Computer Engineering Commons^™

A Low-Power Analog Cell For Implementing Spiking Neural Networks In 65 Nm Cmos, John S. Venker, Luke Vincent, Jeff Dix

Computer Science and Computer Engineering Faculty Publications and Presentations

A Spiking Neural Network (SNN) is realized within a 65 nm CMOS process to demonstrate the feasibility of its constituent cells. Analog hardware neural networks have shown improved energy efficiency in edge computing for real-time-inference applications, such as speech recognition. The proposed network uses a leaky integrate and fire neuron scheme for computation, interleaved with a Spike Timing Dependent Plasticity (STDP) circuit for implementing synaptic-like weights. The low-power, asynchronous analog neurons and synapses are tailored for the VLSI environment needed to effectively make use of hardware SSN systems. To demonstrate functionality, a feedforward Spiking Neural Network composed of two layers, …

Pollutant Forecasting Using Neural Network-Based Temporal Models, Richard Pike

Masters Theses & Specialist Projects

The Jing-Jin-Ji region of China is a highly industrialized and populated area of the country. Its periodic high pollution and smog includes particles smaller than 2.5 μm, known as PM2.5, linked to many respiratory and cardiovascular illnesses. PM2.5 concentration around Jing-Jin-Ji has exceeded China’s urban air quality safety threshold for over 20% of all days in 2017 through 2020.

The quantity of ground weather stations that measure the concentrations of these pollutants, and their valuable data, is unfortunately small. By employing many machine learning strategies, many researchers have focused on interpolating finer spatial grids of PM2.5, or hindcasting PM2.5. However, …

Ethical Implications Of Ai-Based Algorithms In Recruiting Processes: A Study Of Civil Rights Violations Under Title Vii And The Americans With Disabilities Act, Vanessa Rodriguez

Cyber Operations and Resilience Program Graduate Projects

This research paper analyzes the ethical implications of utilizing artificial intelligence, specifically AI-based algorithms in business selection and recruiting processes, with a focus on potential violations under Title VII of the Civil Rights Act of 1964 and Title 1 of the Americans with Disabilities Act (ADA). Amazon’s attempt at launching AI recruiting tools is examined. This paper will assess the fairness of AI recruiting practices, considering data collection, potential biases, and accuracy concerns in its implementation process. Additionally, the paper will provide an overview of federal civil rights statutes enforced by the U.S. Equal Employment Opportunity Commission (EEOC) and recent …

Recycled Polycarbonate And Polycarbonate/Acrylonitrile Butadiene Styrene Feedstocks For Circular Economy Product Applications With Fused Granular Fabrication-Based Additive Manufacturing, Alessia Romani, Marinella Levi, Joshua M. Pearce

Electrical and Computer Engineering Publications

Distributed recycling and additive manufacturing (DRAM) holds enormous promise for enabling a circular economy. Most DRAM studies have focused on single thermoplastic waste stream. This study takes three paths forward from the previous literature: 1) expanding DRAM into high-performance polycarbonate/ acrylonitrile butadiene styrene (PC/ABS) blends, 2) extending PC/ABS blend research into both recycled materials and into direct fused granular fabrication (FGF) 3-D printing and 3) demonstrating the potential of using recycled PC/ABS feedstocks for new applications in circular economy contexts. A commercial open source large-format FGF 3-D printer was modified and used to assess the different printability and accuracy of …

Integrating Ai Into Uavs, Huong Quach

Cybersecurity Undergraduate Research Showcase

This research project explores the application of Deep Learning (DL) techniques, specifically Convolutional Neural Networks (CNNs), to develop a smoke detection algorithm for deployment on mobile platforms, such as drones and self-driving vehicles. The project focuses on enhancing the decision-making capabilities of these platforms in emergency response situations. The methodology involves three phases: algorithm development, algorithm implementation, and testing and optimization. The developed CNN model, based on ResNet50 architecture, is trained on a dataset of fire, smoke, and neutral images obtained from the web. The algorithm is implemented on the Jetson Nano platform to provide responsive support for first responders. …

Potential Security Vulnerabilities In Raspberry Pi Devices With Mitigation Strategies, Briana Tolleson

