Computer Engineering Commons^™

Estimation Of The Berthing Parameter Of Unmanned Surface Vessels Based On 3d Lidar, Haichao Wang, Yong Yin, Qianfeng Jing, Lin Cong

Journal of System Simulation

Abstract: Accurate estimation of berthing parameters is a prerequisite for unmanned surface vessel autonomous berthing. A method for berthing parameter estimation is proposed based on shipborne 3D LiDAR. The method consists of two main modules: ship pose estimation and berthing state estimation. In the berthing position estimation module, raw point cloud data undergoes preprocessing algorithms aims at downsampling and removing outliers. Point cloud registration algorithms are employed to determine the vessel's position during the berthing process. The berthing state estimation module extracts berth boundary information by using the MSAC algorithm, and on the basis of this information, calculates the berthing …

Faids: Artificial Intelligence Developmental Systems Framework For Predicting And Preventing Cyberattacks In Supply Chain Networks, Lordt Becklines

Research & Publications

Cyber threats and attacks disrupt and damages supply chain networks (SCNs), which are complex and interlinked. Current methods to predict and prevent cyberattacks are inadequate and ineffective. This research proposes an AI developmental systems framework (FAIDS) to protect SCNs from cyberattacks. The framework has four components: (1) an AI threat intelligence system; (2) an AI risk assessment system; (3) an AI decision support system; and (4) an AI learning and adaptation system. The framework is tested on a simulated retail SCN. The results show that the framework can predict and prevent cyberattacks and improve the network's resilience and security. The …

Synthetic Data Generation For Accurate, Fair, And Private Recommender Systems, Derek B. Lilienthal

Master's Theses

A notorious challenge for recommender systems on online platforms is to accurately and fairly recommend items that align with users’ preferences while retaining user privacy. These systems often rely on historical data that is partially labeled and includes incomplete user information, leading to the over-representation of majority groups and disproportionately favoring popular items. Substituting user data with synthetic data can address these concerns, but accurately replicating real-world datasets has been a challenging problem. Recent advancements in generative AI have demonstrated the impressive capabilities of diffusion models in generating realistic data across several domains. In this work, we introduce two variations …

Reinforcement Learning Based Planning And Control For Robotic Source Seeking Inspired By Fruit Flies, Gauravkumar Koradiya

Master's Theses

In the exploration of mobile sensing agents such as robots equipped with sensors, these agents have the potential to measure unknown scalar fields, ranging from chemical concentrations to temperature fluctuations. One of the primary interests is the robots’ ability to gravitate towards the peaks or troughs of these fields, a phenomenon termed as “source seeking”. This behavior is invaluable in practical scenarios, like detecting chemical leaks or locating survivors after catastrophes. However, the challenge becomes significantly more pronounced when operating in turbulent flow fields, where the erratic nature of odorant plumes makes predictions and simulations highly unreliable. Interestingly, fruit flies …

Controllable Physics-Informed Latent Space For Acoustic Wave Manipulation, Tristan Shah

Master's Theses

Partial Differential Equations (PDEs) are foundational descriptive tools in scientific and engineering disciplines, enabling the exploration of phenomena from material design to climate dynamics and the propagation of pandemics and wildfires. Establishing control of PDEs, especially when the underlying phenomena are only partially observable, holds great potential for fostering technological innovations. This ambition faces considerable obstacles, as a robust sample-based solution remains elusive. Addressing this challenge, this thesis introduces a framework leveraging physics-informed machine learning (ML) to enable control of PDEs. Central to this framework is an agent, equipped with sensors to perceive its environment, that generates a low-dimensional physics-informed …

An Exploration Of Dimensionality Reduction Of Dynamics On Lie Groups Via Structure-Aware Canonical Correlation Analysis, Wooyoung Chung

Master's Theses

Incorporating prior knowledge into a data-driven modeling problem can drastically improve performance, reliability, and generalization outside of the training sample. The stronger the structural properties, the more effective these improvements become. Manifolds are a powerful nonlinear generalization of Euclidean space for modeling finite dimensions. When additionally assuming that the manifold carries (Lie) group structure, this imposes a drastically stricter global constraint. The range of their applications is very wide and includes the important case of robotic tasks. We apply this idea to Canonical Correlation Analysis (CCA). In traditional CCA one constructs a hierarchical sequence of maximal correlations of up to …

