Computer Engineering Commons^™

Proceedings Of The 2020 Undergraduate Research Conference, Ryan C. Winters, Baylee E. Guillory, Baton Rouge Community College, Grambling State University, Louisiana College, Louisiana State University, Mcneese State University, Northwestern State University Of Louisiana, Southern University And A&M College, University Of Louisiana At Lafayette

Advance: Student Research Experience

Proceedings of the 2020 Undergraduate Research Conference, University of Louisiana at Lafayette, November 9-10, 2020.

Iot Devices In The Public Health Sector, Cayla Young

Cybersecurity Undergraduate Research Showcase

In this research, proper attention is drawn to privacy and security concerns with the integration of Internet of Things (IoT) devices in the public health sector. Often, not much attention is given to IoT devices and its vulnerabilities concerning the medical industry. Effects of COVID-19 contact tracing applications are explored through research of various source types. Mitigation techniques for these privacy and security issues is given. Focus is brought to topics outlining the risks associated with genetic testing companies and the vulnerabilities of data collection and data storage. Recommendations are provided to help consumers avoid these risks. Lastly, a comprehensive …

Improving Pain Management In Patients With Sickle Cell Disease Using Machine Learning Techniques, Fan Yang

Browse all Theses and Dissertations

Sickle cell disease (SCD) is an inherited red blood cell disorder that can cause a multitude of complications throughout a patient's life. Pain is the most common complication and a significant cause of morbidity. Since pain is a highly subjective experience, both medical providers and patients express difficulty in determining ideal treatment and management strategies for pain. Therefore, the development of objective pain assessment and pain forecasting methods is critical to pain management in SCD. On the other hand, the rapidly increasing use of mobile health (mHealth) technology and wearable devices gives the ability to build a remote health intervention …

Enabling Static Program Analysis Using A Graph Database, Jialun Liu

Browse all Theses and Dissertations

This thesis presents the design, the implementation, and the evaluation of a database-oriented static program analysis engine for the PHP programming language. This engine analyzes PHP programs by representing their semantics using a graph-based data structure, which will be subsequently stored into a graph database. Such scheme will fundamentally facilitate various program analysis tasks such as static taint analysis, visualization, and data mining. Specifically, these complex program analysis tasks can now be translated into built-in declarative graph database operations with rich features. Our engine fundamentally differs from other existing static program analysis systems that mainly leverage intermediate representation (IRs) to …

Improving Usability Of Genetic Algorithms Through Self Adaptation On Static And Dynamic Environments, Reamonn Norat

Electronic Theses and Dissertations, 2020-2023

We propose a self-adaptive genetic algorithm, called SAGA, for the purposes of improving the usability of genetic algorithms on both static and dynamic problems. Self-adaption can improve usability by automating some of the parameter tuning for the algorithm, a difficult and time-consuming process on canonical genetic algorithms. Reducing or simplifying the need for parameter tuning will help towards making genetic algorithms a more attractive tool for those who are not experts in the field of evolutionary algorithms, allowing more people to take advantage of the problem solving capabilities of a genetic algorithm on real-world problems. We test SAGA and analyze …

Mfpa: Mixed-Signal Field Programmable Array For Energy-Aware Compressive Signal Processing, Adrian Tatulian

Electronic Theses and Dissertations, 2020-2023

Compressive Sensing (CS) is a signal processing technique which reduces the number of samples taken per frame to decrease energy, storage, and data transmission overheads, as well as reducing time taken for data acquisition in time-critical applications. The tradeoff in such an approach is increased complexity of signal reconstruction. While several algorithms have been developed for CS signal reconstruction, hardware implementation of these algorithms is still an area of active research. Prior work has sought to utilize parallelism available in reconstruction algorithms to minimize hardware overheads; however, such approaches are limited by the underlying limitations in CMOS technology. Herein, the …

Modeling Site Specific Urban Propagation Using A Variable Terrain Radiowave Parabolic Equation - Vertical Plane Launch (Vtrpe-Vpl) Hybrid Technique, Pierre Cadette

