Digital Commons Network^™

Multikernel Embedded Fusion Unet (Mkef-Unet): A Robust Deep Learning Approach For Accurate Segmentation Of Chagas Parasites, Preet Kumar, Carlos Brito-Loeza, Lavdie Rada

Turkish Journal of Electrical Engineering and Computer Sciences

This paper introduces a novel approach for segmenting Chagas parasites on stained blood smear samples from mice during the acute phase of infection with Trypanosoma cruzi utilizing a U-Net-based deep learning model named multikernel embedded fusion UNet (MKEF-UNet). Our proposed model incorporates DenseNet-121 for feature extraction, a classifier module for predicting parasite information, and a segmentation decoder with multiscale feature fusion to generate precise segmentation results. Notably, the integration of the embedded vector module, multikernel convolutions with dilations, and advanced data augmentation techniques significantly enhance the model’s robustness and generalization capabilities. In extensive experiments on the Chagas dataset, MKEF-UNet achieves …

The Artificial Intelligence-Enhanced Echocardiographic Detection Of Congenital Heart Defects In The Fetus: A Mini-Review, Khadiza Tun Suha, Hugh Lubenow, Stefania Soria-Zurita, Marcus Haw, Joseph Vettukattil, Jingfeng Jiang

Michigan Tech Publications

Artificial intelligence (AI) is rapidly gaining attention in radiology and cardiology for accurately diagnosing structural heart disease. In this review paper, we first outline the technical background of AI and echocardiography and then present an array of clinical applications, including image quality control, cardiac function measurements, defect detection, and classifications. Collectively, we answer how integrating AI technologies and echocardiography can help improve the detection of congenital heart defects. Particularly, the superior sensitivity of AI-based congenital heart defect (CHD) detection in the fetus (>90%) allows it to be potentially translated into the clinical workflow as an effective screening tool in …

New Method Of Impact Localization On Plate-Like Structures Using Deep Learning And Wavelet Transform, Asaad Migot, Ahmed Saaudi, Victor Giurgiutiu

Faculty Publications

This paper presents a new methodology for localizing impact events on plate-like structures using a proposed two-dimensional convolutional neural network (CNN) and received impact signals. A network of four piezoelectric wafer active sensors (PWAS) was installed on the tested plate to acquire impact signals. These signals consisted of reflection waves that provided valuable information about impact events. In this methodology, each of the received signals was divided into several equal segments. Then, a wavelet transform (WT)-based time-frequency analysis was used for processing each segment signal. The generated WT diagrams of these segments’ signals were cropped and resized using MATLAB code …

Data-Driven Fusion Of Earth Observation And Hydrologic Data To Forecast Flood Extents At Regional To Continental Scales, Kel N. Markert

Theses and Dissertations

Accurate and timely flood inundation forecasts are crucial for mitigating the devastating impacts of floods, which affect 1.47 billion people globally and cause significant economic losses. While traditional flood forecasting methods, such as hydrologic and hydrodynamic models, have limitations in providing comprehensive and spatially explicit inundation information, recent research has focused on leveraging Earth observation data and machine learning for enhanced flood prediction. This dissertation advances the state-of-the-art in data-driven flood inundation forecasting by exploring the capabilities and scalability of the Forecasting Inundation Extents using Rotated Empirical Orthogonal Function (FIER) framework. This dissertation focuses on investigating different aspects of FIER …

A Gaze-Driven Manufacturing Assembly Assistant System With Integrated Step Recognition, Repetition Analysis, And Real-Time Feedback, Haodong Chen, Niloofar Zendehdel, Ming C. Leu, Zhaozheng Yin

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Modern manufacturing faces significant challenges, including efficiency bottlenecks and high error rates in manual assembly operations. To address these challenges, we implement artificial intelligence (AI) and propose a gaze-driven assembly assistant system that leverages artificial intelligence for human-centered smart manufacturing. Our system processes video inputs of assembly activities using a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network for assembly step recognition, a Transformer network for repetitive action counting, and a gaze tracker for eye gaze estimation. The application of AI integrates the outputs of these tasks to deliver real-time visual assistance through a software interface that displays …

