Physical Sciences and Mathematics Commons^™

Drivers Of 20th Century Sea-Level Change In Southern New Zealand Determined From Proxy And Instrumental Records, Ed Garrett, W. Roland Gehrels, Bruce W. Hayward, Rewi Newnham, Maria J. Gehrels, Craig J. Morey, Sönke Dangendorf

CCPO Publications

In this paper we present new proxy-based sea-level reconstructions for southern New Zealand spanning the last millennium. These palaeo sea-level records usefully complement sparse Southern Hemisphere proxy and tide-gauge sea-level datasets and, in combination with instrumental observations, can test hypotheses about the drivers of 20th century global sea-level change, including land-based ice melt and regional sterodynamics. We develop sea-level transfer functions from regional datasets of salt-marsh foraminifera to establish a new proxy-based sea-level record at Mokomoko Inlet, at the southern tip of the South Island, and to improve the previously published sea-level reconstruction at Pounawea, located about 110 km to …

The Role Of Zooplankton Community Composition In Fecal Pellet Carbon Production In The York River Estuary, Chesapeake Bay, Kristen Nicole Sharpe

Dissertations, Theses, and Masters Projects

The biological pump is a critical component of carbon transformation in aquatic ecosystems, but the role that zooplankton play in carbon production and vertical export is rarely studied in estuaries. Zooplankton produce carbon-rich fecal pellets which sink to depth and can fuel benthic community metabolism. The body size and taxonomic structure of the zooplankton community varies on interannual, seasonal, and diel time scales, and can lead to varying carbon production and export rates. We quantified fecal pellet carbon (FPC) production by the whole mesozooplankton community (> 200 µm) in the York River, a sub-estuary of Chesapeake Bay. Biomass and taxonomic …

Toward A Comprehensive Water Quality Model For The Chesapeake Bay Using Unstructured Grids, Xun Cai

Dissertations, Theses, and Masters Projects

Chesapeake Bay is one of the most productive ecosystems on the US east coast which supports various living resources and habitat, and therefore has significant impacts on human beings and ecosystem health. Developing the capability of accurately simulating the water quality condition in the Chesapeake Bay, such as seasonal hypoxia, phytoplankton production, and nutrient dynamics, helps to better understand the interactions of hydrodynamical and biochemical processes, and more importantly, to predict conditions under changing climate and human intervention. Currently, most Chesapeake Bay models use structured grids that lack the flexibility for local refinements to fit complex geometry over both large …

Abstractive Text Summarization For Tweets, Siyu Chen

Master's Projects

In the high-tech age, we can access a vast number of articles, information, news, and opinion online. The wealth of information allows us to learn about the topics we are interested in more easily and cheaply, but it also requires us to spend an enormous amount of time reading online. Text summarization can help us save a lot of reading time so that we can know more information in a shorter period. The primary goal of text summarization is to shorten the text while including as much vital information as possible in the original text so fewer people use this …

City Of Milpitas Trash Capture Device Program: An Evaluation Of System Performance And Compliance With The Municipal Regional Permit, Joseph Aguilera

Master's Projects

Water pollution negatively impacts the environment and human population. The problem persists despite various mitigation efforts, strategies, and the implementation of regulatory requirements. It is estimated that Californians dispose of approximately 40 million tons of consumer items and waste materials annually (California Department of Resource Recycling and Recovery, 2019). As the population increases, it is expected that negative impacts of trash on the environment will be exacerbated. To address this, municipalities in California apply various methods to reduce trash before it enters ocean waters.

The primary vehicle for urban trash pollutants to reach ocean waters is through storm water conveyance …

Canvas Autoquiz, Archit Jain

Master's Projects

Online learning management platforms such as Canvas are thriving and quickly replacing traditional classrooms, especially during these pandemic-struck times. As more and more quizzes are administered online, we need tools that make the quiz creation process easier and faster. Canvas Autoquiz is a command-line tool that allows instructors to automatically create and upload quizzes of varying difficulty levels. It also allows instructors to export quizzes from one LMS platform to another. This project explores the need, design, and implementation of the tool, and prospective future work.

