Engineering Commons^™

Biom 3110(Honors): Medical Physiology (Syllabus), Bradford D. Pendley

Biomedical Engineering Syllabi Archive

Course Description: Basic principles of human physiology and how these are applied in medicine and biomedical engineering problem and devices; physiological principles using appropriate mathematical and engineering models to construct an understanding of how the body works as an integrated system.

Biom 1720: Introduction To Biomedical Engineering Tools (Syllabus), Stephen Strain

Biomedical Engineering Syllabi Archive

Course Description: Use of computer tools for data analysis, presentation, documentation; introduction to programming; individual and group design projects.

Biom 2720: Experimental Design And Analysis For Biomedical Engineering (Syllabus), Stephen Strain

Biomedical Engineering Syllabi Archive

Course Description: Application of probability, statistics, error analysis, uncertainty in design and performance of biomedical engineering experimentation; discussion and evaluation of common experimental designs for medical device development.

Engr 1009: Preparation For Engineering Problem Solving (Syllabus), Stephen Strain

Biomedical Engineering Syllabi Archive

Course Description: Applications of algebra and trigonometry to engineering problems. Accelerated preparation for an engineering or technology major.

Chemical Decomposition Of Flexible Polyurethane Foam To Generate A Media For Microbial Upcycling, Kaushik Baruah

Dissertations, Master's Theses and Master's Reports

Polyurethane waste is becoming a global concern as a large amount is being disposed of in landfills every year, and only a fraction is being recycled. Several polyurethane recycling techniques exist, of which ammonolysis and base-catalyzed hydrolysis is the least explored. Flexible polyurethane foam (FPUF) decomposition can generate amines that can act as a carbon source for the growth of microbial consortia. This study aims to generate a novel media capable of microbial upcycling via ammonolysis and base-catalyzed hydrolysis of flexible polyurethane foams (FPUFs) using ammonium hydroxide and subsequently determine the reaction conditions for maximum solubilization of polyurethane foam in …

Dynamic Mode Decomposition Approach For Estimating The Shape Of A Cable, Yash Manik Chavan

Dissertations, Master's Theses and Master's Reports

This study investigates the dynamic behavior of a flexible cable with heterogeneous stiffness using a data-driven approach. The study aims to develop accurate models describing intricate structures with rigid or flexible components. To achieve this, reflective markers were attached to the cable at equal spacing, and the motion was manually excited and captured using an 8-camera setup and OptiTrack's Motive software.

The cable displacement data at the marker locations were used as initial conditions for various Dynamic Mode Decomposition (DMD) models. The performance of the data- driven cable model is compared against the performance of the DMD modeling approach, fitting …

Machine Learning And Deep Learning Approaches For Gene Regulatory Network Inference In Plant Species, Sai Teja Mummadi

Dissertations, Master's Theses and Master's Reports

The construction of gene regulatory networks (GRNs) is vital for understanding the regulation of metabolic pathways, biological processes, and complex traits during plant growth and responses to environmental cues and stresses. The increasing availability of public databases has facilitated the development of numerous methods for inferring gene regulatory relationships between transcription factors and their targets. However, there is limited research on supervised learning techniques that utilize available regulatory relationships of plant species in public databases.

This study investigates the potential of machine learning (ML), deep learning (DL), and hybrid approaches for constructing GRNs in plant species, specifically Arabidopsis thaliana, …

Wavelet-Based Real-Time Harmonic Phasor Estimation In Complex Signals With Inter-Harmonics And Transient Events, M A Aziz Jahan

Dissertations, Master's Theses and Master's Reports

Addressing the evolving challenges in modern power systems induced by the integration of Inverter Based Resources (IBRs), such as solar inverters, wind turbine converters, electric vehicle charging stations etc is the main goal of this research. The study focuses on the critical task of harmonic phasor estimation in the presence of sub-harmonics, inter-harmonics, time-varying harmonics, DC offset, transient events, and other power quality disturbances. A novel wavelet-based harmonic phasor estimation method is proposed to enhance accuracy, particularly during transient events, ensuring a nuanced understanding of phase angle variations. The research emphasizes the versatility of wavelet analysis for both steady-state and …