Neurosymbolic Knowledge-Grounded Planning And Reasoning In Ai Systems, Amit Sheth, Vedant Khandelwal, Kaushik Roy, Vishal Pallagani, Megha Chakraborty

Faculty Publications

To build AI systems capable of decision-support assistance, such as AI-assisted healthcare, it is essential to develop user-centric decision-making processes that are robust, interpretable, and capable of effectively processing and acting on natural language interactions. Instruction-based prompting of large language models has demonstrated considerable success in supporting humans with information assistance tasks, including creative writing and content generation. However, recent studies reveal that language models exhibit limitations in performing complex reasoning and planning tasks, such as constructing compositional or hierarchical plans involving multiple reasoning steps. To address these challenges, we propose a neurosymbolic framework that integrates large language models with …

Real-Time Defect Detection And Classification In Robotic Assembly Lines: A Machine Learning Framework, Fadi El Kalach, Mojtaba Farahani, Thorsten Wuest, Ramy Harik

Faculty Publications

Manufacturing systems have witnessed a significant transformation with the introduction of Industry 4.0, introducing new capabilities with the emergence of new technologies. One such instance is the proliferation of sensors enabling the generation and acquisition of vast amounts of data, leading to advancements in Artificial Intelligence (AI) for manufacturing. One field profiting from this is that of Time Series Analytics (TSC) which includes forecasting and classification. TSC can be crucial for fault detection and diagnosis in manufacturing systems. However, there are still challenges in utilizing manufacturing datasets to train and deploy classification algorithms for real time classification. As such this …

Msbzip55 Regulates Salinity Tolerance By Modulating Melatonin Biosynthesis In Alfalfa, Tingting Wang, Jiaqi Yang, Jiamin Cao, Qi Zhang, Huayue Liu, Peng Li, Yizhi Huang, Wenwu Qian, Xiaojing Bi, Hui Wang, Yunwei Zhang

Faculty Publications

No abstract provided.

A Comparison Of Unconventional Microwave And Ultrasound-Assisted Extraction Methods Used For Flavonoids, Istiqomah Rahmawati, Daffa Hafiziaulhaq Azizi, Jihan Nafila Wibowo, Muhammad Reza, Boy Arief Fachri, Bekti Palupi, Meta Fitri Rizkiana, Helda Wika Amini, Ifan Ramadana, Felix Arie Setiawan

Makara Journal of Science

Cocoa pods (Theobroma cacao L.) are a rich source of flavonoids, which are natural antioxidants known for their health benefits. This study investigated the use of microwave-assisted extraction (MAE) and ultrasound-assisted ex-traction (UAE) to extract the maximum flavonoids and antioxidants from cocoa pods. MAE and UAE are efficient and sustainable methods for extracting bioactive compounds like flavonoids and antioxidants from cocoa pods, offer-ing faster extraction, reduced solvent use, and better compound preservation compared to conventional methods. These technologies unlock the untapped potential of cocoa pods for applications in food, cosmetics, and pharmaceuti-cals. The effects of extraction time (2–10 min), microwave …

Cost-Effective Active Laser Scanning System For Depth-Aware Deep-Learning-Based Instance Segmentation In Poultry Processing, Pouya Sohrabipour, Chaitanya Palleria, Amirreza Davar, Siavash Mahmoudi, Philip G. Crandall, Wan Shou, Yu She, Dongyi Wang

Biological and Agricultural Engineering Faculty Publications and Presentations

The poultry industry plays a pivotal role in global agriculture, with poultry serving as a major source of protein and contributing significantly to economic growth. However, the sector faces challenges associated with labor-intensive tasks that are repetitive and physically demanding. Automation has emerged as a critical solution to enhance operational efficiency and improve working conditions. Specifically, robotic manipulation and handling of objects is becoming ubiquitous in factories. However, challenges exist to precisely identify and guide a robot to handle a pile of objects with similar textures and colors. This paper focuses on the development of a vision system for a …

Challenges In Artisanal Small-Scale Gold Mining: Perspectives And Transformations To Sustainability Along Br-163 In Brazil, Carlos Henrique Xavier Araujo, Irfan Ullah, Giorgio De Tomi