Hard Real-Time Linux On A Raspberry Pi For 3d Printing, Alvin Nguyen

Master's Projects

The project presents how a Raspberry Pi with hard real-time enabled Linux can control stepper motors to operate the kinematics of a 3D (three-dimensional) printer. The consistent performance of the Raspberry Pi with the PREEMPT-RT (real-time) patch can satisfy real hard-time requirements for 3D printing kinematics, without introducing dedicated microcontrollers. The Klipper 3D printer firmware enables one of the Raspberry Pi processors to act as the Klipper MCU, the primary controller for the hardware components. This project introduces a software implementation of the control logic for controlling the stepper motors, which utilizes the PCA9685 pwm driver and TB6612 motor drivers …

Gesture Recognition Using Neural Networks, Ashwini Kurady

Master's Projects

The advances in technology have brought in a lot of changes in the way humans go about their lives. This has enhanced the significance of Artificial Neural Networks and Computer Vision- based interactions with the world. Gesture Recognition is one of the major focus areas in Computer Vision. This involves Human Computer Interfaces (HCI) that would capture and understand human actions. In this project, we will explore how Neural Network concepts can be applied in this challenging field of Computer Vision. By leveraging the latest research for Gesture Recognition, we researched on how to capture the movement across different frames …

Predicting Externally Visible Traits From A Dna Sample For Law Enforcement Applications, Niraj Pandkar

Master's Projects

A large majority of crimes such as homicides, sexual assaults and missing person cases are not solved within a reasonable timeframe and become cold cases. The ability to predict visual appearance and ancestry from a DNA sample will provide an unprecedented advancement in such criminal investigations. DNA based prediction of craniofacial features, phenotypes and ancestry can be used to reduce the pool of candidates onto which to perform further investigations. To achieve the above goal, it is first essential to substantiate, model and measure the intrinsic relationship between the genomic markers and phenotypic features. The first step is to standardize …

Hidden Markov Models With Momentum, Andrew Miller

Master's Projects

Momentum is a popular technique for improving convergence rates during gradient descent. In this research, we experiment with adding momentum to the Baum-Welch expectation-maximization algorithm for training Hidden Markov Models. We compare discrete Hidden Markov Models trained with and without momentum on English text and malware opcode data. The effectiveness of momentum is determined by measuring the changes in model score and classification accuracy due to momentum. Experiments indicate that adding momentum to Baum-Welch can reduce the number of iterations required for initial convergence during HMM training, particularly in cases where the model is slow to converge. However, momentum does …

Generative Adversarial Networks For Image-Based Malware Classification, Huy Nguyen

Master's Projects

Malware detection and analysis are important topics in cybersecurity. For efficient malware removal, determination of malware threat levels, and damage estimation, malware family classification plays a critical role. With the rise in computing power and the advent of cloud computing, deep learning models for malware analysis has gained in popularity. In this paper, we extract features from malware executable files and represent them as images using various approaches. We then focus on Generative Adversarial Networks (GAN) for multiclass classification and compare our GAN results to other popular machine learning techniques, including Support Vector Machine

(SVM), XGBoost, and Restricted Boltzmann Machines …

Faking Sensor Noise Information, Justin Chang

Master's Projects

Noise residue detection in digital images has recently been used as a method to classify images based on source camera model type. The meteoric rise in the popularity of using Neural Network models has also been used in conjunction with the concept of noise residuals to classify source camera models. However, many papers gloss over the details on the methods of obtaining noise residuals and instead rely on the self- learning aspect of deep neural networks to implicitly discover this themselves. For this project I propose a method of obtaining noise residuals (“noiseprints”) and denoising an image, as well as …

Robustness Of Image-Based Malware Analysis, Katrina Tran

Master's Projects

Being able to identify malware is important in preventing attacks. Image-based malware analysis is the study of images that are created from malware. Analyzing these images can help identify patterns in malware families. In previous work, "gist descriptor" features extracted from images have been used in malware classification problems and have shown promising results. In this research, we determine whether gist descriptors are robust with respect to malware obfuscation techniques, as compared to Convolutional Neural Networks (CNN) trained directly on malware images. Using the Python Image Library, we create images from malware executables and from malware that we obfuscate. We …

