Computer Engineering Commons^™

An Inductive Ethnographic Study In Elderly Woman Technology Adoption And The Role Of Her Children, Noshad Rahimi, Antonie J, Jetter, Charles M. Weber

Engineering and Technology Management Faculty Publications and Presentations

Elderly woman strives to have a streamlined life surrounded by ease and familiarity. As she is aging, her desire for simplicity grows, her self-efficacy weakens, her prudency intensifies and her overall inclination toward status quo strengthens. As a result, she delays, or refuses, making any decision that might bring complexity and disrupt the continuity in her life, particularly new and unfamiliar technologies (which often bring complexity, before providing ease). Consequently, her technology adoption has a much lower rate than that of other demographics. To open the black box of elderly woman technology adoption process, this study focuses on the role …

Vision-Based Motion For A Humanoid Robot, Khalid Abdullah Alkhulayfi

Dissertations and Theses

The overall objective of this thesis is to build an integrated, inexpensive, human-sized humanoid robot from scratch that looks and behaves like a human. More specifically, my goal is to build an android robot called Marie Curie robot that can act like a human actor in the Portland Cyber Theater in the play Quantum Debate with a known script of every robot behavior. In order to achieve this goal, the humanoid robot need to has degrees of freedom (DOF) similar to human DOFs. Each part of the Curie robot was built to achieve the goal of building a complete humanoid …

Factors Affecting Big Data Technology Adoption, Nayem Rahman

Student Research Symposium

With the advancement of computer science, hardware and software engineering, and computing power, and later with the advent of the internet, social networking tools and other sources such as sensors data growth has increased significantly. These data are called big data which are mostly unstructured, generated in large volumes, data need to be captured in near real-time. To handle big data a completely new set of tools and technologies are being emerged. I have studied big data literature to identify the factors that might influence big data adoption. I was able to list quite a few factors or attributes that …

A Backend Framework For The Efficient Management Of Power System Measurements, Benjamin Mccamish, Rich Meier, Jordan Landford, Robert B. Bass, David Chiu, Eduardo Cotilla-Sanchez

Electrical and Computer Engineering Faculty Publications and Presentations

Increased adoption and deployment of phasor measurement units (PMU) has provided valuable fine-grained data over the grid. Analysis over these data can provide insight into the health of the grid, thereby improving control over operations. Realizing this data-driven control, however, requires validating, processing and storing massive amounts of PMU data. This paper describes a PMU data management system that supports input from multiple PMU data streams, features an event-detection algorithm, and provides an efficient method for retrieving archival data. The event-detection algorithm rapidly correlates multiple PMU data streams, providing details on events occurring within the power system. The event-detection algorithm …

Information Representation And Computation Of Spike Trains In Reservoir Computing Systems With Spiking Neurons And Analog Neurons, Amin Almassian

Dissertations and Theses

Real-time processing of space-and-time-variant signals is imperative for perception and real-world problem-solving. In the brain, spatio-temporal stimuli are converted into spike trains by sensory neurons and projected to the neurons in subcortical and cortical layers for further processing.

Reservoir Computing (RC) is a neural computation paradigm that is inspired by cortical Neural Networks (NN). It is promising for real-time, on-line computation of spatio-temporal signals. An RC system incorporates a Recurrent Neural Network (RNN) called reservoir, the state of which is changed by a trajectory of perturbations caused by a spatio-temporal input sequence. A trained, non- recurrent, linear readout-layer interprets the …

Incorporating Priors For Medical Image Segmentation Using A Genetic Algorithm, Payel Ghosh, Melanie Mitchell, James A. Tanyi, Arthur Y. Hung

Computer Science Faculty Publications and Presentations

Medical image segmentation is typically performed manually by a physician to delineate gross tumor volumes for treatment planning and diagnosis. Manual segmentation is performed by medical experts using prior knowledge of organ shapes and locations but is prone to reader subjectivity and inconsistency. Automating the process is challenging due to poor tissue contrast and ill-defined organ/tissue boundaries in medical images. This paper presents a genetic algorithm for combining representations of learned information such as known shapes, regional properties and relative position of objects into a single framework to perform automated three-dimensional segmentation. The algorithm has been tested for prostate segmentation …

Emerging Adaptive Architectures For Biomolecular Computation, Matthew Fleetwood

Undergraduate Research & Mentoring Program

The goal of this work is to explore applications of reservoir computing in biomolecular computation. Reservoir computing is a unique model for representing a mapping from one instance in time to a specific output. A neural network of randomly connected neurons is linked with a single output neuron or multiple output neurons. The output neurons are capable of mapping inputs to desired outputs using adaptable algorithms. This framework is investigated by using the Python programming language and object oriented design and programming. Neurons are created in programs by bundling information like input data and attributes of the network, which utilize …

High-Performance Computing For Drought Prediction, Henry Cooney

Undergraduate Research & Mentoring Program

In recent decades, there has been considerable interest in using satellite soil moisture data to examine the global water-energy cycle and manage water resources. Current satellites are limited in their sensing depth, and can only directly measure top soil layers. Using a particle filter, this data may be fused with the output of a hydrologic simulation to improve simulation results, and characterize a hydrologic system at the watershed level. However, this approach increases computational requirements dramatically, and requires rethinking to accommodate data scaling and achieve good performance.

We present a detailed performance study of several alternative implementations of the hybrid …

A Verified Information-Flow Architecture, Arthur Azevedo De Amorim, Nathan Collins, André Dehon, Delphine Demange, Cătălin Hriţcu, David Pichardie, Benjamin C. Pierce, Randy Pollack, Andrew Tolmach

Computer Science Faculty Publications and Presentations

SAFE is a clean-slate design for a highly secure computer system, with pervasive mechanisms for tracking and limiting information flows. At the lowest level, the SAFE hardware supports fine-grained programmable tags, with efficient and flexible propagation and combination of tags as instructions are executed. The operating system virtualizes these generic facilities to present an information-flow abstract machine that allows user programs to label sensitive data with rich confidentiality policies. We present a formal, machine-checked model of the key hardware and software mechanisms used to dynamically control information flow in SAFE and an end-to-end proof of noninterference for this model. We …

Sparse Encoding Of Binocular Images For Depth Inference, Sheng Y. Lundquist, Dylan M. Paiton, Peter F. Schultz, Garrett T. Kenyon

Computer Science Faculty Publications and Presentations

Sparse coding models have been widely used to decompose monocular images into linear combinations of small numbers of basis vectors drawn from an overcomplete set. However, little work has examined sparse coding in the context of stereopsis. In this paper, we demonstrate that sparse coding facilitates better depth inference with sparse activations than comparable feed-forward networks of the same size. This is likely due to the noise and redundancy of feed-forward activations, whereas sparse coding utilizes lateral competition to selectively encode image features within a narrow band of depths.