuromorphic Robot In An Open-Field Arena, Tianze Liu, Kang Jun Bai, Hongyu An

Michigan Tech Publications

This study emulates associative learning in rodents by using a neuromorphic robot navigating an open-field arena. The goal is to investigate how biologically inspired neural models can reproduce animal-like learning behaviors in real-world robotic systems. We constructed a neuromorphic robot by deploying computational models of spatial and sensory neurons onto a mobile platform. Different coding schemes—rate coding for vibration signals and population coding for visual signals—were implemented. The associative learning model employs 19 spiking neurons and follows Hebbian plasticity principles to associate visual cues with favorable or unfavorable locations. Our robot successfully replicated classical rodent associative learning behavior by memorizing …

Benchmarking Model Predictive Control And Reinforcement Learning-Based Control For Legged Robot Locomotion In Mujoco Simulation, Shivayogi Akki, Tan Chen

Michigan Tech Publications

Model Predictive Control (MPC) and Reinforcement Learning (RL) are two prominent strategies for controlling legged robots. RL learns control policies through system interaction, adapting to various scenarios, whereas MPC relies on a predefined mathematical model to solve optimization problems in real-time. Despite their widespread use, there is a lack of direct comparative analysis under standardized conditions. This work addresses this gap by benchmarking MPC and RL controllers on a Unitree Go1 quadruped robot within the MuJoCo simulation environment, focusing on a standardized task, straight walking at a constant velocity. Performance is evaluated based on disturbance rejection, energy efficiency, and terrain …

Remote Sensing Of Seismic Signals Via Enhanced Moiré-Based Apparatus Integrated With Active Convolved Illumination, Adrian A. Moazzam, Anindya Ghoshroy, Durdu Güney, Roohollah Askari

Michigan Tech Publications

The remote sensing of seismic waves in challenging and hazardous environments, such as active volcanic regions, remains a critical yet unresolved challenge. Conventional methods, including laser Doppler interferometry, InSAR, and stereo vision, are often hindered by atmospheric turbulence or necessitate access to observation sites, significantly limiting their applicability. To overcome these constraints, this study introduces a Moiré-based apparatus augmented with active convolved illumination (ACI). The system leverages the displacement-magnifying properties of Moiré patterns to achieve high precision in detecting subtle ground movements. Additionally, ACI effectively mitigates atmospheric fluctuations, reducing the distortion and alteration of measurement signals caused by these fluctuations. …

Thermaltrack Dataset - Training Labels - Sequence 1-11, Yiming Yang, Jeremy P. Bos

ThermalTrack

We present a wheel track detection system that leverages RGB-Thermal (RGB-T) imaging, where thermal channels reveal critical temperature differentials between compacted tracks and loose snow - tracks exhibit higher thermal inertia and lower reflectivity, emitting stronger radiation signatures even in visually homogeneous conditions. By fusing these distinctive thermal patterns with RGB spatial information, our method reliably identifies navigable tracks, enabling robust path-following in complete white-out conditions where snow textures and terrain features become indistinguishable.

Thermaltrack Dataset- Training Images- Fused Rgb Lwir- Sequence 1, Yiming Yang, Jeremy Bos

ThermalTrack

We present a wheel track detection system that leverages RGB-Thermal (RGB-T) imaging, where thermal channels reveal critical temperature differentials between compacted tracks and loose snow - tracks exhibit higher thermal inertia and lower reflectivity, emitting stronger radiation signatures even in visually homogeneous conditions. By fusing these distinctive thermal patterns with RGB spatial information, our method reliably identifies navigable tracks, enabling robust path-following in complete white-out conditions where snow textures and terrain features become indistinguishable.