Cybersecurity Undergraduate Research Showcase

For this research project I used a Raspberry Pi device and conducted online research to investigate potential security vulnerabilities along with mitigation strategies. I configured the Raspberry Pi by using the proper peripherals such as an HDMI cord, a microUSB adapter that provided 5V and at least 700mA of current, a TV monitor, PiSwitch, SD Card, keyboard, and mouse. I installed the Rasbian operating system (OS). The process to install the Rasbian took about 10 minutes to boot starting at 21:08 on 10/27/2023 and ending at 21:18. 1,513 megabytes (MB) was written to the SD card running at (2.5 MB/sec). …

Are We Day-Dreaming Our Way To The Future?, John O'Connor

Presentations

This paper explores the transformative impact of technology on society, drawing on Marshall McLuhan’s insights. It scrutinizes the consequences of profit-driven technological progress, particularly in VR, brain-computer interfaces, and AI hallucinations. Critiquing the dominance of industry leaders in AI safety discussions, the paper advocates a balanced, inclusive approach.

Philosophical perspectives on AI and VR prompt questions about their impact on human experience. The paper proposes an educational shift to cultivate human attributes alongside technological skills. Examining AI hallucinations and gaming glitches, it raises concerns about the potential blurring of reality and virtuality.

Connecting technological advancements with environmental challenges, the paper …

Resilient, Sustainable, And Secure Systems Support For Ultra-Low-Power Computational Things, Nicole Tobias

All Dissertations

Wireless battery-free and energy-harvesting devices are expanding the reach and vision of the Internet of Things, where trillions of embedded computational things interconnect ubiquitously around us and inform many different aspects of our everyday lives. Designing these systems without batteries and interconnecting wires lowers maintenance, environmental, and economic costs while also extending device lifetime and deployment opportunities. Over the last decade, research on these ultra-low-power embedded sensors and systems has dramatically increased — enabling new and exciting prospects in many different scientific fields, from smart building and health monitoring applications to animal and activity tracking.

These systems are not without …

From Leanstore To Learnedstore: Using A Learned Index To Improve Database Index Search, Sujit Maharjan

Computer Science and Engineering Faculty Publications - Archive

In the realm of database systems, optimizing B+-tree index performance is of paramount importance to overall database performance. LeanStore, a high-performance OLTP storage engine, has extensively optimized its in-memory B+-tree component as well as its B+-tree -indexed database on the disk. However, B+-tree's lookup time increases linearly with the tree height. This is especially problematic when all or part of its lookup path is on the disk. Recently proposed learned index technique has the potential to significantly improve the performance of the B+-tree -based index by predicting location of the search key, instead of the level-by-Ievel path walk. However, this …

Brunet: Disruption-Tolerant Tcp And Decentralized Wi-Fi For Small Systems Of Vehicles, Nicholas Brunet

Master's Theses

Reliable wireless communication is essential for small systems of vehicles. However, for small-scale robotics projects where communication is not the primary goal, programmers frequently choose to use TCP with Wi-Fi because of their familiarity with the sockets API and the widespread availability of Wi-Fi hardware. However, neither of these technologies are suitable in their default configurations for highly mobile vehicles that experience frequent, extended disruptions. BRUNET (BRUNET Really Useful NETwork) provides a two-tier software solution that enhances the communication capabilities for Linux-based systems. An ad-hoc Wi-Fi network permits decentralized peer-to-peer and multi-hop connectivity without the need for dedicated network infrastructure. …

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 …

Convolution And Autoencoders Applied To Nonlinear Differential Equations, Noah Borquaye

Electronic Theses and Dissertations

Autoencoders, a type of artificial neural network, have gained recognition by researchers in various fields, especially machine learning due to their vast applications in data representations from inputs. Recently researchers have explored the possibility to extend the application of autoencoders to solve nonlinear differential equations. Algorithms and methods employed in an autoencoder framework include sparse identification of nonlinear dynamics (SINDy), dynamic mode decomposition (DMD), Koopman operator theory and singular value decomposition (SVD). These approaches use matrix multiplication to represent linear transformation. However, machine learning algorithms often use convolution to represent linear transformations. In our work, we modify these approaches to …

Early-Warning Prediction For Machine Failures In Automated Industries Using Advanced Machine Learning Techniques, Satnam Singh

