Engineering Commons^™

Omni-Gravity Hydroponics System For Spacecraft, Tara M. Prevo

Undergraduate Research & Mentoring Program

Effective omni-gravity hydroponics will allow astronauts to supplement nutrition and further close the life cycle of water in orbit, lunar, and Martian conditions. This project determines the operational limits of the test cells for the Plant Water Management Hydroponics mission. A scaled 1-g channel was designed by Rihana Mungin to mimic full-scale performance in microgravity that could be tested terrestrially. This project sought to find the limits of operation of the 1-g test cells and identify failure modes that could pose a safety risk in space. The cells were filled at increments of 20% and cycled from 0.184 to 8.33 …

Simulation Of Human Balance Control Using An Inverted Pendulum Model, Joshua E. Caneer

Undergraduate Research & Mentoring Program

The nervous system that human beings use to control balance is remarkably adaptable to a wide variety of environments and conditions. This neural system is likely a combination of many inputs and feedback control loops working together. The ability to emulate this system of balance could be of great value in understanding and developing solutions to proprioceptive disorders and other diseases that affect the human balance control system. Additionally, the process of emulating the human balance system may also have widespread applications to the locomotion capabilities of many types of robots, in both bipedal and non-bipedal configurations.

The goal of …

The Applications Of Grid Cells In Computer Vision, Keaton Kraiger

Undergraduate Research & Mentoring Program

In this study we present a novel method for position and scale invariant object representation based on a biologically-inspired framework. Grid cells are neurons in the entorhinal cortex whose multiple firing locations form a periodic triangular array, tiling the surface of an animal’s environment. We propose a model for simple object representation that maintains position and scale invariance, in which grid maps capture the fundamental structure and features of an object. The model provides a mechanism for identifying feature locations in a Cartesian plane and vectors between object features encoded by grid cells. It is shown that key object features …

Exoskeleton, Vinu Casper, Liliana Fitzpatrick

Engineering and Technology Management Student Projects

This is a research about the marketing plan for exoskeleton wearable devices. The objective is to provide a meaningful Customer Value Proposition to the prospective customers.The Samsung company SWOT analysis is the basis for a marketing strategy. The exoskeleton features and market definition is included in the analysis. A competitor analysis of homogeneus exoskeletons providers is included to review the current market. An exhaustive customer analysis was performed to identify the customer needs as the input for the marketing plan development. The potential market was identified to learn about the exoskeleton market share opportunity. The exoskeleton global market is analyzed …

Exploring And Expanding The One-Pixel Attack, Umairullah Khan, Walt Woods

Undergraduate Research & Mentoring Program

In machine learning research, adversarial examples are normal inputs to a classifier that have been specifically perturbed to cause the model to misclassify the input. These perturbations rarely affect the human readability of an input, even though the model’s output is drastically different. Recent work has demonstrated that image-classifying deep neural networks (DNNs) can be reliably fooled with the modification of a single pixel in the input image, without knowledge of a DNN’s internal parameters. This “one-pixel attack” utilizes an iterative evolutionary optimizer known as differential evolution (DE) to find the most effective pixel to perturb, via the evaluation of …

No-Reference Image Denoising Quality Assessment, Si Lu

Computer Science Faculty Publications and Presentations

A wide variety of image denoising methods are available now. However, the performance of a denoising algorithm often depends on individual input noisy images as well as its parameter setting. In this paper, we present a noreference image denoising quality assessment method that can be used to select for an input noisy image the right denoising algorithm with the optimal parameter setting. This is a challenging task as no ground truth is available. This paper presents a data-driven approach to learn to predict image denoising quality. Our method is based on the observation that while individual existing quality metrics and …

Teen Driver System Modeling: A Tool For Policy Analysis, Celestin Missikpode, Corrine Peek-Asa, Daniel V. Mcgehee, James Torner, Wayne Wakeland, Robert Wallace

Systems Science Faculty Publications and Presentations

Background: Motor vehicle crashes remain the leading cause of teen deaths in spite of preventive efforts. Prevention strategies could be advanced through new analytic approaches that allow us to better conceptualize the complex processes underlying teen crash risk. This may help policymakers design appropriate interventions and evaluate their impacts.

