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Physics-Based Machine Learning Methods For Control And Sensing In Fish-Like Robots, Colin Rodwell

All Dissertations

Underwater robots are important for the construction and maintenance of underwater infrastructure, underwater resource extraction, and defense. However, they currently fall far behind biological swimmers such as fish in agility, efficiency, and sensing capabilities. As a result, mimicking the capabilities of biological swimmers has become an area of significant research interest. In this work, we focus specifically on improving the control and sensing capabilities of fish-like robots.

Our control work focuses on using the Chaplygin sleigh, a two-dimensional nonholonomic system which has been used to model fish-like swimming, as part of a curriculum to train a reinforcement learning agent to …

Design Of Human Serum Albumin And Adenovirus Conjugation Via Catcher/Tag Molecular Glue, Peijie Zhao

McKelvey School of Engineering Graduate Student Theses & Dissertations

Adenovirus (Ad) has been the ideal cargo delivery mechanism, and its moderate immunological response makes it ideal for in vivo gene therapies since its discovery in 1953. However, the robust immunogenicity of the Ad capsid and low vaccine absorption via mucous membranes and epithelium put a limit on the process of developing intranasal vaccines. Efforts are being made to enhance the effectiveness of Ad vectors and numerous studies have demonstrated the remarkable capacity of human serum albumin (HSA) to extend plasma half-life and facilitate targeted intranasal delivery. In this study, we devised an innovative method for employing the Catcher/Tag molecular …

Improving And Integrating Quantitative Single Cell Spatial Biology For Theranostic Medicine, Peng Lu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Disease states are the result of a complex interplay of many different cell types interacting in close proximity in the context of often heterogeneous tissues. Alpha particles are drawing intense research and clinical interest because of their potent cytotoxic effects and their short path lengths. Analyzing the dose distribution and tissue micro-environment for alpha therapy plays a key role in predicting the efficacy of this targeted radiotherapy. However, to date there have been no direct on-tissue analytical methods for alpha dose distributions and the corresponding tissue microenvironments.

Therefore, we have developed a pipeline to overcome this limitation by utilizing quantitative …

Application Of Biochar For Antibiotic-Free Nutrient Recovery From Source-Separated Human Urine, Sayeda Ummeh Masrura

UNLV Theses, Dissertations, Professional Papers, and Capstones

In recent years, the application of biochar as a soil amendment has gained attention as a potential solution for nutrient management and environmental remediation. Biochar, a carbon-rich material produced through the pyrolysis of biomass under oxygen-limited conditions, exhibits unique physicochemical properties making it an effective sorbent for nutrients and contaminants. Its high surface area, porosity, and stability provide an ideal matrix for nutrient retention and release, as well as the immobilization of pollutants. One area of particular interest is the application of biochar for nutrient recovery from source-separated human urine. Source separation of urine has gained momentum as a sustainable …

Optical And Electrical Interfaces For Biological Structures, Mehdi Bandali

Boise State University Theses and Dissertations

This dissertation presents novel solutions for recording optical and electrical data from biological structures. CHAPTER ONE discusses the 16×16 Time correlated SPAD image array I developed in a 180 nm HV CMOS process for single-molecule localization super-resolution microscopy (SRM). This image array has a maximum frame rate of 80 MHz and a fill factor of 17.4 %. To demonstrate SRM with this imaging array, we localized gold nanoparticle displacement at 40 nm. CHAPTER TWO discusses stimulation artifacts' time and frequency domain characteristics and potential strategies for canceling this interference on neural recording. CHAPTER THREE discusses an artifact-resilient neural recording front-end …

Investigating The Influence Of Surface Finishing Methods On Corrosion Of Additively Manufactured Ss316l, Joshua R. Fadeley

Boise State University Theses and Dissertations

Additive Manufacturing (AM), namely with metal, has seen a surge in growth and interest over recent years. A wide array of industries is developing new products to take advantage of this budding technology. In order to enable adoption of this technology, understanding of the differences of AM versus traditional manufacturing on the resultant materials properties of printed components is important. The work done for this study fell into two separate categories of work.

