Physical Sciences and Mathematics Commons^™

Herbicide Management Alters Fungal Biomass And Community Composition In Great Lakes Coastal Wetlands, Erica Danae Lathers

Master's Theses and Doctoral Dissertations

Wetlands provide many ecosystem services, and invasive species and their management can alter these services. Plant responses to wetland management actions are typically monitored, but less is known about how soils respond. Phragmites australis, an invasive wetland plant, can alter soil conditions, potentially impacting soil microbial structure and function. We sampled soil communities in Great Lakes coastal wetlands in southeastern Michigan dominated by Phragmites and sites that had been previously invaded by Phragmites then treated with herbicide from 2011-2017, to understand how herbicide management alters soil fungal communities. We assessed microbial and fungal biomass, fungal community composition, greenhouse gas emissions, …

Inpainting Program, Owen Culmer

Williams Honors College, Honors Research Projects

Image processing is a quickly developing field of computer science, especially with the growth of computer vision and generative artificial intelligence. For my project, I focused on object removal, where a targeted object or area can be removed from a digital image. I first accomplished this by implementing a seam carving algorithm, where rows or columns of pixels called seams are removed from an image, that prioritizes the removal of seams that include targeted areas. I next explored a digital inpainting algorithm that would replace targeted areas, rather than removing them, in order to maintain the exact image dimensions and …

Determination Of Spore Viability In Concrete Across Several Factors Using Most Probable Number, Samuel Boyer

Williams Honors College, Honors Research Projects

To determine the lowest concentration of spore added to polyurethane-cement composite (PUCCO) particles that can still germinate after curing in concrete. This research project is a small addition to the larger research project being undertaken by Mirza Mohammed Rashiduzzaman for his Masters. The larger project involves the use of fungal spores added in concrete to act as a self-healing component when cracks form in the concrete structure over time. These spores are suspended in a protective oil and loaded into small, hardened sponge-like PUCCO cubes to act as growth points when water and air can reach the PUCCO in the …

Modeling Inflation Using A Fast Fourier Transform (Fft), Blake Smith

Williams Honors College, Honors Research Projects

This paper utilizes a Fast Fourier Transform (FFT) algorithm to construct a trigonometric interpolant for the Consumer Price Index (CPI), which is then differentiated and used to obtain a continuous function for “instantaneous” (i.e., month-wise) inflation, as opposed to a 12-month percent-change. Fourier coefficients are analyzed to investigate underlying periodicities in the newly constructed function. This metric does not hold significant predictive value but it may prove helpful in retroactive analysis of inflation trends.

To Mean Or Not To Mean: An Investigation Of Regression To The Mean, Hunter Ellis

Williams Honors College, Honors Research Projects

Regression to the mean is a statistical phenomenon that can hide important characteristics of what is truly happening in a research study. Caused by statistical randomness, regression to the mean occurs when extreme values, high or low, are followed by less extreme values. To correctly deal with it, one must understand what it is and how to distinguish its effect on conclusions made from the data. This paper provides examples of regression to the mean in both a medical and academic performance study and explains simple identifiers one can observe. Those are then followed up by the introduction of the …

Interconnectivity Of Melt During Partial Melting And Deformation Of A Peridotite, Jordan Collins

Williams Honors College, Honors Research Projects

The mechanisms that allow basaltic magma to be transported to the surface are well understood, but not much is known about how the transport channels form as the basalt melt forms at the source. I performed experiments to determine these mechanisms during general shear deformation of a natural olivine aggregate (dunite) from Anita Bay, New Zealand as it partially melts using a high pressure/temperature rock deformation apparatus. Samples were analyzed using the scanning electron microscope and I observed that melt pockets started to form at the grain scale (1-2 vol% melt) prior to becoming interconnected at the sample scale (3 …

Constraining Models For The Origin Of Ultra-High-Energy Cosmic Rays With A Novel Combined Analysis Of Arrival Directions, Spectrum, And Composition Data Measured At The Pierre Auger Observatory, A. Abdul Halim, P. Abreu, M. Aglietta, I. Allekotte, K. Almeida Cheminant, A. Almela, B. Fick, D. Nitz, A. Puyleart, Et Al.

