Engineering Commons^™

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 28, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 30, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 35, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 36, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 37, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 77, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 77 Lidar, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Print, Pattern, Stick: Low–Cost Gecko–Inspired Adhesives Using Embedded Diffraction Structures, Motaz Hassan, Oluwafemi Fayomi, Ajay Mahajan, Joshua Faust

University Research

Gecko-inspired adhesives offer strong, reversible, and directionally tunable adhesion, yet fabrication methods often depend on cleanroom lithography or proprietary molds, limiting scalability and accessibility. This study presents a low-cost, modular fabrication strategy combining high-resolution digital light processing 3D printing with 1000 lines/mm optical diffraction gratings to create hierarchical elastomeric adhesives. The resulting structures feature macroscale micropillars and embedded sub-micron surface topography, enabling effective contact splitting without advanced microfabrication. Mechanical testing reveals a nonlinear increase in shear performance with contact area, with maximum shear forces exceeding 80 N at 103.2 cm2. Peel testing across varied angles and surface areas demonstrates anisotropic …

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 22, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 24, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Challenges And Opportunities In Lentivirus Viral Vector Manufacturing For In Vivo Applications, Eduardo Barbieri, Caryn Heldt

Michigan Tech Publications

The clinical success of chimeric antigen receptor (CAR) T-cell therapies has revolutionized oncology, yet the high costs and logistical complexities of ex vivo manufacturing remain significant barriers to global patient access. In vivo cell therapy, which involves the direct injection of lentiviral vectors (LVVs) to engineer cells within the patient’s body, offers a promising, cost-effective alternative. However, transitioning from ex vivo to in vivo applications necessitates a fundamental shift in LVV biomanufacturing to ensure safety and efficacy. This paper examines the critical bottlenecks in the current LVV production landscape. In upstream processing, we explore LVV particle assembly and maturation mechanisms, …

Neurocore: A Gnn Approach To Configurable Ip Core Identification In Fpga Netlists, Dallin Dahl, Keenan Faulkner, James Usevitch, Jeffrey Goeders

Student Works

Netlist reverse engineering enables many applications, including detecting IP theft, verifying CAD tool correctness, and detecting hardware trojans. However, reconstructing high-level information and circuit structure from a flat, nameless netlist is challenging. In this work we focus on the problem of locating known IP cores in an FPGA netlist, which is especially challenging due to the prevalence of highly configurable IP cores. We present Neurocore: a graph neural network-based approach to classifying nodes in a netlist as instances of known IP cores, and present and evaluate different models for different use cases. We have created a large open-source dataset of …

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. …

Mycelium-Based Composites Using Minimally Processed Industrial Hemp Biomass: Impact Of Species And Feedstock Ratio On Mechanical Performance Compared To Polystyrene Packaging, Radika Bhaskar, Tanisha Rutledge, Kevin Trangone, Oneal Latimore

School of Design and Engineering Papers

Mycelium-based composites (MBCs\) are formed from lignocellulosic substrates and biopolymer matrices derived from fungal mycelium. Due to their low fossil energy demand and biodegradability, MBCs represent a versatile and sustainable material suitable for a range of applications, with increasing interest focused on packaging. Hemp fibers are an example of natural fibers with great promise as a substrate to improve the mechanical properties of MBCs. However, the separation of bast and hurd fiber requires processing and commercial-scale facilities that are logistically challenging and may be cost-prohibitive. Here, the potential for minimally processed hemp, with no separation of fibers, is evaluated for …

Patient‐Specific Computational Flow Simulation Reveals Adverse Hemodynamic Factors Associated With Occlusion Of Directional Branches After Fenestrated‐Branched Endovascular Aneurysm Repair, Kenneth Tran, Jesse Chait, Emmanuel Tenorio, Weiguang Yang, Alison Marsden, Bernardo Mendes, Jason T. Lee, Gustavo S. Oderich

Mechanical Engineering Faculty Publications

Background: Fenestrated and branched endovascular aneurysm repair can be complicated by branch vessel occlusion in the absence of structural stenosis. We hypothesized that computational flow simulation could identify adverse hemodynamic features associated with postfenestrated and branched endovascular aneurysm repair branch occlusion.

