Engineering Commons^™

Regional Sea Level Rise Prediction In Monterey Bay With Lstms And Vertical Land Motion, Branden Lopez

Master's Projects

Earth system data is vast in volume and variety, and is used to forecast weather,

hurricanes, floods, and sea level. Sea Level Rise (SLR) impacts various sectors, espe- cially ecosystems, food production, industry, population, health, and the availability of

clean water. Because of its broad impact, describing the behavior and forecasting SLR is an important topic. Traditional Machine Learning (ML) models vary in use, but many are not capable of capturing all the non-linear spatial and temporal properties of SLR factors. Deep learning models efficaciously handle complex time series data, noise, and high dimensional spaces, making them a focus of …

Examining The Externalities Of Highway Capacity Expansions In California: An Analysis Of Land Use And Land Cover (Lulc) Using Remote Sensing Technology, Serena E. Alexander, Bo Yang, Owen Hussey, Derek Hicks

Mineta Transportation Institute

There are over 590,000 bridges dispersed across the roadway network that stretches across the United States alone. Each bridge with a length of 20 feet or greater must be inspected at least once every 24 months, according to the Federal Highway Act (FHWA) of 1968. This research developed an artificial intelligence (AI)-based framework for bridge and road inspection using drones with multiple sensors collecting capabilities. It is not sufficient to conduct inspections of bridges and roads using cameras alone, so the research team utilized an infrared (IR) camera along with a high-resolution optical camera. In many instances, the IR camera …

Electrical Vehicle Charging Infrastructure Design And Operations, Yu Yang, Hen-Geul Yeh

Mineta Transportation Institute

California aims to achieve five million zero-emission vehicles (ZEVs) on the road by 2030 and 250,000 electrical vehicle (EV) charging stations by 2025. To reduce barriers in this process, the research team developed a simulation-based system for EV charging infrastructure design and operations. The increasing power demand due to the growing EV market requires advanced charging infrastructures and operating strategies. This study will deliver two modules in charging station design and operations, including a vehicle charging schedule and an infrastructure planning module for the solar-powered charging station. The objectives are to increase customers’ satisfaction, reduce the power grid burden, and …

Group-Invariant Reinforcement Learning, Fnu Ankur

Master's Theses

Our work introduces a way to learn an optimal reinforcement learning agent accompanied by intrinsic properties of the environment. The extracted properties helps the agent to extrapolate the learning to unseen states efficiently. Out of all the various types of properties, we are intrigued towards equivariant and invariant properties, which essentially translates to symmetry. Contrary to many approaches, we do not assume the symmetry, rather learn them, making the approach agnostic to the environment and the property. The learned properties offers multiple perspective of the environment to exploit it to benefit decision making while interacting with the environment. By building …

Automatic Presentation Slide Generation Using Llms, Tanya Gupta

Master's Theses

Presentation slides are widely used for conveying information in academic and professional contexts. However, manual slide creation can be time-consuming. Our research focuses on automated slide generation, specifically for scientific research papers. Automating the creation of presentation slides for scientific documents is a rather novel task and hence, there’s limited training data available and there also exists the token constraints of language models like BERT, with a maximum sequence length of 512 tokens. In this study, we fine-tune large language models, including Longformer-Encoder-Decoder (supporting sequences up to 16,834 tokens) and BIGBIRD-Pegasus (supporting sequences up to 4,096 tokens). We tackle this …

Intrinsic Motivation By The Principles Of Non-Linear Dynamical Systems, Phu C. Nguyen

Master's Theses

The design of appropriate control rules for the stabilization of dynamical systems can require quite substantial domain knowledge. Modern AI methodologies, such as Reinforcement Learning, are often used to mitigate the need for such knowledge. However, these can be slow and often rely on at least some hand-designed reward structure, and thus human input, to be more effective. Here, we propose an alternative route to construct rewards requiring only minimal domain knowledge, essentially relying on the structure of the dynamical system itself. For this, we use truncated Lyapunov exponents as rewards to calculate the stabilizing controller from samples. Concretely, the …

Controllability-Constrained Deep Neural Network Models For Enhanced Control Of Dynamical Systems, Suruchi Sharma

Master's Theses

Control of a dynamical system without the knowledge of dynamics is an important and challenging task. Modern machine learning approaches, such as deep neural networks (DNNs), allow for the estimation of a dynamics model from control inputs and corresponding state observation outputs. Such data-driven models are often utilized for the derivation of model-based controllers. However, in general, there are no guarantees that a model represented by DNNs will be controllable according to the formal control-theoretical meaning of controllability, which is crucial for the design of effective controllers. This often precludes the use of DNN-estimated models in applications, where formal controllability …

