Computer Engineering Commons^™

Learning With Your Buddies: An Investigation Of Community Based Ux Design Learning On Discord, Grace Ling

Computer Science and Engineering Master's Theses

Online communities have been a major part of how people connect with others to learn about different perspectives. In this thesis, I examine ways people use Discord, one of the major online community platforms, to learn UX design.

In this research, I designed a study, collected data from the Design Buddies Discord, and conducted semantic content analysis to investigate the community learners, job seekers, and mentors’ dialogues. I then used social network analysis to uncover patterns in connections. Lastly, I conducted a qualitative evaluation to survey and to understand the usefulness of Design Buddies.

The results show that a balance …

The Db Community Vis-À-Vis Environmental, Health, And Societal Grand Challenges: Innovation Engine, Plumber, Or Bystander?, Anastasia Ailamaki, Leilani Battle, Johannes Gehrke, Masaru Kitsuregawa, David Maier, Christopher Re, Meihui Zhang, Magdalena Balazinska

Computer Science Faculty Publications and Presentations

This panel considers the role of the database research community in addressing humanity's greatest challenges. Are we an innovation engine, tool providers, or are we standing on the side while other research communities take the lead?

Mars Prototype Rover Environmental Measurement System, James A. Renick

Computer Engineering

In my senior project, the problem I am trying to solve is how to efficiently design, create, and install an original library onto a Mars prototype rover operating system and to further use that library to integrate a new weather measurement sensor device into the rover system with the necessary software and hardware implementations. This is an important and highly valued problem as many aerospace and other engineering companies utilize rovers and other autonomous systems for important research, explorations, and reconnaissance missions and goals. In solving this problem, I utilized many resources that were available to me such as advisors, …

Researching The Impact Of Cal Poly Classes On Computing Students Perceptions Of Computer Ethics And Social Responsibility, Annie Joss

Computer Engineering

The importance and impact of socio-technical systems are playing an increasing role in the education of computing students. Discussion of ethics and social responsibility has always been a tenant of computer science education; however, research has shown engineering and computer science students lose focus on these values over their engineering education. Cal Poly computing departments have taken steps to emphasize social responsibility and ethics through required and suggested courses. This project focuses on examining the values and beliefs of Cal Poly computing students, who were surveyed over Winter and Spring Quarters in 2022. This project is inspired by Dr. Cech’s …

Developing A Miniature Smart Boat For Marine Research, Michael Isaac Eirinberg

Computer Engineering

This project examines the development of a smart boat which could serve as a possible marine research apparatus. The smart boat consists of a miniature vessel containing a low-cost microcontroller to live stream a camera feed, GPS telemetry, and compass data through its own WiFi access point. The smart boat also has the potential for autonomous navigation. My project captivated the interest of several members of California Polytechnic State University, San Luis Obispo’s (Cal Poly SLO) Marine Science Department faculty, who proposed a variety of fascinating and valuable smart boat applications.

Machine Learning Fmri Autism Classifier, Nicholas Luckenbach

Interdisciplinary Design Senior Theses

Autism Spectrum Disorder (ASD) is typically understood by observing patient behaviors. Advances in neuroimaging research and data collection have made it possible to investigate the neurobiological basis for ASD, and differences between subjects with ASD and healthy controls (HC) have been found using a variety of methods. Functional magnetic resonance imaging (fMRI) in particular has provided evidence for and against certain cognitive models of ASD based on measuring the functional connectivity of subjects’ brains. Various techniques have been explored for applying machine learning (ML) to fMRI data in order to find unique patterns and differences between ASD and HC subjects. …

Therapeutic Mechanical Horse, Cade M. Liberty, Aleya Dolorfino, Peter Philips, Zuzanna Dominik, Luke Watts

Mechanical Engineering

Jack's Helping Hand and its hippotherapy participants required a device to serve as an alternative to a live horseback riding experience that could also increase the range of riders. This would provide more clients with equine-assisted therapy that has proven to better the lives of people with both physical and mental disabilities. Horses can be unpredictable, tall, and sometimes anxiety-inducing, especially for new riders. Our group’s aim was to develop a mechanical horse that will be able to reduce these issues for equine therapy centers and the riders they help. When a rider gets to practice sitting on the horse …

Total Sky Imager Project, Ryan D. Maier, Benjamin Jack Forest, Kyle X. Mcgrath

Mechanical Engineering

Solar farms like the Gold Tree Solar Farm at Cal Poly San Luis Obispo have difficulty delivering a consistent level of power output. Cloudy days can trigger a significant drop in the utility of a farm’s solar panels, and an unexpected loss of power from the farm could potentially unbalance the electrical grid. Being able to predict these power output drops in advance could provide valuable time to prepare a grid and keep it stable. Furthermore, with modern data analysis methods such as machine learning, these predictions are becoming more and more accurate – given a sufficient data set. The …