Domain Specific Aspect Extraction For Product Design, Divyam Sobti

Master's Theses

As technology advances, computers become increasingly proficient at interpreting and translating human language into machine-understandable text. With the help of algorithms in natural language processing (NLP), machines can now translate textual data. These algorithms help identify and extract specific text components known as aspects. The aspects represent specific attributes or topics within textual data. For instance, an Amazon review states,“ This phone has good battery life but poor camera quality,” and attributes like ‘battery life’ and ‘camera quality’ represent aspects in the text. Aspect extraction is a pivotal process involving identifying and isolating key features or topics within text. This …

Analyzing The Usability, Performance, And Cost-Efficiency Of Deploying Ml Models On Various Cloud Computing Platforms, Hongyu Wang

Masters Theses (Archived)

With the enhanced computing capabilities and accessibility to cloud resources, major cloud computing providers such as Google Cloud Platform (GCP), Amazon Web Services (AWS), and Microsoft Azure offer Machine Learning (ML) and AI services. Their primary purpose is to provide efficiency, scalability, and adaptability in modern software development and IT operations while reducing overall costs and operational complexity. However, prospective customers of the services often question which ML-AI service will best suit their organizational and business needs. This study compares and analyzes the usability, performance, and cost-efficiency of deploying Machine Learning (ML) models across three cloud platforms: GCP, AWS, and …

Healthcare Assistance Challenges-Driven Neurosymbolic Ai, Kaushik Roy

Publications

Although Artificial Intelligence technology has proven effective in providing healthcare assistance by analyzing health data, it still falls short in supporting decision-making. This deficiency largely stems from the predominance of opaque neural networks, particularly in mental health care AI applications, which raise concerns about their unpredictable and unverifiable nature. This skepticism hinders the transition from information support to decision support. This presentation will explore neurosymbolic approaches that combine neural networks with symbolic control and verification mechanisms. These approaches aim to unlock AI’s full potential by enhancing information analysis and decision-making support for healthcare assistance.

A Personalized Chat Application For Career Profiling: A Case Study, Dhiraj Choithramani

Harrisburg University Dissertations and Theses

Artificial intelligence (AI) has rapidly transformed numerous fields over the past decade, significantly influencing industries such as software engineering and computer science. One of the most impactful developments in this area is the rise of AI-driven chat applications, which have evolved from simple, rule-based systems to sophisticated platforms capable of simulating human-like conversations. These chatbots are increasingly being utilized across various sectors, including customer service, healthcare, and education, to provide users with quick, personalized responses. This research paper presents a case study of a personalized chat application designed specifically for career profiling, leveraging advanced AI technologies to deliver contextually relevant …

Activity Map Generation And Event-Based Sensor Processing With Spiking Autoencoders And Sparse Dictionary Learning, Jack Easton

Computer Science and Engineering Theses and Dissertations

This thesis explores the potential of Spiking Neural Networks (SNNs) in processing event sensor data and generating high-fidelity activity maps. Event sensors capture asynchronous binary events with high dynamic range, but traditional processing methods often fail to leverage their advantages fully. SNNs, with their asynchronous, event-driven nature, offer a promising alternative.

A Spiking Autoencoder (SAE) was employed in this thesis to handle the stochastic and sparse event data, integrating deep dictionary learning to enhance the feature space and improve activity map quality. The encoder, modeled after the VGG network, extracts features from event streams generated by speckle patterns, which are …

A Step Towards Automated Ethical Analysis In Journalism: Measuring Llms’ Performance In Extracting Sourcing Information, Jingsen Wang

Computer Science and Engineering Master's Theses

This thesis explores the potential of Large Language Models (LLMs) in automating the extraction of sourcing information from news articles, a crucial step towards enhancing transparency and ethical analysis in journalism. We evaluate the performance of two state-of-the-art LLMs, GPT-4 and Claude 3, in identifying and categorizing various source types across four diverse news articles. The thesis employs a zero-shot learning approach with two different prompt designs, assessing the models’ ability to adapt to varying source structures and prompt instructions.

