Engineering Commons^™

Design And Development Of Contactless Capacitive Coupled Electrodes For Cardiovascular Signal Acquisition, Ramya Lakshmi Vs

Theses and Dissertations

In biosignal acquisition and physiological monitoring, developing advanced electrode technologies is crucial in enhancing signal quality, minimizing interference, and improving overall reliability. One such innovative approach is the utilization of capacitive-coupled electrodes, a cutting-edge solution that addresses some of the challenges associated with traditional electrodes in biosignal recording.

Capacitive-based sensors have become prominent in physiological signal measurement over the past decade. The electric field from the external affects both the human body and the electrode. The primary aim while designing the capacitive electrode is to maximize the signal-to-noise ratio. Hence, we introduce shielding and guarding techniques to improve the signal-to-noise …

Optimal Planning And Design Of Operations And Maintenance Systems For Offshore Wind Farms, Md Imran Hasan Tusar

LSU Doctoral Dissertations

Feasible and sustainable sources of power are a critical issue nowadays. Offshore wind farms (OWF) can be a solution to power generation problems, but it is relatively more expensive— their installation cost is more than twice of their comparable size onshore counterparts. The cost that matters most is the operations and maintenance (O&M) cost. Expensive and sophisticated transfer vehicles and highly skilled technicians are needed to conduct O&M activities, resulting in a remarkably higher O&M cost for an offshore wind farm project. Offshore wind turbines can capture more wind than onshore turbines because of their larger structure and location in …

Pet And Polyolefin Plastics Supply Chains In Michigan: Present And Future Systems Analysis Of Environmental And Socio-Economic Impacts, Utkarsh S. Chaudhari, Kamand Sedaghatnia, Barbara K. Reck, Kate Maguire, Anne T. Johnson, David Watkins, Robert M. Handler, Tasmin Hossain, Damon S. Hartley, Vicki S. Thompson, Alejandra Peralta, Jenny L. Apriesnig, David Shonnard

Michigan Tech Publications

Many actions are underway at global, national, and local levels to increase plastics circularity. However, studies evaluating the environmental and socio-economic impacts of such a transition are lacking at regional levels in the United States. In this work, the existing polyethylene terephthalate and polyolefin plastics supply chains in Michigan were compared to a potential future (‘NextCycle’) scenario that looks at increasing Michigan’s overall recycling rate to 45%. Material flow analysis data was combined with environmental and socio-economic metrics to evaluate the sustainability of these supply chains for the modeled scenarios. Overall, the NextCycle scenario for these supply chains achieved a …

A Distributed And Hybrid Ai-Based Security Framework For 5g Real-Time Applications, Ali Ghubaish

McKelvey School of Engineering Graduate Student Theses & Dissertations

This dissertation develops a multifaceted security framework tailored for 5G-enabled real-time Internet of medical things (IoMT) systems to significantly enhance the security infrastructure within healthcare environments. The framework pivots around three core technological advancements: the development of the Light feature Engineering based on the Mean Decrease in Accuracy (LEMDA), the construction of a 5G testbed that serves as a distributed intrusion detection system (IDS), and the implementation of a hybrid deep reinforcement learning (HDRL) method. LEMDA represents a breakthrough in data processing for IoMT systems. By intelligently reducing data complexity, LEMDA enhances the speed and accuracy of threat detection mechanisms, …

Effects Of Rf Signal Eventization Encoding On Device Classification Performance, Michael J. Smith, Michael A. Temple, James W. Dean

Faculty Publications

The results of first-step research activity are presented for realizing an envisioned “event radio” capability that mimics neuromorphic event-based camera processing. The energy efficiency of neuromorphic processing is orders of magnitude higher than traditional von Neumann-based processing and is realized through synergistic design of brain-inspired software and hardware computing elements. Relative to event-based cameras, the development of event-based hardware devices supporting Radio Frequency (RF) applications is severely lagging and considerable interest remains in obtaining neuromorphic efficiency through event-based RF signal processing. In the Operational Technology (OT) protection arena, this includes efficient software computing capability to provide reliable device classification. A …

