Index Catalog // ScholarsArchive@OSU

Ultra-low energy digital controller for battery-free wireless sensor network nodes

Creator:: Nasroullahi, Mohsen
Abstract:: This thesis presents a low-energy application specific digital controller for a battery-free 2.4 GHz wireless sensor network (WSN) node. The digital controller has been designed and fabricated in a standard 0.13 μm CMOS and implements a simple protocol for WSNs. Techniques such as supply voltage reduction and power gating have...
Resource Type:: Masters Thesis
Full Text:: instructors and professors, especially Roger Traylor, for teaching, guiding, mentoring and motivating me

Optimizing performance/watt of embedded SIMD multiprocessors through a priori application guided power scheduling

Creator:: Albright, Ryan K.
Abstract:: A method for improving performance/watt of an embedded single-instruction multiple-data (SIMD) architecture using application-guided a priori scheduling of hardware resources is presented. A multi-core architectural simulator is adopted that accurately estimates power, performance, and utilization of various processor components (logic, interconnect and memory). A greedy search is then performed on...
Resource Type:: Masters Thesis
Full Text:: my advisor Dr. Patrick Chiang. I would also like to thank my committee: Dr. Terri Fiez, Roger

Development of leadership and design skills among ECE juniors and graduate students

Creator:: Luo, Ding
Abstract:: Engineering students spend a significant amount of their undergraduate careers focused on technical theory and practice. This intensive student prepares them for their technical challenges in the future. This training is so intense that often the more 'soft skills' such as communication, leadership, design, and troubleshooting are left out or...
Resource Type:: Masters Thesis
Full Text:: . I thank all my role models: Roger Traylor, Pallavi Dhagat, Pavan Hanumolu, Donald Heer, Stephen

How to Debug Inclusivity Bugs? An Empirical Investigation of Finding-to-Fixing with Information Architecture

Creator:: Guizani, Mariam, Steinmacher, Igor, Emard, Jillian, Fallatah, Abrar, Burnett, Margaret, and Sarma, Anita
Abstract:: Background: Although some previous research has found ways to find inclusivity bugs (biases in software that introduce inequities among cognitively diverse individuals), little attention has been paid to how to go about fixing such bugs. We hypothesized that Information Architecture (IA)--the way information is organized, structured and labeled--may provide the...
Resource Type:: Research Paper

Inclusive Software Design and Its Methods for Beyond-WIMP User Interfaces

Creator:: Fallatah, Abrar
Abstract:: While digital inclusivity researchers and software practitioners have been trying to address exclusion biases in Windows, Icons, Menus, and Pointers (WIMP) user interfaces (UIs) for a long time, little has been done to investigate if and how inclusive software design and its methods that have been devised for WIMP UIs...
Resource Type:: Dissertation
Full Text:: introduced to Human-Computer Interaction (HCI) in 1994 by Roger Coleman to enable independence for older

A domain-specific embedded language for probabilistic programming

Creator:: Kollmansberger, Steven
Abstract:: Functional programming is concerned with referential transparency, that is, given a certain function and its parameter, that the result will always be the same. However, it seems that this is violated in applications involving uncertainty, such as rolling a dice. This thesis defines the background of probabilistic programming and domain-specific...
Resource Type:: Masters Thesis

Analytical Methods for Improving Causal Inference

Creator:: Sinha, Meghamala
Abstract:: Causal inference is an important analytical tool to bridge the gap between prediction and decision-making. However, learning a causal network solely from data is a challenging task. In this work, various techniques have been explored for a better and improved causal network learning from data. Firstly, the problem of learning...
Resource Type:: Dissertation

Uncertainty in Deep Learning with Implicit Neural Networks

Creator:: Ratzlaff, Neale
Abstract:: The ability to extract uncertainties from predictions is crucial for the adoption of deep learning systems to safety-critical applications. Uncertainty estimates can be used as a failure signal, which is necessary for automating complex tasks where safety is a concern. Furthermore, current deep learning systems do not provide uncertainty estimates,...
Resource Type:: Dissertation

Structured learning with latent variables : theory and algorithms

Creator:: Zhao, Kai
Abstract:: Most tasks in natural language processing (NLP) try to map structured input (e.g., sentence or word sequence) to some form of structured output (tag sequence, parse tree, semantic graph, translated/paraphrased/compressed sentence), a problem known as “structured prediction”. While various learning algorithms such as the perceptron, maximum entropy, and expectation-maximization have...
Resource Type:: Dissertation

Engineering Student Perspectives of a New Required Programming Course

Creator:: Garcia Ramirez, Jessica
Abstract:: We present student perceptions of a new first-year engineering programming class that was designed by informed research practices. While the College of Engineering at Oregon State University saw a lot of major switching in the first year, there were not many students switching into computer science (CS). This could have...
Resource Type:: Masters Thesis

ScholarsArchive@OSU

Ultra-low energy digital controller for battery-free wireless sensor network nodes

Optimizing performance/watt of embedded SIMD multiprocessors through a priori application guided power scheduling

Development of leadership and design skills among ECE juniors and graduate students

How to Debug Inclusivity Bugs? An Empirical Investigation of Finding-to-Fixing with Information Architecture

Inclusive Software Design and Its Methods for Beyond-WIMP User Interfaces

A domain-specific embedded language for probabilistic programming

Analytical Methods for Improving Causal Inference

Uncertainty in Deep Learning with Implicit Neural Networks

Structured learning with latent variables : theory and algorithms

Engineering Student Perspectives of a New Required Programming Course

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