Electrical and Computer Engineering Commons^™

A Hierarchical Density-Based Spatial Clustering Of Applications With Noise (Hdbscan) Approach For Identifying Potential Villages In Buleleng Regency, Dina Nur Amalina, Achmad Fauzan

Knowledge Engineering and Data Science

Buleleng Regency, located in Bali Province, possesses diverse village potential, including agricultural production and tourist attractions. However, this potential has not been fully optimized. Therefore, it is important to enhance village potential by clustering villages based on their specific characteristics to identify and prioritize those requiring special attention. This approach aims to promote equitable village development and reduce poverty levels. This study clusters villages in Buleleng Regency based on their potential using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) method. The data utilized in this study comprises village potential data obtained from the Buleleng Regency Statistics Office …

Manifold Learning And Undersampling Approaches For Imbalanced Class Sentiment Classification, L.M. Risman Dwi Jumansyah, Agus Mohamad Soleh, Utami Dyah Syafitri

Knowledge Engineering and Data Science

Movie reviews are crucial in determining a film's success by influencing audience decisions. Automating sentiment classification is essential for efficient public opinion analysis. However, it faces challenges such as high-dimensional data and imbalanced class distributions. This study addresses these issues by applying manifold learning techniques, Principal Component Analysis (PCA) and Laplacian Eigenmaps (LE) to reduce data complexity and undersampling strategies (Random Undersampling (RUS) and EasyEnsemble) to balance data and improve predictions for both sentiment classes. On reviews of The Raid 2: Berandal, EasyEnsemble achieved the highest average G-Mean of 0.694 using Term Frequency-Inverse Document Frequency (TF IDF) features with a …

Constructing Qur’An Recitation Classification Using Alexnet Algorithm, Harits Ar Rosyid, Dzulkifli Abdullah, Mohammed S. Alqahtani

Knowledge Engineering and Data Science

The growing demands for accurate and efficient methods in the Qur'an recitation classification highlight the limitations of existing models, particularly in assisting the memorization process. This study aims to address these challenges by implementing the AlexNet Convolutional Neural Network architecture, widely recognized for its effectiveness in image classification, to classify the Qur'an recitations using the Mel Frequency Cepstral Coefficient (MFCC) as the feature extraction method. The research involves several stages, including data collection, preprocessing (audio segmentation by verse), data augmentation, feature extraction, and classification using the AlexNet architecture, followed by performance evaluation. Key results demonstrate that the combination of MFCC …

Deep Learning Approach For Dental Anomalies X-Ray Imaging Using Yolov8, Amelia Ritahani Ismail, Md Salim Sadman Taseen

Knowledge Engineering and Data Science

Dental X-ray imaging is a critical diagnostic tool for identifying various dental anomalies. However, manual interpretation is time-consuming, prone to human error, and requires specialized expertise. Deep learning models, particularly object detection frameworks like YOLO, have demonstrated promising results in automating medical image analysis. This study aims to develop and evaluate a YOLOv8-based deep learning model for automated detection and classification of 14 dental anomaly categories, including Caries, Crowns, Fillings, Implants, and Periapical lesions. The proposed approach addresses limitations in previous YOLO versions by leveraging anchor-free detection and enhanced feature extraction for improved accuracy. The model was trained on a …

Classification Of Anxiety Levels Entering The World Of Work In Final Year Students Using The Neighbor Weighted K-Nearest Neighbor Method, Awang Hendrianto Pratomo, Muhammad Fahmi Adam, Dessyanto Boedi Prasetyo

Knowledge Engineering and Data Science

This study evaluates the accuracy of the Neighbor Weighted K-Nearest Neighbor (NWKNN) method in classifying the anxiety levels of final-year students as they prepare to enter the workforce, particularly in cases of unbalanced data distribution. The system was developed using the prototype method, and NWKNN was applied to classify anxiety levels into low, medium, and high categories. Testing using the Confusion Matrix demonstrated strong performance, achieving an accuracy of 94% based on a dataset of 1009 students, with a 90:10 ratio of training to test data. The results indicate that NWKNN effectively provides classification input values, making it a reliable …

Comparative Analysis Of Bpnn And Lvq For Sundanese Character Recognition, Haviluddin Haviluddin, Herman Santoso Pakpahan, Dinda Izmya Nurpadillah, Hario Jati Setyadi, Medi Taruk, Rayner Alfred