Journal of Sustainable Mining

Artisanal Small-scale Gold Mining (ASGM) is a sector beset with unique and complex challenges. Recent literature highlights the growing acknowledgment of the critical need for reforms in how the ASGM industry interacts with communities and the environment. Discussions about sustainable transformations go beyond theoretical and conceptual borders, attempting to understand how local transformative events might reflect global developments. Thus, the goal of this article is to give an analysis from the perspective of the actors participating in artisanal gold mining activities. A survey was carried out along BR-163, which runs from Sinop (Mato Grosso) to Santarém (Pará). Fifty-five (55) interviews …

Modeling And Simulations Of An Input Coupler For A High-Power Traveling Wave Tube Amplifier As A Preliminary Investigation Into A Multi-Stream Traveling Wave Tube Amplifier, Ethan Wade

Electrical and Computer Engineering ETDs

The University of New Mexico (UNM) is working on developing a multi-stream Traveling Wave Tube (TWT) amplifier. One of the first steps to be taken during the design of a TWT amplifier is to design an input coupler to couple power from a lower power High Power Microwave (HPM) seed source to the slow wave structure (SWS) of the amplifier. In order to aid in the development of a TWT which operates in X-band, multiple options for input couplers have been designed for use in a greater TWT project at UNM. The primary tasks to be addressed with these input …

Flapping Dynamics And Wing Flexibility Enhance Odor Detection In Blue Bottle Flies, Naeem Haider, Zhipeng Lou, Chengyu Li

Faculty Scholarship

One of the most ancient and evolutionarily conserved behaviors in the animal kingdom involves utilizing wind-borne odor plumes to track essential elements such as food, mates, and predators. Insects, particularly flies, demonstrate a remarkable proficiency in this behavior, efficiently processing complex odor information encompassing concentrations, direction, and speed through their olfactory system, thereby facilitating effective odor-guided navigation. Recent years have witnessed substantial research explaining the impact of wing flexibility and kinematics on the aerodynamics and flow field physics governing the flight of insects. However, the relationship between the flow field and olfactory functions remains largely unexplored, presenting an attractive frontier …

Direct Water Reuse: A Hydraulic Economic Analysis Of Connecting The Wastewater Treatment Effluent To Water Treatment Influent, Brian Barkdoll, Amber G. Strutz

Michigan Tech Publications

Climate change is causing increased flooding and droughts. Droughts can cause drinking water sources to run dry. Therefore, recycling water from the wastewater plant effluent to the water treatment plant influent, also called ‘direct reuse’ is becoming necessary. A connection between a city's wastewater treatment plant (WWTP) effluent and water treatment plant (WTP) influent via pipe was simulated to provide an understanding of the capital costs and feasibility of execution. Hydraulic pipe-flow and pump equations were used to calculate the pipe and pump sizes needed for various flow rate and elevation head values. Various flow rate recycle rates were modeled …

Solid Wastes From Geothermal Energy Production And Implications For Direct Lithium Extraction, William T. Stringfellow, Mary K. Camarillo

Pacific Faculty Work

Direct lithium extraction (DLE) of brines after geothermal power production offers opportunities to produce environmentally benign “green” lithium; however, some environmental impact is inevitable. We examined solid waste production at geothermal power plants in southern California that are also locations for planned DLE facilities. Currently, the geothermal plants in this region produce approximately 79,800 metric tons (wet weight) per year of solid waste, which represents about 28 metric tons per GWh of net electricity production or approximately 500 mg solids per kg geothermal brine. Approximately 15% of this waste requires management as hazardous waste. Solids produced during power production represent …

Intelligent Turning Cyber-Physical Systems Modeling Using Sysml, Prithbey Raj Dey, David Lee Enke, Mario F. Buchely

Engineering Management and Systems Engineering Faculty Research & Creative Works

Cyber-Physical Systems (CPS) support industrial automation that incorporates people, hardware, signal, computation, and control using networking to achieve desired results. The complex automated CPS design demands a standard and comprehensive approach to appropriately identify the system requirements, define the architecture, and model the relationships among the software and hardware components. Systems Modeling Language (SysML) provides the capability for a comprehensive modeling to capture the desired design requirements in the systems architecture. SysML enables performance estimation of the model by analyzing constraints while identifying interactions among the components through various behavioral diagrams. In this paper, SysML is applied to the design …