Mars Rover Mechanical Arm & Turret, Kendall C. Chappell, Kyle D. Peterson, Rodrigo Gonzalez, Sam Cole

Mechanical Engineering

The Rover Mechanical Arm and Turret (RAT) team was originally tasked with designing and building a mechanical arm to attach to the Exo Mars rover: a project headed by Cal Poly professor, Rich Murray. The rover will be the 3rd in a series of rovers sponsored by Murray. Through ideation, comparison studies, research, and prototyping, the RAT team determined a design capable of fulfilling the sponsor’s specifications. The concept design is lightweight, durable, and capable of 4 degrees of freedom. With two links and a mechanical claw, the rover arm has the capability to retrieve small rock samples from Mars's …

Digital Forensics Range, Cody P. Shanahan, Bryson Y. Shishido, Samuel R. Mckee, Justin Siu, Lisa Li, Maxwell Brewer

Computer Engineering

The Digital Forensics Range was developed to serve as an online training for groups interested in computer forensics. This year's team had the goal to expand upon last year, by adding a new forensics image, unity scenario, and additional AWS functionality. The team still wanted to continue with last year's goals of keeping the training easily runnable, quickly deployable, and rapidly scalable through the use of the cloud. Adding to last year's work, this year's team hoped to further increase the educational value of the simulation with more practice, and the addition of feedback. The training is meant to be …

Happiness And Policy Implications: A Sociological View, Sarah M. Kahl

Dissertations, Theses, and Capstone Projects

The World Happiness Report is released every year, ranking each country by who is “happier” and explaining the variables and data they have used. This project attempts to build from that base and create a machine learning algorithm that can predict if a country will be in a “happy” or “could be happier” category. Findings show that taking a broader scope of variables can better help predict happiness. Policy implications are discussed in using both big data and considering social indicators to make better and lasting policies.

Smartphone Control Of Rc Cars, Weston R. Fitzgerald

Electrical Engineering

The smartphone-controlled RC (remote-controlled) car is an inexpensive remote-controlled car designed to be fast and portable. Instead of manufacturing, packaging, and shipping a separate controller, the remote control is implemented in a phone application, which saves time and money in both the design process and the manufacturing process. Utilizing the user’s smartphone is more cost-effective since mobile devices are a common recurrence, and packaging fewer devices results in overall better portability of the product.

This smartphone-controlled car is speedy and intuitive to learn for typical smartphone users. The user can change the car’s speed and direction wirelessly using their phone; …

A Machine Learning Approach To Predicting The Onset Of Type Ii Diabetes In A Sample Of Pima Indian Women, Meriem Benarbia

Dissertations, Theses, and Capstone Projects

Type II diabetes is a disease that affects how the body regulates and uses sugar (glucose) as a fuel. This chronic disease results in too much sugar circulating in the bloodstream. High blood sugar levels can lead to circulatory, nervous, and immune systems disorders. Machine learning (ML) techniques have proven their strength in diabetes diagnosis. In this paper, we aimed to contribute to the literature on the use of ML methods by examining the value of a number of supervised machine learning algorithms such as logistic regression, decision tree classifiers, random forest classifiers, and support vector classifiers to identify factors …

Runtime Energy Savings Based On Machine Learning Models For Multicore Applications, Vaibhav Sundriyal, Masha Sosonkina

Electrical & Computer Engineering Faculty Publications

To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize energy savings under a given performance degradation. Machine learning techniques were utilized to develop performance models which would provide accurate performance prediction with change in operating core-uncore frequency. Experiments, performed on a node (28 cores) of a modern computing platform showed significant energy savings of as much as 26% with performance degradation of as low as 5% under the proposed strategy compared with the execution in the unlimited power case.

Who Is Missing? Characterizing The Participation Of Different Demographic Groups In A Korean Nationwide Daily Conversation Corpus, Haewoon Kwak, Jisun An, Kunwoo Park

Research Collection School Of Computing and Information Systems

A conversation corpus is essential to build interactive AI applications. However, the demographic information of the participants in such corpora is largely underexplored mainly due to the lack of individual data in many corpora. In this work, we analyze a Korean nationwide daily conversation corpus constructed by the National Institute of Korean Language (NIKL) to characterize the participation of different demographic (age and sex) groups in the corpus.