Methods: System Dynamics methodology was used as a new way of representing factors involved in the underlying process of teen crash risk. Systems dynamics modeling is relatively new to public health analytics and is a promising tool to examine relative influence of multiple interacting factors in predicting a health …

Keyword-Based Patent Citation Prediction Via Information Theory, Farshad Madani, Martin Zwick, Tugrul U. Daim

Engineering and Technology Management Faculty Publications and Presentations

Patent citation shows how a technology impacts other inventions, so the number of patent citations (backward citations) is used in many technology prediction studies. Current prediction methods use patent citations, but since it may take a long time till a patent is cited by other inventors, identifying impactful patents based on their citations is not an effective way. The prediction method offered in this article predicts patent citations based on the content of patents. In this research, Reconstructability Analysis (RA), which is based on information theory and graph theory, is applied to predict patent citations based on keywords extracted from …

Exploring Adoption Of Augmented Reality Smart Glasses: Applications In The Medical Industry, Nuri A. Basoglu, Muge Goken, Marina Dabic, Dilek Ozdemir Gungor, Tugrul U. Daim

Engineering and Technology Management Faculty Publications and Presentations

This study explores the use of augmented reality smart glasses (ARSGs) by physicians and their adoption of these products in the Turkish medical industry. Google Glass was used as a demonstrative example for the introduction of ARSGs. We proposed an exploratory model based on the technology acceptance model by Davis. Exogenous factors in the model were defined by performing semi-structured in-depth interviews, along with the use of an expert panel in addition to the technology adoption literature. The framework was tested by means of a field study, data was collected via an Internet survey, and path analysis was used. The …

Combining Algorithms For More General Ai, Mark Robert Musil

Undergraduate Research & Mentoring Program

Two decades since the first convolutional neural network was introduced the AI sub-domains of classification, regression and prediction still rely heavily on a few ML architectures despite their flaws of being hungry for data, time, and high-end hardware while still lacking generality. In order to achieve more general intelligence that can perform one-shot learning, create internal representations, and recognize subtle patterns it is necessary to look for new ML system frameworks. Research on the interface between neuroscience and computational statistics/machine learning has suggested that combined algorithms may increase AI robustness in the same way that separate brain regions specialize. In …

An Exploration Of Software Defined Radio And Gnu Radio Companion For Use In Drone-To-Drone Communication, Amanda K. H. Voegtlin

Undergraduate Research & Mentoring Program

In a world that increasingly relies on automation and intelligent robotics, there is a need for drones to expand their independence and adaptability in navigating their environments. One approach to this problem is the use of wireless communication between units in order to coordinate their sensor data and build real-time maps of the environments they are navigating. However, especially indoors, relying on a fixed transmission tower to provide data to the units faces connectivity challenges.

The purpose of this research was to determine the fitness of an on-drone assembly that uses the the NI B200mini software-defined radio board and Gnu …

Automating Knife-Edge Method Of Thz Beam Characterization, Christopher Charles Faber

Undergraduate Research & Mentoring Program

The goal of this project is to create a time and cost-effective solution for THz beam profiling.

The knife edge method of beam characterization is a technique to verify the intensity profile of a beam involving traveling a blade orthogonal to the beam path and measuring transmission in successive steps. We use a vector network analyzer (VNA) to measure S21 transmission from a THz source. Manual implementation of this method was time-consuming and inefficient.