Martensitic Transformation Induced Strength-Ductility Synergy In Additively Manufactured Maraging 250 Steel By Thermal History Engineering, Shahryar Mooraj, Shuai Feng, Matthew Luebbe, Matthew Register, Jian Liu, Tianyi Li, Baris Yavas, David P. Schmidt, Matthew W. Priddy, Michael B. Nicholas, Victor K. Champagne, Mark Aindow, Haiming Wen, Wen Chen

Materials Science and Engineering Faculty Research & Creative Works

Maraging steels are known for their exceptional strength but suffer from limited work hardening and ductility. Here, we report an intermittent printing strategy to tailor the microstructure and mechanical properties of maraging 250 steel via tuning the thermal history during wire-arc directed energy deposition. By introducing a dwell time between adjacent layers, the maraging 250 steel is cooled below the martensite start temperature, triggering thermally driven martensitic transformation during the printing process. Thermal cycling during subsequent layer deposition results in the formation of reverted austenite which shows a refined microstructure and induces elemental segregation between martensite and reverted austenite. The …

Time-Series Forecasting In Smart Manufacturing Systems: An Experimental Evaluation Of The State-Of-The-Art Algorithms, Mojaba A. Farahani, Fadi El Kalach, Austin Harper, M.R. Mccormick, Ramy Harik, Thorsten Wuest

Faculty Publications

Time-Series Forecasting (TSF) is a growing research area across various domains including manufacturing. Manufacturing can benefit from Artificial Intelligence (AI) and Machine Learning (ML) innovations for TSF tasks. Although numerous TSF algorithms have been developed and proposed over the past decades, the critical validation and experimental evaluation of the algorithms hold substantial value for researchers and practitioners and are missing to date. This study aims to fill this research gap by providing a rigorous experimental evaluation of the state-of-the-art TSF algorithms on thirteen manufacturing-related datasets with a focus on their applicability in smart manufacturing environments. Each algorithm was selected based …

The Interplay Between Endothelial Glycocalyx Maturity And Both The Toxicity And Intracellular Uptake Of Charged Nanoparticles, Claire A. Bridges, Lu D. Fu, Jonathan A. Yeow, Xiaojing D. Huang, Miriam D. Jackson, Rhiannon P. Kuchel, James D. Sterling, Shenda M. Baker, Megan S. Lord

Business and Information Technology Faculty Research & Creative Works

Nanoparticles are widely studied for delivering treatments to target tissues, but few have reached clinical use. Most nanoparticles encounter blood vessels on their way to target tissues. The inner surface of these vessels is lined with endothelial cells covered by a glycocalyx, an extracellular matrix rich in anionic glycans. The role of the glycocalyx in nanoparticle interactions is not well understood. Here, we demonstrate that endothelial cells need extended culture times to synthesize a mature glycocalyx. Our research shows that branched polyethyleneimine functionalized gold nanoparticles bind to endothelial cells expressing either a developing or mature glycocalyx, with the interaction involving …

Enhancing Mucus Flow And Clearance By Grafting Zwitterionic Hydrogel Films To Luminal Surfaces, Ryan Horne, Caleb Escudero, Morgan Ellerman, C. Allan Guymon, Marlan Hansen

Faculty Publications

Objective

To determine the effects of zwitterionic hydrogel films on mucus contact angles, flow, and stasis with respect to medical polymer surfaces, both flat and tubular.

Methods

A zwitterionic hydrogel thin film was photografted onto medical rubber surfaces and compared against non-zwitterionic hydrogel thin films and untreated surfaces to determine its impact on mucus contact angles, mucus flow on sheets and tubes, and mucus plugging.

