Computer Engineering Commons^™

What Drives Blockchain Technology Adoption? A Meta-Analysis Across Tam, Tpb, And Utaut Frameworks, Amir Rahmani, Roohollah Ahmadi, Tugrul Unsal Daim, Mehdi Zamani, Dilek Ozdemir

Engineering and Technology Management Faculty Publications and Presentations

Research on blockchain adoption has expanded rapidly, with most studies conceptualizing adoption as behavioural intention. Technology adoption models, such as the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and the Unified Theory of Acceptance and Use of Technology (UTAUT), are commonly applied to explore blockchain technology adoption (BTA). However, empirical results from the 12 hypotheses associated with these models remain fragmented and, at times, inconsistent, with limited comprehensive quantitative integration. To address this gap, the present study performs a meta-analysis to evaluate the degree and direction of these hypotheses systematically. In accordance with PRISMA guidelines, 149 quantitative …

Improving The Fpga Radio Design Cycle: Implementing Dvb-S2 Modulation Using Verilator And Gnu Radio, Seth Pellegrino

Dissertations and Theses

In embedded digital radio systems, a central challenge is meeting the real-time throughput required to process the samples--especially for space-bound ultra-wideband SDRs which must handle more than 100 Gbps entirely onboard. An FPGA's programmable logic offers sufficient potential, but realizing a particular radio flow is a project usually fraught with defects and long turnaround times. We simulated Verilog modules in a custom harness that adapted a Verilated model to GNU Radio, which allowed for breaking down a complicated radio flow (here, DVB-S2 modulation) into a series of well-bounded problems each with clear criteria for success. The framework, built on open …

Jalzap: An Agile Hybrid For Ai-Accelerated Software Development, Pouya Nouri

University Honors Theses

The rapid adoption of generative AI tools allows software teams to quickly code applications, but it comes at a cost: high scope volatility, technical debt, and unrealistic expectations, which break traditional Agile frameworks. This thesis introduces JALZAP, a lightweight, hybrid Agile framework designed to serve small, high-agility teams facing compressed timelines and unpredictable schedules. This framework was evaluated over 16 weeks through a Portland State University Capstone project working for a pre-seed startup sponsor, where a six-person team built Flowmind: an AI-powered iOS task management app meant to serve users with neurodevelopmental disorders like ADD/ADHD. JALZAP implements structural boundaries, including …

Universal Communications Module: Converting Ieee2030.5 Xml Files To Ansi/Cta-2045, Wyatt Bilodeaux

University Honors Theses

The goal of this thesis is to discuss the method and organization of the Universal Communication module capstone project. This project was part of the Portland State University Electrical engineering degree program. While this thesis is part of the University honors curriculum. This thesis goes into detail about team structure and how a 9 person engineering capstone project was managed and organized. It looks at how we broke down the project into sections to be able to properly distribute our resources. This was one of the earliest and most important challenges that this capstone team had to tackle. This was …

Gps Tracking Smart Dash Cam Capstone Review, Simrah Saleem

University Honors Theses

This thesis details our design, implementation, and collaborative development of an intelligent vehicle logging system built on a Raspberry Pi 5. Unlike standard consumer dash cams that act as closed "black boxes," our system uses a dual-camera stereo vision setup integrated with centimeter-level accuracy. While we successfully built a functional Proof of Concept capable of event-triggered recording, dual-monitor visualization, and smart detection and recognition, this paper focuses on our engineering journey and the real-world challenges we faced. Using an Agile framework, we split into three specialized sub-teams to handle hardware, database, and interface design in parallel. This structure created unique …

The Impact Of Optimization Approximation Algorithms On The Performance Of The Bht-Qaoa, Ali Al-Bayaty, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

This article investigates the performance impact of five classical optimization approximation algorithms on our previously introduced quantum search algorithm, termed the Boolean–Hamiltonians Transform for Quantum Approximate Optimization Algorithm (BHT-QAOA), to effectively search for all best-approximated solutions for Boolean-based problems. These optimization approximation algorithms are BFGS, L-BFGS-B, SLSQP, COBYLA, and COBYQA. Their performance impact is evaluated and compared using two proposed performance metrics—(i) the final number of function evaluations (the lower numbers denote the best optimization approximation algorithms) and (ii) the final quality of qubit measurements (the higher values indicate all best-approximated solutions were found for a problem). Arbitrary classical Boolean …

Multiple-Valued Quantum Automata For Robotics, Yuchen Huang

Dissertations and Theses

This dissertation introduces a new type of quantum automata, their encoding and circuit realization. I concentrate on possible applications in robotics. Several methods and application of quantum automata and quantum circuit-based controllers for elementary robotic systems, with a focus on humanoid robot motion, emotion, and behavior generation are illustrated in detail. The research introduces several novel methodologies that bridge quantum computing principles with robotic control, aiming to overcome the limitations of classical deterministic and probabilistic approaches.