Knowledge Engineering and Data Science

The Sundanese script (Aksara Sunda), an essential part of Sundanese cultural heritage, has been used since the 14th century AD. However, recognizing handwritten Sundanese characters remains challenging due to variations in individual writing styles. This study compares the performance of Backpropagation Neural Network (BPNN) and Learning Vector Quantization (LVQ) for recognizing handwritten Sundanese vowel (Swara) characters. A dataset was collected from 15 individuals, each writing seven Sundanese vowel characters, which were then used for training and testing the recognition models. Experimental results show that BPNN outperforms LVQ, achieving a higher classification accuracy (95.23%), lower Mean Squared Error (MSE), and faster …

Co-Emulation Of Robotics And Software-Defined Radio Based 5g Wireless Communications., Bhaskara Venkata Raju Garuda

Electronic Theses and Dissertations

The convergence of robotics and 5G wireless communication technologies has opened new avenues for real-time, dynamic robotic applications. This dissertation introduces a novel framework that integrates the Robot Operating System (ROS), Software-Defined Radios (SDRs), and 5G wireless networks to achieve seamless coemulation of robotic systems. The research emphasizes the unique features of 5G, such as ultra-low latency and high throughput, which enable critical applications like remote surgery, industrial automation, and autonomous vehicles. The methodology combines ROS for robotic control, SDRs for programmable communication channels, and 5G testbeds for high-speed, reliable data transmission. The experimental evaluation focuses on both position-based and …

Data Security In The Apple Ecosystem: An Evaluation, Kayrene Woods

Cybersecurity Undergraduate Research Showcase

This study provides a comprehensive evaluation of data security within the Apple ecosystem, focusing on the company’s privacy policies, user perceptions, and the effectiveness of its App Store review processes. Employing an interdisciplinary methodology, the research examines Apple’s commitment to data protection, emphasizing transparency and user trust. A survey of user experiences revealed varying levels of engagement and understanding of Apple’s privacy practices, with only 32.8% of respondents having read the Privacy Policy and mixed opinions on its clarity. Additionally, concerns persist about third-party app security, with 39.7% of users expressing apprehension and skepticism about Apple’s App Store review process. …

Motivating Sustainability Through The State Of Biologically Inspired Design, Bryan Watson

Sustainability Conference

There are multiple arguements for sustainability, but one that resonates with environmentalists and the public alike is the need for preservation to all us to discovery natural solutions to our problems. Common examples often given include medical discoveries, unique mechanisms, and new materials. This presentation focuses on two ideas to motivate sustainability. First, what is the current state of biologically inspired design? Is there more to learn from nature? To answer these questions, recent research is presented which examined 660 Biologically Inspired Design samples from three data sources: Google Scholar, Google News, and the Asknature.org “Innovations” database. The data were …

It's Not As Bad As You Think: Detecting Ai-Generated Voices, Yong Qin Xu

Undergraduate Research Symposium Lightning Talks

Advances in machine learning have opened up the world to a brand new frontier of fraudulent phone calls which the average person may not be in any way prepared for. From imitations of a loved one's voice to lifelike mimicry of human callers, telephone scams may become harder than ever to anticipate or prevent now that criminals have the help of AI on their side. This is why in my research paper, I aim to analyze and compare two existing methods of detecting the authenticity of human voice recordings in order to demonstrate and explain currently available technology that's capable …

Modeling W/V-Band Satellite Communications In The Presence Of Noise Jamming, Ryan Michael Eckman

Electrical and Computer Engineering ETDs

Understanding the performance of wideband modulated signals under noise jamming conditions is important for advancing W/V-band military satellite communications. This research developed and validated models to predict performance degradation of a communication channel in the presence of wideband noise jamming. Channel models were developed that included both modulated communication signals and jammer signals. Integrated models included environmental link factors and practical implementation factors. Integrated models were verified and validated using outdoor radio frequency environments utilizing two W/V-band transceivers, a portable W-band jammer, and software defined radios. Experimental results demonstrated excellent agreement with integrated model predictions. Channel degradation due to the …

Sustainable Mobility: Machine Learning-Driven Deployment Of Ev Charging Points In Dublin, Ruairí De Fréin, Alexander Mutiso Mutua Mr

Articles

Electric vehicle (EV) drivers in urban areas face range anxiety due to the fear of running out of charge without timely access to charging points (CPs). The lack of sufficient numbers of CPs has hindered EV adoption and negatively impacted the progress of sustainable mobility. We propose a CP distribution algorithm that is machine learning-based and leverages population density, points of interest (POIs), and the most used roads as input parameters to determine the best locations for deploying CPs. The objects of the following research are as follows: (1) to allocate weights to the three parameters in a $6$ km …

