Computer and Systems Architecture Commons^™

Machine Learning For Kalman Filter Tuning Prediction In Gps/Ins Trajectory Estimation, Peter Wright

Electronic Theses, Projects, and Dissertations

This project is an exploration and implementation of an application using Machine Learning (ML) and Artificial Intelligence (AI) techniques which would be capable of automatically tuning Kalman-Filter parameters used in post-flight trajectory estimation software at Edwards Air Force Base (EAFB), CA. The scope of the work in this paper is to design and develop a skeleton application with modular design, where various AI/ML modules could be developed to plug-in to the application for tuning-switch prediction.

Classification Of Large Scale Fish Dataset By Deep Neural Networks, Priyanka Adapa

Electronic Theses, Projects, and Dissertations

The development of robust and efficient fish classification systems has become essential to preventing the rapid depletion of aquatic resources and building conservation strategies. A deep learning approach is proposed here for the automated classification of fish species from underwater images. The proposed methodology leverages state-of-the-art deep neural networks by applying the compact convolutional transformer (CCT) architecture, which is famous for faster training and lower computational cost. In CCT, data augmentation techniques are employed to enhance the variability of the training data, reducing overfitting and improving generalization. The preliminary outcomes of our proposed method demonstrate a promising accuracy level of …

Melanoma Detection Based On Deep Learning Networks, Sanjay Devaraneni

Electronic Theses, Projects, and Dissertations

Our main objective is to develop a method for identifying melanoma enabling accurate assessments of patient’s health. Skin cancer, such as melanoma can be extremely dangerous if not detected and treated early. Detecting skin cancer accurately and promptly can greatly increase the chances of survival. To achieve this, it is important to develop a computer-aided diagnostic support system. In this study a research team introduces a sophisticated transfer learning model that utilizes Resnet50 to classify melanoma. Transfer learning is a machine learning technique that takes advantage of trained models, for similar tasks resulting in time saving and enhanced accuracy by …

Improving Credit Card Fraud Detection Using Transfer Learning And Data Resampling Techniques, Charmaine Eunice Mena Vinarta

Electronic Theses, Projects, and Dissertations

This Culminating Experience Project explores the use of machine learning algorithms to detect credit card fraud. The research questions are: Q1. What cross-domain techniques developed in other domains can be effectively adapted and applied to mitigate or eliminate credit card fraud, and how do these techniques compare in terms of fraud detection accuracy and efficiency? Q2. To what extent do synthetic data generation methods effectively mitigate the challenges posed by imbalanced datasets in credit card fraud detection, and how do these methods impact classification performance? Q3. To what extent can the combination of transfer learning and innovative data resampling techniques …

Enhancing Accident Investigation Using Traffic Cctv Footage, Aksharapriya Peddi

Electronic Theses, Projects, and Dissertations

This Culminating Experience Project investigated how the densenet-161 model will perform on accident severity prediction compared to proposed methods. The research questions are: (Q1) What is the impact of usage of augmentation techniques on imbalanced datasets? (Q2) How will the hyper parameter tuning affect the model performance? (Q3) How effective is the proposed model compared to existing work? The findings are: Q1. The effectiveness of our model depends on the implementation of augmentation techniques that pay attention to handling imbalanced datasets. Our dataset poses a challenge due to distribution of classes in terms of accident severity. To address this challenge …

Decentralized Machine Learning On Blockchain: Developing A Federated Learning Based System, Nikhil Sridhar

Master's Theses

Traditional Machine Learning (ML) methods usually rely on a central server to per-
form ML tasks. However, these methods have problems like security risks, data
storage issues, and high computational demands. Federated Learning (FL), on the
other hand, spreads out the ML process. It trains models on local devices and then
combines them centrally. While FL improves computing and customization, it still
faces the same challenges as centralized ML in security and data storage.