The dissertation first presents a quantum-circuit-based framework for generating non-repetitive and expressive (e)motions in a humanoid robot actor, using superposition and entanglement to produce …

Artificial Intelligence, Society 5.0 And Smart City Adaptation Initiatives For Businesses: An Integrated Approach, Ines A. M. Gila, Fernando A. F. Ferreira, Neuza C. M. Q. F. Ferreira, Florentin Smarandache, Momtaj Khanam, Tugrul Unsal Daim

Engineering and Technology Management Faculty Publications and Presentations

The mass migration of human populations to urban areas has resulted in unprecedented challenges for city services. To address and find solutions for these emerging issues, decision-makers must embrace the smart city and Society 5.0 paradigms, which comprehensively tackle various dimensions of the problem and ensure adaptability to evolving citizen needs. Central to the success of these paradigms is technology, particularly artificial intelligence (AI). AI’s transformative capabilities enable the expansion of services, automation of tasks, efficient operationalization and processing vast amounts of data to address urban challenges, aligning with several sustainable development goals (SDGs) such as sustainable cities and communities …

Leveraging High-Performance Cloud Computing To Model Underwater Acoustic Propagation And Scattering From Time-Evolving Rough Sea-Surfaces Using The Finite-Difference Time-Domain Method, James Alexander Higgins

Dissertations and Theses

This dissertation presents a two-dimensional (2D) Finite-difference Time-domain (FDTD) model for simulating underwater acoustic propagation and scattering from a one-dimensional (1D) time-evolving rough sea-surface. The techniques discussed are extendable to three spatial dimensions. Traditional acoustic modeling techniques often rely on a "frozen" sea-surface assumption, which proves inadequate for long-duration signals interacting with the time-evolving boundary at many different wave height displacements during its transit. To address this, a new FDTD update equation incorporating a variable subgrid is developed, significantly enhancing spatial accuracy at the boundary without increasing computational cost or compromising stability.

The model's accuracy is rigorously validated against established …

Spike Timing Depended Plasticity Produces Unsupervised Learning Of Synergistic Muscle Feedback In A Synthetical Neural Network, Mark Allen Pupkiewicz

Dissertations and Theses

This study investigates how type Ia feedback from muscle spindles can be organized into groups representing agonistic muscle pairs through Spike Timing Dependent Plasticity (STDP). A single degree of freedom joint is actuated with four biologically modeled muscles forming two agonistic pairs. In order to emulate the sensory dynamics of biological muscle spindles, sensors in the model record the active length and velocity states of each muscle, the two primary factors eliciting type Ia afferent responses. In biological networks, synapses from Ia sensory neurons frequently activate interneurons representing agonistic muscle sources. This research investigates whether this organization can emerge in …

Expanderizing Higher-Order Random Walks, Vedat Levi Alev, Shravas Rao

Computer Science Faculty Publications and Presentations

We study a variant of the down-up (also known as the Glauber dynamics) and up-down walks over an 𝑛-partite simplicial complex, which we call expanderized higher-order random walks—where the sequence of updated coordinates corresponds to the sequence of vertices visited by a random walk over an auxiliary expander graph 𝐻. When 𝐻 is the clique with self-loops on [𝑛], this random walk reduces to the usual down-up walk, and when 𝐻 is the directed cycle on [𝑛], this random walk reduces to the well-known systematic scan Glauber dynamics. We show that whenever the usual higher-order random walks satisfy a log-Sobolev …

Freer Arrows And Why You Need Them In Haskell, Grant Vandomelen, Gan Shen, Lindsey Kupur, Yao Li

Computer Science Faculty Publications and Presentations

Freer monads are a useful structure commonly used in various domains due to their expressiveness. However, a known issue with freer monads is that they are not amenable to static analysis. This paper explores freer arrows, a relatively expressive structure that is amenable to static analysis. We propose several variants of freer arrows. We conduct a case study on choreographic programming to demonstrate the usefulness of freer arrows in Haskell.