Remote Operated Amateur Radio Using Raspberry Pi And Limesdr, Esteban I. Perez

College of Engineering Summer Undergraduate Research Program

This project presents the design and implementation of a portable, remotely programmable amateur radio system operating on the 2-meter and 70-cm bands. The system utilizes Raspberry Pi 5 and LimeSDR hardware to facilitate communication between aerial and ground units, allowing for efficient transmission and reception of synchronization and calibration waveforms. A custom communication protocol incorporating BPSK modulation and a two-tone signaling mechanism enables time-of-flight measurements for calculating distances between nodes. This low-cost solution has potential applications in civilian sectors, such as search and rescue operations, where accurate direction finding and target localization are critical. Full-duplex operation enhances the system's capability …

Measurement Automation & Measurement System Research Endowment, Brian Bivinetto, Beneda Loya, Shiron Bendrihem

College of Engineering Summer Undergraduate Research Program

Road travel safety is always the most important issue in transportation systems. In general, several factors cause road accidents, such as human error, vehicle mechanical failure, roadway limitations (e.g. pavement, lane geometry, etc.), and inclement weather conditions. The major focus of today’s transportation developments is related to making highway transportation safer, smarter, and greener to enhance livability. Many accidents are caused when drivers lack a better understanding of the surrounding traffic conditions because the driver not only needs to control his/her vehicle but also needs to be aware of the movements of the vehicles around him/her. A driver cannot be …

Sal-E: Responsive Space Pathfinder Mission, Liam Duckworth, Berfredd Quezon

College of Engineering Summer Undergraduate Research Program

The main objective of the SAL-E mission is to grow the Cal Poly CubeSat Lab’s (PolySat) capabilities in rapid spacecraft design, as well as to provide the current generation of students with the opportunity to design, test, integrate, and communicate with a satellite. This summer we will be focusing on a few aspects of the spacecraft design and preparing our test facilities for Fall. Specifically, we will be ordering electronics boards that were designed in Spring, testing the boards as they arrive, developing software to interface with the payloads, begin integrated testing, updating the control system in our thermal vacuum …

Exploring Social Networks: An Analysis Of Intra-Organizational Networks, Ximeng Chen, Yiding Cao, Jiachen Liu, Danushka Bandara, Hiroki Sayama

Northeast Journal of Complex Systems (NEJCS)

This research investigates intra-organizational social networks by employing network science methods, aiming to provide actionable insights for both managers and employees. Using datasets from the Colorado Index of Complex Networks (ICON), this study examines four intra-organizational networks from a consulting firm and a research team, focusing on advice requests and skill awareness. The analysis includes network topology, degree distribution, community detection, correlation between communities and node attributes, maximal clique detection, and multilayer network analysis. The findings reveal that intra-organizational networks are intricate and significantly impact organizational efficiency and individual career development. Key insights include the importance of mid-level employees in …

Assessment Of The Factors Affecting The National Ict Broadband Backbone (Nictbb) Systems Restoration Time, Victoria Mahabi

Tanzania Journal of Engineering and Technology (TJET)

This study investigates the factors affecting the prolonged system restoration time when national ICT broadband backbone (NICTBB) services are affected by breakdown incidents. NICTBB is the government-owned backbone infrastructure constructed nationally by the Republic of Tanzania to increase the usage of ICT for equitable and sustainable socio-economic development and accelerate poverty reduction. This study utilised a mixture of exploratory and descriptive study approaches. The sample size determination was executed using purposing and simple random sampling, thus involving 289 respondents. The data reliability test was undertaken. It was followed by factor analysis, analysis of variances (ANOVA), linear regression analysis, and confirmed …

Assessment Of Web Security Vulnerabilities For Common Open Source Virtualization Software, Said Ally

Tanzania Journal of Engineering and Technology (TJET)

Open-source hypervisors have emerged as an integral technology for virtualizing server resources in cloud and data center computing. Hypervisor security efficiency is determined by virtual machine isolation, which is a de facto adoption factor in the selection process, as well as its ability to respond to web attacks. This paper assesses the security performance of Proxmox VE and XenServer for type 1 hypervisors, and Kernel Virtual Machine and Oracle Virtual Box for type 2 hypervisors. Security analysis was conducted using common exposures extracted from vulnerability databases and mapped against the OWASP 2013 and 2017 projects. For clarity, experiments were carried …