Project hardware includes an Arduino, a motor shield, and a ball screw linear rail with stepper motor actuator. Software was created in LabView and data is …

Binder Free Graphene Hybridized Fe3o4 Nanoparticles For Supercapacitor Applications, Nathan D. Jansen

Undergraduate Research & Mentoring Program

In a world with increasing energy demands, the need for safe and mobile energy storage grows. There are a number of renewable energy sources that can be harvested, however peak demand and peak production times tend to not overlap. As the capabilities of collecting the energy grows so does the need to store the energy for later consumption. The two promising methods of storing energy are batteries or supercapacitors. Both technologies employ an electrode consisting of an active material bound to a current collector. This material participates in a redox reaction, storing charge electrochemically to later be used as energy, …

Laser-Scribed Graphene Micro-Supercapacitors, Kimi D. Owens

Undergraduate Research & Mentoring Program

M. F. El-Kady and R. B. Kaner, “Scalable fabrication of high-power graphene micro-supercapacitors for flexible and on-chip energy storage,” Nature Communications, vol. 4, p. 1475, Feb. 2013.

Supercapacitors are electrical components that have higher energy density than regular capacitors. Currently, they are large and bulky which makes it hard to be implemented into smaller electronic devices or on-chip. In Scalable Fabrication of High-power Graphene Micro-supercapacitors for Flexible and On-chip Energy Storage, El-Kady and Kaner developed an inexpensive and reliable method for scaling down supercapacitors to be approximately 7.53 x 5.35 mm. To make the laser-scribed graphene (LSG) micro-supercapacitors, an aqueous …

Learning In Bio-Molecular Computing Systems, Lauren Braun

Undergraduate Research & Mentoring Program

Many potential applications of biochemical computers involve the detection of highly adaptable and dynamic chemical systems, such as emerging pathogens. Current technology is expensive to develop and unique to each application, thus causing limitations in accessibility. In order to make this type of computing a realistic solution to problems in the medical field, a biochemical computer would need to be adaptable to work in a variety of applications. Banda et al. (2014) previously proposed a first dynamic biochemical system that was capable of autonomous learning. For this project we studied a framework similar to Banda’s but in two separate pieces, …

Improvement Of 802.11 Protocol On Fully Programmable Wireless Radio, Eunji Lee

Undergraduate Research & Mentoring Program

The growth in the number of connected device usage has led to a rapidly increased data traffic on wireless network and the demand for access to high speed and stable Internet connection is becoming more prominent. However, current off the shelf wireless cards are not programmable or observable across layers of the standard protocol stack, which leads to poor practical performance. Thus, Wireless Open Access Research Platform (WARP), a scalable wireless platform providing programmable functionality at every layer of the network stack, has been used for the real-time implementation and improvement of 802.11 protocol.

An Analysis Of Lora Low Power Technology And Its Applications, Gomathy Venkata Krishnan

Undergraduate Research & Mentoring Program

The number of Internet of Things (IoT) devices has exponentially increased in the last decade. With the increase in these devices, there is a necessity to effectively connect and control these devices remotely. Cellular technologies cannot handle this demand since they are not cost effective and easy to deploy. This is where LoRa technology comes handy. LoRa is long-range, low-power, low cost technology that supports internet of things applications. LoRa has many advantages in terms of capacity, mobility, battery lifetime and cost. It uses the unlicensed 915MHz ISM band and can be easily deployed.

This research is focused on setting …

Real-Time Object Detection And Tracking On Drones, Tu Le

Undergraduate Research & Mentoring Program

Unmanned aerial vehicles, also known as drones, have been more and more widely used in recent decades because of their mobility. They appear in many applications such as farming, search and rescue, entertainment, military, and so on. Such high demands for drones lead to the need of developments in drone technologies. Next generations of commercial and military drones are expected to be aware of surrounding objects while flying autonomously in different terrains and conditions. One of the biggest challenges to drone automation is the ability to detect and track objects of interest in real-time. While there are many robust machine …

Generating Adversarial Attacks For Sparse Neural Networks, Jack H. Chen, Walt Woods

