Digital Commons Network^™

Machine Learning Algorithms To Detect Cyber-Attack In The Internet Of Things Platform, Maad Kamal Al-Anni, Rafah M. Almuttairi, Ammar A. Al-Hamadani, Khamis A. Zidan, Husam Ibrahiem Husain Alsaadi, Ghaidaa A. Al-Sultany

Iraqi Journal for Computer Science and Mathematics

In response to escalating cyber threats in Internet of Things (IoT) networks, this research investigates several traffic classification techniques by using benchmark datasets, such as CICIoT2023. This dataset was exclusively used to test the effectiveness of the proposed system, which aims to enhance the data processing for machine learning algorithms amid increasingly complex cyber threats. To prepare the naturally unstructured and raw dataset for analysis, Linear Discriminant Analysis (LDA) is applied for dimensionality reduction. The processed data and attributes are then fed into the proposed Fuzzy-Integrated Relevance Vector Machine Classifier (FIRVM), which is implemented in Python 3.10⁺ using the …

Multi Trajectory Guided Model Inversion Attacks, Indrajit Nandi

Master’s Dissertations

Recent advancements in deep learning have brought significant concerns regarding the privacy of training data due to overfitting and memorization, as well as a lack of defense mechanisms. In model inversion attacks, where Attackers aim to reconstruct original private training samples (i.e images) just by using the DNN model’s last layer output. Traditional black-box MI attacks face three key challenges: (1) Non-convex optimization landscapes often trap reconstructions in poor local minima, degrading output quality. (2) Black-box scenarios require excessive queries to approximate gradients, raising detection risks. (3) Unconstrained pixel-space optimization generates unrealistic artifacts since the inverse mapping lacks natural image …

Santa Clara Radio Astronomy Project (Scrap) Iv - Data Processing, Noah Abe, Wesley Durbano

Computer Science and Engineering Senior Theses

The Santa Clara Radio Astronomy Project (SCRAP) IV: Data Processing is the fourth year of a multi-year project that aims to provide SCU with a fully functioning radio telescope in a costefficient manner. The project aims to provide this radio telescope as a hands-on tool that can be used not just for scientific discovery, but also for SCU academic use in a wide variety of different subjects, such as electrical engineering and physics. This year, our SCRAP team aimed to bolster the long-term operation of the telescope, which is a key feature it must have for it to be considered …

An Ai-Driven Microfinance Platform To Enhance The Growth Of Small Businesses, Abdullah Naveed, Yunzhou Wang

Computer Science and Engineering Senior Theses

Microfinance provides financial services to businesses which do not have access to regular banking institutions. There have been many digital applications developed to support microfinance schemes globally. However, the applications currently on the market do not yet fully leverage technology features, which are already being widely used in traditional banking. In conjunction with the Miller Center of Social Entrepreneurship at Santa Clara University, we have developed an application utilizing the latest technologies to help small businesses and lenders in all stages of a microfinance project.

Reclaiming Data To Transform Anti-Trafficking Systems, Laura Hackney

SMU Human Trafficking Data Conference

AnnieCannons believes that the people closest to the problem are closest to the solution. Yet in the anti-trafficking movement, data about survivors is often collected without survivors. Co-founder and CEO Laura Hackney will discuss how reclaiming data can transform systems of care accountability, and prevention. She will share lessons learned from the development of ResourceFull, a digital platform led and co- designed with trafficking survivors to safely connect individuals to trustworthy services and track outcomes across fragmented care systems. There are ethical, technical, and human-centered challenges of collecting and of using data in a space where safety and trust are …

Secure Your Hardware With Randomization And Redundancy, Dennis Cao, Joseph Khamisy, Sumeet Upadhya

Computer Science and Engineering Senior Theses

Differential Fault Analysis (DFA) is a potent hardware attack that threatens cryptographic security by injecting faults into a cipher implementation to reveal secret keys. This project aims to mitigate DFA attacks on the Advanced Encryption Standard (AES) by implementing targeted countermeasures in an embedded AES-128 encryption core. Two key techniques are explored: Randomization and Triple Modular Redundancy (TMR). The randomization approach introduces unpredictability into the encryption process, which involves inserting dummy rounds, artificial noise, and random delays, to disrupt an attacker’s timing and analysis, while TMR provides redundancy by replicating critical rounds of computation and using majority voting to correct …

Real-Time 3d Automated Spotlight Tracking System, Francine Garcia, Mary Hemker

Computer Science and Engineering Senior Theses

Commercially available automated spotlight systems tend to be expensive and unattainable for smaller school or community theaters. This project provides a cheap alternative to automated spotlight systems on the market and is compatible with most stage lighting architecture by using the Digital Multiplex (DMX) Protocol.