Free-Running Ring Oscillators For Crystal-Free Communication Systems: Design, Simulation Challenges, And Frequency Stability Improvements, Haziq Rohail

Dissertations and Theses

This thesis presents techniques for enhancing the frequency stability of ring oscillators (ROs) for crystal-free wireless communication systems. The first major contribution is a tutorial-style study of frequency stability metrics, providing clear definitions, conversions methods, and comparative analysis of commonly used figures of merit such as phase noise, Allan deviation, and jitter. The second contribution addresses the challenges of simulating phase noise in free-running ROs. Key techniques including Periodic Steady-State Noise (PNoise), Harmonic Balance Noise (HBNoise), and transient noise analysis are evaluated in terms of accuracy, convergence behavior, and simulation runtime. Based on these results, practical guidelines are offered for …

Layout-Aware Quantum Circuitry And Algorithmic Extensions To Grover's Algorithm, Ali Al-Bayaty

Dissertations and Theses

Lov K. Grover introduced, in 1996, Grover's algorithm as a quantum search algorithm to find all solutions for quantum oracles representing classical problems. My research observed that the Grover diffusion operator of Grover's algorithm gives wrong solutions when Boolean oracles are designed in some logical structures. Therefore, I invented the new "controlled-diffusion operator" for Boolean oracles as a new approach for Grover's algorithm that always correctly solves the problem of Grover's algorithm.

Another important problem in quantum computing is designing reliable and cost-effective quantum gates using reversible binary logic. In classical logic design, the stage of logic design can be …

Dcai: The 4th International Workshop On Data-Centric Ai, Yanjie Fu, Kunpeng Liu, Dongjie Wang

Computer Science Faculty Publications and Presentations

Machine learning traditionally emphasizes developing models for given datasets, but real-world data is often messy, making model improvement insufficient for enhancing performance. Data-Centric AI (DCAI) is an emerging field that systematically improves datasets, leading to significant practical ML advancements. While experienced data scientists have manually refined datasets through trial-and-error and intuition, DCAI approaches data enhancement as a systematic engineering discipline. DCAI represents a shift from focusing on models to the underlying data used for training and evaluation. Despite the dominance of common model architectures and predictable scaling rules, building and using datasets remain labor-intensive and costly, lacking infrastructure and best …

H2d: Hierarchical Heterogeneous Graph Learning Framework For Drug-Drug Interaction Prediction, Ran Zhang, Xuezhi Wang, Sheng Wang, Kunpeng Liu, Yuanchun Zhou, Pengfei Wang

Computer Science Faculty Publications and Presentations

Accurately predicting Drug-Drug Interactions (DDIs) is critical to designing effective drug combination therapies. Recently, Artificial Intelligence (AI)-powered DDI prediction approaches have emerged as a new paradigm. However, most existing methods oversimplify the complex hierarchical structure within molecules and overlook the multi-source heterogeneous information external to molecules, limiting their modeling and predictive capabilities. To address this, we propose a Hierarchical Heterogeneous graph learning framework for DDI prediction, namely H2D. H2D employs an internal-toexternal, local-to-global hierarchical perspective, exploiting intramolecular multi-granularity structures and inter-molecular biomedical interactions to mutually enhance across hierarchical levels. Extensive experimental results demonstrate H2D’s effectiveness on three …

2024 Summer Proceedings Teuscher Lab, Teuscher Group, Christof Teuscher, Chelsea Ogbede, Lauren Sanday, Sofia Vargas, Artem Arefev

altREU Projects

How will computation evolve in the coming years? What problems can be tackled using artificial intelligence, in a world increasingly driven by data? And how can that data be used to better inform our decisions as a society? In this unique collection of research projects, each chapter represents a distinct work undertaken by a single individual or a group of students as part of the altREU program led by Christof Teuscher. The projects, rooted in applications of artificial intelligence and innovative computation techniques, examine impactful solutions to numerous pressing challenges affecting communities around the world.