Undergraduate Research & Mentoring Program

Neural networks provide state-of-the-art accuracy for image classification tasks. However traditional networks are highly susceptible to imperceivable perturbations to their inputs known as adversarial attacks that drastically change the resulting output. The magnitude of these perturbations can be measured as Mean Squared Error (MSE). We use genetic algorithms to produce black-box adversarial attacks and examine MSE on state-of-the-art networks. This method generates an attack that converts 90% confidence on a correct class to 50% confidence of a targeted, incorrect class after 2000 epochs. We will generate and examine attacks and their MSE against several sparse neural networks. We theorize that …

A Time-Efficient Cmos-Memristive Programmable Circuit Realizing Logic Functions In Generalized And-Xor Structures, Muayad Aljafar, Marek Perkowski, John M. Acken, Robin Tan

Electrical and Computer Engineering Faculty Publications and Presentations

This paper describes a CMOS-memristive Programmable Logic Device connected to CMOS XOR gates (mPLD-XOR) for realizing multi-output functions well-suited for two-level {NAND, AND, NOR, OR}-XOR based design. This structure is a generalized form of AND-XOR logic where any combination of NAND, AND, NOR, OR, and literals can replace the AND level. For mPLD-XOR, the computational delay, which is measured as the number of clock cycles, equals the maximum number of inputs to any output XOR gate of a function assuming that the number of XOR gates is large enough to calculate the outputs of the function simultaneously. The input levels …

Video Frame Interpolation Via Adaptive Separable Convolution, Simon Niklaus, Long Mai, Feng Liu

Computer Science Faculty Publications and Presentations

Standard video frame interpolation methods first estimate optical flow between input frames and then synthesize an intermediate frame guided by motion. Recent approaches merge these two steps into a single convolution process by convolving input frames with spatially adaptive kernels that account for motion and re-sampling simultaneously. These methods require large kernels to handle large motion, which limits the number of pixels whose kernels can be estimated at once due to the large memory demand. To address this problem, this paper formulates frame interpolation as local separable convolution over input frames using pairs of 1D kernels. Compared to regular 2D …

Fast On-Line Kernel Density Estimation For Active Object Localization, Anthony D. Rhodes, Max H. Quinn, Melanie Mitchell

Computer Science Faculty Publications and Presentations

A major goal of computer vision is to enable computers to interpret visual situations—abstract concepts (e.g., “a person walking a dog,” “a crowd waiting for a bus,” “a picnic”) whose image instantiations are linked more by their common spatial and semantic structure than by low-level visual similarity. In this paper, we propose a novel method for prior learning and active object localization for this kind of knowledge-driven search in static images. In our system, prior situation knowledge is captured by a set of flexible, kernel-based density estimations— a situation model—that represent the expected spatial structure of the given situation. These …

Sparse Coding On Stereo Video For Object Detection, Sheng Y. Lundquist, Melanie Mitchell, Garrett T. Kenyon

Computer Science Faculty Publications and Presentations

Deep Convolutional Neural Networks (DCNN) require millions of labeled training examples for image classification and object detection tasks, which restrict these models to domains where such a dataset is available. We explore the use of unsupervised sparse coding applied to stereo-video data to help alleviate the need for large amounts of labeled data. In this paper, we show that unsupervised sparse coding is able to learn disparity and motion sensitive basis functions when exposed to unlabeled stereo-video data. Additionally, we show that a DCNN that incorporates unsupervised learning exhibits better performance than fully supervised networks. Furthermore, finding a sparse representation …

Memcapacitive Devices In Logic And Crossbar Applications, Dat Tran, Christof Teuscher

Electrical and Computer Engineering Faculty Publications and Presentations

Over the last decade, memristive devices have been widely adopted in computing for various conventional and unconventional applications. While the integration density, memory property, and nonlinear characteristics have many benefits, reducing the energy consumption is limited by the resistive nature of the devices. Memcapacitors would address that limitation while still having all the benefits of memristors. Recent work has shown that with adjusted parameters during the fabrication process, a metal-oxide device can indeed exhibit a memcapacitive behavior. We introduce novel memcapacitive logic gates and memcapacitive crossbar classifiers as a proof of concept that such applications can outperform memristor-based architectures. The …