Michigan Tech Publications

The combined fit of the measured energy spectrum and shower maximum depth distributions of ultra-high-energy cosmic rays is known to constrain the parameters of astrophysical models with homogeneous source distributions. Studies of the distribution of the cosmic-ray arrival directions show a better agreement with models in which a fraction of the flux is non-isotropic and associated with the nearby radio galaxy Centaurus A or with catalogs such as that of starburst galaxies. Here, we present a novel combination of both analyses by a simultaneous fit of arrival directions, energy spectrum, and composition data measured at the Pierre Auger Observatory. The …

Climate Change's Effect On Flow Regime, Alexander Ialenti

Williams Honors College, Honors Research Projects

This project will test to see if there is a percent increase in non-perennial streams sampled from 2003-2021. Using data provided by The Cleveland Metroparks, sampling events will be separated by date, flow regime classification, and rain data. Current literature supports the claim that many perennial streams, streams that flow year-round, will become non-perennial streams over time. This shift is predicted to be caused by a change in rain patterns. Both the interval between rain events and the intensity of rainfall per event are predicted to increase. My hypothesis is that there will be an increase in the percentage of …

Ensemble Classification: An Analysis Of The Random Forest Model, Jarod Korn

Williams Honors College, Honors Research Projects

The random forest model proposed by Dr. Leo Breiman in 2001 is an ensemble machine learning method for classification prediction and regression. In the following paper, we will conduct an analysis on the random forest model with a focus on how the model works, how it is applied in software, and how it performs on a set of data. To fully understand the model, we will introduce the concept of decision trees, give a summary of the CART model, explain in detail how the random forest model operates, discuss how the model is implemented in software, demonstrate the model by …

Managing Inventory With A Database, David Bartlett

Williams Honors College, Honors Research Projects

Large commercial companies often use warehouses to store and organize their product inventory. However, manually keeping track of inventory through physical means can be a tedious process and is at risk for a variety of potential issues. It is very easy for records to be inaccurate or duplicated, especially if large reorganizations are undertaken, as this can cause issues such as duplicate product ID numbers. Therefore, it was decided that an inventory management system utilizing a SQL database should be created. The system needed to have capabilities including allowing the entry of product information, the ability to search database records …

Adaptive Critic Optimal Control Of An Uncertain Robot Manipulator With Applications, Ravi Prakash, Laxmidhar Behera, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Realistic manipulation tasks involve a prolonged sequence of motor skills in varying control environments consisting of uncertain robot dynamic models and end-effector payloads. To address these challenges, this article proposes an adaptive critic (AC)-based basis function neural network (BFNN) optimal controller. Using a single neural network (NN) with a basis function, the proposed optimal controller simultaneously learns task-related optimal cost function, robot internal dynamics, and optimal control law. This is achieved through the development of a novel BFNN tuning law using closed-loop system stability. Therefore, the proposed optimal controller provides real-time, implementable, cost-effective control solutions for practical robotic tasks. The …

Coupling-Informed Data-Driven Scheme For Joint Angle And Frequency Estimation In Uniform Linear Array With Mutual Coupling Present, Yanming Zhang, Wenchao Xu, A. Long Jin, Min Li, Peifeng Ma, Lijun Jiang, Steven Gao

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a novel coupling-informed data-driven algorithm tailored for the concurrent estimation of frequency and angle within a uniform linear array (ULA), while addressing the complicating influence of mutual coupling. Leveraging the hybrid dynamic mode decomposition (DMD) methodology, termed as averaged DMD, we incorporate moving average techniques to achieve effective denoising. The averaged DMD further decomposes the received signal into eigenvalues and corresponding eigenvectors. The frequency information is derived from the eigenvalues and the corresponding eigenvectors represent the steering vectors of sources. Subsequently, mutual coupling is informed into the calibration of the steering vector for each source. Specifically, the …

A Tensor-Based Data-Driven Approach For Multidimensional Harmonic Retrieval And Its Application For Mimo Channel Sounding., Yanming Zhang, Wenchao Xu, A. Long Jin, Min Li, Ping Yuan, Lijun Jiang, Steven Gao

Electrical and Computer Engineering Faculty Research & Creative Works

In wireless channel sounding, accurately estimating multiple parameters within a multipath signal, such as azimuth, elevation, Doppler shift, and delay, necessitates addressing the challenges posed by the multidimensional harmonic retrieval (MHR) problem. To overcome these complexities, we propose a framework based on high-order dynamic mode decomposition (HODMD) that designed for robustly estimating frequencies of interest from high-dimensional sinusoidal signals, particularly in additive white Gaussian noise conditions. The HODMD approach, a hybrid algorithm amalgamating high-order singular value decomposition (HOSVD) and dynamic mode decomposition (DMD), operates by initially decomposing observed tensorial data into a core tensor and R mode matrices through HOSVD. …