Electronic Theses, Projects, and Dissertations

This Culminating Experience Project explores the use of machine learning algorithms to detect machine failure. The research questions are: Q1) How does the quality of input data, including issues such as outliers, and noise, impact the accuracy and reliability of machine failure prediction models in industrial settings? Q2) How does the integration of SMOTE with feature engineering techniques influence the overall performance of machine learning models in detecting and preventing machine failures? Q3) What is the performance of different machine learning algorithms in predicting machine failures, and which algorithm is the most effective? The research findings are: Q1) Effective outlier …

Hypothyroid Disease Analysis By Using Machine Learning, Sanjana Seelam

Electronic Theses, Projects, and Dissertations

Thyroid illness frequently manifests as hypothyroidism. It is evident that people with hypothyroidism are primarily female. Because the majority of people are unaware of the illness, it is quickly becoming more serious. It is crucial to catch it early on so that medical professionals can treat it more effectively and prevent it from getting worse. Machine learning illness prediction is a challenging task. Disease prediction is aided greatly by machine learning. Once more, unique feature selection strategies have made the process of disease assumption and prediction easier. To properly monitor and cure this illness, accurate detection is essential. In order …

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 …

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 …

Lung Lesion Segmentation Using Deep Learning Approaches, Sree Snigdha Tummala

Electronic Theses, Projects, and Dissertations

The amount of data generated in the medical imaging field, especially in a modern context, is growing significantly. As the amount of data grows, it's prudent to make use of automated techniques that can leverage datasets to solve problems that are error-prone or have inconsistent solutions.

Deep learning approaches have gained traction in medical imaging tasks due to their superior performance with larger datasets and ability to discern the intricate features of 3D volumes, a task inefficient if done manually. Specifically for the task of lung nodule segmentation, several different methods have been tried before such as region growing etc. …

Development Of A Machine Learning System For Irrigation Decision Support With Disparate Data Streams, Eric Wilkening

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

In recent years, advancements in irrigation technologies have led to increased efficiency in irrigation applications, encompassing the adoption of practices that utilize data-driven irrigation scheduling and leveraging variable rate irrigation (VRI). These technological improvements have the potential to reduce water withdrawals and diversions from both groundwater and surface water sources. However, it is vital to recognize that improved application efficiency does not necessarily equate to increased water availability for future or downstream use. This is particularly crucial in the context of consumptive water use, which refers to water consumed and not returned to the local or sub-regional watershed, representing a …

In Situ Water Sensing Systems: Research On Advancements In Environmental Monitoring, Abigail Seibel

Honors Program: Senior Projects (Public)

In this work, two sensing systems were researched in order to improve in situ environmental monitoring. The first is a pH and Total Alkalinity sensor used to determine these characteristics of sea water. I explored the facets of this sensor over a 7-week internship with Dr. Ellen Briggs in her lab in summer of 2023. The second is a more holistic sensing system that reads temperature, turbidity, and pressure used for studying environmental characteristics of Alaskan bever ponds. Both systems were developed in close collaboration with scientists who are collecting data to better understand the impacts of climate change. Better …

Electronic Note-String Detector, Gavin Garcia-Rossi, Tommy Smail

Electrical Engineering

As the virtual space has become a dominant part of everyone’s day-to-day lives, many normal face-to-face interactions and services have not yet been facilitated by adapting technology. One of these prevailing areas is music lessons. Over Zoom meetings, or other virtual platforms, it is tremendously challenging to teach students. These challenges include recognizing student mistakes audibly and visually, and being able to give confident feedback on the incorrect notes played by learning musicians. Without having to delve into improving the complex systems that would be required to improve audio, video, and connection quality of these connections, we have another solution …

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 …

Quiz Web Application, Dipti Rathod

Electronic Theses, Projects, and Dissertations

The Quiz web application is designed to facilitate the process of quiz creation and participation. This web application mainly consists of three roles: Admin, Instructor, and Student. Each role has specific features, functionalities, and permissions. With a user-friendly interface, the admin role can handle the departments, courses, and instructors. This web application also ensures smooth quiz management, allowing the instructors to schedule the upcoming quizzes, create the questions, and manage the students with ease. Student roles have features like taking quizzes and seeing their results. Additionally, this web application includes a significant feature to prevent cheating during online tests, ensuring …