Electronic Theses and Dissertations, 2020-2023

The development of efficient algorithms for calculating propagation loss in site specific urban environments has been an active area of research for many years. This dissertation demonstrates that, for particular scenarios, a hybrid approach that combines the Variable Terrain Radiowave Parabolic Equation (VTRPE) and Vertical Plane Launch (VPL) models can be used to produce accurate results for a downrange region of interest. The hybrid approach consists of leveraging the 2-D parabolic equation method in the initial propagation region, where backscatter and out of plane energy can be neglected, then transitioning to the more computationally intensive 3-D ray launching method for …

Scalable Communication Frameworks For Multi-Agency Data Sharing, Shafaq Chaudhry

Electronic Theses and Dissertations, 2020-2023

With the rise in frequency and magnitude of natural disasters, there is a need to break down monolithic organizational barriers and engage with community volunteers. This calls for ease of systems interoperability to facilitate communication, data-sharing and scalability of real-time response, essential for crisis communications. We propose two scalable frameworks that enable multi-agency interoperability and real-time data-sharing. The first framework harnesses the power of social media, artificial intelligence, and community volunteers to form an extended rescue-and-response network that alleviates call center burden and augments the finite capacity of dispatch units. Through an "online 9-1-1" service, affected people can request help …

Detecting Small Moving Targets In Infrared Imagery, Adam Cuellar

Electronic Theses and Dissertations, 2020-2023

Deep convolutional neural networks have achieved remarkable results for detecting large and medium sized objects in images. However, the ability to detect smallobjects has yet to achieve the same level performance. Our focus is on applications that require the accurate detection and localization of small moving objects that are distantfrom the sensor. We first examine the ability of several state-of-the-art object detection networks (YOLOv3 and Mask R-CNN) to find small moving targets in infraredimagery using a publicly released dataset by the US Army Night Vision and Electronic Sensors Directorate. We then introduce a novel Moving Target Indicator Network (MTINet) and …

Multi-Element Multi-Datastream Visible Light Communication Networks, Sifat Ibne Mushfique

Electronic Theses and Dissertations, 2020-2023

Because of the exponentially increasing demand of wireless data, the Radio Frequency (RF) spectrum crunch is rising rapidly. The amount of available RF spectrum is being shrunk at a very heavy rate, and spectral management is becoming more difficult. Visible Light Communication (VLC) is a recent promising technology complementary to RF spectrum which operates at the visible light spectrum band (400 THz to 780 THz) and it has 10,000 times bigger bandwidth than radio waves (3 kHz to 300 GHz). Due to this tremendous potential, VLC has captured a lot of interest recently as there is already an extensive deployment …

Investigations On The Use Of Hyperthermia For Breast Cancer Treatment, Sreekala Suseela

Electronic Theses and Dissertations, 2020-2023

Hyperthermia using electromagnetic energy has been proven to be an effective method in the treatment of cancer. Hyperthermia is a therapeutic procedure in which the temperature in the tumor tissue is raised above 42°C without causing any damage to the surrounding healthy tissue. This method has been shown to increase the effectiveness of radiotherapy and chemotherapy. Radio frequencies, microwave frequencies or focused ultrasound can be used to deliver energy to the tumor tissue to attain higher temperatures in the tumor region for hyperthermia application. In this dissertation the use of a near field focused (NFF) microstrip antenna array for the …

Technology Based Audit Tools: Implications For Audit Quality, Annie L. Witte

2020

Technology as an exogenous shock has proven to have pervasive effects on auditing firms, practitioners, regulators, and global markets. However, the dynamic nature of technology makes it uniquely challenging to articulate technology’s largescale implications on the auditing profession in recent times. Understanding how current technology has helped shape the contemporary auditing profession is vital to identify points of inflection within the industry (i.e., areas of risk and change), and key to elucidating the future of where the field is going.