Lifelong Safe Optimal Adaptive Tracking Control Of Nonlinear Strict-Feedback Discrete-Time Systems, Behzad Farzanegan, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a comprehensive approach for achieving multi-task safe optimal adaptive tracking (MSOAT) for a class of nonlinear discrete-time systems, particularly those in strict-feedback form, utilizing a multi-layer neural network (MNN)-based framework. To begin, a cost function with a novel Barrier function (BF) term is introduced for each subsystem to address the weak safely reachable problem, serving as a crucial tool for guiding the system's trajectory toward the safe set while avoiding unwanted sets. To deal with the tracking problem, the Hamilton-Jacobi-Bellman (HJB) framework is used through the actor-critic MNN-based backstepping technique to estimate the solution of the value …

Skipresnet: Crop And Weed Recognition Based On The Improved Resnet, Wenyi Hu, Tian Chen, Chunjie Lan, Shan Liu, Lirong Yin

Electrical & Computer Engineering Faculty Publications

Weeds have a detrimental effect on crop yield. However, the prevailing chemical weed control methods cause pollution of the ecosystem and land. Therefore, it has become a trend to reduce dependence on herbicides; realize a sustainable, intelligent weed control method; and protect the land. In order to realize intelligent weeding, efficient and accurate crop and weed recognition is necessary. Convolutional neural networks (CNNs) are widely applied for weed and crop recognition due to their high speed and efficiency. In this paper, a multi-path input skip-residual network (SkipResNet) was put forward to upgrade the classification function of weeds and crops. It …

Using Environmental Dna Methods For Estimating Juvenile Chinook Salmon Abundance, Jacqueline Marie Bridegum

Cal Poly Humboldt theses and projects

In the Central Valley of California, stocks of Chinook Salmon are declining, and two out of the four ecotypes of Chinook Salmon within the Sacramento River system are federally listed. A monitoring network has been developed to estimate the abundances of downstream migrating Chinook Salmon smolts, but there is high uncertainty in these estimates due to low catch rates. Environmental DNA (eDNA) monitoring, which involves capture of genetic material that organisms release into their surrounding environment, has shown promise for estimating abundance of salmonids and could complement existing monitoring efforts and potentially reduce uncertainty in abundance estimates. This research project …

Incorporating Equity Into Sea-Level Rise Planning: Perspectives From Practitioners Across California, Nayré Herrera

Cal Poly Humboldt theses and projects

Scholars along with national and state governments have increasingly made calls to incorporate an equity lens into climate adaptation processes and into sea-level rise planning specifically. However, the language used in these high-level policies remains vague. There remains a need to learn from practitioners on the ground about the challenges and opportunities for effectively incorporating an equity lens into sea-level rise adaptation and planning efforts. I conducted interviews with 17 individuals who are working within their agencies or organizations to advance equity across the California coast along with a review of 17 California coastal policy documents to gain insights into …

The "Upside-Down" River: Trends In Total Nitrogen, Total Phosphorus, And Microcystin Concentration In The Klamath Basin, 2010-2021, Kayla M. Fitzpatrick

Cal Poly Humboldt theses and projects

The Klamath River tends to experience impaired upstream water quality, which improves downstream. Although this consensus is accepted, an updated spatiotemporal analysis of certain water quality parameters has not been conducted since 2017. With four of the Klamath River’s dams slated for removal by the end of 2024, it is critical to have an updated assessment of current water quality conditions from which to compare conditions following dam removal. In this study, total nitrogen (TN), total phosphorus (TP), and microcystin data from 2010-2021 were analyzed to determine concentration trends from Link Dam to the Klamath River Estuary.