Single-Entity Protein Electrochemistry Of A Diffusion-Limited Enzyme, Ziwen Zhao, Nikolaos Kostopoulos, Sagar Ganguli, Paul Bergstrom, Alina Sekretareva

Michigan Tech Publications

In this work, we present single-entity protein electrochemistry (SEPE) experiments on catalase, along with a theoretical model to simulate its enzymatic activity and predict the experimentally observed current responses. We perform SEPE measurements at various enzyme concentrations and use protein film voltammetry to investigate the origin of the observed electrochemical signals in SEPE. The modeling approach we develop combines finite element simulations in COMSOL Multiphysics with random walk simulations in MATLAB. The enzyme is represented as a partially active sphere, with an arc on the surface corresponding to the enzyme’s diffusion channel leading to the active site. Notably, the model …

Batteryless Nfc-Enabled Wireless Sensor Node: Design, Optimization, And Implementation, Rishin Patra

Dissertations, Master's Theses and Master's Reports

This thesis presents the design, construction, and testing of a batteryless Near-field communication (NFC) powered wireless sensor node intended for maintenance free, short range Internet of Things (IoT) applications. The work focuses on harvesting energy from a 13.56 MHz NFC field to power a very low-power sensing platform capa- ble of measuring temperature, pressure, and humidity without the use of batteries or wired power. The motivation behind this approach is the growing need for reliable, sustainable, and low-maintenance sensing systems that can operate in environments where battery replacement is impractical, undesirable, or environmentally costly. The prototype is built around a …

Modeling, Control And Validation Of A Three-Phase Single-Stage Photovoltaic System, Eubis Pereira Machado, Adeon Cecílio Pinto, Rodrigo Pereira Ramos, Ricardo Menezes Prates, Jadsonlee Da Silva Sá, Joaquim Isídio De Lima, Flavio Bezerra Costa, Damásio Fernandes, Alex Coutinho Pereira

Michigan Tech Publications

The central inverter topology presents some advantages such as simplicity, low cost and high conversion efficiency, being the first option for interfacing photovoltaic mini-generation, whose shading and panel orientation studies are evaluated in the project planning phase. When it uses only one power converter, its control structures must ensure synchronization with the grid, tracking the maximum power generation point, appropriate power quality indices, and control of the active and reactive power injected into the grid. This work develops and contributes to mathematical models, the principles of formation of control structures, the decoupling process of the control loops, the treatment of …

Intelligent Transportation System With 5g Vehicle-To-Everything (V2x): Architectures, Vehicular Use Cases, Emergency Vehicles, Current Challenges, And Future Directions, Vaishali Pawar, Nilima Zade, Deepali Vora, Vaishali Khairnar, Aurenice M. Oliveira, Ketan Kotecha, Ambarish Kulkarni

Michigan Tech Publications

In recent years, connected vehicle technologies emerged to provide a safer and more connected environment for transportation systems, which has further evolved into cellular vehicle-to-everything (C-V2X) technology. The current study contributes in the direction of future research of smart India @2047 in connected vehicles. The study presents the diverse aspects of 5G V2X as a comprehensive overview of Vehicle-to-Everything (V2X) technology that covers the significant scope of V2X technology, including its development, components, functionality, use cases, and future potential. It is observed that few surveys are available that cover limited aspects of 5G V2X communication technologies, with one or two …

Backup Subscription Scheme For Differential Protection Using Iec61850-9-2 Sampled Values, Mohammad Khalili Katoulaei, Aamir Rahmani, Hans Kristian Høidalen, Irina Oleinikova, Bruce A. Mork

Michigan Tech Publications

In IEC-61850-based digital substations, the protection IED’s performance is dependent on merging unit’s vendor implementation, communication networks, and measurement circuit’s health conditions. As the process bus Sampled Value(SV) enables the availability of all sensor data on a communication network, this paper proposes a Backup Subscription scheme (BSS) for a transformer differential protection (87T, PDIF) function. BSS utilizes sensor data in digital substations to achieve a flexible protection scheme using a dynamic subscription feature. Thus, in case of failure of one sensor, differential protection would be maintained. The paper presents the implementation and verification of a prototyped scheme using a Hardware-in-the-loop …

High-Frequency-Based Transmission Line Percentage Differential Protection With Traveling Wave Alignment, Igor F. Prado, Flavio Costa, Kleber M. Silva, Rodrigo P. Medeiros, Bruce A. Mork