Deep Learning In Ai Medical Imaging For Stroke Diagnosis, James Mario Guzman

Master's Theses

Enhancing medical imaging stroke diagnosis applications with artificial intelligence (AI) tools to determine lesion volume, location and clinical metadata is vital toward guiding patient treatment and procedure. A major hardship in developing stroke diagnosis AI tools is the scarcity of publicly available clinical 3D stroke datasets. Through working with Johns Hopkins University, University of Michigan’s ICPSR data repository and SJSU research, we gained access to potentially the largest 3D MRI stroke dataset with clinical metadata annotated by neuroradiologists known as ICPSR 38464. With the ICPSR 38464 dataset recently being available through institutional review board (IRB) approval or exemption, we were …

Detecting The Onion Routing Traffic In Real-Time By Using Reinforcement Learning, Dazhou Liu

Master's Theses

Anonymous networks have been popularly utilized to protect user anonymity and facilitate network security for a decade. However, such networks have been a platform for adversarial affairs and various network attacks including suspicious traffic generators. As a result, detecting anonymous network traffic is one critical task to defend a network against unpredictable attacks. Many new methods using machine learning and deep learning techniques have been proposed. However, many of them rely heavily on a vast amount of labeled data and have complicated architectures. Since network traffic always fluctuates under different network environments, those techniques may degrade in performance due to …

Poriferal Vision: Using Mobilenet To Classify Sponge Spicules Through Transfer Learning, Brian Tran

Master's Projects

Global warming is an ongoing issue where the Earth is rapidly warming up. It negatively affects the growth of coral through ocean warming and ocean acidification. Many coral communities, home to a large variety of marine life, are expected to be severely impacted by these effects. Past evidence suggests that sponges will take over as the primary reef builders since many species of sponges have skeletons made of silica or glass which is not affected by ocean acidification. More research is needed to determine which kinds of sponge will most likely be able to thrive in today’s climate.

This can …

Spartandark: Anonymity Model Integration With A Blockchain Network Using Spartangold, Nishanth Uchil

Master's Projects

Demand for blockchain ecosystems has seen exponential growth in recent times due to its decentralized nature and trustless verification process for the transactions involved. However, transaction data needs to be leveraged for verification, which coupled with the transparent nature of the blockchain ledger, provides sufficient data for malicious entities to reveal identities and even financial history of users. Data masking techniques have been employed over the years to make blockchain transactions anonymous, making them resistant to identity analysis, a key set of methods being zero-knowledge proof (zk-proof) protocols that guarantee zero data leak. In this research, we develop SpartanDark, a …

Identification Of Copy Number Variations (Cnvs) Of Epigenetic Factors Related To The Progression Of Pancreatic Ductal Adenocarcinoma (Pdac), Pavithra Raju

Master's Projects

Pancreatic ductal adenocarcinoma (PDAC) is a formidable challenge in oncology due to its aggressive form and late-stage detection. PDAC is known to be influenced by various epigenetic factors like DNA methylation and histone modifications. This study focuses on copy number variations (CNVs) within epigenetic factors which for their role in early diagnosis. Thus, paving the way for identification of potential biomarkers. The epigenetic pipeline was extended based on CNVs and the CNV modified sequences extracted were compared with the wild type sequences of epigenetic PDAC genes. The epigenetic gene KCNJ11 with copy number gain of CNV id 46771406 was used …

Analyzing The Benthic Cover Of Crustose Coralline Algae Using Mask-R Cnn, Rachana Ravindra

Master's Projects

Coral reefs, supporting 25% of marine biodiversity, confront challenges from local and global impacts like overfishing, runoff, acidification, and warming. Crustose Coralline Algae (CCA), pivotal for reef structure and coral settlement, are underrepresented in research. Current methods like Coral Point Count with Excel Extensions (CPCe) have limitations, relying on image quality and being time-consuming. This paper proposes computer vision and Mask R-CNN, a supervised machine learning model, for CCA analysis in reef images, considering color, texture, and shape. Results indicate promise in clustering and classifying organisms. The innovative technology reduces manual labor, enhancing image analysis, simplifying the understanding of CCA’s …

Pygrapherconnect, Shubham Jain

Master's Projects

The evolving landscape of backend computational systems especially in biomedical research involving heavy data operations which have a gap of not being used properly. It is due to the lack of communication standard between the frontend and backend. This gap presents a problem to researchers who need to use the frontend for visualizing and manipulating their data but also want to do complex analysis. CAPRI a python-based backend system specializing in analyzing Evidential Reasoning data also has the same issue. This project offers a solution PyGrapherConnect module acting as a data conversion layer between CAPRI and PyGrapher, its frontend interface. …