Concentrations of TN …

Recovery Of Riparian Shade And Stream Temperature After Thinning In Coastal Redwood Riparian Forests, Preston J. Selby

Cal Poly Humboldt theses and projects

Historical forest harvest methods such as clear-cut harvesting in the Pacific Northwest have led to dense, even-aged stands, particularly in riparian forests. In an effort to promote the recovery of late-successional conditions, land managers have turned to silvicultural approaches such as thinning, though few studies have evaluated its long-term effects in riparian areas. Due to stream-riparian linkages, changes in riparian forests can directly influence stream ecosystems, particularly thermal regimes, by altering shade and solar radiation influx. Thus, understanding the temporal and spatial impacts of riparian thinning is crucial for land managers interested in thinning as a potential restoration tool for …

Evaluating The Trojan Y Chromosome Strategy For The Removal Of Invasive Sacramento Pikeminnow From The Eel River, Ca, Alexander W. Juan

Cal Poly Humboldt theses and projects

The recent introduction and spread of Sacramento Pikeminnow (Ptychocheilus grandis) in Northern California’s Eel River Basin represents a significant threat and impediment to the recovery of several threatened native fish species. This study was undertaken to evaluate the Trojan Y Chromosome Strategy (TYC) for the extirpation of pikeminnow from the basin. TYC is a genetic biocontrol method that relies on the production and stocking of fish with YY sex chromosomes, which may be phenotypically male (YY male) or female (YY female). These YY fish produce all-male offspring when mating with their wild conspecifics and TYC can lead to …

Drought's Lingering Shadow: Monitoring The Recovery Of Sagebrush Steppe Plant Communities After Four Years Of Experimental Drought, Francisco Jesus Chavez

Cal Poly Humboldt theses and projects

Droughts are projected to increase in frequency and intensity due to climate change, which will significantly impact terrestrial ecosystems. However, the response magnitude will vary depending on a system's resistance and resilience to drought. Furthermore, drought may elicit different responses within the hierarchical levels of plant communities (i.e., individual, community, or ecosystem responses). These effects can extend beyond the drought's duration as legacies persist and influence previously disturbed systems. I assessed whether hierarchical responses occurred within two different adjacent sagebrush plant communities, dominated by Artemisia arbuscula and Artemisia cana, during and after an experimental drought. I used nondestructive point frame …

Barn Owls Exert Top-Down Effects On The Abundance And Behavior Of Rodent Pests, Katherine C. Larson

Cal Poly Humboldt theses and projects

Predators can exert top-down effects on their prey by direct consumption, by imposing risk of predation, or a combination of both. However, empirical evidence for these effects is mixed and inconsistent. Barn owls in agriculture present a system with attributes that should, according to theory, lead to strong top-down effects, but research is limited and the distinction between effects on prey abundance and behavior remains unresolved. Understanding the top-down effect of predators on pests is a cornerstone of integrated pest management and important where barn owls may contribute to the control of rodents on farms. Vegetation can mediate the top-down …

Ai Trustworthy: Ethical Challenges And Strategies, Jian Liu, Iwan Sandjaja, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This paper explores the pivotal role of trust in the widespread application of Artificial Intelligence (AI) across various domains. We review AI applications in sectors like energy, healthcare, and autonomous vehicles and discuss the crisis of human trust they face. This paper introduces a novel framework that delineates the relationship between AI transparency and user trust, highlighting specific industry applications. Through a systematic review of recent literature, we first delve into factors such as emotional response, acceptance, transparency, accuracy, and interpretability that shape human trust in AI. We then underscore the necessity of ethical AI practices and highlight the importance …

Automatic Classification Of Activities In Classroom Videos, Jonathan K. Foster, Matthew Korban, Peter Youngs, Ginger S. Watson, Scott T. Acton

VMASC Publications

Classroom videos are a common source of data for educational researchers studying classroom interactions as well as a resource for teacher education and professional development. Over the last several decades emerging technologies have been applied to classroom videos to record, transcribe, and analyze classroom interactions. With the rise of machine learning, we report on the development and validation of neural networks to classify instructional activities using video signals, without analyzing speech or audio features, from a large corpus of nearly 250 h of classroom videos from elementary mathematics and English language arts instruction. Results indicated that the neural networks performed …

Designing High-Performance Identity-Based Quantum Signature Protocol With Strong Security, Sunil Prajapat, Pankaj Kumar, Sandeep Kumar, Ashok Kumar Das, Sachin Shetty, M. Shamim Hossain

