Learning Proximal Operators With Gaussian Process And Adaptive Quantization In Distributed Optimization, Aldo Duarte Vera Tudela

LSU Doctoral Dissertations

In networks consisting of agents communicating with a central coordinator and working together to solve a global optimization problem in a distributed manner, the agents are often required to solve private proximal minimization subproblems. Such a setting often requires a further decomposition method to solve the global distributed problem, resulting in extensive communication overhead. In networks where communication is expensive, it is crucial to reduce the communication overhead of the distributed optimization scheme. Integrating Gaussian processes (GP) as a learning component to the Alternating Direction Method of Multipliers (ADMM) has proven effective in learning each agent's local proximal operator to …

Cyber Attacks Against Industrial Control Systems, Adam Kardorff

LSU Master's Theses

Industrial Control Systems (ICS) are the foundation of our critical infrastructure, and allow for the manufacturing of the products we need. These systems monitor and control power plants, water treatment plants, manufacturing plants, and much more. The security of these systems is crucial to our everyday lives and to the safety of those working with ICS. In this thesis we examined how an attacker can take control of these systems using a power plant simulator in the Applied Cybersecurity Lab at LSU. Running experiments on a live environment can be costly and dangerous, so using a simulated environment is the …

Autonomous Shipwreck Detection & Mapping, William Ard

LSU Master's Theses

This thesis presents the development and testing of Bruce, a low-cost hybrid Remote Operated Vehicle (ROV) / Autonomous Underwater Vehicle (AUV) system for the optical survey of marine archaeological sites, as well as a novel sonar image augmentation strategy for semantic segmentation of shipwrecks. This approach takes side-scan sonar and bathymetry data collected using an EdgeTech 2205 AUV sensor integrated with an Harris Iver3, and generates augmented image data to be used for the semantic segmentation of shipwrecks. It is shown that, due to the feature enhancement capabilities of the proposed shipwreck detection strategy, correctly identified areas have a 15% …

Cyberinet: Integrated Semi-Modular Sensors For The Computer-Augmented Clarinet, Matthew Bardin

LSU Doctoral Dissertations

The Cyberinet is a new Augmented instrument designed to easily and intuitively provide a method of computer-enhanced performance to the Clarinetist to allow for greater control and expressiveness in a performance. A performer utilizing the Cyberinet is able to seamlessly switch between a traditional performance setting and an augmented one. Towards this, the Cyberinet is a hardware replacement for a portion of a Clarinet containing a variety of sensors embedded within the unit. These sensors collect various real time data motion data of the performer and air fow within the instrument. Additional sensors can be connected to the Cyberinet to …

Software-Defined Networking Security Techniques And The Digital Forensics Of The Sdn Control Plane, Abdullah Alshaya

LSU Doctoral Dissertations

Software-Defined Networking (SDN) is an efficient networking design that decouples the network's control plane from the data plane. When compared to the traditional network architecture, the SDN architecture shares many of the same security issues. The centralized SDN controller makes it easier to control, easier to program in real-time, and more flexible, but this comes at the cost of more security risks. An attack on the control plane layer of the SDN controller is a major security concern.

First, centralized design and the existence of a single point of failure in the control plane compromise the accessibility and availability of …

An Investigation On The Resilience Of Long Short-Term Memory Deep Neural Networks, Christopher Vasquez

LSU Master's Theses

In a world of continuously advancing technology, the reliance on these technologies continues to increase. Recently, transformer networks [22] have been implemented through various projects such as ChatGPT. These networks are extremely computationally demanding and require cutting-edge hardware to explore. However, with the growing increase and popularity of these neural networks, a question of reliability and resilience comes about, especially as the dependency and research on these networks grow. Given the computational demand of transformer networks, we investigate the resilience of the weights and biases of the predecessor of these networks, i.e. the Long Short-Term (LSTM) neural network, through four …

Domain Specific Analysis Of Privacy Practices And Concerns In The Mobile Application Market, Fahimeh Ebrahimi Meymand

LSU Doctoral Dissertations

Mobile applications (apps) constantly demand access to sensitive user information in exchange for more personalized services. These-mostly unjustified-data collection tactics have raised major privacy concerns among mobile app users. Existing research on mobile app privacy aims to identify these concerns, expose apps with malicious data collection practices, assess the quality of apps' privacy policies, and propose automated solutions for privacy leak detection and prevention. However, existing solutions are generic, frequently missing the contextual characteristics of different application domains. To address these limitations, in this dissertation, we study privacy in the app store at a domain level. Our objective is to …