Evaluation Of Business-Driven Reference Architecture For Big Data Analytics Implementation By Public Sector Organizations In Resource-Constrained Setting: A Case Study Of Uganda, Matendo Didas

Tanzania Journal of Engineering and Technology (TJET)

Big Data Analytics (BDA) is a new area at the nexus of agenda, public sector organizations, and government business. It may satisfy the growing need for trustworthy, cost-effective services in the public sector for better, more informed decision-making processes. BDA has been proposed on the planning schedules of several public sector organizations and the government. Therefore, from the previous work, using Uganda as a case study, specifically the Uganda Bureau of Statistics (UBOS), Ministry of Health (MoH), and Ministry of Education and Sports (MoES), this study aims to evaluate a designed Business-Driven Reference Architecture for Big Data Analytics Implementation (BRABDAI) …

Investigations Of Cell Tower Antennas Parameters On Reduction Of Radio Frequency Radiation Levels From Radio Base Stations, Florence U. Rashidi

Tanzania Journal of Engineering and Technology (TJET)

The widespread deployment of mobile cellular base stations in populated areas has raised public health concerns due to increased exposure to radio frequency (RF) radiation emissions. Exposure to high levels of RF radiation can have potential thermal and non-thermal biological effects. Optimizing the configuration of cell tower antenna parameters is crucial for mitigating these radiation levels. This study therefore aims at systematically investigating the influence of different cell tower antenna parameters on reducing the RF radiation levels from mobile base stations. Field measurements were conducted at two cell sites shared by multiple mobile operators. Electric field strengths were measured at …

Data Communication Over Power-Lines: A Review On Technical, And Applications Challenges, Abdi Abdalla

Tanzania Journal of Engineering and Technology (TJET)

This paper presents a review study on the data communication over power-lines, commonly referred to as power-line carrier, power-line communication (PLC), mains communications, or power-line digital subscriber line (PDSL). This study examines the technical and application advantages and challenges associated with adopting PLC as a preferred alternative technology for wideband or broadband data communication. The broader coverage area of the PLC network gives it a distinct advantage over other communication network technologies. Additionally, implementing a communication system using the existing power-line network is more cost-effective and less time-consuming compared to constructing a new network from scratch. However, the primary challenge …

A Measure Of Interactive Complexity In Network Models, Will Deter

Northeast Journal of Complex Systems (NEJCS)

This work presents an innovative approach to understanding and measuring complexity in network models. We revisit several classic characterizations of complexity and propose a novel measure that represents complexity as an interactive process. This measure incorporates transfer entropy and Jensen-Shannon divergence to quantify both the information transfer within a system and the dynamism of its constituents’ state changes. To validate our measure, we apply it to several well-known simulation models implemented in Python, including: two models of residential segregation, Conway’s Game of Life, and the Susceptible-Infected-Susceptible (SIS) model. Our results reveal varied trajectories of complexity, demonstrating the efficacy and sensitivity …

Multivariate Analysis Of The Temporal And Spatial Correlations Of The Global Human Rights Dataset, Amanda Goodrick, Skip Mark, Mikhail G. Filippov, David Cingranelli, Hiroki Sayama

Northeast Journal of Complex Systems (NEJCS)

We propose an extension of a previously proposed method for lead-lag analysis of multivariate time series to include the analysis of spatial correlations. We applied the extended spatial and temporal method to CIRIGHTS, a large global human rights dataset, in order to determine the most influential and most influenced indicators of human rights, freedoms, and atrocities over time. We consider four target countries, each from a different continent. The previously proposed method used a weighted directed network with several lags of each variable as nodes and with edges weighted by transfer entropy. In this study, that method is extended to …

Training Uav Teams With Multi-Agent Reinforcement Learning Towards Fully 3d Autonomous Wildfire Response, Bryce Hopkins

All Theses

As climate-exacerbated wildfires increasingly threaten landscapes and communities, there is an urgent and pressing need for sophisticated fire management technologies. Coordinated teams of Unmanned Aerial Vehicles (UAVs) present a promising solution for detection, assessment, and even incipient-stage suppression – especially when integrated into a multi-layered approach with other recent wildfire management technologies such as geostationary/polar-orbiting satellites and CCTV detection networks. However, there remains significant challenges in developing the necessary sensing, navigation, coordination, and communication subsystems that enable intelligent UAV teams. Further, federal regulations governing UAV deployment and autonomy pose constraints on real-world aerial testing, creating a disconnect between theoretical research …