Robust Ai-Driven Segmentation Of Glioblastoma T1c And Flair Mri Series And The Low Variability Of The Mrimath© Smart Manual Contouring Platform, Yassine Barhoumi, Abdul Hamid Fattah, Nidhal Carla Bouaynaya, Fanny Moron, Jinsuh Kim, Hassan M. Fathallah-Shaykh, Rouba A. Chahine, Houman Sotoudeh

Henry M. Rowan College of Engineering Departmental Research

Patients diagnosed with glioblastoma multiforme (GBM) continue to face a dire prognosis. Developing accurate and efficient contouring methods is crucial, as they can significantly advance both clinical practice and research. This study evaluates the AI models developed by MRIMath© for GBM T1c and fluid attenuation inversion recovery (FLAIR) images by comparing their contours to those of three neuro-radiologists using a smart manual contouring platform. The mean overall Sørensen–Dice Similarity Coefficient metric score (DSC) for the post-contrast T1 (T1c) AI was 95%, with a 95% confidence interval (CI) of 93% to 96%, closely aligning with the radiologists’ scores. For true positive …

Distributed Conflict Detection And Optimal 4d Trajectory Resolution Leveraging Polynomial Based Methods, Michael Klinefelter, Austin Stone, Joshua Miller, Cameron K. Peterson, John Salmon

Student Works

This paper presents a methodology for distributed conflict detection and resolution of aircraft following time-dependent flight paths. We use parametric fifth-order polynomial splines to define the full, time-based paths of vehicles. This representation can be exploited to rapidly detect conflicts and calculate optimal resolution solutions that minimize deviations from the original path. Conflicts are identified using a Sturm sequencing procedure and resolutions are found using gradient-based optimization techniques. Simulations show the locally optimal resolution of complex multi-vehicle conflicts and large-scale scenarios. Also, a method of fitting the flight path model to data sets is presented and flight path trajectories are …

A Rechargeable Metal-Co2 Battery Using Lower-Cost Materials, Chris Fetrow

Nanoscience and Microsystems ETDs

High energy density, low-cost chemical conversion batteries are widely studied for their potential application to applications in which current battery technologies are unsuitable replacements for fossil fuel-powered systems. The demonstration of rechargeable chemical conversion batteries, and particularly batteries without the fatal flaw of high overpotential due to the difficulties inherent to chemical conversion reactions, is of great interest to further both this aim. In this work, a secondary Al-CO2 battery was demonstrated using a homogeneous aluminum iodide redox mediator to enable the battery’s discharge and charge at a high energy density and a low overpotential of 0.05 Volts. The operation …

Coordination Of Srf-Pll And Grid Forming Inverter Control In Microgrid With Solar Pv And Energy Storage, V. Vignesh Babu, J. Preetha Roselyn, Prabha Sundaravadivel

Electrical Engineering Faculty Publications and Presentations

Recently, there has been a huge advancement in renewable energy integration in power systems. Power converters with grid-forming or grid-following topologies are typically employed to link these decentralized power sources to the grid. However, because distributed generation has less inertia than synchronous generators, their use of renewable energy sources threatens the electrical grid's reliability. Suitable control approaches for ensuring frequency and voltage stability in the grid-connected form of operation are established in this study, which offers dynamic, seamless power switching in the islanded mode of operation. In this research, effective Phase Locked Loop (PLL) techniques for grid-forming (GFM) and grid-following …

Recent Progress And Perspectives Of Liquid Organic Hydrogen Carrier Electrochemistry For Energy Applications, Jinyao Tang, Rongxuan Xie, Parsa Pishva, Xiaochen Shen, Yanlin Zhu, Zhenmeng Peng

