Engineering Commons^™

An Investigation Of Information Structures In Dna, Joel Mohrmann

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

The information-containing nature of the DNA molecule has been long known and observed. One technique for quantifying the relationships existing within the information contained in DNA sequences is an entity from information theory known as the average mutual information (AMI) profile. This investigation sought to use principally the AMI profile along with a few other metrics to explore the structure of the information contained in DNA sequences.

Treating DNA sequences as an information source, several computational methods were employed to model their information structure. Maximum likelihood and maximum a posteriori estimators were used to predict missing bases in DNA sequences. …

Next-Generation Crop Monitoring Technologies: Case Studies About Edge Image Processing For Crop Monitoring And Soil Water Property Modeling Via Above-Ground Sensors, Nipuna Chamara

Dissertations and Doctoral Documents from University of Nebraska-Lincoln, 2023–

Artificial Intelligence (AI) has advanced rapidly in the past two decades. Internet of Things (IoT) technology has advanced rapidly during the last decade. Merging these two technologies has immense potential in several industries, including agriculture.

We have identified several research gaps in utilizing IoT technology in agriculture. One problem was the digital divide between rural, unconnected, or limited connected areas and urban areas for utilizing images for decision-making, which has advanced with the growth of AI. Another area for improvement was the farmers' demotivation to use in-situ soil moisture sensors for irrigation decision-making due to inherited installation difficulties. As Nebraska …

Image Filtering To Improve Maize Tassel Detection Accuracy Using Machine Learning Algorithms, Eric Rodene, Gayara Demini Fernando, Ved Piyush, Yufeng Ge, James C. Schnable, Souparno Ghosh, Jinliang Yang

Department of Biological Systems Engineering: Papers and Publications

Unmanned aerial vehicle (UAV)-based imagery has become widely used to collect timeseries agronomic data, which are then incorporated into plant breeding programs to enhance crop improvements. To make efficient analysis possible, in this study, by leveraging an aerial photography dataset for a field trial of 233 different inbred lines from the maize diversity panel, we developed machine learning methods for obtaining automated tassel counts at the plot level. We employed both an object-based counting-by-detection (CBD) approach and a density-based counting-by-regression (CBR) approach. Using an image segmentation method that removes most of the pixels not associated with the plant tassels, the …

Enhancing Estimation Of Cover Crop Biomass Using Field-Based High-Throughput Phenotyping And Machine Learning Models, Geng Bai, Katja Koehler-Cole, David Scoby, Vesh R. Thapa, Andrea D. Basche, Yufeng Ge

Department of Biological Systems Engineering: Papers and Publications

Incorporating cover crops into cropping systems offers numerous potential benefits, including the reduction of soil erosion, suppression of weeds, decreased nitrogen requirements for subsequent crops, and increased carbon sequestration. The aboveground biomass (AGB) of cover crops strongly influences their performance in delivering these benefits. Despite the significance of AGB, a comprehensive field-based high-throughput phenotyping study to quantify AGB of multiple cover crops in the U.S. Midwest has not been found. This study presents a two-year field experiment carried out in Eastern Nebraska, USA, to estimate AGB of five different cover crop species [canola (Brassica napus L.), rye (Secale …

Enhanced Privacy-Enabled Face Recognition Using Κ-Identity Optimization, Ryan Karl

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Facial recognition is becoming more and more prevalent in the daily lives of the common person. Law enforcement utilizes facial recognition to find and track suspects. The newest smartphones have the ability to unlock using the user's face. Some door locks utilize facial recognition to allow correct users to enter restricted spaces. The list of applications that use facial recognition will only increase as hardware becomes more cost-effective and more computationally powerful. As this technology becomes more prevalent in our lives, it is important to understand and protect the data provided to these companies. Any data transmitted should be encrypted …

Development Of A Machine Learning System For Irrigation Decision Support With Disparate Data Streams, Eric Wilkening

