Other Electrical and Computer Engineering Commons^™

Ai-Optimized Resource Management In Next-Gen Wireless Networks, Fatemeh Lotfi

All Dissertations

Next-generation wireless networks must deliver highly adaptive, scalable, and intelligent connectivity to satisfy the heterogeneous demands of emerging services, including enhanced mobile broadband, massive machine-type communications, and ultra reliable low latency applications. The Open Radio Access Network (O-RAN) paradigm has emerged as a key enabler of this vision, introducing openness, virtualization, and artificial intelligence (AI)-driven control into the RAN ecosystem. O-RAN’s disaggregated architecture facilitates multi-vendor interoperability and empowers intelligent management through the RAN Intelligent Controller (RIC). However, achieving real-time, autonomous, and generalized optimization in such a dynamic environment remains a significant challenge due to its distributed nature, non-stationary traffic, and …

Developing Workflows For Passive Acoustic Detection Of Bedload Transport, Quinn Morgan

Dissertations and Theses

Bedload transport is defined as the amount of sediment, including gravel and rocks, traveling down stream. Monitoring bedload transport is important for river safety, hydrological studies and conservation efforts. Existing methods of directly measuring bedload transport (or bedload flux) involve lowering a collection device into a river and measuring the sediment collected; which can be expensive and time consuming. Hydroacoustic sensors, such as hydrophones, have had success tracking bedload flux remotely. This works by measuring the relatively high frequency of sediment impacts to map onto total bedload transported. No perfected method for detection of sediment generated noise (SGN) currently exists. …

Transformer-Based Symbolic Music Generation, Ben Buentello

Master’s Theses

This thesis investigates the capacity of transformer-based architectures to learn generalized musical patterns through symbolic generation. To support this exploration, a complete music generation pipeline was developed, beginning with the construction and classification of a large-scale dataset of over 170,000 MIDI files. The dataset was processed using rule-based heuristics and custom neural classifiers to separate tracks by musical function and contour. A novel tokenization scheme, MINTii, was introduced to encode musical information compactly through interval-based representations, reducing redundancy and promoting generalization. Using this infrastructure, a transformer model was trained to generate single-track melodic sequences. Its performance was evaluated through both …

Predictive Residual Neural Networks For Optical Trapping Of Small Particles, Nasim Mohammadi Estrakhri, Ponthea Zahraii, Saman Kashanchi, Nooshin M. Estakhri

Engineering Faculty Articles and Research

Optical tweezers provide a non-contact method to trap, move, and manipulate micro- and nano-sized objects. Using properly designed dielectric and plasmonic nanostructure configurations, optical tweezers have been tailored to create stable and precise trapping for nanoscale objects. Recent advances in numerical optimization techniques allow further enhancement in nanoscale optical traps through inverse optimization of such configurations. One of the main challenges in such optimization approaches is the time-consuming nature of full-wave simulation of nanostructures and postprocessing steps to extract optical forces. To address this challenge, we introduce a surrogate solver based on residual neural networks that can accurately predict the …

An Investigation Of Information Structures In Dna, Joel Mohrmann

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

The information-containing nature of the DNA molecule has been long known and observed. One technique for quantifying the relationships existing within the information contained in DNA sequences is an entity from information theory known as the average mutual information (AMI) profile. This investigation sought to use principally the AMI profile along with a few other metrics to explore the structure of the information contained in DNA sequences.

Treating DNA sequences as an information source, several computational methods were employed to model their information structure. Maximum likelihood and maximum a posteriori estimators were used to predict missing bases in DNA sequences. …

Inverse Engineering Of Absorption And Scattering In Nanoparticles: A Machine Learning Approach, Alex Vallone, Nooshin M. Estakhri, Nasim Mohammadi Estrakhri

Engineering Faculty Articles and Research

We use a region-specified machine learning approach to inverse design highly absorptive multilayer plasmonic nanoparticles. We demonstrate the design of particles with a wide range of absorption to scattering ratios (i.e., cloaked absorbers and bright absorbers) and for different visible wavelengths.