Faculty Publications

Amidst the global pursuit of clean and sustainable energy, the transition towards a hydrogen economy holds immense promise, yet is encumbered by significant storage challenges. Liquid organic hydrogen carrier (LOHC) electrochemistry emerges as a promising solution to enable efficient and sustainable hydrogen storage, meanwhile broadening the horizons of LOHCs for use as clean, renewable, and intense energy carriers to store and generate electricity. In this perspective, we embark on a review of recent trends and progress in LOHC redox electrochemistry, properties, and applications. We categorize electrochemically active, regenerable LOHCs into alcohols, amines, aromatic compounds, aminoxyl species, and others, based on …

A Deep Learning Framework For Blockage Mitigation In Mmwave Wireless, Ahmed Hazaa Almutairi

Dissertations and Theses

Millimeter-Wave (mmWave) communication is a key technology to enable next generation wireless systems. However, mmWave systems are highly susceptible to blockages, which can lead to a substantial decrease in signal strength at the receiver. Identifying blockages and mitigating them is thus a key challenge to achieve next generation wireless technology goals, such as enhanced mobile broadband (eMBB) and Ultra-Reliable and Low-Latency Communication (URLLC). This thesis proposes several deep learning (DL) frameworks for mmWave wireless blockage detection, mitigation, and duration prediction. First, we propose a DL framework to address the problem of identifying whether the mmWave wireless channel between two devices …

Prediction Of Defects In Deep Drawn Rectangular Parts Using Finite Element Analysis (Fea) And Response Surface Methodology (Rsm), Mustafa M. Semman Eng., Mostafa Shazly, Mohamed H. Gadallah, Tamer Adel Mohamed, Abdallah S. Wifi

Mechanical Engineering

Abstract. Deep drawing defects such as thinning and earing present challenges to sheet metal forming industry while designers tend to favour the usage of new materials and production processes. The present work introduces an integrated approach using Design of Experiment, FEA experimentation and RSM to study the effect of deep drawing process parameters and resulting defects associated with sheet metal forming processes such as thinning and earing particularly in deep drawing of non-circular parts. The Design of Experiment (DoE) is used to generate a series of experiments for different process parameters in deep drawn rectangular products which are then simulated …

The 2019 Raikoke Eruption As A Testbed Used By The Volcano Response Group For Rapid Assessment Of Volcanic Atmospheric Impacts, Jean Paul Vernier, Thomas J. Aubry, Claudia Timmreck, Anja Schmidt, Lieven Clarisse, Fred Prata, Nicolas Theys, Andrew T. Prata, Graham Mann, Hyundeok Choi, Simon Carn, Richard Rigby, Susan C. Loughlin, John A. Stevenson

Michigan Tech Publications

The 21 June 2019 Raikoke eruption (48°N, 153°E) generated one of the largest amounts of sulfur emission to the stratosphere since the 1991 Mt. Pinatubo eruption. Satellite measurements indicate a consensus best estimate of 1.5Tg for the sulfur dioxide (SO2) injected at an altitude of around 14-15km. The peak Northern Hemisphere (NH) mean 525gnm stratospheric aerosol optical depth (SAOD) increased to 0.025, a factor of 3 higher than background levels. The Volcano Response (VolRes) initiative provided a platform for the community to share information about this eruption which significantly enhanced coordination efforts in the days after the eruption. A multi-platform …

Analysis Of Inequality In Household Internet Utilization And Policy Implications, Shanisara Chamwong, Thoedsak Chomtohsuwan, Narissara Charoenphandhu

Journal of Demography

This research investigates the pervasive inequality in household internet access and use that contributes to the digital divide. As the internet becomes an integral part of daily life, variations in access and use carry significant implications for social and economic opportunities. A quantitative approach is applied, analyzing data from The National Statistical Office of Thailand to capture a comprehensive understanding of inequality in household internet access and use, as measured by the Gini coefficient. Five aspects of internet access and use are considered as the determinants of inequality, including internet connectivity, internet affordability, internet quality, device availability, and flexibility and …

Infrared Phase-Change Chiral Metasurfaces With Tunable Circular Dichroism, Haotian Tang, Liliana Stan, David A. Czaplewski, Xiaodong Yang, Jie Gao