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 …

Classification Of Thorax Diseases From Chest X-Ray Images, Sharad Jayusukhbhai Dobariya

Electronic Theses, Projects, and Dissertations

Chest X-ray images are crucial for medical decisions and patient care. However, their manual interpretation is time-consuming and prone to human error. This project aims to create an automated system that uses deep learning techniques to classify thorax disease from chest X-ray images. We are using the NIH Chest X-Ray Dataset, which contains many annotated images, as input data for this project. This approach uses UNet architecture as its classification layer. UNet architecture is well-known for its efficiency in image segmentation. Adding residual blocks enhances this approach's ability to classify images. The goal of this project is to create a …

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.

Implementation Of Adas And Autonomy On Unlv Campus, Zillur Rahman

UNLV Theses, Dissertations, Professional Papers, and Capstones

The integration of Advanced Driving Assistance Systems (ADAS) and autonomous driving functionalities into contemporary vehicles has notably surged, driven by the remarkable progress in artificial intelligence (AI). These AI systems, capable of learning from real-world data, now exhibit the capability to perceive their surroundings via a suite of sensors, create optimal routes from source to destination, and execute vehicle control akin to a human driver.

Within the context of this thesis, we undertake a comprehensive exploration of three distinct yet interrelated ADAS and Autonomy projects. Our central objective is the implementation of autonomous driving(AD) technology at UNLV campus, culminating in …

Cybersecurity In Critical Infrastructure Systems: Emulated Protection Relay, Mitchell Bylak

Computer Science and Computer Engineering Undergraduate Honors Theses

Cyber-attacks on Critical Systems Infrastructure have been steadily increasing across the world as the capabilities of and reliance on technology have grown throughout the 21st century, and despite the influx of new cybersecurity practices and technologies, the industry faces challenges in its cooperation between the government that regulates law practices and the private sector that owns and operates critical infrastructure and security, which has directly led to an absence of eas- ily accessible information and learning resources on cybersecurity for use in public environments and educational settings. This honors research thesis addresses these challenges by submitting the development of an …

Docai, Riley Badnin, Justin Brunings

Computer Science and Software Engineering

DocAI presents a user-friendly platform for recording, transcribing, summarizing, and classifying doctor-patient consultations. The application utilizes AssemblyAI for conversational transcription, and the user interface allows users to either live-record consultations or upload an existing MP3 file. The classification process, powered by 'ml-classify-text,' organizes the consultation transcription into SOAP (Subjective, Objective, Assessment, and Plan) format – a widely used method of documentation for healthcare providers. The result of this development is a simple yet effective interface that effectively plays the role of a medical scribe. However, the application is still facing challenges of inconsistent summarization from the AssemblyAI backend. Future work …

Renovating The Ipmu Via Internet Of Things For Pollutant Emission Estimations In Poultry Facilities, Joshua Dotto

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

The emissions of ammonia (NH₃), particulate matter (PM_2.5), and carbon dioxide (CO₂) are major concerns in poultry facilities. They can pose environmental concerns and nuances. Robust and affordable measurement systems are needed to accurately measure in-barn concentrations and quantify the emissions.

The Intelligent Portable Monitoring Unit (iPMU or PMU3) developed in 2016 was reconstructed into PMU4 to include upgraded NH₃ and PM_2.5 sensors and wireless connectivity for a low-cost, robust, and accurate air quality monitoring device with contactless data transfer using the concept of Internet of Things (IoT). In addition, a user-friendly …

Assessing Blockchain’S Potential To Ensure Data Integrity And Security For Ai And Machine Learning Applications, Aiasha Siddika

Master of Science in Information Technology Theses

The increasing use of data-centric approaches in the fields of Machine Learning and Artificial Intelligence (ML/AI) has raised substantial issues over the security, integrity, and trustworthiness of data. In response to this challenge, Blockchain technology offered a promising and practical solution, as its inherent characteristics as a decentralized distributed ledger, coupled with cryptographic processes, offer an unprecedented level of data confidentiality and immutability. This study examines the mutually beneficial connection between Blockchain technology and ML/AI, using Blockchain's inherent capacity to protect against unauthorized alterations of data during the training phase of ML models. The method involves building valid blocks of …