Unobtrusive Data Collection In Clinical Settings For Advanced Patient Monitoring And Machine Learning, Walker Arce

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

When applying machine learning to clinical practice, a major hurdle that will be encountered is the lack of available data. While the data collected in clinical therapies is suitable for the types of analysis that are needed to measure and track clinical outcomes, it may not be suitable for other types of analysis. For instance, video data may have poor alignment with behavioral data, making it impossible to extract the videos frames that directly correlate with the observed behavior. Alternatively, clinicians may be exploring new data modalities, such as physiological signal collection, to research methods of improving clinical outcomes that …

Dense & Attention Convolutional Neural Networks For Toe Walking Recognition, Junde Chen, Rahul Soangra, Marybeth Grant-Beuttler, Y. A. Nanehkaran, Yuxin Wen

Physical Therapy Faculty Articles and Research

Idiopathic toe walking (ITW) is a gait disorder where children’s initial contacts show limited or no heel touch during the gait cycle. Toe walking can lead to poor balance, increased risk of falling or tripping, leg pain, and stunted growth in children. Early detection and identification can facilitate targeted interventions for children diagnosed with ITW. This study proposes a new one-dimensional (1D) Dense & Attention convolutional network architecture, which is termed as the DANet, to detect idiopathic toe walking. The dense block is integrated into the network to maximize information transfer and avoid missed features. Further, the attention modules are …

Spam Detection Using Machine Learning And Deep Learning, Olubodunde Agboola

LSU Doctoral Dissertations

Text messages are essential these days; however, spam texts have contributed negatively to the success of this communication mode. The compromised authenticity of such messages has given rise to several security breaches. Using spam messages, malicious links have been sent to either harm the system or obtain information detrimental to the user. Spam SMS messages as well as emails have been used as media for attacks such as masquerading and smishing ( a phishing attack through text messaging), and this has threatened both the user and service providers. Therefore, given the waves of attacks, the need to identify and remove …

Exploratory Analysis Of Machine Learning For Images: Methods And Applications, Meenu Ajith

Electrical and Computer Engineering ETDs

This research focuses on implementing four different applications of machine learning on images. The various categories of digital images considered for these applications are grayscale, RGB, and infra-red images. The first framework uses an unsupervised learning strategy for detecting fire and smoke from an infra-red image dataset. This problem was solved using a classical machine learning algorithm since the dataset was small and unlabeled. Next, a semi-supervised deep learning model was used for facial expression recognition. Here we detect emotions from a moderately large dataset containing labeled and unlabeled grayscale images. The third application focused on single image superresolution, which …

Combinatorial Cnn Meta-Structures In Deep Learning Applications, Aswathy Rajendra Kurup

Electrical and Computer Engineering ETDs

The study aims in applying combinatorial structures motivated from the basic CNN in various applications. Convolutional neural networks (CNNs) have grown to be very popular in the field of deep learning. The ability of such networks to learn both Spatial and temporal characteristics in the data have helped in deploying them in various fields. Through this research we explore different kinds of CNN-derived architectures and how these structures can be trained and setup in combinatorial environment to solve problems in deep learning applications. First, we introduce the basic idea of a deep learning meta-structures which is used in the application …

Cloud-Based Machine Learning And Sentiment Analysis, Emmanuel C. Opara

College of Graduate Studies: Theses & Dissertations

The role of a Data Scientist is becoming increasingly ubiquitous as companies and institutions see the need to gain additional insights and information from data to make better decisions to improve the quality-of-service delivery to customers. This thesis document contains three aspects of data science projects aimed at improving tools and techniques used in analyzing and evaluating data. The first research study involved the use of a standard cybersecurity dataset and cloud-based auto-machine learning algorithms were applied to detect vulnerabilities in the network traffic data. The performance of the algorithms was measured and compared using standard evaluation metrics. The second …

Explainable Data-Driven Motor Condition Monitoring And Fault Disgnosis, Yuming Wang

Theses and Dissertations--Electrical and Computer Engineering

Industrial motors are widely used in various fields such as power generation, mining, and manufacturing. Motor faults and time-consuming maintenance process will lead to serious economic losses in this context. To monitor motor faults and detect motor conditions, different types of sensors that can test vibration and current signals are mounted on motors. However, the main challenge was how to use information gained by sensors to analyze or diagnose motor conditions.