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Integrating Phase-Change Materials in Meta surfaces Has Emerged as a Powerful Strategy to Realize Optical Devices with Tunable Electromagnetic Responses. Here, Phase-Change Chiral Meta surfaces based on GST-225 Material with the Designed Trapezoid-Shaped Resonators Are Demonstrated to Achieve Tunable Circular Dichroism (CD) Responses in the Infrared Regime. the Asymmetric Trapezoid-Shaped Resonators Are Designed to Support Two Chiral Plasmonic Resonances with OppositeCDresponses for Realizing SwitchableCDbetween Negative and Positive Values using the GST Phase Change from Amorphous to Crystalline. the Electromagnetic Field Distributions of the Chiral Plasmonic Resonant Modes Are Analyzed to Understand the Chiroptical Responses of the Meta surface. Furthermore, the …

Deep Learning-Based Breast Cancer Diagnosis With Multiview Of Mammography Screening To Reduce False Positive Recall Rate, Meryem Altın Karagöz, Özkan Ufuk Nalbantoğlu, Derviş Karaboğa, Bahriye Akay, Alper Baştürk, Halil Ulutabanca, Serap Doğan, Damla Coşkun, Osman Demi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Breast cancer is the most prevalent and crucial cancer type that should be diagnosed early to reduce mortality. Therefore, mammography is essential for early diagnosis owing to high-resolution imaging and appropriate visualization. However, the major problem of mammography screening is the high false positive recall rate for breast cancer diagnosis. High false positive recall rates psychologically affect patients, leading to anxiety, depression, and stress. Moreover, false positive recalls increase costs and create an unnecessary expert workload. Thus, this study proposes a deep learning based breast cancer diagnosis model to reduce false positive and false negative rates. The proposed model has …

Experimental Investigation On Downstream Characteristics Of S809 Airfoil With And Without Passive Flow Control Device, Mano S

Theses and Dissertations

The wake behaviour of extended flat plate (EFP) and serration in the trailing edge of S809 airfoil is presented in this experimental study using wind tunnel testing for several freestream turbulence intensities (TI). The clustering wind turbines in wind parks has recently been an important problem, as it involves determining the number of wind turbines to be installed to increase the power output.

The downstream wake characteristics are one of the most significantparameter that influence the performance of subsequent wind turbines.These wind turbine blades are made up of airfoil cross sections and need to be studied in detail. A series …

Assessing The Benefits Of Electrification For The Mackinac Island Ferry From An Environmental And Economic Perspective, Siddharth Gopujkar, Jeremy Worm

Michigan Tech Publications

Ferry electrification has gained attention in the last decade as a potential path to reduce greenhouse gas emissions. This study, conducted by APS LABS at Michigan Technological University for the Mackinac Economic Alliance (MEA) and funded by the Michigan Economic Development Corporation (MEDC), looked at the feasibility and potential benefits of electrification of a particular vessel that is part of a ferry service from Mackinaw City, Michigan, USA, to Mackinac Island, Michigan, USA. The study included a comprehensive analysis of the feasibility of retrofitting the current configuration of the ferry into an all-electric ferry based on the availability of components …

Estimation Of Useful-Stage Energy Returns On Investment For Fossil Fuels And Implications For Renewable Energy Systems, Emmanuel Aramendia, Paul E. Brockway, Peter G. Taylor, Jonathan B. Norman, Matthew K. Heun, Zeke Marshal;

University Faculty Publications and Creative Works

The net energy implications of the energy transition have so far been analysed at best at the final energy stage. Here we argue that expanding the analysis to the useful stage is crucial. We estimate fossil fuelsʼ useful-stage energy returns on investment (EROIs) over the period 1971–2020, globally and nationally, and disaggregate EROIs by end use. We find that fossil fuelsʼ useful-stage EROIs (~3.5:1) are considerably lower than at the final stage (~8.5:1), due to low final-to-useful efficiencies. Further, we estimate the final-stage EROI for which electricity-yielding renewable energy would deliver the same net useful energy as fossil fuels (EROI …