Michigan Tech Publications

This paper presents a wavelet-based differential protection algorithm for transmission lines. It uses restraint and operating components obtained with high-frequency components of a few kHz from instantaneous energy values of the real-time boundary stationary wavelet transform instead of low-frequency components from phasor estimation. It does not require capacitive current suppression as well. Therefore, the proposed method overcomes the limitations of conventional percentage differential protection. Furthermore, the proposed technique uses traveling wave theory to perform the current sample alignment at a few kHz, thereby, not requiring the global positioning system (GPS). The performance of the proposed method is evaluated through extensive …

Design And Analysis Of Self-Tanked Stepwise Charging Circuit For Four-Phase Adiabatic Logic, William Morell, Jin Woo Choi

Michigan Tech Publications

Adiabatic logic has been proposed as a method for drastically reducing power consumption in specialized low-power circuits. They often require specialized clock drivers that also function as the main power supply, in contrast to standard CMOS logic, and these power clocks are often a point of difficulty in the design process. A novel, stepwise charging driver circuit for four-phase adiabatic logic is proposed and validated through a simulation study. The proposed circuit consists of two identical driver circuits each driving two opposite adiabatic logic phases. Its performance relative to ideal step-charging and a standard CMOS across mismatched phase loads is …

Plc Multi-Robot Integration Via Ethernet For Human Operated Quality Sampling, Jeevan S. Devagiri, Paniz Khanmohammadi Hazaveh, Nathir Rawashdeh, Sai Revanth Reddy Dudipala, Pratik Mohan Desmuhk, Aditya Prasad Karmarkar

Michigan Tech Publications

In automation, quality control inspection is a critical requirement to ensure product standards. The goal of this work is to insure product quality without interrupting the production line flow. The multi-robot system presented, connects a programmable logic controller (PLC), as the main controller, to a conveyor belt and two FANUC industrial robotic arms via EtherNet/IP. Human interaction is implemented to pick a work piece from the moving conveyor and return it with a quality label. This label is used by the PLC to execute the correct robot action; either to return the inspected part to the conveyor or discard it …

Channel Prediction For Underwater Acoustic Communication: A Review And Performance Evaluation Of Algorithms, Haotian Liu, Lu Ma, Zhaohui Wang, Gang Qiao

Michigan Tech Publications

Underwater acoustic (UWA) channel prediction technology, as an important topic in UWA communication, has played an important role in UWA adaptive communication network and underwater target perception. Although many significant advancements have been achieved in underwater acoustic channel prediction over the years, a comprehensive summary and introduction is still lacking. As the first comprehensive overview of UWA channel prediction, this paper introduces past works and algorithm implementation methods of channel prediction from the perspective of linear, kernel-based, and deep learning approaches. Importantly, based on available at-sea experiment datasets, this paper compares the performance of current primary UWA channel prediction algorithms …

V2i-Based Adaptive Collision Avoidance For Safety And Traffic Efficiency, Vaishnavi Balambeed

Dissertations, Master's Theses and Master's Reports

To leverage the growing communication and connectivity among modern vehicles, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) systems are increasingly being used to implement active safety applications. However, current research often overlooks the impact of algorithms such as collision avoidance on traffic flow efficiency. This work investigates the adaptation of a collision avoidance algorithm implemented in V2I to incorporate a variable time headway and spacing control strategy. The proposed approach aims to maintain higher average speeds among vehicles, lower individual vehicle’s waiting, and travel times in the vicinity of the infrastructural unit while simultaneously avoiding collisions; thereby enhancing both safety and traffic …

Implementing Associative Learning Using Neuromorphic Robot, Vinay Kumar Pillalamarri

Dissertations, Master's Theses and Master's Reports

Associative learning, a key cognitive process seen across the animal kingdom, enables organisms to form connections between stimuli and adapt their behaviors based on past experiences. A particularly powerful example is fear conditioning, where animals learn to associate a neutral stimulus with an aversive one, allowing them to predict and avoid potential threats. Inspired by this mechanism, this project implements associative learning on an unmanned ground vehicle (UGV) to develop adaptive behavior through neuromorphic principles. Utilizing Nengo for neural modeling, the UGV learns to associate visual (red color) and tactile (vibration) stimuli through Hebbian learning, a biologically inspired synaptic adaptation …