Our findings reveal that while LLMs show promise in extracting sourcing information, their performance varies significantly across different article types …

Evaluating The Role Of Data Enrichment Approaches Towards Rare Event Analysis In Manufacturing, Chathurangi Shyalika, Ruwan Wickramarachchi, Fadi El Kalach, Ramy Harik, Amit P. Sheth

Publications

Rare events are occurrences that take place with a significantly lower frequency than more common, regular events. These events can be categorized into distinct categories, from frequently rare to extremely rare, based on factors like the distribution of data and significant differences in rarity levels. In manufacturing domains, predicting such events is particularly important, as they lead to unplanned downtime, a shortening of equipment lifespans, and high energy consumption. Usually, the rarity of events is inversely correlated with the maturity of a manufacturing industry. Typically, the rarity of events affects the multivariate data generated within a manufacturing process to be …

Exploiting Physical Side-Channel Information For Offensive And Defensive Ends, Sisheng Liang

All Dissertations

Side-channel information consists of side effects of computation that range from microarchitectural to physical phenomena. Empirical studies have demonstrated the practical exploitability of these side effects in real-world systems for malicious attacks and effective defenses. In this dissertation, we discover, analyze, and exploit certain physical side-channel information for end-to-end attacks and defense across three studies.

In the first study, we demonstrate a new DNN model extraction attack named Clairvoyance that exploits certain far-field electromagnetic signals emitted from a GPU to steal DNN models several meters away from the victim machine, even with some physical obstacles in between. Using Clairvoyance, an …

Improving Expressive Capacity Of Deep Neural Networks, Clayton Harper

Computer Science and Engineering Theses and Dissertations

Deep learning has had remarkable success in a variety of fields. However, architectures often rely on hyperparameter searches and heuristics for improved model performance. Performing hyperparameter searches is an arduous task--often time-consuming and potentially expensive to run on accelerated hardware. As a result, practitioners often rely on heuristics which may lead to sub-optimal results. In the context of deep learning, hyperparameters are set by the user prior to the training process and remain fixed. Deep learning uses gradient descent to learn complex feature representations from data, limiting human intervention. While the weights of the architecture can learn directly through data …

Task Management Application, Dhaval Chaturbhai Hirpara

Electronic Theses, Projects, and Dissertations

The Task Management Application is a web-based platform designed to facilitate efficient task and project management, similar to other Project Management Tools like Jira, Trello, ClickUp, Wrike, Zoho Projects, and Asana. The application features three distinct roles: Administrator, Project Manager, and Employee, each with specific functionalities and permissions to streamline workflow.

Administrator: This role encompasses comprehensive project oversight, including adding, viewing, and managing project managers, supervising ongoing projects, and viewing employee details.

Project Manager: Project Managers can manage employees, assign tasks, and oversee project progress effortlessly.

Employee: Employees have dedicated functionalities to view and manage tasks assigned …

Crime Data Prediction Based On Geographical Location Using Machine Learning, Sai Bharath Yarlagadda

Electronic Theses, Projects, and Dissertations

This project employs machine learning methods like K Nearest Neighbors (KNN), Random Forest, Logistic Regression, and Decision Tree algorithms to monitor crime data based on location and pinpoint areas with risks. The project implements and tunes the four models to improve the precision of predicting crime levels. These models collaborate to offer a trustworthy evaluation of crime patterns. K Nearest Neighbors (KNN) categorizes locations by examining the proximity of data points considering coordinates and other factors to identify trends linked to increased crime data. Logistic Regression gauges the likelihood of crime incidents by studying the connection, between factors (like location …

Service Connect, Namrata Bomble

Electronic Theses, Projects, and Dissertations

ServiceConnect is an innovative web-based marketplace platform designed to revolutionize how local services are accessed and managed in the US. By connecting service providers and customers directly, ServiceConnect provides a simple, secure, user-friendly platform for a range of services such as home repairs, tutoring, pet care and more. Featuring convenient booking tools that increase efficiency while simultaneously building trust among both parties involved. ServiceConnect stands out with its comprehensive service range, user-friendly interface, secure payment processing and rigorous verification process for service providers. Leveraging advanced technologies like ReactJS on the frontend, Node.js & Express on the backend and MongoDB for …