Methods: Patients undergoing 4-vessel fenestrated and branched endovascular aneurysm repair for Extent II to IV thoracoabdominal aortic aneurysms were retrospectively reviewed. Branches that occluded without identifiable kinking or stenosis on computed tomography were included, along with an equal cohort of anatomy-matched patent controls. Patient-specific pulsatile rigid-wall simulations were performed using SimVascular with individualized geometries and boundary conditions. Abnormal time-averaged …

Data File For Kaizen Event Research, Jen Schoenherr

Michigan Tech Research Data

Research was performed to determine if Kaizen teams that used Cartooning as one of the lean tools available for problem solving were more successful in achieving the stated Kaizen goals

Construction Project Performance Research: A Bibliometric, Scientometric, And Qualitative Review (1989–2023), Abdelnaser Abdelhameed, Mohamed S. Yamany, Ahmed Abdelaty, Emad Elbeltagi

Faculty Publications

Despite the significant increase in publications on construction project performance (CPP), there is a deficiency of research that rigorously assesses and synthesizes previous studies to delineate the field’s development, themes, and research gaps. This article employs quantitative and qualitative methodologies to critically evaluate studies on CPP published over the last three decades and indexed in the Scopus database. The quantitative approach includes bibliometric searches and scientometric analyses to assess the extent of research interest and achievements. The qualitative methodology aims to conduct thorough content analysis to classify existing material based on prevalent themes. The results demonstrate an exponential growth of …

Ice-Templated Zwitterionic Sponge Hydrogels For Stable And Efficient Solar Desalination In High-Salinity Brines, Louis D. Zhang, Yanhui Zhang, Peng Xiao, Chang Zhang

University Research

Solar-driven steam generation (SSG) offers a sustainable pathway for desalination, yet achieving temperature-regulated control over macroporous structures in salt-tolerant hydrogels remains a critical challenge. Here, we report a carbon black-coated PDMAPS sponge hydrogel (PDMAPS-CB-SH) fabricated via an ice-templated polymerization strategy, where the pore size and connectivity are tuned by regulating ice-crystal growth at different prefreezing temperatures. The optimized PDMAPS-CB-SH integrates abundant interconnected pores with the intrinsic antipolyelectrolyte effect of zwitterionic networks, enabling rapid water transport and stable swelling in brines up to 10 wt % NaCl. Upon incorporation of carbon black nanoparticles, the hydrogel evaporator achieves a high evaporation rate …

After A Wildfire: Considerations For Building Environmental Testing, Andrew J. Whelton, E. Bollens, C. Ferrarezzi

Resilience to Emergencies and Disasters

No abstract provided.

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. …

Data For "Unified Vapor Pressure Correlations For Lanthanide And Actinide Chlorides: An In-Depth Statistical And Thermodynamic Approach", J. Marvin Torrie, Nicolas Marvin Christensen, Kyle Duke, Carlos Mejia, Larry Baxter, Devin Rappleye

ScholarsArchive Data

This submission contains machine readable data (.csv) for all data from the publication "Unified Vapor Pressure Correlations for Lanthanide and Actinide Chlorides: An In-depth Statistical and Thermodynamic Approach". It contains the data contained in all figures in the paper and its supplementary material except for the water vapor pressure data in Figures 1 and 2. In addition, this submission contains all the original vapor pressure measurements (241 sets) from the sources reviewed in the paper.  Sufficient information is provided that readers can identify the original sources of these data sets if needed.