The first effort of this work involved the installation, certification, and operation of the first laser powder bed fusion (LPBF) printer at the Center for Advanced Energy …

Additive Manufacturing Of Microelectronic Devices For Extreme Environments, Nicholas Mckibben

Boise State University Theses and Dissertations

This work encompasses a comprehensive exploration of advanced manufacturing techniques for fabricating microelectronic devices compatible with extreme environments. The research integrates the fields of chemistry, nanomaterials, and additive manufacturing to achieve remarkable progress in sensor development, with a particular emphasis on aerosol jet printing (AJP), surface acoustic wave (SAW) devices, patterned graphene growth, and surface functionalization. The investigation begins with the AJP process development of a commercial silver nanoparticle ink, culminating in the fabrication of a piezoelectric SAW transducer that was prototyped as a high-temperature thermometer, showing excellent linearity when validated to 200°C. Next, the investigation continued with the development …

The Private Pilot Check Ride: Applying The Spacing Effect Theory To Predict Time To Proficiency For The Practical Test, Michael Scott Harwin

Theses and Dissertations

This study examined the relationship between a set of targeted factors and the total flight time students needed to become ready to take the private pilot check ride. The study was grounded in Ebbinghaus’s (1885/1913/2013) forgetting curve theory and spacing effect, and Ausubel’s (1963) theory of meaningful learning. The research factors included (a) training time to proficiency, which represented the number of training days needed to become check-ride ready; (b) flight training program (Part 61 vs. Part 141); (c) organization offering the training program (2- or 4-year college/university vs. FBO); (d) scheduling policy (mandated vs. student-driven); and demographical variables, which …

Underwater Image Enhancement: A Pipeline For Underwater Computer Visions, Humberto Lebron Rivera

Theses and Dissertations

Ocean exploration has surged in popularity and significance in recent years, including diverse areas like maritime archeology, underwater resources, and submerged structure inspection. The activities mentioned above heavily depend on vision and imagery, a challenge in the unpredictable marine world. This thesis presents a conditional generative adversarial network model for image-to-image translation problems. We designed and trained the model with the end goal of enhancing underwater images. Five metrics were employed for validation to quantify our model’s resulting enhanced images. By doing so, we aim to establish a pipeline that can leverage aerial computer vision algorithms for marine applications.

Our …

Tissue-Engineered Models For Investigating Nerve Infiltration In Fibrotic Microenvironments, Nikolas Ala-Kokko

Graduate Theses and Dissertations

Neuron infiltration is involved in a multitude of diseased states and is critical in the regeneration of physiologic conditions. Modeling of innervation in three-dimensional, heterogenous, environments would allow early investigation in the crosstalk of neurons and native cells in normal and pathophysiologic microenvironments to gain an understanding of mechanistic functions. This would allow for greater insight into the role of metabolism, cell adhesion, microenvironmental alternations, cellular crosstalk, and signaling pathways that play a role in the promotion and prevention of neurite growth. In this thesis, with the construction of collagen-based hydrogels as the foundation for cell culture, we investigate cell …

Development Of Lignin-Based Copolymers And Polymer Blends, David Chem

Graduate Theses and Dissertations

Increasing industrial interest in reducing fossil fuels in virgin polymer production and incorporating bio-based polymeric materials has led researchers to explore the potential of utilizing plant-based polymers such as lignin. Chapter 1 of this thesis presents a comprehensive overview of copolymerization and blending techniques for lignin and synthetic polymers, emphasizing different types of modifications and copolymerization techniques as well as key parameters such as temperature, reaction time, pH level, and solution compositions. These valorization methods are directed towards the development of high-value lignin-based materials with properties well-suited for various commercial applications. Chapter 2 offers a fundamental exploration into the amination …

