Engineering Commons^™

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 …

Understanding Quadrature Modulation By Designing A 7mhz Iq Test Bench To Encode The Polybius Square, William Lee Bradley

Dissertations and Theses

This thesis outlines the design of an IQ Test Bench that allows for experimentation of quadrature modulation techniques. Quadrature modulation utilizes two signals I and Q, 90° out of phase from each other, to greatly increase communication data rates. Using Desmos, a thorough mathematical analysis of waveform mixing is presented, and constellation diagrams are plotted from the results. From this an ancient fire signaling technique known as the Polybius Square is encoded into the system. The IQ Test Bench is built from fundamental components that would be contained within an RFFE: a local oscillator and two frequency mixers. The LO …

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 …

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%.

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 …

A Distributed Trust Model Simulator For Energy Grid Of Things Distributed Energy Resource Management System, Abdullah Barghouti

Dissertations and Theses

The evolution of networks into more distributed, self-reliant nodes has mitigated single-point failures that plagued traditional centralized networks. Applied to power grids, distributed systems can increase the integrity and availability of grid services while also offering a power management solution. However, while distributed networks provide scalability, security, and sustainability compared to centralized networks, their distributed nature makes them harder for anomaly detection and prevention. Incorporating a Distributed Trust Model (DTM) System into an Energy Grid of Things Distributed Energy Resource Management System (EGOT DERMS) allows grid participants to be characterized and their communication to be analyzed for possible attacks. A …

The Future Of Camera Technology With Atul Ingle, Atul Ingle

PDXPLORES Podcast

Single-photon camera sensors have the potential to revolutionize digital camera technology. These sensors can capture individual photons at high speeds, circumventing some of the limitations of current digital camera technologies. One challenge, however, is the sheer amount of data generated by the sensor, which is a hindrance to their widespread adoption. With the support of the National Science Foundation, Assistant Professor Atul Ingle is developing algorithms to solve this problem--algorithms that could enable the adoption of single-photon sensors in applications ranging from autonomous vehicles to medical imaging and the cameras in cellphones.

Methodologies For Quantum Circuit And Algorithm Design At Low And High Levels, Edison Tsai

Dissertations and Theses

Although the concept of quantum computing has existed for decades, the technology needed to successfully implement a quantum computing system has not yet reached the level of sophistication, reliability, and scalability necessary for commercial viability until very recently. Significant progress on this front was made in the past few years, with IBM planning to create a 1000-qubit chip by the end of 2023, and Google already claiming to have achieved quantum supremacy. Other major industry players such as Intel and Microsoft have also invested significant amounts of resources into quantum computing research.

Any viable computing system requires both hardware and …

Poster: Indoor Navigation For Visually Impaired People With Vertex Colored Graphs, Pei Du, Nirupama Bulusu

Electrical and Computer Engineering Faculty Publications and Presentations

Visually impaired people face many daily encumbrances. Traditional visual enhancements do not suffice to navigate indoor environments. In this paper, we explore path finding algorithms such as Dijkstra and A* combined with graph coloring to find a safest and shortest path for visual impaired people to navigate indoors. Our mobile application is based on a database which stores the locations of several spots in the building and their corresponding label. Visual impaired people select the start and destination when they want to find their way, and our mobile application will show the appropriate path which guarantees their safety.

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?

Privacy-Preserving Information Security For The Energy Grid Of Things, Mohammed Alsaid, Nirupama Bulusu, Abdullah Bargouti, N. Sonali Fernando, John M. Acken, Tylor E. Slay, Robert B. Bass

Electrical and Computer Engineering Faculty Publications and Presentations

Smart grid infrastructure relies on information exchange between multiple actors in order to ensure system reliability. These actors include but are not limited to smart loads, grid control, and energy management technologies. As information exchange between these actors is susceptible to cyber-attacks, security and privacy issues are indispensable to ensure a reliable and stable grid. This position paper proposes a privacy-preserving, trust-augmented secure scheme for a smart grid implementation.

A Graph-Based Approach To Boundary Estimation With Mobile Sensors, Sean Onufer Stalley, Dingyu Wang, Gautam Dasarathy, John Lipor

Electrical and Computer Engineering Faculty Publications and Presentations

We consider the problem of adaptive sampling for boundary estimation, where the goal is to identify the two dimensional spatial extent of a phenomenon of interest. Motivated by applications in estimating the spread of wildfires with a mobile sensor, we present a novel graph-based algorithm that is efficient in both the number of samples taken and the distance traveled. The key idea behind our approach is that by sampling locations close to known cut edges (edges whose vertices lie on opposite sides of the boundary), we can reliably find additional cut edges. Our approach repeats this process of using the …

Concolic Execution Of Nmap Scripts For Honeyfarm Generation, Zhe Li, Bo Chen, Wu-Chang Feng, Fei Xie