High-Throughput Computational Framework For High-Order Anharmonic Thermal Transport In Cubic And Tetragonal Crystals, Zhi Li, Huiju Lee, Chris Wolverton, Yi Xia

Mechanical and Materials Engineering Faculty Publications and Presentations

Accurate first-principles prediction of lattice thermal conductivity (κ L) remains challenging in identifying materials with extreme thermal behavior. While the harmonic approximation with three-phonon scattering (HA + 3ph) is now routine, reliable κ L prediction often requires higher-order anharmonic effects, including self-consistent phonon renormalization, three- and four-phonon scattering, and off-diagonal heat flux (SCPH + 3, 4ph + OD). We present a state-of-the-art high-throughput workflow that unifies these effects and apply it to 773 cubic and tetragonal crystals spanning diverse chemistries and structures. From 562 dynamically stable compounds, we assess the hierarchical impacts of higher-order anharmonicity. For around 60% of materials, …

Use Of Gamma-Ray Spectroscopy In Thickness Gauging Of A Complex-Shaped Lead Shield, Joseph T. Graham, Brian Durtschi, Ashish Avachat, Seth Kilby

Nuclear Engineering and Radiation Science Faculty Research & Creative Works

Methods for measuring the thickness of lead shielding based on ⁶⁰Co gamma-ray spectroscopy are presented. In applications where a shield's thickness is multiple mean free paths and the shield has a complex shape (i.e. cannot be approximated as a simple solid such as a slab, sphere, semi-infinite medium, etc.), the necessary buildup factors are not available. Thus, determination of shield thickness by means of the Beer–Lambert law requires separating the counts from uncollided photons from the scattered photon contribution. It is demonstrated how the 1332 keV gamma ray of ⁶⁰Co can be used to precisely quantify lead thicknesses …

Modeling And Characterizing The Electron Backscatter In A Cylindrical Anode-Based Distributed X-Ray Source, Jordan Fox, Seth Kilby, Hyoung Koo Lee, Ayodeji Alajo, Ashish Avachat

Nuclear Engineering and Radiation Science Faculty Research & Creative Works

Upcoming advancements in computed tomography architectures warrants the investigation of new X-ray source designs and the impacts that electron backscatter can have on these designs. One such design being investigated is a distributed, cylindrical anode-based X-ray source. For such a distributed X-ray source, we developed a modeling pipeline for simulating electron optics and transport to characterize the quality of the primary X-ray beam and the electron backscatter behavior. We report our results on the energy distributions of the bremsstrahlung spectra; electron backscatter ratio; and spatial, temporal, and energy distributions of backscattered electrons that return to the anode.

Full Core Fuel Burnup Assessment Of The Itu Triga Mark Ii Research Reactor Using Gamma Spectroscopy, Z. Boduroglu, A. Kaya, O. Erbay, I. A. Reyhancan, M. S. Kiziltas, T. Akyurek

Nuclear Engineering and Radiation Science Faculty Research & Creative Works

This study investigates the burnup distribution of fuel elements in the ITU TRIGA Mark II research reactor core through gamma spectroscopy using¹³⁷Cs as a burnup indicator. Non-Destructive Assay (NDA) techniques were employed to analyze fuel depletion, utilizing a specialized fuel investigation system installed above the reactor pool. The results indicate that the highest burnup values, exceeding 5 %, were concentrated in the inner core rings (B and C) due to intense neutron flux exposure, while the outer rings (D, E, F) exhibited lower burnup levels. To address this asymmetric distribution, a proposed fuel reshuffling strategy was analyzed, aiming …

Numerical Investigation Of A Dual-Mode Shape Memory Alloy Stent Enabling Secondary Expansion Via Focused Ultrasound: A Potential Strategy For Correcting In-Stent Restenosis, Stephen Asare, Lucinda Duncan, Josiah Owusu-Danquah, Brian L. Davis

Civil and Environmental Engineering Faculty Publications

Coronary stent implantation is an invasive procedure performed to correct atherosclerosis. The aftermath of this procedure is identified with neointimal hyperplasia, which contributes to in-stent restenosis (ISR), and thus remains a significant clinical challenge. This study introduces a novel, noninvasive conceptual approach for addressing ISR through the thermal activation of shape memory alloy (SMA) stent using focused ultrasound (FU) in a controlled manner to restore luminal patency. COMSOL Multiphysics and ABAQUS finite element software were employed to perform the numerical modeling to simulate the thermal and mechanical responses of the SMA stent integrated into the arterial wall. After 15 s …