Circuit boards with Ultra-Wideband (UWB) radio frequency track an actor’s coordinates in a 3D space defined by four UWB anchors. A Raspberry Pi single-board computer acts as a controller and converts the coordinates into pan and tilt angles and maps these calculations to values that the spotlight can read in order to move. These values …

Boundary Value Problems Associated With Morse-Novikov Cohomology Groups Of Riemannian Manifolds With Boundary, Qusay S. A. Al-Zamil, Mohammed Y. Abass

Iraqi Journal for Computer Science and Mathematics

In this paper, we provide an affirmative response to the following question: if Morse-Novikov cohomology groups of M with non-empty boundary ∂M do not vanish, then what topological or analytical(geometrical) constraints can be enforced to ensure that the equation 𝖽θω=η is solvable for any non-trivialprescribed [η] in absolute Hθk(M) or relative Hθk(M,∂M) Morse-Novikov cohomology groups?. Where 𝖽θω=𝖽ω+θ∧ω and 0≠[θ]∈HdR1(M) for any ω∈ …

Retracted: A Novel Scheme To Optimize Lsb Steganography Based On A Logistic Chaotic Map And Genetic Algorithm, Dena Abu Laila, Ibrahim Moh'd Obeidt, Mohammad Aljaidi, Mohammed Amin Almaiah, Muneer Albourini, Qais Al-Na'amneh, Ghassan Samara, Rami Shehab, Khaled Momani

Iraqi Journal for Computer Science and Mathematics

Steganography has gained importance in various fields, including cloud security, internet banking, military applications, and medical imaging. Additionally, it is popular due to its many uses, becoming a hot research topic. The Least Significant Bit (LSB) approach is among the simplest ways to embed secret data in a cover image. We proposed a new scheme using LSB with the genetic algorithm GA to find the optimal solution permutation for embedding the pixel assortment of the image where data is to be concealed, and a chaotic genetic algorithm (CGA) to efficiently find the best crossover and mutation performance in the chromosomes. …

Hybrid Methods Of Two Conjugate Gradient Coefficients With Modified Condition For Solving Unconstrained Optimization, Yasir Salih, Sudradjat

Iraqi Journal for Computer Science and Mathematics

The conjugate gradient (CG) technique is an efficient method for solving nonlinear optimization problems, especially for large scale problems. This is due to simplicity, low memory requirement, and computational cost, which satisfy the sufficient descent condition. This paper presents a new modification of the PRP method which can be considered as a convex combination of PRP and DPRP methods. The performance of the new method is analyzed to ensure the descent property under exact line search. Numerical experiments indicate that this method has good convergence performance and is promising.

Open-Source Cubesat Flight Software And Simulation, James Gruber

Computer Engineering

An open-source FreeRTOS-based flight software framework for CubeSats, with an example flight software package and simulation demonstrating real-time attitude control.

How Long Can I Transmit? A Mobility Aware Mmwave-Based Uav Communication Framework, Shawon Mitra

Master’s Dissertations

One primary focus of next generation wireless communication networks is the millimeterwave (mmWave) spectrum, typically considered in the 30 GHz to 300 GHz frequency range. Despite their promise of high data rates, mmWaves su↵ers from severe attenuation while passing through obstacles. Unmanned aerial vehicles (UAVs) have been proposed to o↵set this limitation on account of their additional degrees of freedom, which can be leveraged to provide line of sight (LoS) transmission paths. While some prior works have proposed analytical frameworks to compute the LoS probability for static ground users and a UAV, the same is lacking for mobile users on …

Distributed Formation Control Of Nonholonomic Mobile Robots: Safety-Critical Leader-Follower Approach With Obstacle Avoidance And Dynamic Reconfiguration, Kelvin C. Villago