Application Of Fusion Based Deep Learning Models To Improve Millimeter Wave Beamforming, Abishek Subramanian

Dissertations, Master's Theses and Master's Reports

This study addresses the challenge of selecting millimeter Wave (mmWave) beamforming pairs for vehicle-to-infrastructure (V2I) communication, to mitigate latency in highly dynamic vehicular environments. We investigate the use of out-of-band sensor data as side information to model mmWave ray tracing paths and predicting a subset of top-K optimal beamforming pairs for efficient and low-latency searches. Unimodal-Fusion Deep Learning (F-DL) networks was applied to enhance mmWave beamforming process. We started by first investigating the centralized architecture, and then explored a novel distributed architecture through federated learning to minimize resource and latency overheads. The distributed architecture incorporates two biased client selection strategies: …

Data Driven And Machine Learning Based Modeling And Predictive Control Of Combustion At Reactivity Controlled Compression Ignition Engines, Behrouz Khoshbakht Irdmousa

Dissertations, Master's Theses and Master's Reports

Reactivity Controlled Compression Ignition (RCCI) engines operates has capacity to provide higher thermal efficiency, lower particular matter (PM), and lower oxides of nitrogen (NOx) emissions compared to conventional diesel combustion (CDC) operation. Achieving these benefits is difficult since real-time optimal control of RCCI engines is challenging during transient operation. To overcome these challenges, data-driven machine learning based control-oriented models are developed in this study. These models are developed based on Linear Parameter-Varying (LPV) modeling approach and input-output based Kernelized Canonical Correlation Analysis (KCCA) approach. The developed dynamic models are used to predict combustion timing (CA50), indicated mean effective pressure (IMEP), …

Transient Simulations Of Power Systems With Inverter Interfaced Resources, Gaurish Shreedhar Gokhale

Dissertations, Master's Theses and Master's Reports

Renewable energy sources are interfaced with the electrical grid using power electronic inverters. These inverter-interfaced resources have been deployed for nearly 20 years. Still, NERC only recently highlighted the vast gap between the actual behavior of these inverters during power system transients and those observed in simulations. Simulation models need significant improvements, mainly for developing accurate inverter current controls, phase-locked loops, and fault response during different power priority modes. Additionally, only time-domain electromagnetic transient simulation tools can fully represent the fault response of the inverter-interfaced resources.

The developed simulation model of the inverter-interfaced resource is based on the recommendations made …

The Integration Of Neuromorphic Computing In Autonomous Robotic Systems, Md Abu Bakr Siddique

Dissertations, Master's Theses and Master's Reports

Deep Neural Networks (DNNs) have come a long way in many cognitive tasks by training on large, labeled datasets. However, this method has problems in places with limited data and energy, like when planetary robots are used or when edge computing is used [1]. In contrast to this data-heavy approach, animals demonstrate an innate ability to learn by communicating with their environment and forming associative memories among events and entities, a process known as associative learning [2-4]. For instance, rats in a T-maze learn to associate different stimuli with outcomes through exploration without needing labeled data [5]. This learning paradigm …

Developing Robust Autonomous Vehicles With Ros, Dylan J. Kangas

Dissertations, Master's Theses and Master's Reports

The demand for autonomous vehicles (AVs) is rising across both military and civilian sectors. These unmanned systems offer numerous advantages, such as improved efficiency, safety, and adaptability. Addressing this demand requires the development of resilient and versatile autonomous vehicles crucial for the transport and reconnaissance markets.

The sensory perception of autonomous vehicles of any kind is paramount to their ability to navigate and localize in their environment. Factors such as sensor noise, erroneous readings, and deliberate attacks should all be considered when developing a robust autonomous system. This work aims to quantify the degradation of sensor data which causes mapping …