Investigating Lattice-Based Cryptography, Michaela Molina

Master's Projects

Cryptography is important for data confidentiality, integrity, and authentication. Public key cryptosystems allow for the encryption and decryption of data using two different keys, one that is public and one that is private. This is beneficial because there is no need to securely distribute a secret key. However, the development of quantum computers implies that many public-key cryptosystems for which security depends on the hardness of solving math problems will no longer be secure. It is important to develop systems that have harder math problems which cannot be solved by a quantum computer.

In this project, two public-key cryptosystems which …

A Novel Handover Method Using Destination Prediction In 5g-V2x Networks, Pooja Shyamsundar

Master's Projects

This paper proposes a novel approach to handover optimization in fifth generation vehicular networks. A key principle in designing fifth generation vehicular network technology is continuous connectivity. This makes it important to ensure that there are no gaps in communication for mobile user equipment. Handovers can cause disruption in connectivity as the process involves switching from one base station to another. Issues in the handover process include poor load management for moving traffic resulting in low bandwidth or connectivity gaps, too many hops resulting in multiple unneccessary handovers, short dwell times and ineffective base station selection resulting in delays and …

Improving User Experiences For Wiki Systems, Parth Patel

Master's Projects

Wiki systems are web applications that allow users to collaboratively manage the content. Such systems enable users to read and write information in the form of web pages and share media items like videos, audios, books etc. Yioop is an open-source web portal with features of a search engine, a wiki system and discussion groups. In this project I have enhanced Yioop’s features for improving the user experiences. The preliminary work introduced new features like emoji picker tool for direct messaging system, unit testing framework for automating the UI testing of Yioop and redeeming advertisement credits back into real money. …

Analysis Of Public Sentiment Of Covid-19 Pandemic, Vaccines, And Lockdowns, Devinesh Singh

Master's Projects

CoV-2 pandemic prompted lockdown measures to be implemented worldwide; these directives were implemented nationwide to stunt the spread of the infection. Throughout the lockdowns, millions of individuals resorted to social media for entertainment, communicate with friends and family, and express their opinions about the pandemic. Simultaneously, social media aided in the dissemination of misinformation, which has proven to be a threat to global health. Sentiment analysis, a technique used to analyze textual data, can be used to gain an overview of public opinion behind CoV-2 from Twitter and TikTok. The primary focus of the project is to build a deep …

Factors Affecting The Production Of Berries Of The Red Huckleberry Plant In The Redwood Experimental Forest, Kagat G. Mcquillen

Cal Poly Humboldt theses and projects

Vaccinium parvifolium (red huckleberry) is a culturally and commercially valued food for different coastal tribes of northwestern California.

Today some tribes are regaining access to ancestral lands, and Indigenous researchers are working to document information about culturally significant plant species and their management to reclaim traditional ecological knowledge and restore food sovereignty.

The present study, focused on red huckleberry, Vaccinium parvifolium, took place in the ancestral territory of the Yurok Tribe, currently managed by the US Forest Service as the Redwood Experimental Forest (REF) in Klamath, CA. Geographic Information Systems-based mapping, forest ecological field data collection, and a literature …

It Permeated Everything: A Lived Experience Of Slow Violence And Toxicological Disaster, Tara Jo Holmberg

Antioch University Dissertations & Theses

Impacts of disasters on individuals are dependent on numerous factors: local to international political dynamics, socioeconomics, geography, educational background, and outside support among others. Currently, much of disaster research focuses on those of natural origin, acute and large-scale environmental events, emergency management, and the ability of individuals, communities, and societies to prepare for, and recover from, likely known disasters in their region. However, there is a lack of data about individual experiences through ‘invisible’ anthropogenic disasters, especially those that fall under the umbrella of slow environmental violence (Davies, 2019; Rice, 2016). Through critical phenomenological autoethnography, I examine an individual experience …