Results

Zwitterionic and conventional hydrogel films significantly reduce the mucus contact angles and the tilt required to initiate mucus flow on sheets and in tubular systems. Preliminary experiments show that these films may also shorten …

Layout-Aware Quantum Circuitry And Algorithmic Extensions To Grover's Algorithm, Ali Al-Bayaty

Dissertations and Theses

Lov K. Grover introduced, in 1996, Grover's algorithm as a quantum search algorithm to find all solutions for quantum oracles representing classical problems. My research observed that the Grover diffusion operator of Grover's algorithm gives wrong solutions when Boolean oracles are designed in some logical structures. Therefore, I invented the new "controlled-diffusion operator" for Boolean oracles as a new approach for Grover's algorithm that always correctly solves the problem of Grover's algorithm.

Another important problem in quantum computing is designing reliable and cost-effective quantum gates using reversible binary logic. In classical logic design, the stage of logic design can be …

Dynamic Spectral And Systems-Theoretic Approaches For Inference And Control Of Heterogeneous Complex Networks, Bharat Kumar Singhal

McKelvey School of Engineering Graduate Student Theses & Dissertations

Networks of nonlinear systems are commonly employed to describe a diverse range of phenomena across physics, engineering, neuroscience, and biology. The undesirable behaviors of such systems, in the form of neurological disorders, power grid failures, or ecological collapses, have spurred significant interest in understanding their dynamic structures and developing effective control strategies. These systems are typically large-scale, consist of heterogeneous units, and are partially observable with limited measurement data, presenting theoretical and computational challenges for control design and connectivity inference. This thesis addresses these challenges by developing novel algorithms for pattern formation in populations of stable limit-cycle oscillators and connectivity …

Dynamic Spectral And Systems-Theoretic Approaches For Inference And Control Of Heterogeneous Complex Networks, Bharat Kumar Singhal

McKelvey School of Engineering Graduate Student Theses & Dissertations

Networks of nonlinear systems are commonly employed to describe a diverse range of phenomena across physics, engineering, neuroscience, and biology. The undesirable behaviors of such systems, in the form of neurological disorders, power grid failures, or ecological collapses, have spurred significant interest in understanding their dynamic structures and developing effective control strategies. These systems are typically large-scale, consist of heterogeneous units, and are partially observable with limited measurement data, presenting theoretical and computational challenges for control design and connectivity inference. This thesis addresses these challenges by developing novel algorithms for pattern formation in populations of stable limit-cycle oscillators and connectivity …

Conversational Voice User Interfaces Supporting Individuals With Down Syndrome: A Literature Review, Franceli L. Cibrian, Concepción Valdez, Lauren Min, Vivian Genaro Motti

Engineering Faculty Articles and Research

Conversational Voice User Interfaces (CVUIs) are widely used in commercial applications such as personal assistants. CVUIs are beneficial for most users as they enable interaction through speech and natural language. However, recent studies indicate that underrepresented user groups, such as individuals with speech impairments and specifically those with Down syndrome, face challenges in using voice commands to control CVUIs. The anatomical and physiological differences affecting the voice, speech, fluency, and prosody of users with Down syndrome hinder their experience with CVUIs. This article presents the results of 43 papers related to the use of voice user interfaces supporting individuals with …

Friction-Based Recycling: An Evaluation Of Friction Extrusion For Fabricating Ti-6al-4 V Wire Fabricated From Machining Chip Feedstock, Devesh Kumar Chouhan, Mageshwari Komarasamy, Scott B. Taysom, Nicole R. Overman, Nathan L. Canfield, Timothy J. Roosendaal, Anthony P. Reynolds, Scott A. Whalen

Faculty Publications

Titanium and its alloys are used in aviation and automobile industries due to their remarkable strength to weight ratio, but machining loss commonly is high with ~ 80 wt% of the material being converted to scrap. Recycling post-consumer Ti scrap directly into solid bulk products is a potential solution for repurposing valuable material. Further, eliminating fresh Ti sponge during recycling might lead to lower energy and greenhouse gas emissions. In this study, a solid-phase process known as friction extrusion was utilized to recycle Ti-6Al-4 V machining chips into solid wires which could be used as feedstock in additive manufacturing. The …

Experiment-Based Superposition Thermal Modeling Of Laser Powder Bed Fusion, Cody S. Lough, Tao Liu, Robert G. Landers, Douglas A. Bristow, James A. Drallmeier, Ben Brown, Edward C. Kinzel