The first paper (sole-authored) is a literature review that synthesizes auditing studies across methodologies, including archival, experimental, and qualitative methods. …

Design And Analysis Of A Pavement Marker Detection System, Timothy L. Johnson Ii

Theses and Dissertations--Civil Engineering

Personal injuries and property damage due to the failure of snow-plowable pavement markers which detach from pavement surfaces has led to the development of new all-plastic pavement markers which are located entirely below the planar surface of the pavement. The new all-plastic design pushes existing solutions used to avoid striping over highway reflectors into obsolescence since current solutions operate using electromagnets to sense the metal housings of snow-plowable pavement markers. A replacement solution is currently sought by the highway maintenance industry and three different marker detection methods were developed and tested on real-world highways with both new and aging pavement …

Opendrop Software Development, Jiajun Guan

Electrical Engineering

To be able to transfer and mix solutions more efficiently and accurately during biology experiments, the Electrical Engineering department at Cal Poly University is planning to purchase or engineer the OpenDrop device. The OpenDrop device uses electro-wetting technology to control the movement of small droplets of water on a planar electrode array to transport and mix different solutions. This device is an open-source project and could be purchased online through the GaudiLab. The basic code of the system could be found in GitHub. With this code as a reference, the goal of this senior project is to develop the firmware, …

Provably Trustworthy And Secure Hardware Design With Low Overhead, Qutaiba Alasad

Electronic Theses and Dissertations, 2020-2023

Due to the globalization of IC design in the semiconductor industry and outsourcing of chip manufacturing, 3PIPs become vulnerable to IP piracy, reverse engineering, counterfeit IC, and hardware Trojans. To thwart such attacks, ICs can be protected using logic encryption techniques. However, strong resilient techniques incur significant overheads. SCAs further complicate matters by introducing potential attacks post-fabrication. One of the most severe SCAs is PA attacks, in which an attacker can observe the power variations of the device and analyze them to extract the secret key. PA attacks can be mitigated via adding large extra hardware; however, the overheads of …

Automated Testing And Bug Reproduction Of Android Apps, Yu Zhao

Theses and Dissertations--Computer Science

The large demand of mobile devices creates significant concerns about the quality of mobile applications (apps). The corresponding increase in app complexity has made app testing and maintenance activities more challenging. During app development phase, developers need to test the app in order to guarantee its quality before releasing it to the market. During the deployment phase, developers heavily rely on bug reports to reproduce failures reported by users. Because of the rapid releasing cycle of apps and limited human resources, it is difficult for developers to manually construct test cases for testing the apps or diagnose failures from a …

Coverage Guided Differential Adversarial Testing Of Deep Learning Systems, Jianmin Guo, Houbing Song, Yue Zhao, Yu Jiang

Publications

Deep learning is increasingly applied to safety-critical application domains such as autonomous cars and medical devices. It is of significant importance to ensure their reliability and robustness. In this paper, we propose DLFuzz, the coverage guided differential adversarial testing framework to guide deep learing systems exposing incorrect behaviors. DLFuzz keeps minutely mutating the input to maximize the neuron coverage and the prediction difference between the original input and the mutated input, without manual labeling effort or cross-referencing oracles from other systems with the same functionality. We also design multiple novel strategies for neuron selection to improve the neuron coverage. The …

Alone: A Dataset For Toxic Behavior Among Adolescents On Twitter, Thilini Wijesiriwardene, Hale Inan, Ugur Kursuncu, Manas Gaur, Valerie L. Shalin, Krishnaprasad Thirunarayan, Amit P. Sheth, I. Budak Arpinar

Publications

The convenience of social media has also enabled its misuse, potentially resulting in toxic behavior. Nearly 66% of internet users have observed online harassment, and 41% claim personal experience, with 18% facing severe forms of online harassment. This toxic communication has a significant impact on the well-being of young individuals, affecting mental health and, in some cases, resulting in suicide. These communications exhibit complex linguistic and contextual characteristics, making recognition of such narratives challenging. In this paper, we provide a multimodal dataset of toxic social media interactions between confirmed high school students, called ALONE (AdoLescents ON twittEr), along with descriptive …