Taxi Travel Time Based Geographically Weighted Regression Model (Gwr) For Modeling Public Housing Prices In Singapore, Yi’An Wang, Fangyi Cai, Shih-Fen Cheng, Bo Wu, Kai Cao

Research Collection School Of Computing and Information Systems

In this research, a taxi travel time based Geographically Weighted Regression model (GWR) is proposed and utilized to model the public housing price in the case study of Singapore. In addition, a comparison between the proposed taxi data driven GWR and other models, such as ordinary least squares model (OLS), GWR model based on Euclidean distance and GWR model based on public transport travel time, have also been carried out. Results indicates that taxi travel time based GWR model has better fitting performance than the OLS model, and slightly better than the Euclidean distance-based GWR model, however, it is not …

Strainer: State Transcript Rating For Informed News Entity Retrieval, Thomas M. Gerrity

Master's Theses

Over the past two decades there has been a rapid decline in public oversight of state and local governments. From 2003 to 2014, the number of journalists assigned to cover the proceedings in state houses has declined by more than 30\%. During the same time period, non-profit projects such as Digital Democracy sought to collect and store legislative bill and hearing information on behalf of the public. More recently, AI4Reporters, an offshoot of Digital Democracy, seeks to actively summarize interesting legislative data.

This thesis presents STRAINER, a parallel project with AI4Reporters, as an active data retrieval and filtering system for …

Accelerating Graphics Rendering On Risc-V Gpus, Joshua Simpson

Master's Theses

Graphics Processing Units (GPUs) are commonly used to accelerate massively parallel workloads across a wide range of applications from machine learning to cryptocurrency mining. The original application for GPUs, however, was to accelerate graphics rendering which remains popular today through video gaming and video rendering. While GPUs began as fixed function hardware with minimal programmability, modern GPUs have adopted a design with many programmable cores and supporting fixed function hardware for rasterization, texture sampling, and render output tasks. This balance enables GPUs to be used for general purpose computing and still remain adept at graphics rendering. Previous work at the …

A Study Of Grammar-Based Fuzzing Approaches, Ziwei Wu

Master's Theses

Fuzzing is the process of finding security vulnerabilities in code by creating inputs that will activate the exploits. Grammar-based fuzzing uses a grammar, which represents the syntax of all inputs a target program will accept, allowing the fuzzer to create well-formed complex inputs. This thesis conducts an in-depth study on two blackbox grammar-based fuzzing methods, GLADE and Learn&Fuzz, on their performance and usability to the average user. The blackbox fuzzer Radamsa was also used to compare fuzzing effectiveness. From our results in fuzzing PDF objects, GLADE beats both Radamsa and Learn&Fuzz in terms of coverage and pass rate. XML inputs …

Specialized Named Entity Recognition For Breast Cancer Subtyping, Griffith Scheyer Hawblitzel

Master's Theses

The amount of data and analysis being published and archived in the biomedical research community is more than can feasibly be sifted through manually, which limits the information an individual or small group can synthesize and integrate into their own research. This presents an opportunity for using automated methods, including Natural Language Processing (NLP), to extract important information from text on various topics. Named Entity Recognition (NER), is one way to automate knowledge extraction of raw text. NER is defined as the task of identifying named entities from text using labels such as people, dates, locations, diseases, and proteins. There …

Improving Relation Extraction From Unstructured Genealogical Texts Using Fine-Tuned Transformers, Carloangello Parrolivelli

Master's Theses

Though exploring one’s family lineage through genealogical family trees can be insightful to developing one’s identity, this knowledge is typically held behind closed doors by private companies or require expensive technologies, such as DNA testing, to uncover. With the ever-booming explosion of data on the world wide web, many unstructured text documents, both old and new, are being discovered, written, and processed which contain rich genealogical information. With access to this immense amount of data, however, entails a costly process whereby people, typically volunteers, have to read large amounts of text to find relationships between people. This delays having genealogical …

Low-Cost Uav Swarm For Real-Time Object Detection Applications, Joel Valdovinos Miranda

Master's Theses

With unmanned aerial vehicles (UAVs), also known as drones, becoming readily available and affordable, applications for these devices have grown immensely. One type of application is the use of drones to fly over large areas and detect desired entities. For example, a swarm of drones could detect marine creatures near the surface of the ocean and provide users the location and type of animal found. However, even with the reduction in cost of drone technology, such applications result costly due to the use of custom hardware with built-in advanced capabilities. Therefore, the focus of this thesis is to compile an …

Viability And Implementation Of A Vector Cryptography Extension For Risc-V, Jonathan W. Skelly

Master's Theses

RISC-V is an open-source instruction-set architecture (ISA) forming the basis of thousands of commercial and experimental microprocessors. The Scalar Cryptography extension ratified in December 2021 added scalar instructions that target common hashing and encryption algorithms, including SHA2 and AES. The next step forward for the RISC-V ISA in the field of cryptography and digital security is the development of vector cryptography instructions.