VMASC Publications

Due to the rapid advancement of quantum computers, there has been a furious race for quantum technologies in academia and industry. Quantum cryptography is an important tool for achieving security services during quantum communication. Designated verifier signature, a variant of quantum cryptography, is very useful in applications like the Internet of Things (IoT) and auctions. An identity-based quantum-designated verifier signature (QDVS) scheme is suggested in this work. Our protocol features security attributes like eavesdropping, non-repudiation, designated verification, and hiding sources attacks. Additionally, it is protected from attacks on forgery, inter-resending, and impersonation. The proposed scheme benefits from the traditional designated …

Embedding Software Engineering In Mixed Methods: Computationally Enhanced Risk Communication, Ann Marie Reinhold, Madison H. Munro, Elizabeth A. Shanahan, Ross J. Gore, Barry C. Ezell, Clemente I. Izurieta

VMASC Publications

Mixed methods research ameliorates many convergent research challenges within the contemporary sociotechnical landscape. We suggest the integration of software engineering in mixed methods studies is a critical step to address some of the remaining and persistent challenges. One such research challenge where software engineering is particularly well suited is in hazard preparedness—in particular, the creation of risk communication messages to mitigate or prevent harm. Computationally enhanced risk communication is convergent research that integrates software engineering and social science research for the benefit of protecting humans and infrastructure. To this end, we developed a mixed methods framework for the efficient construction …

Cyclegan-Gradient Penalty For Enhancing Android Adversarial Malware Detection In Gray Box Setting, Fabrice Setephin Atedjio, Jean-Pierre Lienou, Frederica F. Nelson, Sachin S. Shetty, Charles A. Kamhoua

VMASC Publications

Adversarial attacks pose significant threats to Android malware detection by undermining the effectiveness of machine learning-based systems. The rapid increase in Android apps complicates the management of malicious software that can compromise user defense solutions. Many current Android defense techniques rely on deep learning methods. Malicious users exploit GAN-based attacks to achieve adversarial attack transferability and deceive target models by crafting adversarial examples based on known models. We propose a new model based on a Cycle Generative Adversarial Network (CycleGAN) to detect GAN-based attacks. This model incorporates a gradient penalty to enhance the detection rate of the target model. Our …

A Defensive Strategy Against Android Adversarial Malware Attacks, Fabrice Setephin Atedjio, Jean-Pierre Lienou, Frederica F. Nelson, Sachin S. Shetty, Charles A. Kamhoua

VMASC Publications

Due to the popularity of Android mobile devices over the past ten years, malicious Android applications have significantly increased. Systems utilizing machine learning techniques have been successfully applied for Android malware detection to counter the constantly changing Android malware threats. However, attackers have developed new strategies to circumvent these systems by using adversarial attacks. An attacker can carefully craft a malicious sample to deceive a classifier. Among the evasion attacks, there is the more potent one, which is based on solid optimization constraints: the Carlini-Wagner attack. Carlini-Wagner is an attack that uses margin loss, which is more efficient than cross-entropy …

Designing A Blockchain-Empowered Telehealth Artifact For Decentralized Identity Management And Trustworthy Communication: Interdisciplinary Approach, Xueping Liang, Nabid Alam, Tahmina Sultana, Eranga Bandara, Sachin Shetty

VMASC Publications

Background: Telehealth played a critical role during the COVID-19 pandemic and continues to function as an essential component of health care. Existing platforms cannot ensure privacy and prevent cyberattacks.

Objective: The main objectives of this study are to understand existing cybersecurity issues in identity management and trustworthy communication processes in telehealth platforms and to design a software architecture integrated with blockchain to improve security and trustworthiness with acceptable performance.

Methods: We improved personal information security in existing telehealth platforms by adopting an innovative interdisciplinary approach combining design science, social science, and computer science in the health care domain, with prototype …

Enhancing Cyber Resilience Through Traffic Generation Patterns In Complex Networks: A Study On Cascading Failures, Aymar Le Père Tchimwa Bouom, Jean-Pierre Lienou, Wilson Ejuh Geh, Frederica Nelson, Sachin Shetty, Charles Kamhoua

VMASC Publications

Network resilience is the capacity of a network to maintain and restore its fundamental operations during or after a failure. This paper investigates the resilience of communication networks with heterogeneous nodes, with host nodes that generate and receive packets and routers that only forward packets. We focus on how traffic generation patterns, defined as the distribution of data packet creation across hosts, affect network resilience. While previous studies identified optimal host placements that balance traffic loads and enhance network performance, this research explores how traffic generation patterns influence network resilience, particularly during cascading failures, where the failure of one node …