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Parts experience significant local thermal variations during the Laser Powder Bed Fusion (LPBF) metal Additive Manufacturing (AM) process, providing a potential source of defects. Near real-time thermal predictions can enable better process planning and facilitate corrections on subsequent layers to enable the engineering of laser parameter and scan path combinations that avoid defect inducing scenarios. This paper considers an experiment-based Discrete Green's Function (DGF) thermal model for temperature field prediction in LPBF. An analytical framework is developed and used to calculate an experimental DGF (i.e., powder bed's single pulse temperature response) from spatiotemporal Short-Wave Infrared (SWIR) camera data. The extracted …

Mg And Fe Co-Doped Perovskites For The Electrochemical Oxidative Coupling Of Methane In Solid Oxide Electrolyzers, Luke H. Denoyer

Chemical and Biological Engineering ETDs

More efficient usage of shale gas reserves and natural gas resources will allow for higher olefin yields and lower greenhouse gas emissions. The oxidative coupling of methane (OCM) is a direct pathway for converting methane to ethylene at temperatures >700 °C. The OCM process is typically regulated via the choice of catalyst, reactor conditions, temperature range and gas mixing. The electrochemical oxidative coupling of methane (E-OCM) is a modification to the OCM reaction by controlling the oxygen content via externally applied potentials to an SOEC. Perovskite oxide catalysts are useful for E-OCM due to their flexible bond networks and desired …

Error-Based Dynamic Velocity Range Of Piv Processing Algorithms, Gauresh Raj Jassal, Bryan E. Schmidt

Faculty Scholarship

The ability of PIV processing algorithms to accurately determine velocity vectors across the range of motion present in PIV images is characterized by the algorithm’s dynamic velocity range (DVR). Conventionally, the DVR of PIV is defined using the ratio between the maximum and minimum resolvable particle displacements, with the minimum based on the uncertainty in the location of a single particle in the optical system. In this work, it is demonstrated that this definition is inadequate in practice, as it ignores many factors which affect the accuracy of an algorithm when determining small displacements, and the error in vectors with …

Uncertainty Propagation And Sensitivity Analysis For Constrained Optimization Of Nuclear Waste Vitrification, Lagrande Gunnell, Xiaonan Lu, John D. Vienna, Dong-Sang Kim, Brian J. Riley, John Hedengren

Faculty Publications

The vitrification of high-level waste (HLW) by heating a mixture of glass-forming chemicals (GFCs) with the waste can be improved using a constrained optimization problem. This study explores how different uncertainty propagation (UP) methods implemented with the optimization process can affect the glass formulation of nuclear waste glasses. UP is the effort of propagating uncertain inputs through a system to understand and quantify output distributions. Uncertainty intervals are crafted from output distributions to inform the optimization algorithm. UP is often implemented with Monte Carlo (MC) sampling for large nonlinear systems, which can be difficult to implement within a constrained optimization …

Understanding Ionic Transport In Perovskite Lithium-Ion Conductor Li3/8Sr7/16Ta3/4Hf1/4O3: A Neutron Diffraction And Molecular Dynamics Simulation Study †, Danyi Sun, Nan Wu, Yeting Wen, Shichen Sun, Yufang He, Ke Huang, Cheng Li, Bin Ouyang, Ralph E. White, Kevin Huang

Faculty Publications

Solid-state Li-ion electrolytes (SSEs) are essential for the development of next-generation solid-state Li-metal batteries and new Li-extraction electrochemical cells. Among these, the perovskite-type SSE Li3/8Sr7/16Ta3/4Hf1/4O3 (LSTH) has garnered attention for Li-extraction applications, owing to its outstanding chemical and thermal stability and high ionic conductivity. However, its precise crystal structure and Li-ion transport mechanisms remain insufficiently understood. This study addresses these gaps by employing neutron diffraction to resolve LSTH's crystallography and machine learning force field (MLFF) based MD simulations to elucidate ionic transport mechanisms. A single-phase LSTH, synthesized via the sol–gel method, exhibits a room-temperature bulk conductivity of 0.418 mS cm−1 …