Engineering Commons^™

Current Research Progress On Electrode Materials For All-Vanadium Redox Flow Batteries, Wen-Qi Wang, Jie Jin, Li-Min Wang, Xin-Yue Liu, Tao Cheng, Yong Hou, Han Xue, Zhi-Yu Wang, Bo Liu, Jia-Bao Liu, Xu-Bin Lu

Journal of Electrochemistry

The redox active species in all-vanadium redox flow batteries (VRFBs) reside in the electrolyte, while the heterogeneous reactions occur on the electrode surface; the electrode is therefore the decisive platform for dynamic adsorption, electron transfer, and ion conversion, especially for the VO²⁺/VO₂⁺ and V²⁺/V³⁺ couples. One of the major challenges for VRFBs is the slow charge transfer in VO²⁺/VO₂⁺ and V²⁺/V³⁺ reactions, mainly caused by poor catalytic performance of electrodes and weak adhesion of catalysts to electrodes. This review focuses on the key challenges and recent …

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 …

Remote Sensing Of Dynamic Ground Motion Via A Moiré-Based Apparatus, Adrian Ali Moazzam, Nontawat Srisapan, Gregory Waite, Durdu Guney, Roohollah Askari

Michigan Tech Publications

Highlights: What are the main findings? A Moiré-based optical apparatus enables long-range, non-contact ground displacement measurement in hazardous environments. Controlled indoor and outdoor experiments demonstrate reliable detection of dynamic and seismic-like ground motions with sub-millimeter resolution. What are the implications of the main findings? System performance is evaluated under atmospheric turbulence and wind, revealing key limits and mitigation strategies for field deployment. The proposed approach provides a low-cost, scalable complement to traditional seismic and geodetic monitoring techniques. Ground-based remote sensing of seismic and geophysical displacements remains a major challenge due to environmental hazards, signal attenuation, and practical deployment limitations of …

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

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 …

Strategic Research On Rebco High-Temperature Superconducting Tapes, Tianping Ying, Dongliang Wang, Pengtao Yang, Lin Zhao, Zhongtang Xu, Ziyi Liu, Chao Yao, Yanwei Ma, Xingjiang Zhou, Jinguang Cheng, Zhong Fang

Bulletin of Chinese Academy of Sciences (Chinese Version)

High-temperature superconducting materials, represented by REBCO (REBa2Cu3O7-δ, where RE denotes rare-earth elements), are of significant strategic importance in fields such as energy, healthcare, and large-scale scientific facilities due to their excellent performance in the liquid nitrogen temperature range. However, they still face severe challenges in long-tape uniformity, production cost, and engineering reliability. Future development must shift toward a “material-processing-application” collaborative innovation model, aiming at enhancing flux pinning, optimizing the interfaces and mechanical properties of the multilayer structure, and integrating scalable and intelligent fabrication technologies to promote the low-cost and stable production of high-performance tapes. This study analyzes the core application …

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 …

Contrasting Coastal Dune Environments In Chile, Aurora Christianson

Student Research Symposium (SRS)

Emerging coastalization and urbanization threats to the prehistoric Concón Dunes and Humedal de Mantagua coastal area of Chile is being investigated by researchers via uncrewed aircraft systems (UAS). Four UAS were utilized: Anzu Raptor T, DJI Mavic 3E, DJI Mavic 3M, and DJI Air 3 to collect various images of the coastal dunes. Multispectral and RGB cameras gather images by photogrammetry to create orthomosaics and monitor vegetation indexes in Pix4Dmapper. Thermal cameras provided images in rainbow, white hot infrared, and black hot infrared schemes to monitor wildlife and vegetation. The normalized difference vegetation index (NDVI) was calculated to visualize overall …

Numerical Investigation Of Highly Efficient Chlorine-Doped Perovskite Solar Cells, Md Abdul K Sheikh, Md Shazarul Islam, Hasina Huq

Electrical and Computer Engineering Faculty Publications

In this study, we present a comprehensive numerical investigation of chlorine-doped perovskite solar cells using the SCAPS-1D simulation framework, with the device structure ITO/ZnO/CH3NH3PbI3−xClx/NiOx/Au. This work focuses on optimizing active-layer properties and compositional engineering to enhance photovoltaic performance. Initially, the influence of absorber thickness on device parameters was investigated, revealing that CH3NH3PbI3 achieves optimal performance at 800 nm thickness, delivering a power conversion efficiency (PCE) of 24.17%, along with a short circuit current density (Jsc) of 25.31 mA cm−2, an open circuit voltage (Voc) of 1.15 V, and a fill factor (FF) of 82.75%. Subsequently, chlorine incorporation was systematically varied …

Design And Economic Assessment Of A Sustainable Standalone Photovoltaic System For Hospital Lift Applications, Tasneem M. Abed, Saad S. Eskander, Mohamed A. Elsayes, Mohamed Zaki

Mansoura Engineering Journal

This study presents an optimized design and operation strategy for a stand-alone solar photovoltaic power system to drive a 4.5 kW elevator at Mansoura University Hospital, Egypt. The research aims to reduce the battery bank capacity and overall system cost while maximizing the utilization of solar panels. A novel "Optimum Operator System" is proposed, incorporating three power switches controlled by an Arduino microcontroller. The system's performance is analyzed through extensive modeling, simulation, and experimental validation. Results show a significant reduction in battery bank capacity, with 7 fewer 100 Ah batteries required compared to conventional systems. The proposed method significantly enhances …

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 …

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 12, 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 16, 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 20, 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 34, 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 76, 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 11, 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 13, 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 14, 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 15, 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 17, 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 18, 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 23, 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 26, 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 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.