Ri2ap: Robust And Interpretable 2d Anomaly Prediction In Assembly Pipelines, Chathurangi Shyalika, Kaushik Roy, Renjith Prasad, Fadi El Kalach, Yuxin Zi, Priya Mittal, Vignesh Narayanan, Ramy Harik, Amit Sheth

Publications

Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1 measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data are expensive), and the complex …

05.20.2024 Orsp Connect, Liz Williamson

ORED Newsletter

NIH RPPR Update (Research Performance Progress Report)

Nanozyme: Combining Power Of Natural Enzymes And Artificial Catalysis, Peng Du, Lizeng Gao, Jian Jiao, Kelong Fan, Xiyun Yan

Bulletin of Chinese Academy of Sciences (Chinese Version)

Nanozymes represent a novel class of artificial enzymes and biocatalysts, possessing both the physical and chemical properties of nanomaterials along with unique enzyme-like catalytic activities, which breaks the boundary between inorganic materials and organic life. Unlike natural enzymes, traditional enzyme mimics and chemical catalysts, nanozymes exhibit catalytic activity that can be regulated by their nanoscale physical and chemical properties. They are characterized by good stability, high- and lowtemperature resistance, acid and alkali resistance, adjustable activity, and multifunctionality. As a result, nanozymes have garnered widespread attention in the fields of biomedicine, environment treatment, green agriculture, new energy resources, and have begun …

New Frontier In Race For Deep Space Exploration: Lunar Water Resources, Yong Wei, Honglei Lin, Fei He, Hui Zhang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Deep space exploration has become the commanding heights of science and technology competition. Since the beginning of the 21st century, China has successfully completed the lunar exploration missions of “orbiting, landing, and returning” in just twenty years, and upgraded to a new roadmap of “survey, construction, and utilization”. Meanwhile, lunar exploration worldwide has shown a trend towards normalization and commercialization. The research on lunar water resources has sparked widespread interest and intense competition among countries and space agencies, marking a new focus in human’s deep space exploration. The exploration of lunar water can help reveal crucial processes in the formation …

A Multi-Material Platform For Imaging Of Single Cell-Cell Junctions Under Tensile Load Fabricated With Two-Photon Polymerization, Jordan Rosenbohm, Grayson Minnick, Bahareh Tajvidi Safa, Amir M. Esfahani, Xiaowei Jin, Haiwei Zhai, Nickolay V. Lavrik, Ruiguo Yang

Department of Mechanical and Materials Engineering: Faculty Publications

We previously reported a single-cell adhesion micro tensile tester (SCAμTT) fabricated from IP-S photoresin with two-photon polymerization (TPP) for investigating the mechanics of a single cell-cell junction under defined tensile loading. A major limitation of the platform is the autofluorescence of IP-S, the photoresin for TPP fabrication, which significantly increases background signal and makes fluorescent imaging of stretched cells difficult. In this study, we report the design and fabrication of a new SCAμTT platform that mitigates autofluorescence and demonstrate its capability in imaging a single cell pair as its mutual junction is stretched. By employing a two-material design using IP-S …

Deep Clustering Of Tabular Data By Weighted Gaussian Distribution Learning, Shourav B. Rabbani, Ivan V. Medri, Manar D. Samad

Computer Science Faculty Research

Deep learning methods are primarily proposed for supervised learning of images or text with limited applications to clustering problems. In contrast, tabular data with heterogeneous features pose unique challenges in representation learning, where deep learning has yet to replace traditional machine learning. This paper addresses these challenges in developing one of the first deep clustering methods for tabular data: Gaussian Cluster Embedding in Autoencoder Latent Space (G-CEALS). G-CEALS is an unsupervised deep clustering framework for learning the parameters of multivariate Gaussian cluster distributions by iteratively updating individual cluster weights. The G-CEALS method presents average rank orderings of 2.9(1.7) and 2.8(1.7) …

Comparative Analysis Of Water-Induced Response In 3d-Printed Scf/Abs Composites Under Controlled Diffusion, Samiul Alam, Md Tareq Hassan, Joshua Merrell, Juhyeong Lee

