Computer Engineering Commons^™

Digital Forensics For Investigating Control-Logic Attacks In Industrial Control Systems, Nauman Zubair

LSU New Orleans Theses and Dissertations

Programmable logic controllers (PLC) are required to handle physical processes and thus crucial in critical infrastructures like power grids, nuclear facilities, and gas pipelines. Attacks on PLCs can have disastrous consequences, considering attacks like Stuxnet and TRISIS. Those attacks are examples of exploits where the attacker aims to inject into a target PLC malicious control logic, which engineering software compiles as a reliable code. When investigating a security incident, acquiring memory can provide valuable insight such as runtime system activities and memory-based artifacts which may contain the attacker's footprints. The existing memory acquisition tools for PLCs require a hardware-level debugging …

Improving Detection And Classification Of Steady-State Visual Evoked Potentials In Brain-Computer Interfaces, Hadi Habibzadeh

Legacy Theses & Dissertations (2009 - 2024)

Brain-computer interfaces (BCIs) provide an alternative communication channel to send computer commands without requiring any voluntary muscle control. They benefit people with limited communication capabilities due to motor function deficits, spinal cord injuries, and neurological disorders. They may also have applications for healthy individuals.

Domain Aware Deep Learning For Wireless Physical Layer, Shuvam Chakraborty

Legacy Theses & Dissertations (2009 - 2024)

Wireless receiver design for OFDM systems is well investigated with classical signal processing tools, which lack the capacity to extract intrinsic channel effects in received signal and lead to high decoding error in receiver. Current deep learning techniques have shown improvement in such cases. But these models are mostly being developed as black box without any anchor to the theory of wireless signal propagation, which leads to surface level information gain and lacks generalizability. We propose deep learning models where the hyperparameters and learning objectives are derived from domain knowledge of wireless signal propagation. These models not only increase the …

Sequence-Based Bioinformatics Approaches To Predict Virus–Host Relationships In Archaea And Eukaryotes, Yingshan Li

School of Computing: Dissertations, Theses, and Student Research

Viral metagenomics is independent of lab culturing and capable of investigating viromes of virtually any given environmental niches. While numerous sequences of viral genomes have been assembled from metagenomic studies over the past years, the natural hosts for the majority of these viral contigs have not been determined. Different computational approaches have been developed to predict hosts of bacteria phages. Nevertheless, little progress has been made in the virus-host prediction, especially for viruses that infect eukaryotes and archaea. In this study, by analyzing all documented viruses with known eukaryotic and archaeal hosts, we assessed the predictive power of four computational …

A Low-Power, Low-Area 10-Bit Sar Adc With Length-Based Capacitive Dac, Zhili Pan

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

A 2.5 V single-ended 10-bit successive-approximation-register analog-to-digital converter (SAR ADC) based on the TSMC 65 nm CMOS process is designed with the goal of achieving low power consumption (33.63 pJ/sample) and small area (2874 µm^2 ). It utilizes a novel length-based capacitive digital-to-analog converter (CDAC) layout to achieve low total capacitance for power efficiency, and a custom static asynchronous logic to free the dependence on a high-frequency external clock source. Two test chips have been designed and the problems found through testing the first chip are analyzed. Multiple improved versions of the ADC with minor variations are implemented on the …

Transforming Character Faces Based On Perceived Personality Traits, Kara Porter

All Theses

The ability to read other human's faces is a crucial part of everyday life. Subconsciously, the human brain analyzes someone's face within the first few seconds of seeing it, making a variety of conclusions ~\cite{FacePerp} such as gathering information about emotional state and assuming character traits this person might possess. The purpose of this thesis is to create a tool that allows a user to alter features of a character's three dimensional (3D) face mesh to look increasingly or decreasingly like the character possesses certain personality traits. Using a sample set of randomly generated faces, a survey is conducted to …

Scalable Data-Driven Predictive Modeling And Analytics For Cho Process Development Optimization, Sarah Mbiki

All Dissertations

In 1982, the FDA approved the first recombinant therapeutic protein, and since then, the biopharmaceutical industry has continued to develop innovative and highly effective biological drugs for various illnesses¹. These drugs are produced using host organisms that are modified to hold the genetic encoding of the targeted protein¹. Of the many host organisms, Chinese hamster ovary (CHO) cells are often used due to capability to perform posttranslational modification (PTM): which allows human-like synthesis of proteins unlikely to invoke immunogenicity in humans ^1,2.