Evaluating Differences In Optical Properties Of Indolent And Aggressive Murine Breast Tumors Using Quantitative Diffuse Reflectance Spectroscopy, Joel Rodriguez Troncoso, Umme Marium Mim, Jesse D. Ivers, Santosh K. Paidi, Mason G. Harper, Khue G. Nguyen, Sruthi Ravindranathan, Lisa Rebello, David E. Lee, David A. Zaharoff, Ishan Barman, Narasimhan Rajaram

Biomedical Engineering Faculty Publications and Presentations

We used diffuse reflectance spectroscopy to quantify tissue absorption and scattering-based parameters in similarly sized tumors derived from a panel of four isogenic murine breast cancer cell lines (4T1, 4T07, 168FARN, 67NR) that are each capable of accomplishing different steps of the invasion-metastasis cascade. We found lower tissue scattering, increased hemoglobin concentration, and lower vascular oxygenation in indolent 67NR tumors incapable of metastasis compared with aggressive 4T1 tumors capable of metastasis. Supervised learning statistical approaches were able to accurately differentiate between tumor groups and classify tumors according to their ability to accomplish each step of the invasion-metastasis cascade. We investigated …

Ai Assisted Workflows For Computational Electromagnetics And Antenna Design, Oameed Noakoasteen

Electrical and Computer Engineering ETDs

These days large volumes of data can be recorded and manipulated with relative ease. If valuable information can be extracted from them, these vast amounts of data can be a rich resource not just for the digital economy but also for scientific discovery and development of technology. When it comes to deriving valuable information from data, Machine Learning (ML) emerges as the key solution. To unlock the potential benefits of ML to science and technology, extensive research is needed to explore what algorithms are suitable and how they can be applied.

To shine light on various ways that ML can …

A Practical Low-Dimensional Feature Vector Generation Method Based On Wavelet Transform For Psychophysiological Signals, Erdem Erkan, Yasemi̇n Erkan

Turkish Journal of Electrical Engineering and Computer Sciences

High-dimensional feature vectors entail computational cost and computational complexity. However, a successful classification can be obtained with an optimally sized feature vector consisting of distinctive features. With the widespread use of the internet and mobile devices, the need for systems with low computational costs is increasing day by day. In this study, starting from the idea that each motor imagery is represented as a subject-specific pattern in the brain, we propose a new and practical method that can generate a low-dimensional feature vector based on wavelet transform. The feature vector is obtained from the correlation between each trial and each …

A Comparative Study Of Yolo Models And A Transformer-Based Yolov5 Model For Mass Detection In Mammograms, Damla Coşkun, Dervi̇ş Karaboğa, Alper Baştürk, Bahri̇ye Akay, Özkan Ufuk Nalbantoğlu, Serap Doğan, İshak Paçal, Meryem Altin Karagöz

Turkish Journal of Electrical Engineering and Computer Sciences

Breast cancer is a prevalent form of cancer across the globe, and if it is not diagnosed at an early stage it can be life-threatening. In order to aid in its diagnosis, detection, and classification, computer-aided detection (CAD) systems are employed. You Only Look Once (YOLO)-based CAD algorithms have become very popular owing to their highly accurate results for object detection tasks in recent years. Therefore, the most popular YOLO models are implemented to compare the performance in mass detection with various experiments on the INbreast dataset. In addition, a YOLO model with an integrated Swin Transformer in its backbone …

Machine Learning Based Bioinformatics Analysis Of Intron Usage Alterations And Metabolic Regulation In Adipose Browning, Hamza Umut Karakurt, Pinar Pi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Adipose tissue is the major energy depot of the body and is considered an endocrine organ. Adipose tissue involves many different cell types, first and foremost, the adipocytes. White adipose cells that store fat and brown adipocytes that take part in lipid oxidation and heat generation are the most common cell types in adipose tissue. Even though brown adipocytes which have a high number of mitochondria and high fat-burning capacity are rare in adults, they are abundant in newborns and rodents. White adipocytes can gain a temporal brown-like character with a process called browning, which can be induced with cold …