Department of Biological Systems Engineering: Dissertations and Theses

In recent years, advancements in irrigation technologies have led to increased efficiency in irrigation applications, encompassing the adoption of practices that utilize data-driven irrigation scheduling and leveraging variable rate irrigation (VRI). These technological improvements have the potential to reduce water withdrawals and diversions from both groundwater and surface water sources. However, it is vital to recognize that improved application efficiency does not necessarily equate to increased water availability for future or downstream use. This is particularly crucial in the context of consumptive water use, which refers to water consumed and not returned to the local or sub-regional watershed, representing a …

Statistical And Machine Learning Approaches To Describe Factors Affecting Preweaning Mortality Of Piglets, Md Towfiqur Rahman, Tami M. Brown-Brandl, Gary A. Rohrer, Sudhendu R. Sharma, Vamsi Manthena, Yeyin Shi

Department of Biological Systems Engineering: Papers and Publications

High preweaning mortality (PWM) rates for piglets are a significant concern for the worldwide pork industries, causing economic loss and well-being issues. This study focused on identifying the factors affecting PWM, overlays, and predicting PWM using historical production data with statistical and machine learning models. Data were collected from 1,982 litters from the United States Meat Animal Research Center, Nebraska, over the years 2016 to 2021. Sows were housed in a farrowing building with three rooms, each with 20 farrowing crates, and taken care of by well-trained animal caretakers. A generalized linear model was used to analyze the various sow, …

Statistical And Machine Learning Approaches To Describe Factors Affecting Preweaning Mortality Of Piglets, Md Towfiqur Rahman, Tami M. Brown-Bandl, Gary A. Rohrer, Sudhendu R. Sharma, Vamsi Manthena, Yeyin Shi

Department of Biological Systems Engineering: Papers and Publications

High preweaning mortality (PWM) rates for piglets are a significant concern for the worldwide pork industries, causing economic loss and well-being issues. This study focused on identifying the factors affecting PWM, overlays, and predicting PWM using historical production data with statistical and machine learning models. Data were collected from 1,982 litters from the U.S. Meat Animal Research Center, Nebraska, over the years 2016 to 2021. Sows were housed in a farrowing building with three rooms, each with 20 farrowing crates, and taken care of by well-trained animal caretakers. A generalized linear model was used to analyze the various sow, litter, …

Statistical And Machine Learning Approaches To Describe Factors Affecting Preweaning Mortality Of Piglets, Md Towfiqur Rahman, Tami M. Brown-Bandl, Gary A. Rohrer, Sudhendu R. Sharma, Vamsi Manthena, Yeyin Shi

Department of Biological Systems Engineering: Papers and Publications

High preweaning mortality (PWM) rates for piglets are a significant concern for the worldwide pork industries, causing economic loss and well-being issues. This study focused on identifying the factors affecting PWM, overlays, and predicting PWM using historical production data with statistical and machine learning models. Data were collected from 1,982 litters from the U.S. Meat Animal Research Center, Nebraska, over the years 2016 to 2021. Sows were housed in a farrowing building with three rooms, each with 20 farrowing crates, and taken care of by well-trained animal caretakers. A generalized linear model was used to analyze the various sow, litter, …

Development Of A Scalable Edge-Cloud Computing Based Variable Rate Irrigation Scheduling Framework, Eric J. Wilkening, Derek M. Heeren, Yeyin Shi, Abia Katimbo, Precious N. Amori, Guillermo R. Balboa, Laila A. Puntel, Kuan Zhang, Daran R. Rudnick

Department of Biological Systems Engineering: Conference Presentations and White Papers

Currently, variable-rate precision irrigation (VRI) scheduling methods require large amounts of data and processing time to accurately determine crop water demands and spatially process those demands into an irrigation prescription. Unfortunately, irrigated crops continue to develop additional water stress when the previously collected data is being processed. Machine learning is a helpful tool, but handling and transmitting large datasets can be problematic; more rural areas may not have access to necessary wireless data transmission infrastructure to support cloud interaction. The introduction of “edge-cloud” processing to agricultural applications has shown to be effective at increasing data processing speed and reducing the …