Despite all the positive attributes, many challenges are associated with CHO cell cultures, …

A Study Of Heart Disease Diagnosis Using Machine Learning And Data Mining, Intisar Ahmed

Electronic Theses, Projects, and Dissertations

Heart disease is the leading cause of death for people around the world today. Diagnosis for various forms of heart disease can be detected with numerous medical tests, however, predicting heart disease without such tests is very difficult. Machine learning can help process medical big data and provide hidden knowledge which otherwise would not be possible with the naked eye. The aim of this project is to explore how machine learning algorithms can be used in predicting heart disease by building an optimized model. The research questions are; 1) What Machine learning algorithms are used in the diagnosis of heart …

Nigeria’S Budding Digital Economy: Coping With Disruptive Technology, Olugbenga Agboola

Economic and Financial Review

For a thriving and inclusive digital economy, African countries such as Nigeria need to build the critical foundations of the digital economy (World Bank Group, 2019). These foundations are interdependent and require public and private sector solutions.

The Effects Of Virtual Reality On Mental Health Software User Satisfaction And Retention, William Hooten

Honors Theses

Mental health issues have become increasingly important in today's society. With that being said, researchers and consumers are looking for new ways to manage and treat mental health using new technologies in labs and the consumer space. This innovation has led to the presence of mobile self-help mental health applications, applications for peoples’ phones that are used to manage symptoms of mental health problems, such as depression and anxiety, track goals, meditate, and more. However, mobile mental health applications, and mobile applications in general, have a problem concerning user satisfaction and overall user retention – studies have shown that 95% …

Sequential Frame-Interpolation And Dct-Based Video Compression Framework, Yeganeh Jalalpour, Wu-Chi Feng, Feng Liu

Computer Science Faculty Publications and Presentations

Video data is ubiquitous; capturing, transferring, and storing even compressed video data is challenging because it requires substantial resources. With the large amount of video traffic being transmitted on the internet, any improvement in compressing such data, even small, can drastically impact resource consumption. In this paper, we present a hybrid video compression framework that unites the advantages of both DCT-based and interpolation-based video compression methods in a single framework. We show that our work can deliver the same visual quality or, in some cases, improve visual quality while reducing the bandwidth by 10--20%.

Cyber Resilience Analytics For Cyber-Physical Systems, Md Ariful Haque

Electrical & Computer Engineering Theses & Dissertations

Cyber-physical systems (CPSs) are complex systems that evolve from the integrations of components dealing with physical processes and real-time computations, along with networking. CPSs often incorporate approaches merging from different scientific fields such as embedded systems, control systems, operational technology, information technology systems (ITS), and cybernetics. Today critical infrastructures (CIs) (e.g., energy systems, electric grids, etc.) and other CPSs (e.g., manufacturing industries, autonomous transportation systems, etc.) are experiencing challenges in dealing with cyberattacks. Major cybersecurity concerns are rising around CPSs because of their ever-growing use of information technology based automation. Often the security concerns are limited to probability-based possible attack …

Lung Cancer Type Classification, Mohit Ramajibhai Ankoliya

Electronic Theses, Projects, and Dissertations

Lung cancer is the third most common cancer in the U.S. This research focuses on classifying lung cancer cells based on their tumor cell, shape, and biological traits in images automatically obtained by passing through the

convolutional layers. Additionally, I classify whether the lung cell is adenocarcinoma, large cell carcinoma, squamous cell carcinoma, or normal cell carcinoma. The benefit of this classification is an accurate prognosis, leading to patients receiving proper therapy. The Lung Cancer CT(Computed Tomography) image dataset from Kaggle has been drawn with 1000 CT images of various types of lung cancer. Two state-of-the-art convolutional neural networks (CNNs) …

Panodepth – Panoramic Monocular Depth Perception Model And Framework, Adley K. Wong