Feature Selection Optimization With Filtering And Wrapper Methods: Two Disease Classification Cases, Serhat Ati̇k, Tuğba Dalyan

Turkish Journal of Electrical Engineering and Computer Sciences

Discarding the less informative and redundant features helps to reduce the time required to train a learning algorithm and the amount of storage required, improving the learning accuracy as well as the quality of results. In this study, we present different feature selection approaches to address the problem of disease classification based on the Parkinson and Cardiac Arrhythmia datasets. For this purpose, first we utilize three filtering algorithms including the Pearson correlation coefficient, Spearman correlation coefficient, and relief. Second, metaheuristic algorithms are compared to find the most informative subset of the features to obtain better classification accuracy. As a final …

Fuzzycsampling: A Hybrid Fuzzy C-Means Clustering Sampling Strategy For Imbalanced Datasets, Abdullah Maraş, Çi̇ğdem Erol

Turkish Journal of Electrical Engineering and Computer Sciences

Classification model with imbalanced datasets is recently one of the most researched areas in machine learning applications since they induce to the emergence of low-performing machine learning models. The imbalanced datasets occur if target variables have an uneven number of examples in a dataset. The most prevalent solutions to imbalanced datasets can be categorized as data preprocessing, ensemble techniques, and cost-sensitive learning. In this article, we propose a new hybrid approach for binary classification, named FuzzyCSampling, which aims to increase model performance by ensembling fuzzy c-means clustering and data sampling solutions. This article compares the proposed approaches' results not only …

Effect Of The Homogenization Process On Titanium Oxide-Reinforced Nanocellulose Composite Membranes, Taufiq Mahsuli, Aisyah Larasati, Aminnudin Aminnudin, Jibril Maulana

Journal of Mechanical Engineering Science and Technology (JMEST)

Indonesian pineapple production can reach 200 tons per day; however, pineapples generate a significant amount of waste. Pineapple peel waste can be used to make membranes. Composite membranes containing TiO2 have dense properties, low porosity, and increase the mechanical strength of the cellulose sheet. This research uses various ultrasonic homogenizers to homogenize the distribution of nanocellulose and TiO2 (50% and 100% power with 30, 60, and 90 minutes). The casting method is used to shape the membrane. The SEM test shows that the higher the power used and the longer the sonication time, the less agglomeration of about 1.63%/ cm2 …

Artificial Neural Network-Based Modeling Of Performance Spark Ignition Engine Fuelled With Bioethanol And Gasoline, Susi Marianingsih, Farhanna Mar’I, Hendry Y. Nanlohy

Journal of Mechanical Engineering Science and Technology (JMEST)

Machine learning technology can distinguish the relationship between engine characteristics and performances. Therefore, the goal of the present work is to predict the performance parameters of a singlecylinder 4-stroke gasoline engine at different ignition timings using a blended mixture of gasoline and bioethanol by an artificial neural network (ANN). Experimental data for training and testing in the proposed ANN was obtained at a dynamic speed and full load condition. An ANN model was developed based on standard Back-Propagation algorithm for the spark ignition engine. Multi-layer perception network (MLP) was used for non-linear mapping between the input and output parameters. An …

Thermo-Kinetic Parameters Governing Decomposition Of Amino Acids: A Computational Study, Mubarak Taleb Alkwradi

Thesis/ Dissertation Defenses

Amino acid model compounds are important nitrogenated species that in various categories of biomass. These compounds exhibit unique fragmentation pathways because of their adjacent amino and hydroxyl groups. Comprehending the decomposition chemistry of amino acids is essential to reduce harmful nitrogen emissions during biomass thermal treatment. This research reports the kinetic and thermodynamic properties of amino acids, and sulfur-containing amino acid, based on accurate density functional theory calculations coupled with kinetic modelling. The first part of the thesis provides comprehensive thermochemical parameters for the complete set of amino acids in the gas phase. The optimized geometries of amino acid compounds …

