Computer Engineering Commons^™

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 …

Identifying Online Sexual Predators Using Support Vector Machine, Yifan Li

Dissertations

A two-stage classification model is built in the research for online sexual predator identification. The first stage identifies the suspicious conversations that have predator participants. The second stage identifies the predators in suspicious conversations. Support vector machines are used with word and character n-grams, combined with behavioural features of the authors to train the final classifier. The unbalanced dataset is downsampled to test the performance of re-balancing an unbalanced dataset. An age group classification model is also constructed to test the feasibility of extracting the age profile of the authors, which can be used as features for classifier training. The …

Detection Of Pathological Hfo Using Supervised Machine Learning And Ieeg Data, Isabel L. Sicardi Rosell

Dissertations

Epilepsy is the second most common neurological disorder and it affects approxi mately 50 million people worldwide. One of the main characteristics of this disorder is the presence of recurrent seizures which tend to be controlled through medication. Nonetheless, 20% of the patients with this disorder are resistant to drug treatment meaning that they need to go through alternative procedures.

Classification Of Animal Sound Using Convolutional Neural Network, Neha Singh

Dissertations

Recently, labeling of acoustic events has emerged as an active topic covering a wide range of applications. High-level semantic inference can be conducted based on main audioeffects to facilitate various content-based applications for analysis, efficient recovery and content management. This paper proposes a flexible Convolutional neural network-based framework for animal audio classification. The work takes inspiration from various deep neural network developed for multimedia classification recently. The model is driven by the ideology of identifying the animal sound in the audio file by forcing the network to pay attention to core audio effect present in the audio to generate Mel-spectrogram. …

Synthetic Data Generation Using Wasserstein Conditional Gans With Gradient Penalty (Wcgans-Gp), Manhar Singh Walia

Dissertations

With data protection requirements becoming stricter, the data privacy has become increasingly important and more crucial than ever. This has led to restrictions on the availability and dissemination of real-world datasets. Synthetic data offers a viable solution to overcome barriers of data access and sharing. Existing data generation methods require a great deal of user-defined rules, manual interactions and domainspecific knowledge. Moreover, they are not able to balance the trade-off between datausability and privacy. Deep learning based methods like GANs have seen remarkable success in synthesizing images by automatically learning the complicated distributions and patterns of real data. But they …

Confusion Modelling - An Estimation By Semantic Embeddings, Praveen Mohanprasad

Dissertations

Approaching the task of coherence assessment of a conversation from its negative perspective ‘confusion’ rather than coherence itself, has been attempted by very few research works. Influencing Embeddings to learn from similarity/dissimilarity measures such as distance, cosine similarity between two utterances will equip them with the semantics to differentiate a coherent and an incoherent conversation through the detection of negative entity, ‘confusion’. This research attempts to measure coherence of conversation between a human and a conversational agent by means of such semantic embeddings trained from scratch by an architecture centralising the learning from the distance between the embeddings. State of …

Finetuning Pre-Trained Language Models For Sentiment Classification Of Covid19 Tweets, Arjun Dussa

Dissertations

It is a common practice in today’s world for the public to use different micro-blogging and social networking platforms, predominantly Twitter, to share opinions, ideas, news, and information about many things in life. Twitter is also becoming a popular channel for information sharing during pandemic outbreaks and disaster events. The world has been suffering from economic crises ever since COVID-19 cases started to increase rapidly since January 2020. The virus has killed more than 800 thousand people ever since the discovery as per the statistics from Worldometer [1] which is the authorized tracking website. So many researchers around the globe …

Improving Transfer Learning For Use In Multi-Spectral Data, Yuvraj Sharma

Dissertations

Recently Nasa as well as the European Space Agency have made observational satellites images public. The main reason behind opening it to public is to foster research among university students and corporations alike. Sentinel is a program by the European Space Agency which has plans to release a series of seven satellites in lower earth orbit for observing land and sea patterns. Recently huge datasets have been made public by the Sentinel program. Many advancements have been made in the field of computer vision in the last decade. Krizhevsky, Sutskever & Hinton, 2012, revolutionized the field of image analysis by …