Computer Science Faculty Publications and Presentations

Attackers rely upon a vast array of tools for automating attacksagainst vulnerable servers and services. It is often the case thatwhen vulnerabilities are disclosed, scripts for detecting and exploit-ing them in tools such asNmapandMetasploitare released soonafter, leading to the immediate identification and compromise ofvulnerable systems. Honeypots, honeynets, tarpits, and other decep-tive techniques can be used to slow attackers down, however, such approaches have difficulty keeping up with the sheer number of vulnerabilities being discovered and attacking scripts that are being released. To address this issue, this paper describes an approach for applying concolic execution on attacking scripts in Nmap in …

An Automated Ar-Based Annotation Tool For Indoor Navigation For Visually Impaired People, Pei Du, Nirupama Bulusu

Computer Science Faculty Publications and Presentations

Low vision people face many daily encumbrances. Traditional visual enhancements do not suffice to navigate indoor environments, or recognize objects efficiently. In this paper, we explore how Augmented Reality (AR) can be leveraged to design mobile applications to improve visual experience and unburden low vision persons. Specifically, we propose a novel automated AR-based annotation tool for detecting and labeling salient objects for assisted indoor navigation applications like NearbyExplorer. NearbyExplorer, which issues audio descriptions of nearby objects to the users, relies on a database populated by large teams of volunteers and map-a-thons to manually annotate salient objects in the environment like …

Modeling The Effect Of The Covid-19 Pandemic On Azithromycin Prescription In General Practices Across The Uk, Oluwasegun Isaac Daramola

altREU Projects

In the early months of the COVID-19 pandemic, it was reported that some antibiotics were prescribed as a remedy for viral treatment and prophylaxis based on non-randomized, uncontrolled short clinical trials. A major antibiotic consulted being Azithromycin; a broad-spectrum macrolide selected based on its immunomodulatory effects in chronic inflammatory lung diseases, with a seasonal prescription increase of 21.5% in March 2020 compared to March 2019.

To analyze the effect and possible antimicrobial resistance impact of the pandemic on Azithromycin prescription across general practices in the United Kingdom (UK), this study uses a time series decomposition modeling method to compare a …

Developing A Strategy For Creating Affordable Student Housing Solutions, Juan D. Campolargo

altREU Projects

College is becoming more and more expensive, and students are graduating with more and more debt. In 2021, we have almost 1.6 trillion dollars of student loan debt. While students are in college, they need a place to live, and this project will be about how to develop a strategy for creating affordable student housing solutions.

Housing should not be another cause of concern to students or really anyone.

The way it’s done is that people get more loans to pay for housing, or they work as much as they can when they’re not studying to pay for rent. Affordable …

Digitally Reporting Trail Obstructions In Forest Park, Colton S. Maybee

REU Final Reports

The inclusion of technology on the trail can lead to better experiences for everyone involved in the hobby. Hikers can play a more prominent role in the maintenance of the trails by being able to provide better reports of obstructions while directly on the trail. This paper goes into the project of revamping the obstruction report system applied at Forest Park in Portland, Oregon. Most of my contributions to the project focus on mobile app development with some research into path planning algorithms related to the continuations of this project.

Forest Park Trail Monitoring, Adan Robles, Colton S. Maybee, Erin Dougherty

REU Final Reports

Forest Park, one of the largest public parks in the United States with over 40 trails to pick from when planning a hiking trip. One of the main problems this park has is that there are too many trails, and a lot of the trails extend over 3 miles. Due to these circumstances’ trails are not checked frequently and hikers are forced to hike trails in the area with no warnings of potential hazards they can encounter. In this paper I researched how Forest Park currently monitors its trails and then set up a goal to solve the problem. We …

Online Grocery Shopping: A Staple Of The Present -- And Maybe The Future?, Henry B. Chao

altREU Projects

This project aims to understand how the COVID-19 pandemic has affected people's food shopping tendencies, specifically focusing on the use of online grocers. It uses data collected by a team of researchers led out of Portland State University. This data consists of roughly 8000 surveys distributed starting in September of 2020. The survey asks respondents to discuss their demographics, household resources, and shopping tendencies.

In particular, this project wants to understand how inclined a respondent is to use online grocers based on their personal experience with COVID-19. To do this, the project uses a Latent Class Model (LCM) to classify …

Multi-Agent Radiation Localization, Teresa Nguyen

REU Final Reports

Advancement of radiation detection technology is an ongoing process, and adjustments are made based on pre-existing conditions of radiation presence--both natural and man made. Tools that are currently used for safely detecting radiation in urban environments exist in several forms: drones, robots, or handheld radiation detection devices. This is a harm reductive way to explore radiation-infected environments while preserving human health as best as possible. In order for these autonomous platforms to successfully detect radiation sources, an algorithm needs to be created that is capable of gathering crucial data on its own with little to no human interference. Machine learning …