Cozie Apple: An Ios Mobile And Smartwatch Application For Environmental Quality Satisfaction And Physiological Data Collection, Federico Tartarini, Mario Frei, Stefano Schiavon, Xuan Yun Chua, Clayton Miller

Research Collection College of Integrative Studies

Collecting feedback from people in indoor and outdoor environments is traditionally challenging and complex to achieve in a reliable, longitudinal, and non-intrusive way. This paper introduces Cozie Apple, an open-source mobile and smartwatch application for iOS devices. This platform allows people to complete a watch-based micro-survey and provide real-time feedback about environmental conditions via their Apple Watch. It leverages the inbuilt sensors of the smartwatch to collect physiological (e.g., heart rate, activity) and environmental (sound level) data. This paper outlines data collected from 48 research participants who used the platform to report perceptions of urban-scale environmental comfort (noise and thermal) …

The Building Data Genome Directory – An Open, Comprehensive Data Sharing Platform For Building Performance Research, Xiaoyu Jin, Chun Fu, Hussain Kazmi, Atilla Balint, Ada Canaydin, Matias Quintana, Filip Biljecki, Fu Xiao, Clayton Miller

Research Collection College of Integrative Studies

The building sector plays a crucial role in the worldwide decarbonization effort, accounting for significant portions of energy consumption and environmental effects. However, the scarcity of open data sources is a continuous challenge for built environment researchers and practitioners. Although several efforts have been made to consolidate existing open datasets, no database currently offers a comprehensive collection of building data types with all subcategories and time granularities (e.g., year, month, and sub-hour). This paper presents the Building Data Genome Directory, an open data-sharing platform serving as a one-stop shop for the data necessary for vital categories of building energy research. …

Conductive 3d Nano-Biohybrid Systems Based On Densified Carbon Nanotube Forests And Living Cells, Roya Bagheri, Alicia K. Ball, Masoud Kasraie, Aparna Chandra, Xinqian Chen, Ibrahim Miskioglu, Zhiying Shan, Parisa Pour Shahid Saeed Abadi

Michigan Tech Publications

Conductive biohybrid cell-material systems have applications in bioelectronics and biorobotics. To date, conductive scaffolds are limited to those with low electrical conductivity or 2D sheets. Here, 3D biohybrid conductive systems are developed using fibroblasts or cardiomyocytes integrated with carbon nanotube (CNT) forests that are densified due to interactions with a gelatin coating. CNT forest scaffolds with a height range of 120–240 µm and an average electrical conductivity of 0.6 S/cm are developed and shown to be cytocompatible as evidenced from greater than 89% viability measured by live-dead assay on both cells on day 1. The cells spread on top and …

Evaluation Of Nanoclay Additives For Improving Resistance To Moisture Damage In Hot Mix Asphalt (Hma), Amro El Badawy, Ashraf Rahim

Mineta Transportation Institute

Transportation has an enormous impact on the U.S. economy and on the lives of all Americans. Many modes of transportation rely on pavement, but pavement conditions deteriorate over time because of the combined effects of traffic and climate. Exposure to moisture often causes premature failure of asphalt pavements as it reduces the stiffness of the asphalt and enables stripping of the asphalt from the aggregate. This research evaluates the effectiveness of clay nanomaterials (i.e., nanoclays) in improving the resistance of hot mix asphalt (HMA) to moisture damage and compares the enhancement results to anti-stripping additives commonly used in pavement construction. …

Elevation-Distributed Multistage Reverse Osmosis Desalination With Seawater Pumped Storage, Hani E. Elsayed-Ali

Electrical & Computer Engineering Faculty Publications

A seawater reverse osmosis (RO) plant layout based on multistage RO with stages located at different elevations above sea level is described. The plant uses the weight of a seawater column from pumped storage as head pressure for RO (gravity-driven multistage RO) or to supplement high-pressure pumps used in RO (gravity-assisted multistage RO). The use of gravitational force reduces the specific energy for RO compared to using high-pressure pumps. By locating the RO stages at different elevations based on demand sites, the total specific energy consumption for RO and permeate transport to different elevations above sea level is reduced from …

View Synthesis With Scene Recognition For Cross-View Image Localization, Uddom Lee, Peng Jiang, Hongyi Wu, Chunsheng Xin