Machine-Learning Approaches For Developing An Autograder For High School-Level Cs-For-All Initiatives, Sirazum Munira Tisha

LSU Doctoral Dissertations

Most existing autograders used for grading programming assignments are based on unit testing, which is tedious to implement for programs with graphical output and does not allow testing for other code aspects, such as programming style or structure. We present a novel autograding approach based on machine learning that can successfully check the quality of coding assignments from a high school-level CS-for-all computational thinking course. For evaluating our autograder, we graded 2,675 samples from five different assignments from the past three years, including open-ended problems from different units of the course curriculum. Our autograder uses features based on lexical analysis …

A Machine Learning Approach To Robotic Additive Manufacturing Of Uv-Curable Polymers Using Direct Ink Writing, Luis A. Velazquez

LSU Master's Theses

This thesis presents the design and implementation of a robotic additive manufacturing system that uses ultraviolet (UV)-curable thermoset polymers. Its design considers future applications involving free-standing 3D printing by means of partial UV curing and the fabrication of samples that are reinforced with fillers or fibers to manufacture complex-shape objects.

The proposed setup integrates a custom-built extruder with a UR5e collaborative manipulator. The capabilities of the system were demonstrated using Anycubic resin formulations containing fumed silica (FS) at varying weight fractions from 2.8 to 8 wt%. To fully cure the specimens after fabrication, a UV chamber was used. Then, measurements …

Enabling The Human Perception Of A Working Camera In Web Conferences Via Its Movement, Anish Shrestha

LSU Master's Theses

In recent years, video conferencing has seen a significant increase in its usage due to the COVID-19 pandemic. When casting user’s video to other participants, the videoconference applications (e.g. Zoom, FaceTime, Skype, etc.) mainly leverage 1) webcam’s LED-light indicator, 2) user’s video feedback in the software and 3) the software’s video on/off icons to remind the user whether the camera is being used. However, these methods all impose the responsibility on the user itself to check the camera status, and there have been numerous cases reported when users expose their privacy inadvertently due to not realizing that their camera is …

Compilation Optimizations To Enhance Resilience Of Big Data Programs And Quantum Processors, Travis D. Lecompte

LSU Doctoral Dissertations

Modern computers can experience a variety of transient errors due to the surrounding environment, known as soft faults. Although the frequency of these faults is low enough to not be noticeable on personal computers, they become a considerable concern during large-scale distributed computations or systems in more vulnerable environments like satellites. These faults occur as a bit flip of some value in a register, operation, or memory during execution. They surface as either program crashes, hangs, or silent data corruption (SDC), each of which can waste time, money, and resources. Hardware methods, such as shielding or error correcting memory (ECM), …

Device Free Indoor Localization Of Human Target Using Wifi Fingerprinting, Prasanga Neupane

LSU Master's Theses

Indoor localization of human objects has many important applications nowadays. Proposed here is a new device free approach where all the transceiver devices are fixed in an indoor environment so that the human target doesn't need to carry any transceiver device with them. This work proposes radio-frequency fingerprinting for the localization of human targets which makes this even more convenient as radio-frequency wireless signals can be easily acquired using an existing wireless network in an indoor environment. This work explores different avenues for optimal and effective placement of transmitter devices for better localization. In this work, an experimental environment is …

Evaluating Serverless Computing, Charitra Maharjan

LSU Master's Theses

Function as a Service (FaaS) is gaining admiration because of its way of deploying the computations to serverless backends in the different clouds. It transfers the complexity of provisioning and allocating the necessary resources for an application to the cloud providers. The cloud providers also give an illusion of always availability of resources to the users. Among the cloud providers, AWS serverless platform offers a new paradigm for developing cloud applications without worrying about the underlying hardware infrastructure. It manages not only the resource provisioning and scaling of an application but also provides an opportunity to reimagine the cloud infrastructure …

A Deep Reinforcement Learning Approach With Prioritized Experience Replay And Importance Factor For Makespan Minimization In Manufacturing, Jose Napoleon Martinez

LSU Doctoral Dissertations

In this research, we investigated the application of deep reinforcement learning (DRL) to a common manufacturing scheduling optimization problem, max makespan minimization. In this application, tasks are scheduled to undergo processing in identical processing units (for instance, identical machines, machining centers, or cells). The optimization goal is to assign the jobs to be scheduled to units to minimize the maximum processing time (i.e., makespan) on any unit.