Controlled Radiation Capsule For Precision And Rapid Cancer Treatment, Hoseon Lee, Zsolt Kollar, Bailey R. White, Junia Nguyen, David Roque, Sowjanya Palagani

Symposium of Student Scholars

This research aims to transform cancer treatment through the optimization of brachytherapy, with a focus on reducing treatment duration, setup complexities, and financial burdens, all while emphasizing patient safety. Patients living at a distance from radiation clinics, particularly those undergoing extended Low Dose Radiation brachytherapy, often struggle with the formidable financial challenges associated with securing nearby accommodations. In response to these issues, the research introduces a radiation capsule designed to condense the conventional six-month treatment period to approximately just one week, thereby significantly reducing the duration of required accommodations. This capsule is especially relevant considering the construction cost of $40 …

Towards A Virtual Reality Visualization Of Hand-Object Interactions To Support Remote Physical Therapy, Trudi Di Qi, Louanne Boyd, Scott Fitzpatrick, Meghna Raswan, Franceli L. Cibrian

Engineering Faculty Articles and Research

Improving object manipulation skills through hand-object interaction exercises is crucial for rehabilitation. Despite limited healthcare resources, physical therapists propose remote exercise routines followed up by remote monitoring. However, remote motor skills assessment remains challenging due to the lack of effective motion visualizations. Therefore, exploring innovative ways of visualization is crucial, and virtual reality (VR) has shown the potential to address this limitation. However, it is unclear how VR visualization can represent understandable hand-object interactions. To address this gap, in this paper, we present VRMoVi, a VR visualization system that incorporates multiple levels of 3D visualization layers to depict movements. In …

Occlusive Membranes For Guided Regeneration Of Inflamed Tissue Defects., Woojin Choi, Utkarsh Mangal, Jin-Young Park, Ji-Yeong Kim, Taesuk Jun, Ju Won Jung, Moonhyun Choi, Sungwon Jung, Milae Lee, Ji-Yeong Na, Du Yeol Ryu, Jin Man Kim, Jae-Sung Kwon, Won-Gun Koh, Sangmin Lee, Patrick T J Hwang, Kee-Joon Lee, Ui-Won Jung, Jae-Kook Cha, Sung-Hwan Choi, Jinkee Hong

Henry M. Rowan College of Engineering Departmental Research

Guided bone regeneration aided by the application of occlusive membranes is a promising therapy for diverse inflammatory periodontal diseases. Symbiosis, homeostasis between the host microbiome and cells, occurs in the oral environment under normal, but not pathologic, conditions. Here, we develop a symbiotically integrating occlusive membrane by mimicking the tooth enamel growth or multiple nucleation biomineralization processes. We perform human saliva and in vivo canine experiments to confirm that the symbiotically integrating occlusive membrane induces a symbiotic healing environment. Moreover, we show that the membrane exhibits tractability and enzymatic stability, maintaining the healing space during the entire guided bone regeneration …

The Effects Of Engineering Summer Camps On Middle And High School Students’ Engineering Interest And Identity Formation: A Multi-Methods Study, Timothy Robinson, Adam Kirn, Jenny Amos, Indira Chatterjee

Journal of Pre-College Engineering Education Research (J-PEER)

This multi-methods study explores changes in engineering interest and identity of middle and high school students (n = 79) attending introductory-level engineering summer camps at a large western land grant university. Middle school is a critical time when student interest, identity, and subsequently career choice begin to emerge and hence it is important that at this age students are given accurate information about engineering majors in college and future career opportunities in engineering. Data were collected over a period of two years in six summer camps. Three separate populations of middle and high school students participated in the summer …