Security Concerns On Machine Learning Solutions For 6g Networks In Mmwave Beam Prediction, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Umit Cali, Devrim Unal

Engineering Technology Faculty Publications

6G – sixth generation – is the latest cellular technology currently under development for wireless communication systems. In recent years, machine learning (ML) algorithms have been applied widely in various fields, such as healthcare, transportation, energy, autonomous cars, and many more. Those algorithms have also been used in communication technologies to improve the system performance in terms of frequency spectrum usage, latency, and security. With the rapid developments of ML techniques, especially deep learning (DL), it is critical to consider the security concern when applying the algorithms. While ML algorithms offer significant advantages for 6G networks, security concerns on artificial …

A Comparison Of Deep Learning Algorithms On Image Data For Detecting Floodwater On Roadways, Sarp Salih, Kuzlu Murat, Zhao Yanxiao, Cetin Mecit

Engineering Technology Faculty Publications

Object detection and segmentation algorithms evolved significantly in the last decade. Simultaneous object detection and segmentation paved the way for real-time applications such as autonomous driving. Detection and segmentation of (partially) flooded roadways are essential inputs for vehicle routing and traffic management systems. This paper proposes an automatic floodwater detection and segmentation method utilizing the Mask Region-Based Convolutional Neural Networks (Mask-R-CNN) and Generative Adversarial Networks (GAN) algorithms. To train the model, manually labeled images with urban, suburban, and natural settings are used. The performances of the algorithms are assessed in accurately detecting the floodwater captured in images. The results show …

Bfv-Based Homomorphic Encryption For Privacy-Preserving Cnn Models, Febrianti Wibawa, Ferhat Ozgur Catak, Salih Sarp, Murat Kuzlu

Engineering Technology Faculty Publications

Medical data is frequently quite sensitive in terms of data privacy and security. Federated learning has been used to increase the privacy and security of medical data, which is a sort of machine learning technique. The training data is disseminated across numerous machines in federated learning, and the learning process is collaborative. There are numerous privacy attacks on deep learning (DL) models that attackers can use to obtain sensitive information. As a result, the DL model should be safeguarded from adversarial attacks, particularly in medical data applications. Homomorphic encryption-based model security from the adversarial collaborator is one of the answers …

A Pilot Course As A Step Towards New Academic Programs In Renewable Energies, Otilia Popescu, Orlando Ayala, Isaac Flory, Jose Fernandez, Vukica Jovanović

Engineering Technology Faculty Publications

The challenges arising from climate change have never before in human history been more pressing for solutions. Addressing pollution and the transition to clean energies are essential problems to solve in the upcoming decades. The process of transitioning to renewable energies has started already, with some states leading the process. As the development of industries sees a fast growth, the supply of qualified engineers and technicians to support these industries needs to keep up. At the community college level, some efforts have already started to introduce courses on renewable energies as well as boot camps or certifications to prepare the …

Defensive Distillation-Based Adversarial Attack Mitigation Method For Channel Estimation Using Deep Learning Models In Next-Generation Wireless Networks, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Umit Cali, Ozgur Guler

Engineering Technology Faculty Publications

Future wireless networks (5G and beyond), also known as Next Generation or NextG, are the vision of forthcoming cellular systems, connecting billions of devices and people together. In the last decades, cellular networks have dramatically grown with advanced telecommunication technologies for high-speed data transmission, high cell capacity, and low latency. The main goal of those technologies is to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, smart cities, smart grids, advanced manufacturing, and many more. The key motivation of NextG networks is to meet the high demand for those …

Security Hardening Of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks, Ferhat Ozgur Catak, Murat Kuzlu, Haolin Tang, Evren Catak, Yanxiao Zhao