Master's Theses

Networked control systems for multi-agent robotics have emerged as a critical paradigm for executing complex coordinated tasks in diverse environments. While formation control serves as the backbone of such systems, real-world deployment introduces significant challenges including communication constraints, environmental obstacles, and the need for adaptive reconfiguration. This research addresses these challenges by developing a novel unified framework that seamlessly integrates obstacle avoidance algorithms with dynamic formation reconfiguration capabilities, specifically designed for communication-limited networked control architectures. The proposed framework represents a significant advancement over existing approaches by simultaneously handling both static and dynamic obstacles while maintaining system cohesion under communication constraints. …

Open Source Asic Design Curriculum, Francisco Wilken

Master's Theses

The ever-growing importance of Application-Specific Integrated Circuits (ASICs) in a high-compute world necessitates that college graduates entering the workforce are well prepared to design them. This thesis details the design of novel ASIC curriculum, using open source tools to teach at the undergraduate level. By moving to a higher level of abstraction than classical transistor-focused coursework, chip design material can be made accessible earlier in an undergraduate degree. Additionally, open source tools provide a powerful, free, and portable platform for students to create their own designs, solving assignments focused on industry readiness. Finally, this thesis studies the results and challenges …

Framework For Multi-Agent Coordination And Distributed Localization In Micro-Uavs, Minwoo Park, Nikolas Tambornini

Electrical Engineering

Micro-UAVs (unmanned aerial vehicles) due to their inexpensive nature and compact form factor have shown an increase in prevalence throughout a multitude of applications including, but not limited to: search and rescue, military reconnaissance, and agriculture monitoring. However, for a majority of these high impact applications, a swarm of micro-UAVs are required and furthermore mandate that they are able to cooperatively and autonomously coordinate with each other. For long, controlling and communicating between a user and a singular micro-UAV has been a well known and solved problem, however the same can't be said for swarms of micro-UAVs. This project seeks …

Farmnav-Uav: A Field-Ready Autonomous Uav For Tracking Static And Dynamic Trajectories In Agricultural Applications, Veera Venkata Ram Murali Krishna Rao Muvva

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

This study presents the design and development of a custom-built uncrewed aerial vehicle (UAV) tailored for precision agriculture. Unlike commercial UAVs, which are often constrained by proprietary systems and limited hardware customization, the proposed platform emphasizes modularity, upgradeability, and cost-effectiveness. The UAV is equipped with a Cube Blue flight controller for reliable low-level actuation and a Raspberry Pi 4 companion computer that executes a Model Predictive Control (MPC) algorithm for high-level trajectory optimization and stability enhancement.

Most conventional UAV autopilot systems rely on non-optimal control strategies, such as proportional–integral–derivative (PID) controllers, which can be inadequate for dynamic or resource-constrained environments. …

Adversarial Deep Reinforcement Learning For Tank Duel Simulation Using Lidar-Based Observations, Braedan Kennedy

Master's Theses

Previous research has demonstrated that reinforcement learning agents can learn to steer differential-drive robots around obstacles using 2D lidar scans as observations. However, these studies typically treat all range returns as undifferentiated obstacles—objects to avoid—without distinguishing between different object types. This thesis builds upon previous research by introducing an adversarial task in which an agent must interpret raw range readings to both avoid static obstacles and identify, pursue, and engage a hostile target.

To investigate this problem, this thesis introduces TankGame, a novel, lightweight 2D tank duel simulator. Each agent receives a 360° lidar scan, controls its motion via tread …

Fault Tolerant Dynamic Task Allocation For Heterogeneous Multi-Robot Systems, Jack R. Cline

Master's Theses

This research presents a novel approach to dynamic task allocation in heterogeneous multi-robot systems with integrated fault detection capabilities. As multiple industries are becoming more reliant on multi-robot systems for tasks, maintaining operational efficiency despite robot failures becomes critical. We propose a framework that combines optimization-based task allocation with a Kalman filter that estimates task progress for anomaly detection to identify unreliable agents and dynamically redistribute tasks. Observing values such as the normalized innovation squared (NIS), covariance, and progress rate, the algorithm can designate a robot as faulty. Embedding information about which robots are faulty in the task algorithm allows …