Electrical & Computer Engineering Faculty Publications

Image-based localization has been widely used for autonomous vehicles, robotics, augmented reality, etc., and this is carried out by matching a query image taken from a cell phone or vehicle dashcam to a large scale of geo-tagged reference images, such as satellite/aerial images or Google Street Views. However, the problem remains challenging due to the inconsistency between the query images and the large-scale reference datasets regarding various light and weather conditions. To tackle this issue, this work proposes a novel view synthesis framework equipped with deep generative models, which can merge the unique features from the outdated reference dataset with …

Security Of Internet Of Things (Iot) Using Federated Learning And Deep Learning — Recent Advancements, Issues And Prospects, Vinay Gugueoth, Sunitha Safavat, Sachin Shetty

Electrical & Computer Engineering Faculty Publications

There is a great demand for an efficient security framework which can secure IoT systems from potential adversarial attacks. However, it is challenging to design a suitable security model for IoT considering the dynamic and distributed nature of IoT. This motivates the researchers to focus more on investigating the role of machine learning (ML) in the designing of security models. A brief analysis of different ML algorithms for IoT security is discussed along with the advantages and limitations of ML algorithms. Existing studies state that ML algorithms suffer from the problem of high computational overhead and risk of privacy leakage. …

Opioid Use Disorder Prediction Using Machine Learning Of Fmri Data, A. Temtam, Liangsuo Ma, F. Gerard Moeller, M. S. Sadique, K. M. Iftekharuddin, Khan M. Iftekharuddin (Ed.), Weijie Chen (Ed.)

Electrical & Computer Engineering Faculty Publications

According to the Centers for Disease Control and Prevention (CDC) more than 932,000 people in the US have died since 1999 from a drug overdose. Just about 75% of drug overdose deaths in 2020 involved Opioid, which suggests that the US is in an Opioid overdose epidemic. Identifying individuals likely to develop Opioid use disorder (OUD) can help public health in planning effective prevention, intervention, drug overdose and recovery policies. Further, a better understanding of prediction of overdose leading to the neurobiology of OUD may lead to new therapeutics. In recent years, very limited work has been done using statistical …

Ict Security Tools And Techniques Among Higher Education Institutions: A Critical Review, Miko Nuñez, Xavier-Lewis Palmer, Lucas Potter, Chris Jordan Aliac, Lemuel Clark Velasco

Electrical & Computer Engineering Faculty Publications

Higher education institutions (HEIs) are increasingly relying on digital technologies for classroom and organizational management, but this puts them at higher risk for information and communication (ICT security attacks. Recent studies show that HEIs have experienced more security breaches in ICT security composed of both cybersecurity an information security. A literature review was conducted to identify common ICT security practices in HEIs over the last decade. 11 journal articles were profiled and analyzed, revealing threats to HEIs’ security and protective measures in terms of organizational security, technological security, physical security, and standards and frameworks. Security tools and techniques were grouped …

Special Section Editorial: Artificial Intelligence For Medical Imaging In Clinical Practice, Claudia Mello-Thoms, Karen Drukker, Sian Taylor-Phillips, Khan Iftekharuddin, Marios Gavrielides

Electrical & Computer Engineering Faculty Publications

This editorial introduces the JMI Special Section on Artificial Intelligence for Medical Imaging in Clinical Practice.

On The Chronological Understanding Of The Homogeneous Dielectric Barrier Discharge, Xinpei Lu, Zhi Fang, Dong Dai, Tao Shao, Feng Liu, Cheng Zhang, Dawei Liu, Chunqi Jiang

Electrical & Computer Engineering Faculty Publications

Dielectric barrier discharges (DBD) are widely utilised non-equilibrium atmospheric pressure plasmas with a diverse range of applications, such as material processing, surface treatment, light sources, pollution control, and medicine. Over the course of several decades, extensive research has been dedicated to the generation of homogeneous DBD (H-DBD), focussing on understanding the transition from H-DBD to filamentary DBD and exploring strategies to create and sustain H-DBD. This paper first discusses the influence of various parameters on DBD, including gas flow, dielectric material, surface conductivity, and mesh electrode. Secondly, a chronological literature review is presented, highlighting the development of H-DBD and the …