Using Efficient Vision Transformers To Improve Perception Systems In Autonomous Off-Road Vehicles, Adam S. Pickeral

All Theses

The development of autonomous vehicles has become one of the greatest research endeavors in recent years. These vehicles rely on many complex systems working in tandem to make decisions. For practical use and safety reasons, these systems must not only be accurate, but also be quick to make decisions. In Autonomous Vehicle research, the environment perception system is one of the key com- ponents of development. The environment perception system allows the vehicle to understand its surroundings using cameras, light detection and ranging (LiDAR), and other sensor systems or modalities. Deep learning computer vision algorithms have shown to be the …

Authenticated Diagnosing Of Covid-19 Using Deep Learning-Based Ct Image Encryption Approach, Mohamed Attia Abdelgwad, Amira Hassan Abed, Mahmoud Bahloul

Future Computing and Informatics Journal

Researchers are motivated to use artificial intelligence in biometrics, medical imaging encryption, as well as cybersecurity due to its rapid progress. An encryption method for CT scans—which are used to diagnose COVID-19 disease—is proposed in this study. The suggested encryption method creates a connection among an individual's face picture and CT image to increase confidentiality. The simple CT picture is first enhanced with a host image. An encryption key is multiplied by the final result. This key is produced by applying a Convolutional Neural Network (CNN) to recognize characteristics from people's face photographs. Additionally, a straightforward CNN with three convolutional …

Balloon Borne Gps-Enabled Radiosondes That Enable Simultaneous Multi-Point Atmospheric Sensing With A Single Ground Station, Peter A. Ribbens

Doctoral Dissertations and Master's Theses

Radiosondes are balloon borne atmospheric instruments that are a critical tool for understanding dynamics in the lower layers of the atmosphere. The low-cost radiosondes developed in the Space and Atmospheric Instrumentation Lab have been further developed to improve the system's use as a science-quality atmospheric instrument that is unique in its ability to simultaneously track multiple sondes with a single ground station. Sensors to measure temperature and pressure were added to improve measurements of the atmospheric state. A printed circuit board shield and 3D-printed shell were designed to make mass manufacturing possible. A thermistor-based temperature sensor was developed and tested …

Parallel Communications In Single-Core Optical Fibers Utilizing Spatial Domain Multiplexing And Wavelength Division Multiplexing, Mingxuan Tu

Theses and Dissertations

This endeavor proposes a hybrid parallel optical communication architecture in single-core fibers combining WDM in O-band (1270 nm, 1310 nm, 1310 nm, and 1330 nm) and two-channel SDM. The proposed architecture applies carefully designed delays in each propagating channel to achieve synchronization and, therefore, realizes parallel transmission in single-core fibers without the help of Serializer/De-serializers (SerDes). A mathematical model of SDM, calculations on delays between each optical channel, simulation of the hybrid architecture, experimental setup, and the testing and analysis of channel performance have been provided. A design of a photonic integrated circuit based on thin film lithium niobate electro-optic …

Enhancing Direction Finding Accuracy In Perturbed Digital Arrays Via Rf Ranging-Based Self Calibration, Ariel Freiman

Master's Theses

Direction finding with radio-frequency (RF) waves have numerous applications in radio navigation, wireless localization, emergency aid, and air traffic control, among others. Direction-finding using digital arrays outperforms traditional analog techniques but requires precise knowledge of the location of the array elements to obtain accurate results. Array perturbations can lead to algorithm failures and false detection, compromising direction-finding capabilities.

This research proposes implementing a Matched Filter - Least Square (MF-LS) algorithm for Two-Way Ranging (TWR) to enhance direction-finding accuracy in arrays with perturbed element locations. The MF-LS algorithm leverages the properties of matched filters to accurately determine element positions by measuring …

Shuffled Faster Than Nyquist Signaling For Spectrally Efficient And Secure Wireless Communication, John Gharib

Master's Theses

This thesis investigates the implementation and performance of Shuffled Faster than Nyquist (SFTN) signaling, a communication method that enhances spectral efficiency and provides physical layer security (PLS) in wireless communications. In Faster than Nyquist signaling, the Nyquist inter-symbol interference (ISI) criterion is exceeded, thereby increasing spectral efficiency. By varying the transmission rate of symbols above the Nyquist rate, SFTN signaling is able to obfuscate the timing of transmitted symbols with ISI. The work in this thesis evaluates the performance of SFTN in Additive White Gaussian Noise (AWGN) channels and the MATLAB 802.11ax fading channels. Results show that while SFTN signaling …