This thesis introduces a new approach combining Federated Learning and Decen-
tralized Machine Learning (DML), which operates on an Ethereum Virtual Machine
(EVM) compatible blockchain. The …

Qasm-To-Hls: A Framework For Accelerating Quantum Circuit Emulation On High-Performance Reconfigurable Computers, Anshul Maurya

Theses and Dissertations

High-performance reconfigurable computers (HPRCs) make use of Field-Programmable Gate Arrays (FPGAs) for efficient emulation of quantum algorithms. Generally, algorithm-specific architectures are implemented on the FPGAs and there is very little flexibility. Moreover, mapping a quantum algorithm onto its equivalent FPGA emulation architecture is challenging. In this work, we present an automation framework for converting quantum circuits to their equivalent FPGA emulation architectures. The framework processes quantum circuits represented in Quantum Assembly Language (QASM) and derives high-level descriptions of the hardware emulation architectures for High-Level Synthesis (HLS) on HPRCs. The framework generates the code for a heterogeneous architecture consisting of a …

Towards Multi-Modal Interpretable Video Understanding, Quang Sang Truong

Graduate Theses and Dissertations

This thesis introduces an innovative approach to video comprehension, which simulates human perceptual mechanisms and establishes a comprehensible and coherent narrative representation of video content. At the core of this approach lies the creation of a Visual-Linguistic (VL) feature for an interpretable video portrayal and an adaptive attention mechanism (AAM) aimed at concentrating solely on principal actors or pertinent objects while modeling their interconnections. Taking cues from the way humans disassemble scenes into visual and non-visual constituents, the proposed VL feature characterizes a scene via three distinct modalities: (i) a global visual environment, providing a broad contextual comprehension of the …

Remote Side-Channel Disassembly On Field-Programmable Gate Arrays, Brandon R. Baggett

Graduate Theses and Dissertations (2019 - present)

Over the last two decades, side-channel vulnerabilities have shown to be a major threat to embedded devices. Most side-channel research has developed our understanding of the vulnerabilities to cryptographic devices due to their implementation and how we can protect them. However, side-channel leakage can yield useful information about many other processes that run on the device. One promising area that has received little attention is the side-channel leakage due to the execution of assembly instructions. There has been some work in this area that has demonstrated the idea’s potential, but so far, this research has assumed the adversary has physical …

Low-Power, Event-Driven System On A Chip For Charge Pulse Processing Applications, Joseph A. Schmitz

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

This dissertation presents an electronic architecture and methodology capable of processing charge pulses generated by a range of sensors, including radiation detectors and tactile synthetic skin. These sensors output a charge signal proportional to the input stimulus, which is processed electronically in both the analog and digital domains. The presented work implements this functionality using an event-driven methodology, which greatly reduces power consumption compared to standard implementations. This enables new application areas that require a long operating time or compact physical dimensions, which would not otherwise be possible. The architecture is designed, fabricated, and tested in the aforementioned applications to …

Knowing Just Enough To Be Dangerous: The Sociological Effects Of Censoring Public Ai, David Hopkins

Cybersecurity Undergraduate Research Showcase

This paper will present the capabilities and security concerns of public AI, also called generative AI, and look at the societal and sociological effects of implementing regulations of this technology.

Screensafefuture: A Parent-Empathetic And Practical Mhealth Application For Toddlers' Brain Development Addressing Screen-Addiction Challenges, Nafisa Anjum

Master of Science in Information Technology Theses

The surging incidents of infants and toddlers screen addiction in the United States are becoming a pressing concern due to its detrimental and compound impact on cognitive development, mental health, and physical growth. To address this era's critical child health and human development problem, we propose an innovative mHealth application-- ScreenSafeFuture-- in this paper. ScreenSafeFuture provides practical and parent-friendly solutions that seamlessly fit into parents' busy lifestyles, also acknowledging the effectiveness and convenience of smartphones as a healthcare tool. Our offering includes essential features designed to enhance the experience between parents and their children under 3 years old. With an …

Studazon, Gage Stevens, Michelle Thoi, Umangkumar Patel, Gavin Minney

Harrisburg University Research Symposium: Highlighting Research, Innovation, & Creativity

Studazon is a book listing for students to list textbooks they no longer need. Studazon acts as an advertising site to help connect students who have textbooks with students who need those textbooks.

Integrating Nist And Iso Cybersecurity Audit And Risk Assessment Frameworks Into Cameroonian Law, Bernard Ngalim

Journal of Cybersecurity Education, Research and Practice

This paper reviews cybersecurity laws and regulations in Cameroon, focusing on cybersecurity and information security audits and risk assessments. The importance of cybersecurity risk assessment and the implementation of security controls to cure deficiencies noted during risk assessments or audits is a critical step in developing cybersecurity resilience. Cameroon's cybersecurity legal framework provides for audits but does not explicitly enumerate controls. Consequently, integrating relevant controls from the NIST frameworks and ISO Standards can improve the cybersecurity posture in Cameroon while waiting for a comprehensive revision of the legal framework. NIST and ISO are internationally recognized as best practices in information …