Ev Charging Behavior Analysis Using Hybrid Intelligence For 5g Smart Grid, Yi Shen, Wei Fang, Feng Ye, Michel Kadoch

Electrical and Computer Engineering Faculty Publications

With the development of the Internet of Things (IoT) and the widespread use of electric vehicles (EV), vehicle-to-grid (V2G) has sparked considerable discussion as an energy-management technology. Due to the inherently high maneuverability of EVs, V2G systems must provide on-demand service for EVs. Therefore, in this work, we propose a hybrid computing architecture based on fog and cloud with applications in 5G-based V2G networks. This architecture allows the bi-directional flow of power and information between schedulable EVs and smart grids (SGs) to improve the quality of service and cost-effectiveness of energy service providers. However, it is very important to select …

Metadata Management For Clinical Data Integration, Ningzhou Zeng

Theses and Dissertations--Computer Science

Clinical data have been continuously collected and growing with the wide adoption of electronic health records (EHR). Clinical data have provided the foundation to facilitate state-of-art researches such as artificial intelligence in medicine. At the same time, it has become a challenge to integrate, access, and explore study-level patient data from large volumes of data from heterogeneous databases. Effective, fine-grained, cross-cohort data exploration, and semantically enabled approaches and systems are needed. To build semantically enabled systems, we need to leverage existing terminology systems and ontologies. Numerous ontologies have been developed recently and they play an important role in semantically enabled …

Algorithms For Achieving Fault-Tolerance And Ensuring Security In Cloud Computing Systems, Md. Tariqul Islam

Theses and Dissertations--Computer Science

Security and fault tolerance are the two major areas in cloud computing systems that need careful attention for its widespread deployment. Unlike supercomputers, cloud clusters are mostly built on low cost, unreliable, commodity hardware. Therefore, large-scale cloud systems often suffer from performance degradation, service outages, and sometimes node and application failures. On the other hand, the multi-tenant shared architecture, dynamism, heterogeneity, and openness of cloud computing make it susceptible to various security threats and vulnerabilities. In this dissertation, we analyze these problems and propose algorithms for achieving fault tolerance and ensuring security in cloud computing systems.

First, we perform a …

Greater Cybersecurity Threat Predictions With A Primer In Machine Learning, Samir Souidi, Stanley Mierzwa

Center for Cybersecurity

Why is it that Big Data services such as Netflix can predict, with reasonable accuracy, the movies and programs that I may be interested in when I connect to their service? When I access Amazon.com Online Shopping, I get a good glimpse of items I purchased in the past, and at what timeframe, and also a prediction if it is time to repurchase it? Machine Learning (ML) and Artificial Intelligence (AI) probably hold the key to the reasons these predictions are performed so well. So, why can’t we predict, at least at a small-scale level, when and what type of …

Technological Challenges And Innovations In Cybersecurity And Networking Technology Program, Syed R. Zaidi, Ajaz Sana, Aparicio Carranza

Publications and Research

This era is posing a unique challenge to the Cybersecurity and related Engineering Technology areas, stimulated by the multifaceted technological boom expressed in accelerated globalization, digital transformation, the cloud, mobile access apps, and the Internet of Things (IoT)—where more and more devices are connected to the Internet every day. As the use of new Internet-based technologies increase; so does the risk of theft and misuse of sensitive information. This demands the awareness of cyber-criminality and the need for cyber hygiene in corporations, small businesses, and the government. As the need for experienced cybersecurity specialists has skyrocketed in recent years and …

Relational Sequential Decision Making, Kaushik Roy

Publications

Markov Decision Processes(MDPs) are the standard for sequential decision making. Comprehensive theory and methods have been developed to deal with solving MDPs in the propositional setting. Real world domains however are naturally represented using objects and relationships. To this effect, relational adaptations of algorithms to solve MDPs have been proposed in recent years. This paper presents a study of these techniques both in the model based and model free setting.