Master's Theses

Depth perception has become a heavily researched area as companies and researchers are striving towards the development of self-driving cars. Self-driving cars rely on perceiving the surrounding area, which heavily depends on technology capable of providing the system with depth perception capabilities. In this paper, we explore developing a single camera (monocular) depth prediction model that is trained on panoramic depth images. Our model makes novel use of transfer learning efficient encoder models, pre-training on a larger dataset of flat depth images, and optimizing the model for use with a Jetson Nano. Additionally, we present a training and optimization framework …

An Overview Of Bidirectional Electric Vehicles Charging System As A Vehicle To Anything (V2x) Under Cyber–Physical Power System (Cpps), Onur Elma, Umit Cali, Murat Kuzlu

Engineering Technology Faculty Publications

Nowadays, EVs are rapidly increasing in popularity, and are accepted as the vehicles of the future all over the world. The most important components are their battery and charging systems. The energy capacity of EVs’ batteries has a significant potential to supply different energy requirements. Therefore, EVs must be designed in accordance with bidirectional power flow, and Electric Vehicle Supply Equipment (EVSE) should be upgraded as Electric Vehicle Power Exchange Equipment (EVPE). This power exchange infrastructure can be called Vehicle-to-Anything (V2X). V2X will also be the key solution for energy grids of the future that will turn into a much …

Infrastructure Development For Personalized Risk Prediction To Reduce Cardiovascular Disease In Childhood Cancer Survivors, Suman Shrestha

Dissertations and Theses (Open Access)

Although childhood cancer survivors have lengthy life expectancies, they run the risk of experiencing long-term health issues as a result of their treatment. The most frequent non-cancerous cause of morbidity and mortality for these survivors is cardiac disease. Radiation therapy (RT) has been linked in numerous cohort studies to a higher chance of developing a late cardiac disease in these survivors, and this risk rises with higher mean heart doses and increased RT exposure to larger cardiac volumes. Since, the heart is a heterogeneous organ made up of several distinct substructures, RT dose received by the entire heart does not …

Effective Short Text Classification Via The Fusion Of Hybrid Features For Iot Social Data, Xiong Luo, Zhijian Yu, Zhigang Zhao, Wenbing Zhao, Jenq-Haur Wang

Electrical and Computer Engineering Faculty Publications

Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive …

The Importance Of Hand Motions For Communication And Interaction In Virtual Reality, Alex Adkins

All Dissertations

Virtual reality (VR) is a growing method of communication and play. Recent advances have enabled hand-tracking technologies for consumer VR headsets, allowing virtual hands to mimic a user's real hand movements in real-time. A growing number of users now utilize hand-tracking when using VR to manipulate objects or to create gestures when interacting with others. As VR grows as a tool and communication platform, it is important to understand how the rising prevalence of hand-tracking technology might affect users' experiences.

The goal of this dissertation is to investigate, through a series of experiments, how using hand motions in VR influences …

Stuck-At-Fault Immunity Enhancement Of Memristor-Based Edge Ai Systems, Md. Oli-Uz-Zaman

Graduate Theses and Dissertations (2019 - present)

Deep Neural Networks (DNN) are widely used in edge AI. But the complex perception and decision-making demand the overlarge computation and make the DNN architecture very sophisticated. Memristors have multilevel resistance property that enables faster in-memory DNN computation to remove the bottleneck caused by the von Neumann architecture and CMOS technology. However, the Stuck-At-Fault (SAF) defect of memristor generated from immature fabrication and heavy device utilization makes the memristor-based edge AI commercially unavailable. To mitigate this problem, an Adaptive Mapping Method (AMM) is proposed in this project. Based on the analysis for the VGG8 model with CIFAR10 dataset, the experiment …

Online/Incremental Learning To Mitigate Concept Drift In Network Traffic Classification, Alberto R. De La Rosa

Open Access Theses & Dissertations

Communication networks play a large role in our everyday lives. COVID19 pandemic in 2020 highlighted their importance as most jobs had to be moved to remote work environments. It is possible that the spread of the virus, the death toll, and the economic consequences would have been much worse without communication networks. To remove sole dependence on one equipment vendor, networks are heterogeneous by design. Due to this, as well as their increasing size, network management has become overwhelming for network managers. For this reason, automating network management will have a significant positive impact. Machine learning and software defined networking …