An Adaptive Image Restoration Algorithm Based On Hybrid Total Variation Regularization, Cong Thang Pham, Thi Thu Thao Tran, Hung Vi Dang, Hoai Phuong Dang

Turkish Journal of Electrical Engineering and Computer Sciences

In imaging systems, the mixed Poisson-Gaussian noise (MPGN) model can accurately describe the noise present. Total variation (TV) regularization-based methods have been widely utilized for Poisson-Gaussian removal with edge-preserving. However, TV regularization sometimes causes staircase artifacts with piecewise constants. To overcome this issue, we propose a new model in which the regularization term is represented by a combination of total variation and high-order total variation. We study the existence and uniqueness of the minimizer for the considered model. Numerically, the minimization problem can be efficiently solved by the alternating minimization method. Furthermore, we give rigorous convergence analyses of our algorithm. …

Deep Learning-Based Classification Of Chaotic Systems Over Phase Portraits, Sezgi̇n Kaçar, Süleyman Uzun, Burak Aricioğlu

Turkish Journal of Electrical Engineering and Computer Sciences

This study performed a deep learning-based classification of chaotic systems over their phase portraits. To the best of the authors' knowledge, such classification studies over phase portraits have not been conducted in the literature. To that end, a dataset consisting of the phase portraits of the most known two chaotic systems, namely Lorenz and Chen, is generated for different values of the parameters, initial conditions, step size, and time length. Then, a classification with high accuracy is carried out employing transfer learning methods. The transfer learning methods used in the study are SqueezeNet, VGG-19, AlexNet, ResNet50, ResNet101, DenseNet201, ShuffleNet, and …

Transmission Network Planning For Realistic Egyptian Systems Via Encircling Prey Based Algorithms, Abdullah M. Shaheen, Ragab Elsehiemy, Mohammed Kharrich, Salah Kamel

Turkish Journal of Electrical Engineering and Computer Sciences

Transmission network planning problem (TNPP) is one of the pertinent issues of the planning activities in power systems. It aims to optimally pick out the routs, types, and number of the new installed lines to confront the expected future loading conditions. In this line, this study proposes a new economic model to the TNPP. The aim of the model is to find the optimal transmission routes at least investment and operating costs. Three recent algorithms called grey wolf optimization algorithm (GWOA), spotted hyena optimization algorithm (SHOA) and whale optimization algorithm (WOA) are developed to solve the TNPP. The concept of …

Basismap: Sequence-Based Similarity Search For Geomagnetic Positioning, Tevfi̇k Kadioğlu, Burcu Erkmen

Turkish Journal of Electrical Engineering and Computer Sciences

Indoor localization has become a popular topic with the development of location-based services (LBS) and indoor navigation systems. Beside these circumstances indoor positioning has been the focus of attention for researchers as the most important component of these applications. Many signals are used as distinguishable features for indoor positioning. RF-based Wi-Fi and BLE systems are the most popular ones and these have been preferred because of their high distinguishable feature. The use of geomagnetism, a natural signal found all over the world, has also been of interest to many researchers. Geomagnetic signals being distorted in the indoor area due to …

The Effects Of The Dielectric Substrate Thickness And The Loss Tangent On The Absorption Spectrum: A Comprehensive Study Considering The Resonance Type, The Ground Plane Coupling, And The Characterization Setup, Umut Köse, Evren Ekmekçi̇

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, the effects of dielectric substrate thickness and the dielectric loss tangent on the absorption spectrum are investigated parametrically in S-band. The study has been conducted on two different absorber topologies, one is closed ring resonator (CRR) and the other is composed of a split ring resonator (SRR), to observe the effects on both LC - and dipole-type resonances. The studies on the substrate thickness have been performed both numerically and experimentally, whereas the studies on the dielectric loss tangent have been performed numerically. The results agree with the literature such that the substrate thickness has significant effects …

Variational Autoencoder-Based Anomaly Detection In Time Series Data For Inventory Record Inaccuracy, Hali̇l Arğun, Sadetti̇n Emre Alpteki̇n