Mechanical and Aerospace Engineering Faculty Publications

Additive manufacturing (AM) or 3D printing of fiber-reinforced composites (FRCs) has garnered significant interests for its versatility in creating intricate parts and rapid prototyping due to cost-effectiveness. Although short fiber-reinforced thermoplastic composites are challenging to manufacture, their mechanical properties are enhanced. However, void formation during printing is a key issue, impacting mechanical properties and facilitating water ingress, affecting long-term durability. This work studies water diffusion characteristics and the associated hydro-aging of 3Dprinted short carbon fiber (SCF)/acrylonitrile butadiene styrene (ABS) composites with controlled water diffusion. Effects of material type (ABS and SCF/ABS), 3D printing path (horizontal and vertical filament orientation), and …

Winning Battle For Key And Core Technologies In Emerging Fields—Inspiration Based On 863 Program Related Projects, Guangzu Bai, Li Li, Hongfei Meng, Qiang Wang, Xiaoyang Cao, Anrong Liu, Bo Cheng, Mimi Zhan, Jing Li, Leiei Cui, Xiangwan Du

Bulletin of Chinese Academy of Sciences (Chinese Version)

Emerging technology fields have become the main battleground for strategic competition among major powers today, with key and core technologies serving as crucial approach in shaping a nation’s international competitive advantage. This study, from the perspective of national strategy, profoundly understands the significant importance of winning the key and core technology battle in emerging fields. Based on this understanding, it starts with a comparison between the implementation background of the 863 Program and the current reality. It systematically summarizes valuable experiences from projects aimed at advancing key and core technologies in emerging fields, and puts forward reflections and suggestions for …

Structural Fiber Mesh Reinforcement Of A Polymeric Heart Valve: Improving Valve Durability And Leaflet Closure Effectiveness, Peter J. Choi, Hugo Zazueta, John A. Acevedo, Philip Park

Civil Engineering Faculty Publications

Objective: Bioprosthetic valves using either porcine or bovine pericardium have been widely used for transcatheter heart valve replacements. However, producing bioprosthetic valves is not a sustainable solution. Acquiring animal tissue is often not readily available and costly and requires time-consuming modifications. Moreover, its primary tissue failure requires reoperation after roughly 15 years. The suggested replacement of porcine and bovine leaflets with biocompatible polymers appears to be an attractive alternative to bioprosthetic valves. In this study, engineered fiber-reinforced polymers were developed, which are less degenerative and offer greater hemocompatibility in relation to mechanical valves.
Methods: Polydimethylsiloxane (PDMS) polymer reduces calcification significantly …

Unveiling Anomalies: A Survey On Xai-Based Anomaly Detection For Iot, Esin Eren, Feyza Yildirim Okay, Suat Özdemi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

In recent years, the rapid growth of the Internet of Things (IoT) has raised concerns about the security and reliability of IoT systems. Anomaly detection is vital for recognizing potential risks and ensuring the optimal functionality of IoT networks. However, traditional anomaly detection methods often lack transparency and interpretability, hindering the understanding of their decisions. As a solution, Explainable Artificial Intelligence (XAI) techniques have emerged to provide human-understandable explanations for the decisions made by anomaly detection models. In this study, we present a comprehensive survey of XAI-based anomaly detection methods for IoT. We review and analyze various XAI techniques, including …

Text-To-Sql: A Methodical Review Of Challenges And Models, Ali Buğra Kanburoğlu, Faik Boray Tek

Turkish Journal of Electrical Engineering and Computer Sciences

This survey focuses on Text-to-SQL, automated translation of natural language queries into SQL queries. Initially, we describe the problem and its main challenges. Then, by following the PRISMA systematic review methodology, we survey the existing Text-to-SQL review papers in the literature. We apply the same method to extract proposed Text-to-SQL models and classify them with respect to used evaluation metrics and benchmarks. We highlight the accuracies achieved by various models on Text-to-SQL datasets and discuss execution-guided evaluation strategies. We present insights into model training times and implementations of different models. We also explore the availability of Text-to-SQL datasets in non-English …