Story Of Your Lazy Function’S Life: A Bidirectional Demand Semantics For Mechanized Cost Analysis Of Lazy Programs, Liyao Xia, Laura Israel, Maite Kramarz, Stephanie Weirich, Koen Claessen, Nicolas Coltharp, Yao Li

Computer Science Faculty Publications and Presentations

Lazy evaluation is a powerful tool that enables better compositionality and potentially better performance in functional programming, but it is challenging to analyze its computation cost. Existing works either require manually annotating sharing, or rely on separation logic to reason about heaps of mutable cells. In this paper, we propose a bidirectional demand semantics that allows for extrinsic reasoning about the computation cost of lazy programs without relying on special program logics. To show the effectiveness of our approach, we apply the demand semantics to a variety of case studies including insertion sort, selection sort, Okasaki's banker's queue, and the …

Design And Test Of Asynchronous Systems Using The Link And Joint Model, Ebelechukwu Esimai

Dissertations and Theses

Asynchronous circuits offer numerous advantages, including low energy consumption and good composability and scalability. However, they remain meagerly adopted in the mainstream semiconductor industry. One reason is the limited number of design tools available to help designers navigate design complexity, particularly the myriad of asynchronous implementation styles.

This dissertation focuses on managing the myriad of asynchronous implementation styles by utilizing a circuit-neutral model, called Links and Joints, and embedding this Link-Joint approach into a design flow. Although years of past work have already laid the groundwork, the work in this dissertation identifies and addresses key missing pieces.

First, the …

A Deep Learning Framework For Blockage Mitigation In Mmwave Wireless, Ahmed Hazaa Almutairi

Dissertations and Theses

Millimeter-Wave (mmWave) communication is a key technology to enable next generation wireless systems. However, mmWave systems are highly susceptible to blockages, which can lead to a substantial decrease in signal strength at the receiver. Identifying blockages and mitigating them is thus a key challenge to achieve next generation wireless technology goals, such as enhanced mobile broadband (eMBB) and Ultra-Reliable and Low-Latency Communication (URLLC). This thesis proposes several deep learning (DL) frameworks for mmWave wireless blockage detection, mitigation, and duration prediction. First, we propose a DL framework to address the problem of identifying whether the mmWave wireless channel between two devices …

Flexible Strain Gauge Sensors As Real-Time Stretch Receptors For Use In Biomimetic Bpa Muscle Applications, Rochelle Jubert

Student Research Symposium

This work presents a novel approach to real-time length sensing for biomimetic Braided Pneumatic Actuators (BPAs) as artificial muscles in soft robotics applications. The use of artificial muscles enables the development of more interesting robotic designs that no longer depend on single rotation joints controlled by motors. Developing robots with these capabilities, however, produces more complexities in control and sensing. Joint encoders, the mainstay of robotic feedback, can no longer be used, so new methods of sensing are needed to get feedback on muscle behavior to implement intelligent controls. To address this need, flexible strain gauge sensors from Portland company, …

Uncovering The Critical Drivers Of Blockchain Sustainability In Higher Education Using A Deep Learning-Based Hybrid Sem-Ann Approach, Mohammed Alshamsi, Mostafa Al-Emran, Tugrul Daim, Mohammed A. Al-Sharafi, Gulin Idil Sonmezturk Bolatan, Khaled Shaalan

Engineering and Technology Management Faculty Publications and Presentations

The increasing popularity of Blockchain technology has led to its adoption in various sectors, including higher education. However, the sustainability of Blockchain in higher education is yet to be fully understood. Therefore, this research examines the determinants affecting Blockchain sustainability by developing a theoretical model that integrates the protection motivation theory (PMT) and expectation confirmation model (ECM). Based on 374 valid responses collected from university students, the proposed model is evaluated through a deep learning-based hybrid structural equation modeling (SEM) and artificial neural network (ANN) approach. The PLS-SEM results confirmed most of the hypotheses in the proposed model. The sensitivity …

Mmwave Rat Optimization: Mac Layer Initial Access Design And Transport Layer Integration, Suresh Srinivasan

Dissertations and Theses

MmWave Radio Access Technology (RAT) is a promising technology for wireless communication due its large bandwidth and is already being deployed in 5G cellular and emerging WiFi technologies. MmWave systems use highly directional beams with narrow beamwidths to overcome the high path loss associated with their frequency bands. A mmWave radio can be used either in a standalone mode (where all radios use the same technology) or simultaneously with other technologies such as LTE and low frequency WiFi in a communication mode commonly referred to as integrated mode. This thesis proposes two methods to optimize mmWave RAT performance in both …

Multi-Agent Deep Reinforcement Learning For Radiation Localization, Benjamin Scott Totten

Dissertations and Theses

For the safety of both equipment and human life, it is important to identify the location of orphaned radioactive material as quickly and accurately as possible. There are many factors that make radiation localization a challenging task, such as low gamma radiation signal strength and the need to search in unknown environments without prior information. The inverse-square relationship between the intensity of radiation and the source location, the probabilistic nature of nuclear decay and gamma ray detection, and the pervasive presence of naturally occurring environmental radiation complicates localization tasks. The presence of obstructions in complex environments can further attenuate the …