Real-Time Arrhythmia Detection Using Convolutional Neural Networks, Thong Vu, Tyler Petty, Kemal Yakut, Muhammad Usman, Wei Xue, Francis M. Haas, Robert A. Hirsh, Xinghui Zhao

Henry M. Rowan College of Engineering Departmental Research

Cardiovascular diseases, such as heart attack and congestive heart failure, are the leading cause of death both in the United States and worldwide. The current medical practice for diagnosing cardiovascular diseases is not suitable for long-term, out-of-hospital use. A key to long-term monitoring is the ability to detect abnormal cardiac rhythms, i.e., arrhythmia, in real-time. Most existing studies only focus on the accuracy of arrhythmia classification, instead of runtime performance of the workflow. In this paper, we present our work on supporting real-time arrhythmic detection using convolutional neural networks, which take images of electrocardiogram (ECG) segments as input, and classify …

Mechanisms And Opportunities For Rational In Silico Design Of Enzymes To Degrade Per- And Polyfluoroalkyl Substances (Pfas), Melissa Marciesky, Diana S. Aga, Ian M. Bradley, Nirupam Aich, Carla Ng

Department of Civil and Environmental Engineering: Faculty Publications

Per and polyfluoroalkyl substances (PFAS) present a unique challenge to remediation techniques because their strong carbon−fluorine bonds make them difficult to degrade. This review explores the use of in silico enzymatic design as a potential PFAS degradation technique. The scope of the enzymes included is based on currently known PFAS degradation techniques, including chemical redox systems that have been studied for perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) defluorination, such as those that incorporate hydrated electrons, sulfate, peroxide, and metal catalysts. Bioremediation techniques are also discussed, namely the laccase and horseradish peroxidase systems. The redox potential of known reactants and …

Integration Of Algae And Biomass Processes To Synthesize Renewable Bioproducts For The Circular Economy, Jessica Martin

USF Tampa Graduate Theses and Dissertations

Rapid population growth and global industrialization have substantially heightened the demand for fossil-based fuels and products in various sectors of the global economy, including energy production, transportation fuels, and as raw materials for petrochemicals. The intense consumption of fossil fuels has caused immense environmental impacts, especially pertaining to carbon dioxide emissions. Shifting to renewable feedstocks (raw materials) is expected to reduce these emissions by lowering the carbon footprint of fuels and products compared to traditional fossil-derived alternatives. This transition aligns with the goal of creating a sustainable and circular economy, emphasizing efficient resource use, and reducing waste generation through recycling …

Exploring The Interactions Of Copper High Aspect Ratio Structures (Cuhars), A Novel Metal-Organic Biohybrid Nanocomposite, With Chondrocytes, Tasneem Khasru

Master's Theses

Osteoarthritis (OA) is a prevalent degenerative joint disorder characterized by cartilage lesions and subchondral bone destruction (1). Addressing OA is challenging due to cartilage's avascular and aneural nature, unique cellular arrangement, and dense extracellular matrix (ECM). Current pharmacological treatments are mainly symptomatic and are unable to halt disease progression or reverse cartilage damage . Surgical interventions also come with complications. Tissue-engineered grafts using biomaterials and molecular manipulation have emerged as potential treatments for cartilage regeneration. Copper-containing scaffolds have shown promise in bone and cartilage regeneration. Nanotechnology offers new avenues, with nanoparticles and nanofibers being used in tissue engineering research. However, …

Improving Cellphone-Based Bio-Imaging Technique For Fluorescence Detection, Erteza T. Efaz

LSU Master's Theses

The research presented in this thesis focuses on the design, development, and evaluation of a fluorescence detection system. The system is implemented primarily as an Android application, Auto Camera, which leverages smartphone cameras to capture and analyze fluorescent images. The application provides a user-friendly interface with some configurable features like exposure time, ISO speed, and storage limit; as well as defining detection thresholds and setting acquisition intervals. This study begins with the architectural framework of the Android application, which is written in Java using Android Studio. The API compatibility is set to version 33, and users are prompted to grant …