Graphical User Interface For Evidential Reasoning Models, Rohin Gopalakrishnan

Master's Projects

The Capri system is an evidential reasoning system based on the belief function calculus to support automated reasoning and decision making in uncertain environments. Example domains of application include, medical diagnosis, as well as identifying biological biomarkers. The purpose of this project is to build a Python web-based and app-based Graphical User Interface (GUI), called PyGrapher, that facilitates building graphical evidential reasoning models. The graphical models built using PyGrapher will then be converted to a form that is suitable for input to the Capri system. The PyGrapher system provides an intuitive means to build and manipulate evidential reasoning models as …

Mild Cognitive Impairment And Alzheimer’S Disease Detection And Testing Interface (Mci-Addti) Modeller10.4 Integrating Structure-Function Prediction Modules, Grant Galileo Jacobson

Master's Projects

In the population of adult human patients who over express Beta and Tau Amyloids, it is unclear why 40% of them do not have Alzheimer’s Disease (AD), when all patients with AD have an overexpression of Beta and Tau Amyloids. The MCI-AD-DTI project’s epigenetic pipeline is an evolving computation tool that seeks epigenetic-related information related to the observed disparity. The MCI-AD-DTI’s epigenetic pipeline’s ability to identify mutations currently relies solely on PyPDB for verification of its protein functionality evaluation. The assessment process of the industry standard application, Modeller10.4, is independent from the current epigenetic pipeline’s protein evaluation algorithm. Thus, this …

Serverless Architecture For Machine Learning, Ikshaku Goswami

Master's Projects

Serverless computing is an area under cloud computing which does not require individual management of cloud infrastructure and services. It is the groundwork behind Function as a Service or FaaS cloud computing technique. FaaS provides a stateless event-driven orchestration of functions and services for applications deployed in the cloud, without having to manage the servers and other infrastructure resources. This event driven architecture is being well utilized to manage different web-applications and services. Machine learning can bring a unique challenge to serverless computing, as it involves high-intensive tasks which requires voluminous data. In such a scenario it becomes essential to …

Uncertainty-Aware And Explainable Artificial Intelligence For Identification Of Human Errors In Nuclear Power Plants, Bhavya Reddy Kotla

Master's Projects

Nuclear Power Plants (NPPs) can face challenges in maintaining standard operations due to a range of issues, including human mistakes, mechanical breakdowns, electrical problems, measurement errors, and external influences. Swift and precise detection of these issues is crucial for stabilizing the NPPs. Identifying such operational anomalies is complex due to the numerous potential scenarios. Additionally, operators need to promptly discern the nature of an incident by tracking various indicators, a process that can be mentally taxing and increase the likelihood of human errors. Inaccurate identification of problems leads to inappropriate corrective actions, adversely affecting the safety and efficiency of NPPs. …

Metagenomic Survey Of Marine 16s Bacterial Communities Off Palmer Station In Antarctica, Daniel Salter

Master's Projects

This project surveys the metagenomic bacterial community composition in marine surface waters off Palmer Station, Western Antarctic Peninsula and correlates findings with temperature and salinity data. Marine bacterial communities play a vital role in nutrient cycling, but data on surface waters in this region are limited. Analyzing fifteen samples of 16S sequencing data from three austral summers, consistent dominance was observed by the classes Alphaproteobacteria, Gammaproteobacteria, and Flavobacteria. Correlation analysis confirmed significant relationships between taxa and environmental conditions. The observed trends suggest varying abilities of phyla to resist and adapt to changing environmental conditions. Notably, Alphaproteobacteria demonstrated adaptability to favorable …

Evaluation Of The Effect Of Walnut Extract On Sp1-Related Pathways, Jihan Yehia

Master's Projects

Walnut extract (WE) has shown promising anti-cancer effects, such as inducing apoptosis and moderating cell cycle progression. A previous study by Dr. Brandon White’s Lab at San Jose State University hypothesizes that WE can downregulate the expression of the pro-tumoral specificity protein 1 (Sp1) in triple negative breast cancer (TNBC). This project builds an RNA-seq pipeline that runs differential gene expression (DGE) analysis to study the effect of WE on TNBC, thereby offering a wider perspective on genes that may be affected by this treatment. The data used in this project originated from Illumina and Nanopore sequencing methods, and DGE …

Identifying Potential Alzheimer’S Disease Biomarkers Beyond Amyloid-Beta And Tau, Frank Cai