Machine learning is a popular technology in recent years, and it's very suitable for crunching and analyzing data. As an important subset of machine learning, deep learning is suitable …

Recent Advances And Trends Of Predictive Maintenance From Data-Driven Machine Prognostics Perspective, Yuxin Wen, Md. Fashiar Rahman, Honglun Xu, Tzu-Liang Bill Tseng

Engineering Faculty Articles and Research

In the Engineering discipline, prognostics play an essential role in improving system safety, reliability and enabling predictive maintenance decision-making. Due to the adoption of emerging sensing techniques and big data analytics tools, data-driven prognostic approaches are gaining popularity. This paper aims to deliver an extensive review of recent advances and trends of data-driven machine prognostics, with a focus on their applications in practice. The primary purpose of this review is to categorize existing literature and report the latest research progress and directions to support researchers and practitioners in acquiring a clear comprehension of the subject area. This paper first summarizes …

A Machine Learning Approach To Delineating Neighborhoods From Geocoded Appraisal Data, Rao Hamza Ali, Josh Graves, Stanley Wu, Jenny Lee, Erik Linstead

Engineering Faculty Articles and Research

Identification of neighborhoods is an important, financially-driven topic in real estate. It is known that the real estate industry uses ZIP (postal) codes and Census tracts as a source of land demarcation to categorize properties with respect to their price. These demarcated boundaries are static and are inflexible to the shift in the real estate market and fail to represent its dynamics, such as in the case of an up-and-coming residential project. Delineated neighborhoods are also used in socioeconomic and demographic analyses where statistics are computed at a neighborhood level. Current practices of delineating neighborhoods have mostly ignored the information …

Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead

Engineering Faculty Articles and Research

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also …

Pixel-Level Deep Multi-Dimensional Embeddings For Homogeneous Multiple Object Tracking, Mateusz Mittek

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

The goal of Multiple Object Tracking (MOT) is to locate multiple objects and keep track of their individual identities and trajectories given a sequence of (video) frames. A popular approach to MOT is tracking by detection consisting of two processing components: detection (identification of objects of interest in individual frames) and data association (connecting data from multiple frames). This work addresses the detection component by introducing a method based on semantic instance segmentation, i.e., assigning labels to all visible pixels such that they are unique among different instances. Modern tracking methods often built around Convolutional Neural Networks (CNNs) and additional, …

Multi-Column Neural Networks And Sparse Coding Novel Techniques In Machine Learning, Ammar O. Hoori

Theses and Dissertations

Accurate and fast machine learning (ML) algorithms are highly vital in artificial intelligence (AI) applications. In complex dataset problems, traditional ML methods such as radial basis function neural network (RBFN), sparse coding (SC) using dictionary learning, and particle swarm optimization (PSO) provide trivial results, large structure, slow training, and/or slow testing. This dissertation introduces four novel ML techniques: the multi-column RBFN network (MCRN), the projected dictionary learning algorithm (PDL) and the multi-column adaptive and non-adaptive particle swarm optimization techniques (MC-APSO and MC-PSO). These novel techniques provide efficient alternatives for traditional ML techniques. Compared to traditional ML techniques, the novel ML …

Design Of A Distributed Real-Time E-Health Cyber Ecosystem With Collective Actions: Diagnosis, Dynamic Queueing, And Decision Making, Yanlin Zhou

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

In this thesis, we develop a framework for E-health Cyber Ecosystems, and look into different involved actors. The three interested parties in the ecosystem including patients, doctors, and healthcare providers are discussed in 3 different phases. In Phase 1, machine-learning based modeling and simulation analysis is performed to remotely predict a patient's risk level of having heart diseases in real time. In Phase 2, an online dynamic queueing model is devised to pair doctors with patients having high risk levels (diagnosed in Phase 1) to confirm the risk, and provide help. In Phase 3, a decision making paradigm is proposed …