Engineering Technology Faculty Publications

Next-generation communication networks, also known as NextG or 5G and beyond, are the future data transmission systems that aim to connect a large amount of Internet of Things (IoT) devices, systems, applications, and consumers at high-speed data transmission and low latency. Fortunately, NextG networks can achieve these goals with advanced telecommunication, computing, and Artificial Intelligence (AI) technologies in the last decades and support a wide range of new applications. Among advanced technologies, AI has a significant and unique contribution to achieving these goals for beamforming, channel estimation, and Intelligent Reflecting Surfaces (IRS) applications of 5G and beyond networks. However, the …

Development Of Experiential Learning Experiences For K-12 Students Focusing On Smart Cities, Murat Kuzlu, Vukica Jovanovic, Nathan Puryear, Patrick J. Martin, Sherif Abdelwahed, Özgür Güler

Engineering Technology Faculty Publications

The main objective of this paper is to describe a project focused on the development of experiential learning experiences for undergraduate and graduate students focusing on smart cities. The future workforce needs students with various data analytics skills, service reliability, and sustainability. The team of researchers from Old Dominion University and Virginia Commonwealth University is developing a virtual smart city lab environment at both universities and collaborating on multiple research projects. The main purpose of this virtual labs is to provide a testbed that can be used for students who are interested in careers related to cyber-physical systems (CPS). These …

Strange Stones Of Skull Creek: Basalt Glacial Erratics And Omars In Eastern Nebraska, Robert M. Joeckel, Jesse T. Korus, Judith Turk, C. C. Arps, N. V. Arps, Leslie M. Howard

Conservation and Survey Division

We describe unusual stream- reworked glacially transported rocks (erratics) from a locality 50 km east of the limit of all pre- Illinoian (pre- 190 ka) Pleistocene glaciations in the central USA. Almost all these erratics consist of the igneous rock basalt, and of those, the vast majority have at least one fl at, smooth face. Some have two or more such faces that meet at obtuse angles along one or more well- defi ned, straight edges. We attribute these features, as well as laminations, plumose marks, and other features, to columnar jointing in ancient lava fl ows and shallow intrusions. …

The Impact Of Sampling Methodology On Soil Bulk Density Measurement By The Clod Method, Aldi J. Airori, Trinity Baker, Judith Turk

Conservation and Survey Division

The clod method is a widely used and accurate bulk density method. However, its use is limited to sampling from soil pits. This study was conducted to: 1) determine whether clods collected from cores provide similar bulk density measurements to those collected from soil pits and 2) evaluate the impact of various clod bulk density methods on carbon stock calculation. Clods were collected from soil pits, 5.1 cm soil cores, and 8.9 cm soil cores. Three-dimensional laser scanning was used to measure the volume of the soil clods before and after oven-drying and bulk density was calculated as the dry …

2022 Nebraska Water Leaders Academy Final Report, Mark E. Burbach, Robert Matthew Joeckel, Brooke Mott, Gina S. Matkin

Conservation and Survey Division

Fifteen participants completed the 2022 Water Leaders Academy bringing the total number of graduates to 168 since the inception of the program in 2011. Assessments of participants’ transformational leadership skills, champion of innovation skills, water knowledge, engagement with water issues, civic capacity, entrepreneurial leadership behaviors, and boundary spanning skills increased significantly over the course of the year, according to both the participants and their raters. Feedback from the participants was highly positive and constructive. Academy planners are addressing participant concerns. Results of the program assessment indicate that the curriculum is meeting Academy objectives. Therefore, only minor changes are planned for …

Characterization Of Cyclopropyl Synthases Involved In The Maturation Of Ribosomally Synthesized And Posttranslationally Modified Peptides, Yi Lien

Electronic Theses and Dissertations

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a large class of natural products with significant human health implications. RiPPs are synthesized from a genetically encoded precursor peptide that undergoes significant modifications by maturing enzymes, or maturases. Recently, radical-S-adenosylmethionine (rSAM) enzymes have emerged as an important family of RiPP maturases. rSAM enzymes have been shown to install ether, thioether, and carbon-carbon bonds on the precursor peptide. These modifications usually define the backbone structure of the mature RiPP. This thesis describes the characterization of a novel RiPP modification catalyzed by the radical S-adenosylmethionine enzyme TigE. TigE belongs to the TIG biosynthetic …