Machine learning methods have the potential to "learn" structures in the distribution of job times that could lead to improved optimization performance and time over traditional optimization methods, as well as to adapt …

Practical Considerations And Applications For Autonomous Robot Swarms, Rory Alan Hector

LSU Doctoral Dissertations

In recent years, the study of autonomous entities such as unmanned vehicles has begun to revolutionize both military and civilian devices. One important research focus of autonomous entities has been coordination problems for autonomous robot swarms. Traditionally, robot models are used for algorithms that account for the minimum specifications needed to operate the swarm. However, these theoretical models also gloss over important practical details. Some of these details, such as time, have been considered before (as epochs of execution). In this dissertation, we examine these details in the context of several problems and introduce new performance measures to capture practical …

Machine Learning Assisted Discovery Of Shape Memory Polymers And Their Thermomechanical Modeling, Cheng Yan

LSU Doctoral Dissertations

As a new class of smart materials, shape memory polymer (SMP) is gaining great attention in both academia and industry. One challenge is that the chemical space is huge, while the human intelligence is limited, so that discovery of new SMPs becomes more and more difficult. In this dissertation, by adopting a series of machine learning (ML) methods, two frameworks are established for discovering new thermoset shape memory polymers (TSMPs). Specifically, one of them is performed by a combination of four methods, i.e., the most recently proposed linear notation BigSMILES, supplementing existing dataset by reasonable approximation, a mixed dimension (1D …

Ux/U-Eye: Designing Graphical User Interfaces For Exclusive Eye Gaze Control, Timothy Curol

Honors Theses

No abstract provided.

Generative Adversarial Networks Take On Hand Drawn Sketches: An Application To Louisiana Culture And Mardi Gras Fashion, Stephanie Hines

Honors Theses

No abstract provided.

Efficient Information Retrieval For Software Bug Localization, Saket Khatiwada

LSU Doctoral Dissertations

Software systems are often shipped with defects. When a bug is reported, developers use the information available in the associated report to locate source code fragments that need to be modified to fix the bug. However, as software systems evolve in size and complexity, bug localization can become a tedious and time-consuming process. Contemporary bug localization tools utilize Information Retrieval (IR) methods for automated support to minimize the manual effort. IR methods exploit the textual content of bug reports to capture and rank relevant buggy source files. However, for an IR-based bug localization tool to be useful, it must achieve …

90snet:, Seth Richard

Honors Theses

No abstract provided.

Interpretable And Anti-Bias Machine Learning Models For Human Event Sequence Data, Zihan Zhou

LSU Doctoral Dissertations

Growing volumes and varieties of human event sequence data are available in many applications such as recommender systems, social network, medical diagnosis, and predictive policing. Human event sequence data is usually clustered and exhibits self-exciting properties. Machine learning models especially deep neural network models have shown great potential in improving the prediction accuracy of future events. However, current approaches still suffer from several drawbacks such as model transparency, unfair prediction and the poor prediction accuracy due to data sparsity and bias. Another issue in modeling human event data is that data collected from real word is usually incomplete, and even …

Asynchronous, Distributed Optical Mutual Exclusion And Applications, Ahmed Bahaael Mansour

LSU Doctoral Dissertations

Silicon photonics have drawn much recent interest in the setting of intra-chip andmodule communication. In this dissertation, we address a fundamental computationalproblem, mutual exclusion, in the setting of optical interconnects. As a main result, wepropose an optical network and an algorithm for it to distribute a token (shared resource)mutually exclusively among a set ofnprocessing elements. Following a request, the tokenis granted in constant amortized time andO(n) worst case time; this assumes constantpropagation time for light within the chip. Additionally, the distribution of tokens is fair,ensuring that no token request is denied more thann−1 times in succession; this is thebest possible. …

Evaluating Word Embedding Models For Traceability, Mahfuza Khatun, Mahfuza Khatun

LSU Master's Theses

ABSTRACT

Traceability link recovery (TLR) is a software engineering activity that helps to ensure software quality and assists with keeping track of changes by establishing links between software artifacts that are a part of the software engineering process, such as requirements, use cases, source code, test cases, and documentation. Software requirement artifacts are typically written in natural language. An Information Retrieval process is frequently used in many software activities, including the TLR activity. Recently, Word Embedding (WE) techniques have been used in many natural language processing tasks as well as in TLR tasks. We investigate the effectiveness of WE techniques …

Fatigue Monitoring Through Wearable Sensors For Construction Workers, Srikanth Sagar Bangaru