Smart Service Function Chain System For Dynamic Traffic Steering Using Reinforcement Learning (Chrl), Ahmed Nadhum, Ahmed Al-Saadi

Karbala International Journal of Modern Science

The rapid development of the Internet and network services coupled with the growth of communication infrastructure necessitates the employment of intelligent systems. The complexity of the network is heightened by these systems, as they offer diverse services contingent on traffic type, user needs, and security considerations. In this context, a service function chain offers a toolkit to facilitate the management of intricate network systems. However, various traffic types require dynamic adaptation in the sets of function chains. The problem of optimizing the order of service functions in the chain must be solved using the proposed approach, along with balancing the …

Streamlined Hpc Environments With Cvmfs And Cybergis-Compute, Alexander C. Michels, Mit Kotak, Anand Padmanabhan, Shaowen Wang

I-GUIDE Forum

High-Performance Computing (HPC) resources provide the potential for complex, large-scale modeling and analysis, fueling scientific progress over the last few decades, but these advances are not equally distributed across disciplines. Those in computational disciplines are often trained to have the necessary technical skills to utilize HPC (e.g. familiarity with the terminal), but many disciplines face technical hurdles when trying to apply HPC resources to their work. This unequal familiarity with HPC is increasingly a problem as cross-discipline teams work to tackle critical interdisciplinary issues like climate change and sustainability. CyberGIS-Compute is middle-ware designed to democratize to HPC services with the …

Enabling Intelligent Network Management Through Multi-Agent Systems: An Implementation Of Autonomous Network System, Petro Mushidi Tshakwanda

Electrical and Computer Engineering ETDs

This Ph.D. dissertation presents a pioneering Multi-Agent System (MAS) approach for intelligent network management, particularly suited for next-generation networks like 5G and 6G. The thesis is segmented into four critical parts. Firstly, it contrasts the benefits of agent-based design over traditional micro-service architectures. Secondly, it elaborates on the implementation of network service agents in Python Agent Development Environment (PADE), employing machine learning and deep learning algorithms for performance evaluation. Thirdly, a new scalable approach, Scalable and Efficient DevOps (SE-DO), is introduced to optimize agent performance in resource-constrained settings. Fourthly, the dissertation delves into Quality of Service (QoS) and Radio Resource …

Prirpt: Practical Blockchain-Based Privacy-Preserving Reporting System With Rewards, Rui. Shi, Yang Yang, Huamin. Feng, Feng. Yuan, Huiqin. Xie, Jianyi. Zhang

Research Collection School Of Computing and Information Systems

In order to obtain evidence of a crime timely, most authorities encourage whistleblowers to provide valuable reports by rewarding them with prizes. However, criminals will try their best to delete or tamper with the reports and even threaten and revenge the whistleblowers to escape punishment. Hence, to make the reporting system work, it is essential to ensure the integrity of reported messages and the anonymity of the reporting and rewarding procedures in the reporting system. Most existing schemes for this problem are generally based on ring signatures, which incur high computational overhead and imperfect anonymity. In this paper, we introduce …

Balanced Blended Space: Proposing A Universal Theoretical Framework For Combinative Reality, David Smith, Frederick Bianchi

Publications and Research

In today's fragmented societies, a unified framework for communication and collaboration across different realities is crucial. We introduce Balanced Blended Space (BBS) as a framework for describing combinative reality, encompassing virtual, physical, and conceptual realms, all intrinsically connected. Interactions within these environments shape our perceptual space. This paper outlines key axiomatic assumptions, criteria for a universal framework, and fundamental terminology. We identify deep symmetries enabling the BBS framework, including Cognitive and Computational Symmetry, Physical and Virtual Symmetry, Mediation Pathway Symmetry, Space-Time Symmetry, and Sensory Symmetry. We propose tests to determine its viability, emphasizing virtual intelligence as a collaborative partner. We …

A Social Profile-Based E-Learning Model, Xola Ntlangula

African Conference on Information Systems and Technology

Many High Education Institutions (HEIs) have migrated to blended or complete online learning to cater for less interruption with learning. As such, there is a growing demand for personalized e-learning to accommodate the diversity of students' needs. Personalization can be achieved using recommendation systems powered by artificial intelligence. Although using student data to personalize learning is not a new concept, collecting and identifying appropriate data is necessary to determine the best recommendations for students. By reviewing the existing data collection capabilities of the e-learning platforms deployed by public universities in South Africa, we were able to establish the readiness of …