Nondestructive Burnup Evaluation And Gamma Spectroscopy Analysis Of Spent Fuel Elements In The Itu Triga Mark Ii Research Reactor, A. Kaya, O. Erbay, Z. Boduroglu, I. A. Reyhancan, M. S. Kiziltas, T. Akyurek

Nuclear Engineering and Radiation Science Faculty Research & Creative Works

This study presents a comprehensive burnup analysis of all fuel elements in the ITU TRIGA Mark II research reactor core using non-destructive assay (NDA) techniques based on gamma spectroscopy. Two distinct fuel inspection systems were employed to measure the gamma activity, with¹³⁷Cs used as the primary burnup indicator due to its strong correlation with fuel depletion. The results show a clear burnup pattern, with higher values in the inner core rings that gradually decreases toward the outer rings. This asymmetric burnup distribution underscores the need for reactor core reconfiguration, for which an optimized layout is proposed. Additionally, gamma …

Hybrid Data-Driven Cement-Stabilized Soil Design: An Integration Of Machine Learning, Multi-Objective Optimization, And Life Cycle Assessment, Chikezie Chimere Onyekwena, Yunli Li, Ikenna J. Okeke, Ubani Obinna Uzodimma, Monday Uchenna Okoronkwo, Wenping Wu

Chemical and Biochemical Engineering Faculty Research & Creative Works

Soil stabilization is crucial in geotechnical engineering, yet conventional methods are often time-consuming, resource-intensive, and environmentally unsustainable. Despite growing interest in Machine Learning (ML) and optimization tools for mix design, few studies integrate these methods with decision-making techniques and environmental assessment to support practical implementation. This study proposes a hybrid data-driven framework for predicting strength, optimizing mix compositions, and evaluating environmental impacts via life cycle assessment of cement-stabilized soft soils. Six ML models were evaluated, and the top-performing eXtreme Gradient Boosting (XGB) model was further improved using the Grey Wolf Optimizer (GWO). The optimized XGB-GWO model, integrated with a polynomial …

Nanocatalytic Enhancement Of Local Heat Transfer In Continuous-Flow Thermal Reactors, Nasser Zouli, Nujud Maslamani, Ayman Yousef, Muthanna H. Al-Dahhan

Chemical and Biochemical Engineering Faculty Research & Creative Works

An experimental investigation was conducted to evaluate the thermal conductivity (TC) and local heat-transfer coefficients (LHTCs) of nanofluids containing alumina (Al₂O₃), hematite (Fe₂O₃), and copper oxide (CuO) nanoparticles dispersed in deionized water. A newly developed non-invasive LHTC probe was integrated into the inner wall of the test section to enable direct quantification of interfacial heat-transfer performance. The measurements were conducted under laminar and turbulent flow conditons across Reynolds numbers ranging from 1000 to 10,000. The selected nanoparticles were chosen based on their high intrinsic thermal conductivity, cost effectiveness, and, in the case …

Generative Artificial Intelligence In Aircraft Design Optimization, Xiaosong Du

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Aircraft design optimization is essential for improving aircraft performance (such as reduced fuel consumption and lowered noise), which leads to more efficient, sustainable, and affordable aircraft. Conventional aircraft design adopts physics-based simulation models, but iteratively evaluating simulation models is computationally intensive, or even practically impossible. Meanwhile, artificial intelligence (AI) emerges as a revolutionary game changer in the modern engineering industry, including aircraft design optimization. Generative AI (genAI), one of the groundbreaking AI methods, has been advancing aircraft design optimization from various aspects, including intelligent parameterization, predictive modeling, training facilitation, and constraints handling. However, there is a lack of a review …