Automated Statistical Structural Testing Techniques And Applications, Yang Shi

Dissertations and Theses

Statistical structural testing(SST) is an effective testing technique that produces random test inputs from probability distributions. SST shows superiority in fault-revealing power over random testing and deterministic approaches since it heritages the merits from both of them. SST ensures testing thoroughness by setting up a probability lower-bound criterion for each structural cover element and test inputs that exercise a structural cover element sampled from the probability distribution, ensuring testing randomness. Despite the advantages, SST is not a widely used approach in practice. There are two major limitations. First, to construct probability distributions, a tester must understand the underlying software's structure, …

A Method For Comparative Analysis Of Trusted Execution Environments, Stephano Cetola

Dissertations and Theses

The problem of secure remote computation has become a serious concern of hardware manufacturers and software developers alike. Trusted Execution Environments (TEEs) are a solution to the problem of secure remote computation in applications ranging from "chip and pin" financial transactions to intellectual property protection in modern gaming systems. While extensive literature has been published about many of these technologies, there exists no current model for comparing TEEs. This thesis provides hardware architects and designers with a set of tools for comparing TEEs. I do so by examining several properties of a TEE and comparing their implementations in several technologies. …

Towards Adaptive, Self-Configuring Networked Unmanned Aerial Vehicles, Nirupama Bulusu, Ehsan Aryafar, Feng Liu

Computer Science Faculty Publications and Presentations

Networked drones have the potential to transform various applications domains; yet their adoption particularly in indoor and forest environments has been stymied by the lack of accurate maps and autonomous navigation abilities in the absence of GPS, the lack of highly reliable, energy-efficient wireless communications, and the challenges of visually inferring and understanding an environment with resource-limited individual drones. We advocate a novel vision for the research community in the development of distributed, localized algorithms that enable the networked drones to dynamically coordinate to perform adaptive beam forming to achieve high capacity directional aerial communications, and collaborative machine learning to …

A Golden Age For Computing Frontiers, A Dark Age For Computing Education?, Christof Teuscher

Electrical and Computer Engineering Faculty Publications and Presentations

There is no doubt that the body of knowledge spanned by the computing disciplines has gone through an unprecedented expansion, both in depth and breadth, over the last century. In this position paper, we argue that this expansion has led to a crisis in computing education: quite literally the vast majority of the topics of interest of this conference are not taught at the undergraduate level and most graduate courses will only scratch the surface of a few selected topics. But alas, industry is increasingly expecting students to be familiar with emerging topics, such as neuromorphic, probabilistic, and quantum computing, …

Automated Test Generation For Validating Systemc Designs, Bin Lin

Dissertations and Theses

Modern system design involves integration of all components of a system on a single chip, namely System-on-a-Chip (SoC). The ever-increasing complexity of SoCs and rapidly decreasing time-to-market have pushed the design abstraction to the electronic system level (ESL), in order to increase design productivity. SystemC is a widely used ESL modeling language that plays a central role in modern SoCs design process. ESL SystemC designs usually serve as executable specifications for the subsequent SoCs design flow. Therefore, undetected bugs in ESL SystemC designs may propagate to low-level implementations or even final silicon products. In addition, modern SoCs design often involves …

On The (Im)Practicality Of Adversarial Perturbation For Image Privacy, Arezoo Rajabi, Rakesh B. Bobba, Mike Rosulek, Charles Wright, Wu-Chi Feng

Computer Science Faculty Publications and Presentations

Image hosting platforms are a popular way to store and share images with family members and friends. However, such platforms typically have full access to images raising privacy concerns. These concerns are further exacerbated with the advent of Convolutional Neural Networks (CNNs) that can be trained on available images to automatically detect and recognize faces with high accuracy.

Recently, adversarial perturbations have been proposed as a potential defense against automated recognition and classification of images by CNNs. In this paper, we explore the practicality of adversarial perturbation based approaches as a privacy defense against automated face recognition. Specifically, we first …

Extending The Functional Subnetwork Approach To A Generalized Linear Integrate-And-Fire Neuron Model, Nicholas Szczecinski, Roger Quinn, Alexander J. Hunt

Mechanical and Materials Engineering Faculty Publications and Presentations

Engineering neural networks to perform specific tasks often represents a monumental challenge in determining network architecture and parameter values. In this work, we extend our previously-developed method for tuning networks of non-spiking neurons, the “Functional subnetwork approach” (FSA), to the tuning of networks composed of spiking neurons. This extension enables the direct assembly and tuning of networks of spiking neurons and synapses based on the network’s intended function, without the use of global optimization ormachine learning. To extend the FSA, we show that the dynamics of a generalized linear integrate and fire (GLIF) neuronmodel have fundamental similarities to those of …

Flight Simulator Modeling Using Recurrent Neural Networks, Nickolas Sabatini, Andreas Natsis

Undergraduate Research & Mentoring Program

Recurrent neural networks (RNNs) are a form of machine learning used to predict future values. This project uses RNNs tor predict future values for a flight simulator. Coded in Python using the Keras library, the model demonstrates training loss and validation loss, referring to the error when training the model.