Turkish Journal of Electrical Engineering and Computer Sciences

Retail companies monitor inventory stock levels regularly and manage them based on forecasted sales to sustain their market position. Inventory accuracy, defined as the difference between the warehouse stock records and the actual inventory, is critical for preventing stockouts and shortages. The root causes of inventory inaccuracy are the employee or customer theft, product damage or spoilage, and wrong shipments. In this paper, we aim at detecting inaccurate stocks of one of Turkey's largest supermarket chain using the variational autoencoder (VAE), which is an unsupervised learning method. Based on the findings, we showed that VAE is able to model the …

Binary Text Classification Using Genetic Programming With Crossover-Based Oversampling For Imbalanced Datasets, Mona Aljero, Nazi̇fe Di̇mi̇li̇ler

Turkish Journal of Electrical Engineering and Computer Sciences

It is well known that classifiers trained using imbalanced datasets usually have a bias toward the majority class. In this context, classification models can present a high classification performance overall and for the majority class, even when the performance for the minority class is significantly lower. This paper presents a genetic programming (GP) model with a crossover-based oversampling technique for oversampling the imbalanced dataset for binary text classification. The aim of this study is to apply an oversampling technique to solve the imbalanced issue and improve the performance of the GP model that employed the proposed technique. The proposed technique …

A Robust Model For Spot Virtual Machine Bidding In The Cloud Market Using Information Gap Decision Theory (Igdt), Mona Naghdehforoushha, Mehdi Dehghan Takht Fooladi, Mohammed Hossein Rezvani, Mohammad Mehdi Gilanian Sadeghi

Turkish Journal of Electrical Engineering and Computer Sciences

The spot market is one of the most common cloud markets where cloud providers, such as Amazon EC2, rent their surplus computing resources at lower prices in the form of spot virtual machines (SVMs). In this market, which is often managed through an auction mechanism, users seek optimal bidding strategies for renting SVMs to minimize cost and risk. Uncertainty in the price of SVMs and their low availability/reliability is a challenging issue to bid on the user side. In this paper, we present a robust model for minimizing the cost of executing tasks by considering the uncertainty of the price …

Lvq Treatment For Zero-Shot Learning, Firat İsmai̇loğlu

Turkish Journal of Electrical Engineering and Computer Sciences

In image classification, there are no labeled training instances for some classes, which are therefore called unseen classes or test classes. To classify these classes, zero-shot learning (ZSL) was developed, which typically attempts to learn a mapping from the (visual) feature space to the semantic space in which the classes are represented by a list of semantically meaningful attributes. However, the fact that this mapping is learned without using instances of the test classes affects the performance of ZSL, which is known as the domain shift problem. In this study, we propose to apply the learning vector quantization (LVQ) algorithm …

An Xai Approach For Covid-19 Detection Using Transfer Learning With X-Ray Images, Salih Sarp, Ferhat Ozgur Catak, Murat Kuzlu, Umit Cali, Huseyin Kusetogullari, Yanxiao Zhao, Gungor Ates, Ozgur Guler

Engineering Technology Faculty Publications

The coronavirus disease (COVID-19) has continued to cause severe challenges during this unprecedented time, affecting every part of daily life in terms of health, economics, and social development. There is an increasing demand for chest X-ray (CXR) scans, as pneumonia is the primary and vital complication of COVID-19. CXR is widely used as a screening tool for lung-related diseases due to its simple and relatively inexpensive application. However, these scans require expert radiologists to interpret the results for clinical decisions, i.e., diagnosis, treatment, and prognosis. The digitalization of various sectors, including healthcare, has accelerated during the pandemic, with the use …

Defending Ai-Based Automatic Modulation Recognition Models Against Adversarial Attacks, Haolin Tang, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Yanxiao Zhao

Engineering Technology Faculty Publications

Automatic Modulation Recognition (AMR) is one of the critical steps in the signal processing chain of wireless networks, which can significantly improve communication performance. AMR detects the modulation scheme of the received signal without any prior information. Recently, many Artificial Intelligence (AI) based AMR methods have been proposed, inspired by the considerable progress of AI methods in various fields. On the one hand, AI-based AMR methods can outperform traditional methods in terms of accuracy and efficiency. On the other hand, they are susceptible to new types of cyberattacks, such as model poisoning or adversarial attacks. This paper explores the vulnerabilities …