Master's Projects

Alzheimer's Disease (AD) and other forms of Mild Cognitive Impairment (MCI) affect millions of people around the world. The buildup of Amyloid-Beta (Aβ) and Tau proteins in the brain produced by amyloid precursor protein (APP) has been identified as an important cofactor in the onset and progression of AD. However, although patients diagnosed with AD exhibit Aβ and Tau buildup, about 40% of the subjects with Aβ and Tau buildup are not diagnosed with AD. In this project, we hypothesize the involvement of other epigenetic interactions between APP and related genes in addition to the buildup of Aβ and Tau …

Evalsql - Automated Assessment Of Database Queries, Damanpreet Kaur

Master's Projects

In computer science programs, database is a fundamental subject taught through several undergraduate courses. These courses develop theoretical and practical concepts of databases. Building queries is a key aspect of this learning process, and students are assessed through assignments and quizzes. However, grading these assignments can be time-consuming for professors, and students usually receive feedback only after the deadlines have passed. As a result, students may miss the opportunity to improve their work and achieve better grades. To address this issue, it would be beneficial to provide students with immediate feedback on their submissions. EvalSQL is an automated system that …

Enhancing The Queueing Process For Yioop's Scheduler, Gargi Sheguri

Master's Projects

Indexing in search engines is the process of storing information related to crawled pages to facilitate searches. A crucial determinant of the success of a search engine is the efficiency of the indexing process utilized, which greatly affects both the speed and relevancy of search results. Yioop is an open-source web search engine that employs an inverted index strategy, wherein each term is mapped to a list of the documents it appeared in while crawling.

The primary aim of this project is to better the indexing system used by Yioop, and thus improve the quality of the Search Engine Results …

Graph Based System For Evidential Reasoning, Divyarajsinh Chauhan

Master's Projects

In the modern data driven world, graph editing tools have become very essential as they provide means to understand, visualize and manipulate complex relationships between various datasets. They have especially played a crucial role in the space of evidential reasoning, where it has made a significant impact in the decision making process by developers, analysts and researchers to understand and represent the connection in the data. Existing tools fail to handle huge amounts of data efficiently and also don’t have the features required to handle tasks related to evidential reasoning.To address these gaps, we developed Pygrapher Web UI tool. We …

Estimating Air Pollution Levels Using Machine Learning, Srujay Rao Devaraneni

Master's Projects

Air pollution has emerged as a substantial concern, especially in developing countries worldwide. An important aspect of this issue is the presence of PM2.5. Air pollutants with a diameter of 2.5 or less micrometers are known as PM2.5. Due to their size, these particles are a serious health risk and can quickly infiltrate the lungs, leading to a variety of health problems. Due to growing concerns about air pollution, technology like automatic air quality measurement can offer beneficial assistance for both personal and business decisions. This research suggests an ensemble machine learning model that can efficiently replace the standard air …

Multimap Implementation In Openjdk, Nishant Yadav

Master's Projects

A key-value pair is an elementary data model in which a unique key is associated with a given value. This association between the key and the value allows for a quick lookup of data based on the key and hence is extensively used in programming languages, NoSQL databases, caches, session management, etc. In Java OpenJDK, this elementary data model is implemented by the interface Map, which allows efficient storage and retrieval of data but can only store a single value against each key. In this project, we have implemented a MultiMap data structure in OpenJDK which allows associating multiple values …

Xai-Driven Cnn For Diabetic Retinopathy Detection, Vikas Shenoy Pete

Master's Projects

Diabetes, a chronic metabolic disorder, poses a significant health threat with potentially severe consequences, including diabetic retinopathy, a leading cause of blindness. In this project, we tackle this threat by developing a Convolutional Neural Network (CNN) to support the diagnosis based on eye images. The aim is early detection and intervention to mitigate the effects of diabetes on eye health. To enhance transparency and interpretability, we incorporate explainable AI techniques. This research not only contributes to the early diagnosis of diabetic eye disease but also advances our understanding of how deep learning models arrive at their decisions, fostering trust and …

Chapter 3: Basis, Basis Vectors, And Inner Product, Hiu Yung Wong

Faculty Research, Scholarly, and Creative Activity

No abstract provided.

Chapter 27: Bloch Sphere And Single-Qubit Arbitrary Unitary Gate, Hiu Yung Wong

Faculty Research, Scholarly, and Creative Activity

No abstract provided.

Chapter 28: Quantum Phase Estimation, Hiu Yung Wong

Faculty Research, Scholarly, and Creative Activity

No abstract provided.