Sentry V3: Extending Context Switches On A Trusted Secure Coprocessor, Mark Kong

Master's Theses

Software correctness and integrity is only ensured through trust in the un- derlying hardware. However, modern computer systems are complex to design and secure. Thus, given the choice between performance and security, companies will often prioritize performance, resulting in vulnerable systems. This creates exploitable systems that must be patched retroactively because business value performance over security. One approach to this issue is to separate the root of security from the rest of the system to create a minimal trusted computing base. Trustguard is one instance of this. Trustguard implements a Containment Architecture with Ver- ified Output (CAVO) model which shows …

Ecen191/Robo150 Robotics Tools: Evaluating The Impact Of Project-Based Learning On Teaching Effectiveness, Nikhil Satyala

UNL Faculty Course Portfolios

The course selected for this study aims to introduce the skills and knowledge needed to utilize software and hardware tools used to design and operate robots. In this introductory course, undergraduate students in the Robotics Engineering major are provided with the opportunity to broaden their skill set with focus on specializing in one of three engineering disciplines (Software, Electrical and Mechanical). The primary goal of this course is to teach how develop the proficiency in basic concepts in robotics and enable the students to build and program a small-scale fully functional robotic arm. In this portfolio study, I investigated the …

Sla Evaluation And Composition In Reconfigurable Cloud-Based Services, Michael Iannelli

Dissertations, Theses, and Capstone Projects

Given the business model of offering data and computing services in a cloud setting, a major question arises: How do the services of one cloud provider compare to those of others? With the ubiquitous use of smartphones and tablets, the ability of a cloud provider to support QoS and client mobility becomes paramount. This research proposes a methodology for evaluating service-level agreements (SLAs) between cloud providers and their consumers, with a particular focus on dynamic SLA composition to adapt to changes in the application requirements and the external environment—such as traffic surges, security threats, or evolving business models.

In one …

Meta-Learning Hyperparameters For Foundation Model Adaptation In Remote-Sensing Imagery, Zichen Tian, Yaoyao Liu, Qianru Sun

Research Collection School Of Computing and Information Systems

Training large foundation models of remote-sensing (RS) images is almost impossible due to the limited and long-tailed data problems. Fine-tuning natural image pre-trained models on RS images is a straightforward solution. To reduce computational costs and improve performance on tail classes, existing methods apply parameter-efficient fine-tuning (PEFT) techniques, such as LoRA and AdaptFormer. However, we observe that fixed hyperparameters -- such as intra-layer positions, layer depth, and scaling factors, can considerably hinder PEFT performance, as fine-tuning on RS images proves highly sensitive to these settings. To address this, we propose MetaPEFT, a method incorporating adaptive scalers that dynamically adjust module …

Adversarial Robustness In Advanced Machine Learning Models Integrating Graph Neural Networks And Large Language Models, Mahmoud Nazzal

Dissertations

Artificial intelligence (AI) has achieved remarkable performances across various domains. In most real-world applications, data often takes relational forms, such as graphs and networks, or sequential forms, such as text and time series. As AI evolves, specialized models have emerged to handle these structures; Graph Neural Networks (GNNs) for relational mining and Large Language Models (LLMs) for sequential understanding. Despite their success, these models face challenges in security, robustness, and interpretability. GNNs excel in relational reasoning but are vulnerable to adversarial manipulation and lack interpretability, while LLMs are strong in linguistic reasoning and generalization yet struggle with relational data and …

Gamified Gait Rehabilitation Via Real-Time Biofeedback And Adaptive Hip-Exoskeleton Control, Mariya Huzaifa Tohfafarosh

Theses

Gait impairments arise from systemic diseases, age-related degeneration, musculoskeletal dysfunctions, or neurological conditions. While traditional rehabilitation can be effective, they often face challenges such as high costs, inaccessibility, and low patient engagement. To address these challenges, my work introduces a virtual reality-based rehabilitation (VRBR) system, integrating real-time motion and electromyographic (EMG) muscle activation feedback with a gamified virtual environment for enhanced adaptability and engagement. The system includes a custom-designed hip-exoskeleton that provides adaptive spring-like assistance or resistance, supporting both mobility-impaired users and strength training. Assistance levels can be tuned to match the user's progress. Additionally, a custom pressure insole was …

Codelympics: An Educational Game, Shreyas Raghunath, Riley Guioguio

Computer Science and Engineering Senior Theses

This thesis addresses the growth of Computer Science and the increasing demand for resources that educate people about programming. The central problem is that existing resources either fall short in engaging the user, or are ineffective at communicating the basics of programming. Through a year of development and testing, we developed an application to serve as a bridge between games and educational content, making programming skills fun and accessible for younger students with no prior experience.