Training Uav Teams With Multi-Agent Reinforcement Learning Towards Fully 3d Autonomous Wildfire Response, Bryce Hopkins

All Theses

As climate-exacerbated wildfires increasingly threaten landscapes and communities, there is an urgent and pressing need for sophisticated fire management technologies. Coordinated teams of Unmanned Aerial Vehicles (UAVs) present a promising solution for detection, assessment, and even incipient-stage suppression – especially when integrated into a multi-layered approach with other recent wildfire management technologies such as geostationary/polar-orbiting satellites and CCTV detection networks. However, there remains significant challenges in developing the necessary sensing, navigation, coordination, and communication subsystems that enable intelligent UAV teams. Further, federal regulations governing UAV deployment and autonomy pose constraints on real-world aerial testing, creating a disconnect between theoretical research …

Using Efficient Vision Transformers To Improve Perception Systems In Autonomous Off-Road Vehicles, Adam S. Pickeral

All Theses

The development of autonomous vehicles has become one of the greatest research endeavors in recent years. These vehicles rely on many complex systems working in tandem to make decisions. For practical use and safety reasons, these systems must not only be accurate, but also be quick to make decisions. In Autonomous Vehicle research, the environment perception system is one of the key com- ponents of development. The environment perception system allows the vehicle to understand its surroundings using cameras, light detection and ranging (LiDAR), and other sensor systems or modalities. Deep learning computer vision algorithms have shown to be the …

Remodel-Fpga: Reconfigurable Memory-Centric Array Processor Architecture For Deep-Learning Acceleration On Fpga, Md Arafat Kabir

Graduate Theses and Dissertations

Deep-Learning has become a dominant computing paradigm across a broad range of application domains. Different architectures of Deep-Networks like CNN, MLP, and RNN have emerged as the prominent machine-learning approaches for today’s application domains. These architectures are heavily data-dependent, requiring frequent access to memory. As a result, these applications suffer the most from the memory bottleneck of the von Neumann architectures. There is an imminent need for memory-centric architectures for deep-learning and big-data analytic applications that are memory intensive. Modern Field Programmable Gate Arrays (FPGAs) are ideal programmable substrates for creating customized Processor in/near Memory (PIM) accelerators. Modern FPGAs contain …

Development Of Feature Extraction Models To Improve Image Analysis Applications In Cancer, Yu Shi

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

Cancer poses a significant global health challenge. With an estimated 20 million new cases diagnosed worldwide in 2022 and 9.7 million fatalities attributable to the disease, the economic burden of cancer is immense. It impacts healthcare systems and imposes substantial costs for its care on patients and their families. Despite advancements in early detection, prevention, and treatment that have reduced overall cancer mortality rates, the growing prevalence of cancer, particularly among younger individuals, remains a pressing issue.

Recent advancements in medical imaging technology have progressed significantly with the help of emerging computer vision and artificial intelligence (AI) technology. Despite these …

Story Of Your Lazy Function’S Life: A Bidirectional Demand Semantics For Mechanized Cost Analysis Of Lazy Programs, Liyao Xia, Laura Israel, Maite Kramarz, Stephanie Weirich, Koen Claessen, Nicolas Coltharp, Yao Li

Computer Science Faculty Publications and Presentations

Lazy evaluation is a powerful tool that enables better compositionality and potentially better performance in functional programming, but it is challenging to analyze its computation cost. Existing works either require manually annotating sharing, or rely on separation logic to reason about heaps of mutable cells. In this paper, we propose a bidirectional demand semantics that allows for extrinsic reasoning about the computation cost of lazy programs without relying on special program logics. To show the effectiveness of our approach, we apply the demand semantics to a variety of case studies including insertion sort, selection sort, Okasaki's banker's queue, and the …

Iot Technologies To Enable Location Based Services (Lbs) For Smart Tourism, Dharmik Chhatbar

University Honors Program Senior Projects

Location-based Services (LBS) are special types of services that are triggered to deliver certain content (e.g., text, image, video) to users with respect to the users’ exact or approximate location or proximity to certain points of interest. LBS can address the field of smart museums with a goal of enhancing the museum experience for visitors with dynamic and personalized content. In this UHP thesis, we explore a set of Internet of Things (IoT) technologies that can enable the work of LBS in the field of smart museums. The project envisions an immersive museum journey where visitors can seamlessly access interactive …