Shift-Symmetric Configurations In Two-Dimensional Cellular Automata: Irreversibility, Insolvability, And Enumeration, Peter Banda, John S. Caughman Iv, Martin Cenek, Christof Teuscher

Mathematics and Statistics Faculty Publications and Presentations

The search for symmetry as an unusual yet profoundly appealing phenomenon, and the origin of regular, repeating configuration patterns have been for a long time a central focus of complexity science, and physics.

Here, we introduce group-theoretic concepts to identify and enumerate the symmetric inputs, which result in irreversible system behaviors with undesired effects on many computational tasks. The concept of so-called configuration shift-symmetry is applied on two-dimensional cellular automata as an ideal model of computation. The results show the universal insolvability of “non-symmetric” tasks regardless of the transition function. By using a compact enumeration formula and bounding the number …

Video Frame Interpolation Via Adaptive Convolution, Simon Niklaus, Long Mai, Feng Liu

Computer Science Faculty Publications and Presentations

Video frame interpolation typically involves two steps: motion estimation and pixel synthesis. Such a two-step approach heavily depends on the quality of motion estimation. This paper presents a robust video frame interpolation method that combines these two steps into a single process. Specifically, our method considers pixel synthesis for the interpolated frame as local convolution over two input frames. The convolution kernel captures both the local motion between the input frames and the coefficients for pixel synthesis. Our method employs a deep fully convolu- tional neural network to estimate a spatially-adaptive con- volution kernel for each pixel. This deep neural …

Bayesian Optimization For Refining Object Proposals, Anthony D. Rhodes, Jordan Witte, Melanie Mitchell, Bruno Jedynak

Computer Science Faculty Publications and Presentations

We develop a general-purpose algorithm using a Bayesian optimization framework for the efficient refinement of object proposals. While recent research has achieved substantial progress for object localization and related objectives in computer vision, current state-of-the-art object localization procedures are nevertheless encumbered by inefficiency and inaccuracy. We present a novel, computationally efficient method for refining inaccurate bounding-box proposals for a target object using Bayesian optimization. Offline, image features from a convolutional neural network are used to train a model to predict an object proposal’s offset distance from a target object. Online, this model is used in a Bayesian active search to …

Cyberpdx: A Camp For Broadening Participation In Cybersecurity, Wu-Chang Feng, Robert Liebman, Lois Delcambre, Michael Mooradian Lupro, Tim Sheard, Scott Britell, Gerald W. Recktenwald

University Studies Faculty Publications and Presentations

With society’s increasing dependence on technology infrastructure, the importance of securing the computers, networks, data, and algorithms that run our digital and physical lives is becoming critical. To equip the next generation of citizens for the challenges ahead, an effort is underway to introduce security content early in a student’s academic career. It is important that these efforts broaden participation and increase diversity in the field. While many camps and curricula focus on introducing technical content and skills related to cybersecurity, such approaches can prematurely limit how students view career opportunities in the field, potentially limiting those who ultimately pursue …

Proving Non-Deterministic Computations In Agda, Sergio Antoy, Michael Hanus, Steven Libby

Computer Science Faculty Publications and Presentations

We investigate proving properties of Curry programs using Agda. First, we address the functional correctness of Curry functions that, apart from some syntactic and semantic differences, are in the intersection of the two languages. Second, we use Agda to model non-deterministic functions with two distinct and competitive approaches incorporating the non-determinism. The first approach eliminates non-determinism by considering the set of all non-deterministic values produced by an application. The second approach encodes every non-deterministic choice that the application could perform. We consider our initial experiment a success. Although proving properties of programs is a notoriously difficult task, the functional logic …

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 …