This thesis examines if it is viable to add vector implementations of existing RISC-V scalar cryptography instructions to the existing vector instruction format, and what improvements they can make to the execution of SHA2 and AES algorithms. Vector …

Local Learning Algorithms For Stochastic Spiking Neural Networks, Bleema Rosenfeld

Dissertations

This dissertation focuses on the development of machine learning algorithms for spiking neural networks, with an emphasis on local three-factor learning rules that are in keeping with the constraints imposed by current neuromorphic hardware. Spiking neural networks (SNNs) are an alternative to artificial neural networks (ANNs) that follow a similar graphical structure but use a processing paradigm more closely modeled after the biological brain in an effort to harness its low power processing capability. SNNs use an event based processing scheme which leads to significant power savings when implemented in dedicated neuromorphic hardware such as Intel’s Loihi chip.

This work …

Outdoor Operations Of Multiple Quadrotors In Windy Environment, Deepan Lobo

Dissertations

Coordinated multiple small unmanned aerial vehicles (sUAVs) offer several advantages over a single sUAV platform. These advantages include improved task efficiency, reduced task completion time, improved fault tolerance, and higher task flexibility. However, their deployment in an outdoor environment is challenging due to the presence of wind gusts. The coordinated motion of a multi-sUAV system in the presence of wind disturbances is a challenging problem when considering collision avoidance (safety), scalability, and communication connectivity. Performing wind-agnostic motion planning for sUAVs may produce a sizeable cross-track error if the wind on the planned route leads to actuator saturation. In a multi-sUAV …

Design And Control Of Next-Generation Uavs For Effectively Interacting With Environments, Caiwu Ding

Dissertations

In this dissertation, the design and control of a novel multirotor for aerial manipulation is studied, with the aim of endowing the aerial vehicle with more degrees of freedom of motion and stability when interacting with the environments. Firstly, it presents an energy-efficient adaptive robust tracking control method for a class of fully actuated, thrust vectoring unmanned aerial vehicles (UAVs) with parametric uncertainties including unknown moment of inertia, mass and center of mass, which would occur in aerial maneuvering and manipulation. The effectiveness of this method is demonstrated through simulation. Secondly, a humanoid robot arm is adopted to serve as …

Towards A Cross-Layer Coupled Design Framework For Big Data Workflows, Qianwen Ye

Dissertations

The processing and analysis of big data increasingly rely on workflow technologies for knowledge discovery and scientific innovation. The execution of such workflows goes far beyond the capability and capacity of single computers and is now commonly supported on reliable and scalable data storage and analysis platforms in distributed environments, such as the Hadoop ecosystem. Workflow performance largely depends on how big data systems are configured and used. For example, the makespan of a big data workflow is affected by multiple layers of big data systems, including the parallel computing engine it runs on, the resource manager that orchestrates various …

Rechargeable Multi-Cell Battery, Wei Qiao, Taesic Kim, Liyan Qu

Department of Electrical and Computer Engineering: Faculty Publications

A method for power management of a multi - cell battery includes identifying a desired power value and voltage value , determining a battery voltage value and a battery current value for a battery , determining a number of battery banks from a plurality of battery banks to use for the battery , where each battery bank includes one or more battery cells ( or battery modules ) , checking availability of each of the one or more battery cells ( or battery modules ) , selecting one or more battery banks from the plurality of battery banks , where …

Un-Fair Trojan: Targeted Backdoor Attacks Against Model Fairness, Nicholas Furth

Theses

Machine learning models have been shown to be vulnerable against various backdoor and data poisoning attacks that adversely affect model behavior. Additionally, these attacks have been shown to make unfair predictions with respect to certain protected features. In federated learning, multiple local models contribute to a single global model communicating only using local gradients, the issue of attacks become more prevalent and complex. Previously published works revolve around solving these issues both individually and jointly. However, there has been little study on the effects of attacks against model fairness. Demonstrated in this work, a flexible attack, which we call Un-Fair …

Meta-Algorithms In Machine Learning, Vishnu Pendyala

Open Educational Resources

This presentation explores how to make the best of models impacted by bias and variance. Meta-learning minimizes loss. Ensemble methods, including Bagging, Adaboost, Random Forest, Gradient Boosting, and Stacking, are discussed. These methods perturb data (X or Y) using techniques like bootstrap sampling, k-fold sampling, weighted sampling, and random subspaces. Models are generated in parallel or sequentially, with aggregation strategies such as mean, mode, weighted response, and metamodel. The presentation also touches upon deep learning and one-shot learning, and explains how distances become less meaningful in high dimensions.

More details: https://events.vtools.ieee.org/m/315184

Video Recording: https://ieeetv.ieee.org/video/meta-algorithms-in-machine-learning