Image Processing Techniques For Water Droplet Penetration Time And Contact Angle Estimation, Sai Balaji Jai Kumar

UNLV Theses, Dissertations, Professional Papers, and Capstones

Water droplet behavior on soil surfaces plays a critical role in numerous environmental processes, including soil erosion, hydrological dynamics, and ecosystem health. Accurate characterization of soil water repellency, quantified by parameters such as water droplet penetration time (WDPT) and contact angles (WDCA), is essential for informed decision-making in agricultural management, forestry practices, and land-use planning. Despite the significance of these parameters, challenges exist in reliably estimating them due to the complex and dynamic nature of soil-water interactions. This thesis address challenges in estimating WDPT and WDCA, by leveraging state-of-the-art image processing techniques and machine learning algorithms. The research focuses on …

Automated Measurement Of The Water Drop Penetration Time For The Analysis Of Soil Water Repellency, Danxu Wang

UNLV Theses, Dissertations, Professional Papers, and Capstones

In this work, we develop an innovative system for the automated measurement of Water Drop Penetration Time (WDPT) - a parameter that is conventionally used for evaluating soil water repellency (SWR). Increased SWR can be a reason for plant stress and poor crop yields, create a risk of potential water runoff and floods and thus can pose risks to life and property loss. Timely evaluation of soil conditions can save resources and win time for responding to environmental disasters. Manual measurements of WDPT are labor-intensive, subjective, tend to produce variability of outcomes, and also not always available in remote or …

Real-Time Gun Detection In Video Streams Using Yolo V8, Harish Kumar Reddy Kunchala

Electronic Theses, Projects, and Dissertations

In this research, we advance the domain of public safety by developing a machine learning model that utilizes the YOLO v8 architecture for real-time detection of firearms in video streams. A diverse and extensive dataset, capturing a range of firearms in varying lighting and backgrounds, was meticulously assembled and preprocessed to enhance the model's adaptability to real-world scenarios. Leveraging the YOLO v8 framework, known for its real-time object detection accuracy, the model was fine-tuned to accurately identify firearms across different shapes and orientations.

The training phase capitalized on GPU computing and transfer learning to expedite the learning process while preserving …

Society Management App, Ruchit Rakholiya

Electronic Theses, Projects, and Dissertations

A comprehensive solution as native mobile application which is feasible economical and fast, which will establish the authenticity and reliability for society management overcoming the drawbacks of current system. In today's fast-paced technological ecosystem, the capacity to readily store and access information is becoming increasingly important. Residential societies, where individuals live together and manage collective resources, often require a large number of documents, registrations, vehicle parking records, and other forms of paperwork. The complexity and volume of these documents can lead to inefficiencies and frustrations among residents and management alike.

Development And Implementation Of A Gps-Agnostic Drone Localization System, Alex Peterson

Boise State University Theses and Dissertations

This research develops a GPS-denied state estimation system to localize and orient a drone for touch-based installations on power line towers and cables. As opposed to environments like underground tunnels or building interiors, our system effectively identifies and utilizes sparse landmarks such as towers, cables, and ground features. Our approach utilizes Simultaneous Localization and Mapping (SLAM) to create and reference three-dimensional maps in real time. Specifically, we employ Georgia Tech Smoothing and Mapping (GTSAM), proposed by Georgia Tech's BORG Lab, a factor graph-based data structure consisting of measurement factors and unknown pose variables that we are implementing for solving the …

Development Of The Structure And Control System Of A Stewart Platform Robot For Human Balance Recovery Interventions, Rhobenn R. Alvarez Zambrano

Theses and Dissertations

In this thesis, the process to design a Stewart platform parallel robot for balance recovery with given assembly constraints and mobility requirements is described. A Model Based Design (MBD) approach in MATLAB was used as a tool to model and optimize the design of the platform through quick and repeatable workspace and movements simulations. An algorithm based on Inverse Kinematics was used to find the most adequate Stewart platform configuration which yields a workspace that fulfills the design goals best. Solidworks was used as a 3D CAD Modeling tool to elaborate machining blueprints while ensuring that each piece fits accurately …