Realization Of Multi-Valued Logic Using Optical Quantum Computing, Sophie Choe

Dissertations and Theses

Quantum computing is a paradigm of computing using physical systems, which operate according to quantum mechanical principles. Since 2017, functioning quantum processing units with limited capabilities are available on the cloud. There are two models of quantum computing in the literature: discrete variable and continuous variable models. The discrete variable model is an extension of the binary logic of digital computing with quantum bits |0⟩ and |1⟩ . In the continuous variable model, the quantum state space is infinite-dimensional and the quantum state is expressed with an infinite number of basis elements.

In the physical implementation of quantum computing, however, …

Panoramas From Photons, Sacha Jungerman, Atul Ingle, Mohit Gupta

Computer Science Faculty Publications and Presentations

Scene reconstruction in the presence of high-speed motion and low illumination is important in many applications such as augmented and virtual reality, drone navigation, and autonomous robotics. Traditional motion estimation techniques fail in such conditions, suffering from too much blur in the presence of high-speed motion and strong noise in low-light conditions. Single-photon cameras have recently emerged as a promising technology capable of capturing hundreds of thousands of photon frames per second thanks to their high speed and extreme sensitivity. Unfortunately, traditional computer vision techniques are not well suited for dealing with the binary-valued photon data captured by these cameras …

Learned Compressive Representations For Single-Photon 3d Imaging, Felipe Gutierrez-Barragan, Fangzhou Mu, Andrei Ardelean, Atul Ingle, Claudio Bruschini, Edoardo Charbon, Yin Li, Mohit Gupta, Andreas Velten

Computer Science Faculty Publications and Presentations

Single-photon 3D cameras can record the time-of-arrival of billions of photons per second with picosecond accuracy. One common approach to summarize the photon data stream is to build a per-pixel timestamp histogram, resulting in a 3D histogram tensor that encodes distances along the time axis. As the spatio-temporal resolution of the histogram tensor increases, the in-pixel memory requirements and output data rates can quickly become impractical. To overcome this limitation, we propose a family of linear compressive representations of histogram tensors that can be computed efficiently, in an online fashion, as a matrix operation. We design practical lightweight compressive representations …

Sequential Frame-Interpolation And Dct-Based Video Compression Framework, Yeganeh Jalalpour, Wu-Chi Feng, Feng Liu

Computer Science Faculty Publications and Presentations

Video data is ubiquitous; capturing, transferring, and storing even compressed video data is challenging because it requires substantial resources. With the large amount of video traffic being transmitted on the internet, any improvement in compressing such data, even small, can drastically impact resource consumption. In this paper, we present a hybrid video compression framework that unites the advantages of both DCT-based and interpolation-based video compression methods in a single framework. We show that our work can deliver the same visual quality or, in some cases, improve visual quality while reducing the bandwidth by 10--20%.

Splitting Gaussian Densities To Minimize Variance Along A Direction Of Nonlinearity, Amit Kumar

Dissertations and Theses

Nonlinear functions of random vectors are frequently used in signal processing, and especially in state space tracking algorithms. Many of these algorithms require a way of estimating the probability density of the state vector at the output of the nonlinear function. Algorithms derived from Kalman Filter, such as Extended Kalman Filter and Unscented Kalman Filter, are popular choices for this, but they only estimate mean and covariance which may be insufficient to describe the non-Gaussian densities. On the other hand, Monte Carlo methods such as particle filters can be more capable but require much more computation. Gaussian mixture filters aim …

A Privacy-Preserving Strategy For The Trust Layer Of The Energy Grid Of Things Distributed Energy Resource Management System, Mohammed Abdullah Alsaid

Dissertations and Theses

Emergent from the shadows of the traditional grid flaws, the Smart Grid (SG) idea was born and led by government mandates toward cleaner energy production. The SG represents the next generation of electricity distribution systems that subsume recent technological innovations. It uses digital communication between its components and entities to attain more automation, self-sufficiency, and reliability. Unfortunately, this relatively new concept is not flawless; the intrinsic reliance on increased digital communication spreads open attack paths for adversaries. Therefore, finding solutions that address information exchange vulnerabilities has become imperative.

The Energy Grid of Things (EGoT) is Portland State University's implementation of …