LSU Doctoral Dissertations

About 40% of the US construction workforce experiences high-level fatigue, which leads to poor judgment, increased risk of injuries, a decrease in productivity, and a lower quality of work. Excessive fatigue from working in unpleasant working conditions, long working hours, or heavy workloads can aggravate fatigue's adverse effects, leading to work-related musculoskeletal disorders (WMSDs) and productivity loss. Therefore, it is essential to monitor fatigue to reduce the adverse effects and preventing long-term health problems. However, since fatigue demonstrates itself in several complex processes, there is no single standard measurement method for fatigue detection. This research aims to develop a system …

Distance-Based Formation Control Using Decentralized Sensing With Infrared Photodiodes, Steven Williams

LSU Master's Theses

This study presents an onboard sensor system for determining the relative positions of mobile robots, which is used in decentralized distance-based formation controllers for multi-agent systems. This sensor system uses infrared photodiodes and LEDs; its effective use requires coordination between the emitting and detecting robots. A technique is introduced for calculating the relative positions based on photodiode readings, and an automated calibration system is designed for future maintenance. By measuring the relative positions of their neighbors, each robot is capable of running an onboard formation controller, which is independent of both a centralized controller and a global positioning-like system (e.g., …

Low-Cost Hardware-In-The-Loop (Hil) Simulator For Simulation And Analysis Of Embedded Systems With Non-Real-Time Applications, Chanuka S. Elvitigala

LSU Master's Theses

Hardware-In-the-Loop (HIL) simulation is an approach that is used for embedded systems testing which was popular in the early days of embedded systems. This technology was wrapped and was stored away for a long time. One of the main reasons is that the less use of embedded systems in society. Recent developments in embedded systems, data science, and control systems lead to a focus on testing these systems. The earliest method of testing these systems was by doing destructive testing which is a waste of money, resources, and time. The HIL provided a safe and economical way of testing the …

Reinforcement Learning Approach For Inspect/Correct Tasks, Hoda Nasereddin

LSU Doctoral Dissertations

In this research, we focus on the application of reinforcement learning (RL) in automated agent tasks involving considerable target variability (i.e., characterized by stochastic distributions); in particular, learning of inspect/correct tasks. Examples include automated identification & correction of rivet failures in airplane maintenance procedures, and automated cleaning of surgical instruments in a hospital sterilization processing department. The location of defects and the corrective action to be taken for each varies from task episode. What needs to be learned are optimal stochastic strategies rather than optimization of any one single defect type and location. RL has been widely applied in robotics …

Monitoring Of Remote Hydrocarbon Wells Using Azure Internet Of Things, Derek W. Staal

LSU Master's Theses

Remote monitoring of hydrocarbon wells is a tedious and meticulously thought out task performed to create a cyber-physical bridge between the asset and the owner. There are many systems and techniques on the market that offer this solution but due to their lack of interoperability and/or decentralized architecture they begin to fall apart when remote assets become farther away from the client. This results in extreme latency and thus poor decision making. Microsoft's Azure IoT Edge was the focus of this writing. Coupled with off-the-shelf hardware, Azure's IoT Edge services were integrated with an existing unit simulating a remote hydrocarbon …

An Approach To Counting Vehicles From Pre-Recorded Video Using Computer Algorithms, Mishuk Majumder

LSU Master's Theses

One of the fundamental sources of data for traffic analysis is vehicle counts, which can be conducted either by the traditional manual method or by automated means. Different agencies have guidelines for manual counting, but they are typically prepared for particular conditions. In the case of automated counting, different methods have been applied, but You Only Look Once (YOLO), a recently developed object detection model, presents new potential in automated vehicle counting. The first objective of this study was to formulate general guidelines for manual counting based on experience gained in the field. Another goal of this study was to …

Optimizing The Performance Of Multi-Threaded Linear Algebra Libraries Based On Task Granularity, Shahrzad Shirzad

LSU Doctoral Dissertations

Linear algebra libraries play a very important role in many HPC applications. As larger datasets are created everyday, it also becomes crucial for the multi-threaded linear algebra libraries to utilize the compute resources properly. Moving toward exascale computing, the current programming models would not be able to fully take advantage of the advances in memory hierarchies, computer architectures, and networks. Asynchronous Many-Task(AMT) Runtime systems would be the solution to help the developers to manage the available parallelism. In this Dissertation we propose an adaptive solution to improve the performance of a linear algebra library based on a set of compile-time …