Cometrics: A New Software Tool For Behavior‑Analytic Clinicians And Machine Learning Researchers, Walker S. Arce, Seth G. Walker, Morgan L. Hurtz

Department of Electrical and Computer Engineering: Faculty Publications

Cometrics is a Microsoft Windows compatible clinical tool for the collection and recording of frequency- and duration-based target behaviors, physiological signals, and video data. This software package is designed to record in-vivo observational and physiological data. In addition, we have included features that allow observers to capture video from real-time camera feeds and import saved video for retroactive data collection. By using Microsoft Excel-based spreadsheets, also called keystroke files, assessment and treatment sessions are exported into a single document using the click of a button. Integrated interobserver agreement metrics allow comparisons across primary and reliability observers, with the output exported …

High-Throughput Phenotyping Of Plant Leaf Morphological, Physiological, And Biochemical Traits On Multiple Scales Using Optical Sensing, Huichun Zhang, Lu Wang, Xiuliang Jin, Liming Bian, Yufeng Ge

Department of Biological Systems Engineering: Papers and Publications

Acquisition of plant phenotypic information facilitates plant breeding, sheds light on gene action, and can be applied to optimize the quality of agricultural and forestry products. Because leaves often show the fastest responses to external environmental stimuli, leaf phenotypic traits are indicators of plant growth, health, and stress levels. Combination of new imaging sensors, image processing, and data analytics permits measurement over the full life span of plants at high temporal resolution and at several organizational levels from organs to individual plants to field populations of plants. We review the optical sensors and associated data analytics used for measuring morphological, …

Unobtrusive Data Collection In Clinical Settings For Advanced Patient Monitoring And Machine Learning, Walker Arce

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

When applying machine learning to clinical practice, a major hurdle that will be encountered is the lack of available data. While the data collected in clinical therapies is suitable for the types of analysis that are needed to measure and track clinical outcomes, it may not be suitable for other types of analysis. For instance, video data may have poor alignment with behavioral data, making it impossible to extract the videos frames that directly correlate with the observed behavior. Alternatively, clinicians may be exploring new data modalities, such as physiological signal collection, to research methods of improving clinical outcomes that …

Characterization Of Physical And Biochemical Traits In Wheat And Corn Plants Using High Throughput Image Analysis, Kantilata Thapa

Department of Biological Systems Engineering: Dissertations and Theses

Plant phenotyping has been recognized as a rapidly growing field of research due to the labor-intensive, destructive, and time-consuming nature of traditional phenotyping methods. These phenotyping bottlenecks can be addressed by advancements in image-based phenotyping like RGB and hyperspectral imaging for the assessment of plant traits important for breeding purposes. This study aims (1) to characterize the physical and biochemical traits of wheat and corn plants using RGB and hyperspectral imaging in the greenhouse, and (2) to estimate leaf nitrogen (N), phosphorus (P), and potassium (K) content using hyperspectral imaging and an analytical spectral device (ASD spectrometer) and compare the …

Finite Element-Based Machine Learning Model For Predicting The Mechanical Properties Of Composite Hydrogels, Yasin Shokrollahi, Pengfei Dong, Peshala T. Gamage, Nashaita Patrawalla, Vipuil Kishore, Hozhabr Mozafari, Linxia Gu

Department of Mechanical and Materials Engineering: Faculty Publications

In this study, a finite element (FE)-based machine learning model was developed to predict the mechanical properties of bioglass (BG)-collagen (COL) composite hydrogels. Based on the experimental observation of BG-COL composite hydrogels with scanning electron microscope, 2000 microstructural images with randomly distributed BG particles were created. The BG particles have diameters ranging from 0.5 μm to 1.5 μm and a volume fraction from 17% to 59%. FE simulations of tensile testing were performed for calculating the Young’s modulus and Poisson’s ratio of 2000 microstructures. The microstructural images and the calculated Young’s modulus and Poisson’s ratio by FE simulation were used …

Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data, Ali Al-Ramini, Mahdi Hassan, Farahnaz Fallahtafti, Mohammad Ali Takallou, Basheer Qolomany, Iraklis I. Pipinos, Fadi Alsaleem, Sara A. Myers

Department of Mechanical and Materials Engineering: Faculty Publications

Peripheral artery disease (PAD) manifests from atherosclerosis, which limits blood flow to the legs and causes changes in muscle structure and function, and in gait performance. PAD is underdiagnosed, which delays treatment and worsens clinical outcomes. To overcome this challenge, the purpose of this study is to develop machine learning (ML) models that distinguish individuals with and without PAD. This is the first step to using ML to identify those with PAD risk early. We built ML models based on previously acquired overground walking biomechanics data from patients with PAD and healthy controls. Gait signatures were characterized using ankle, knee, …

Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data, Ali Al-Ramini, Mahdi Hassan, Farahnaz Fallahtafti, Mohammad Ali Takallou, Hafizur Rahman, Basheer Qolomany, Iraklis I. Pipinos, Fadi M. Alsaleem, Sara A. Myers

Department of Mechanical and Materials Engineering: Faculty Publications

Peripheral artery disease (PAD) manifests from atherosclerosis, which limits blood flow to the legs and causes changes in muscle structure and function, and in gait performance. PAD is underdiagnosed, which delays treatment and worsens clinical outcomes. To overcome this challenge, the purpose of this study is to develop machine learning (ML) models that distinguish individuals with and without PAD. This is the first step to using ML to identify those with PAD risk early. We built ML models based on previously acquired overground walking biomechanics data from patients with PAD and healthy controls. Gait signatures were characterized using ankle, knee, …

Sers Spectroscopy With Machine Learning To Analyze Human Plasma Derived Sevs For Coronary Artery Disease Diagnosis And Prognosis, Xi Huang, Bo Liu, Shenghan Guo, Weihong Guo, Ke Liao, Guoku Hu, Wen Shi, Mitchell Kuss, Michael J. Duryee, Daniel R. Anderson, Yongfeng Lu, Bin Duan

Department of Electrical and Computer Engineering: Faculty Publications

Coronary artery disease (CAD) is one of the major cardiovascular diseases and represents the leading causes of global mortality. Developing new diagnostic and therapeutic approaches for CAD treatment are critically needed, especially for an early accurate CAD detection and further timely intervention. In this study, we successfully isolated human plasma small extracellular vesicles (sEVs) from four stages of CAD patients, that is, healthy control, stable plaque, non-ST-elevation myocardial infarction, and ST-elevation myocardial infarction. Surface-enhanced Raman scattering (SERS) measurement in conjunction with five machine learning approaches, including Quadratic Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor, Artificial Neural network, were then …

Sers Spectroscopy With Machine Learning To Analyze Human Plasma Derived Sevs For Coronary Artery Disease Diagnosis And Prognosis, Xi Huang, Bo Liu, Shenghan Guo, Weihong Guo, Ke Liao, Guoku Hu, Wen Shi, Mitchell Kuss, Michael J. Duryee, Daniel R. Anderson, Yongfeng Lu, Bin Duan

Department of Electrical and Computer Engineering: Faculty Publications

Coronary artery disease (CAD) is one of the major cardiovascular diseases and represents the leading causes of global mortality. Developing new diagnostic and therapeutic approaches for CAD treatment are critically needed, especially for an early accurate CAD detection and further timely intervention. In this study, we successfully isolated human plasma small extracellular vesicles (sEVs) from four stages of CAD patients, that is, healthy control, stable plaque, non-ST-elevation myocardial infarction, and ST-elevation myocardial infarction. Surface-enhanced Raman scattering (SERS) measurement in conjunction with five machine learning approaches, including Quadratic Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor, Artificial Neural network, were then …