Knowledge-Infused Statistical Learning For Social Good, Kaushik Roy, Manas Gaur

Publications

Humans are able to provide symbolic knowledge in structured form for potential use by an AI system in learning human-desirable concepts. In clinical settings, for instance, prediction of patient outcomes by an AI can be guided by knowledge from patient history. This history contains concepts such as treatment information, observational and drug-related information, mental health conditions, and severity of disease/disorder. Additionally, there is also often a certain graphical structure to the knowledge among the concepts, for example, ”patient symptoms cause certain tests to be taken”, which in turn affects the prescription of medication. This type of structure between human interpretable …

Image Instance Segmentation: Using The Cirsy System To Identify Small Objects In Low Resolution Images, Orghomisan William Omatsone

Dissertations

The CIRSY system (or Chick Instance Recognition System) is am image processing system developed as part of this research to detect images of chicks in highly-populated images that uses the leading algorithm in instance segmentation tasks, called the Mask R-CNN. It extends on the Faster R-CNN framework used in object detection tasks, and this extension adds a branch to predict the mask of an object along with the bounding box prediction. Mask R-CNN has proven to be effective ininstance segmentation and object de-tection tasks after outperforming all existing models on evaluation of the Microsoft Common Objects in Context (MS COCO) …

Content-Based Filtering Recommendation Approach To Label Irish Legal Judgements, Sandesh Gangadhar

Dissertations

Machine learning approaches are applied across several domains to either simplify or automate tasks which directly result in saved time or cost. Text document labelling is one such task that requires immense human knowledge about the domain and efforts to review, understand and label the documents. The company Stare Decisis summarises legal judgements and labels them as they are made available on Irish public legal source www.courts.ie. This research presents a recommendation-based approach to reduce the time for solicitors at Stare Decisis by reducing many numbers of available labels to pick from to a concentrated few that potentially contains the …

Machine Learning Assisted Gait Analysis For The Determination Of Handedness In Able-Bodied People, Hugh Gallagher

Dissertations

This study has investigated the potential application of machine learning for video analysis, with a view to creating a system which can determine a person’s hand laterality (handedness) from the way that they walk (their gait). To this end, the convolutional neural network model VGG16 underwent transfer learning in order to classify videos under two ‘activities’: “walking left-handed” and “walking right-handed”. This saw varying degrees of success across five transfer learning trained models: Everything – the entire dataset; FiftyFifty – the dataset with enough right-handed samples removed to produce a set with parity between activities; Female – only the female …

An Examination Of The Smote And Other Smote-Based Techniques That Use Synthetic Data To Oversample The Minority Class In The Context Of Credit-Card Fraud Classification, Eduardo Parkinson De Castro

Dissertations

This research project seeks to investigate some of the different sampling techniques that generate and use synthetic data to oversample the minority class as a means of handling the imbalanced distribution between non-fraudulent (majority class) and fraudulent (minority class) classes in a credit-card fraud dataset. The purpose of the research project is to assess the effectiveness of these techniques in the context of fraud detection which is a highly imbalanced and cost-sensitive dataset. Machine learning tasks that require learning from datasets that are highly unbalanced have difficulty learning since many of the traditional learning algorithms are not designed to cope …

Transformer Neural Networks For Automated Story Generation, Kemal Araz

Dissertations

Towards the last two-decade Artificial Intelligence (AI) proved its use on tasks such as image recognition, natural language processing, automated driving. As discussed in the Moore’s law the computational power increased rapidly over the few decades (Moore, 1965) and made it possible to use the techniques which were computationally expensive. These techniques include Deep Learning (DL) changed the field of AI and outperformed other models in a lot of fields some of which mentioned above. However, in natural language generation especially for creative tasks that needs the artificial intelligent models to have not only a precise understanding of the given …