This thesis details the development of Codelympics, an educational game designed to teach the basics of computer programming, including control flow, functions, variables, and …

Are Cycles Of Neural Activity The Algorithm Of The Brain?, Edwin Omondi Onyango

Computer Science Senior Theses

We propose that precisely timed neural activity cycles can serve as structural primitives for memory and computation in a system that exhibits associative learning like the brain. Inspired by biologically grounded mechanisms such as calcium-dependent plasticity, spike-timing-dependent learning, and phase-sensitive excitability, we construct a spiking neural network model in which repeated temporal coincidences drive the formation of self-sustaining activity loops. These cycles, once formed, persist as dynamic memory traces: not stored as static weights, but as reverberating patterns that replay in time when these loops are restarted. We show that noise alone fails to induce stable structure, but even sparse, …

Ams-Hd: Acute Mountain Sickness Detection With Hyperdimensional Computing, M. Hassan Najafi, Mehran Shoushtari Moghadam

Faculty Scholarship

Acute mountain sickness (AMS) is a potentially life-threatening condition that affects many individuals traveling to high altitudes. Early diagnosis is crucial, especially for travelers who may not have immediate access to medical resources. While traditional machine learning (ML) methods have been used to detect AMS using biomedical data (e.g., heart rate, blood oxygen saturation, respiration rate, blood pressure, and body temperature), hyperdimensional computing (HDC) has yet to be explored for this purpose using the few of biomedical data. Previous classification methods fall short of balancing accuracy with low hardware complexity, but HDC offers a promising solution. HDC provides a hardware-efficient …

Endo Almost 3–Absorbing Sub-Modules (Modules) And Related Concepts, Wafaa H. Hanoon, Mahmood S. Fiadh, Marwah W. Allami

Iraqi Journal for Computer Science and Mathematics

The concept of Endo Almost 3-Absorbing sub-modules (modules) is presented in this study, along with observations and the connections between Endo 2-Absorbing sub-modules (modules), Endo Approximately 2-Absorbing sub-modules (modules), Endo quasi-prime sub-modules (modules), and Endo prime sub-modules (modules). The study provides a range of attributes, illustrations, and justifications for these concepts. We aim to utilize the ramifications of this research to develop new ideas based on Endo Almost 3-Absorbing sub-modules (modules). Along with observations and an exploration of the relationships between Endo 2-Absorbing sub-modules (modules), Endo Approximately 2-Absorbing sub-modules (modules), Endo quasi-prime sub-modules (modules), and Endo prime sub-modules (modules), the …

Retracted: A Group Decision-Making For Selecting Multi-Deep Face Recognition Models, Abdulbasit Alazzawi, Qahtan M. Yas, Burhan Albayati

Iraqi Journal for Computer Science and Mathematics

Deep face recognition is a significant area of biometric authentication that addresses challenges such as low resolution, varying facial expressions, and inconsistent lighting. This paper presents a robust deep-learning approach to tackle these challenges. The study aims to employ multi-criteria decision-making techniques and verify the influence of individual and group expert opinions in decision-making. However, balancing criteria such as accuracy, sensitivity, specificity, precision, and recall remain challenging across different models. To fill this gap, the study utilized a decision-support framework that included the fuzzy analytical hierarchical process to set criteria weights based on expert input and the Technique for Order …

Ecg-Based Biometric Key Generation Using Principal Component Analysis And Machine Learning, Ahmad Abadleh

Iraqi Journal for Computer Science and Mathematics

Biometric authentication techniques are fast becoming imperative methods for secure identifications in a wide range of applications, while most of the traditional systems are easily spoofed or forged. In this paper a new approach of deriving a unique biometric key from an electrocardiogram signal is presented owing to physiological uniqueness of heart activity. In this respect, by focusing on RR intervals extracted from ECG signals, the PCA is applied in order to reduce its dimensionality and then form a compact and distinctive biometric key. Thereafter, a Random Forest classifier was used in evaluating the effectiveness of features, where a high …