Ag-Iot For Crop And Environment Monitoring: Past, Present, And Future, Nipuna Chamara, Md Didarul Islam, Geng Bai, Yeyin Shi, Yufeng Ge

Department of Biological Systems Engineering: Papers and Publications

CONTEXT: Automated monitoring of the soil-plant-atmospheric continuum at a high spatiotemporal resolution is a key to transform the labor-intensive, experience-based decision making to an automatic, data-driven approach in agricultural production. Growers could make better management decisions by leveraging the real-time field data while researchers could utilize these data to answer key scientific questions. Traditionally, data collection in agricultural fields, which largely relies on human labor, can only generate limited numbers of data points with low resolution and accuracy. During the last two decades, crop monitoring has drastically evolved with the advancement of modern sensing technologies. Most importantly, the introduction …

Machine Learning-Based Device Type Classification For Iot Device Re- And Continuous Authentication, Kaustubh Gupta

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Today, the use of Internet of Things (IoT) devices is higher than ever and it is growing rapidly. Many IoT devices are usually manufactured by home appliance manufacturers where security and privacy are not the foremost concern. When an IoT device is connected to a network, currently there does not exist a strict authentication method that verifies the identity of the device, allowing any rogue IoT device to authenticate to an access point. This thesis addresses the issue by introducing methods for continuous and re-authentication of static and dynamic IoT devices, respectively. We introduce mechanisms and protocols for authenticating a …

Ultra-Broadband And Polarization-Insensitive Metasurface Absorber With Behavior Prediction Using Machine Learning, Shobhit K. Patel, Juveriya Parmar, Vijay Katkar, Fahad Ahmed Al-Zahrani, Kawsar Ahmed

Department of Mechanical and Materials Engineering: Faculty Publications

The solar spectrum energy absorption is very important for designing any solar absorber. The need for absorbing visible, infrared, and ultraviolet regions is increasing as most of the absorbers absorb visible regions. We propose a metasurface solar absorber based on Ge₂Sb₂Te₅ (GST) substrate which increases the absorption in visible, infrared and ultraviolet regions. GST is a phase-changing material having two different phases amorphous (aGST) and crystalline (cGST). The absorber is also analyzed using machine learning algorithm to predict the absorption values for different wavelengths. The solar absorber is showing an ultra-broadband response covering a 0.2–1.5 …

Investigation Of The Prevalence Of Faults In The Heating, Ventilation, And Air-Conditioning Systems Of Commercial Buildings, Amir Ebrahimifakhar

Durham School of Architectural Engineering and Construction: Dissertations, Thesis, and Student Research

This dissertation describes a large-scale investigation of heating, ventilation, and air-conditioning (HVAC) fault prevalence in commercial buildings in the United States. A multi-year dataset with 36,556 pieces of HVAC equipment including air handling units (AHUs), air terminal units (ATUs), and packaged rooftop units (RTUs) was analyzed to determine values for several HVAC fault prevalence metrics. The primary source of data for this study comes from three commercial fault detection and diagnostics (FDD) providers. Since each FDD provider uses different terms to refer to the same fault in an HVAC system, a mapping function was created for each FDD provider’s dataset, …

Uavs For Vegetation Monitoring: Overview And Recent Scientific Contributions, Ana I. De Castro, Yeyin Shi, Joe Mari Maja, Jose M. Peña

Department of Biological Systems Engineering: Papers and Publications

This paper reviewed a set of twenty-one original and innovative papers included in a special issue on UAVs for vegetation monitoring, which proposed new methods and techniques applied to diverse agricultural and forestry scenarios. Three general categories were considered: (1) sensors and vegetation indices used, (2) technological goals pursued, and (3) agroforestry applications. Some investigations focused on issues related to UAV flight operations, spatial resolution requirements, and computation and data analytics, while others studied the ability of UAVs for characterizing relevant vegetation features (mainly canopy cover and crop height) or for detecting different plant/crop stressors, such as nutrient content/deficiencies, water …

Breast Cancer Detection From Histopathology Images Using Machine Learning Techniques: A Bibliometric Analysis, Shubhangi A. Joshi, Anupkumar M. Bongale Dr., Arunkumar M. Bongale Dr.

Library Philosophy and Practice (e-journal)

Computer aided diagnosis has become upcoming area of research over past few years. With the advent of machine learning and especially deep learning techniques, the scenario of work flow management in healthcare sector is changing drastically. Artificial intelligence has shown potential in the field of breast cancer care. With datasets for machine learning frameworks getting eventually richer with time, we can definitely get newer insights in the field of breast cancer care. This will help in narrowing down the treatment range for patients and increasing patient survivability. The purpose of this study was to perform bibliometric analysis of the literature …

Quantitative Analysis Of Research On Artificial Intelligence In Retinopathy Of Prematurity, Ranjana Agrawal, Manasi Anup Agrawal, Sucheta Kulkarni, Ketan Kotecha, Rahee Walambe

Library Philosophy and Practice (e-journal)

Retinopathy of Prematurity (ROP) is a disease of the eye and a potential source of blindness in low birth weight preterm infants. It is preventable if diagnosed and treated on time. Artificial Intelligence (AI) has played an important role in developing automated screening systems to assist medical experts. There are many traditional literature review articles available that focus on the scientific content of ROP-AI. The researchers also require a bibliometric analysis to become acquainted with the competing groups and new trends in this field. This paper gives a brief overview of ROP and AI systems for ROP screening with a …

Liver Segmentation And Liver Cancer Detection Based On Deep Convolutional Neural Network: A Brief Bibliometric Survey, Kiran Malhari Napte Mr., Anurag Mahajan Dr.

Library Philosophy and Practice (e-journal)

Background: This study analyzes liver segmentation and cancer detection work, with the perspectives of machine learning and deep learning and different image processing techniques from the year 2012 to 2020. The study uses different Bibliometric analysis methods.

Methods: The articles on the topic were obtained from one of the most popular databases- Scopus. The year span for the analysis is considered to be from 2012 to 2020. Scopus analyzer facilitates the analysis of the databases with different categories such as documents by source, year, and county and so on. Analysis is also done by using different units of analysis such …

A Bibliometric Analysis Of Plant Disease Classification With Artificial Intelligence Based On Scopus And Wos, Shivali Amit Wagle, Harikrishnan R

Library Philosophy and Practice (e-journal)

The maneuver of Artificial Intelligence (AI) techniques in the field of agriculture help in the classification of diseases. Early prediction of the disease benefits in taking relevant management steps. This is an important step towards controlling the disease growth that will yield good quality products to fulfill the global food demand. The main objective of this paper is to study the extent of research work done in this area of plant disease classification. The paper discusses the bibliometric analysis of plant disease classification with AI in Scopus and Web of Science core collection (WOS) database in analyzing the research by …

Investigating The Effect Of E30 Fuel On Long Term Vehicle Performance, Adaptability And Economic Feasibility, Adil Alsiyabi, Seth Stroh, Rajib Saha

Department of Chemical and Biomolecular Engineering: Faculty Publications

Due to the drawbacks associated with the use of petroleum derived fuels, the use of more sustainable fuel sources has garnered increasing attention in several sectors including road transportation. However, the transition away from gasoline is often hindered by the inability of currently operating vehicles to efficiently run under alternative fuels. Therefore, the logical short-term alternative is to transition to clean fuel sources including higher-ethanol fuel blends that are compatible with current fuel systems and spark-ignition engines. In this work, the long-term adaptability and economic feasibility of non-flex vehicles to consume a 30% ethanol (E30) fuel blend was investigated. Sixteen …

Editorial: Predictive Modeling Of Human Microbiota And Their Role In Health And Disease, Hyun-Seob Song, Stephen R. Lindemann, Dong-Yup Lee

Department of Biological Systems Engineering: Papers and Publications

No abstract provided.