Computer Engineering Commons^™

Design Of A Canine Inspired Quadruped Robot As A Platform For Synthetic Neural Network Control, Cody Warren Scharzenberger

Dissertations and Theses

Legged locomotion is a feat ubiquitous throughout the animal kingdom, but modern robots still fall far short of similar achievements. This paper presents the design of a canine-inspired quadruped robot named DoggyDeux as a platform for synthetic neural network (SNN) research that may be one avenue for robots to attain animal-like agility and adaptability. DoggyDeux features a fully 3D printed frame, 24 braided pneumatic actuators (BPAs) that drive four 3-DOF limbs in antagonistic extensor-flexor pairs, and an electrical system that allows it to respond to commands from a SNN comprised of central pattern generators (CPGs). Compared to the previous version …

A Resource Constrained Shortest Paths Approach To Reducing Personal Pollution Exposure, Elling Payne

REU Final Reports

As wildfires surge in frequency and impact in the Pacific Northwest, in tandem with increasingly traffic-choked roads, personal exposure to harmful airborne pollutants is a rising concern. Particularly at risk are school-age children, especially those living in disadvantaged communities near major motorways and industrial centers. Many of these children must walk to school, and the choice of route can effect exposure. Route-planning applications and frameworks utilizing computational shortest paths methods have been proposed which consider personal exposure with reasonable success, but few have focused on pollution exposure, and all have been limited in scalability or geographic scope. This paper addresses …

Creating A 3d Printed Bipedal Robot’S Ankle And Foot With Human-Like Motion, Tylise E. Fitzgerald

Maseeh Summer Undergraduate Research Experience

Humanoid robots are being created to replace humans in dangerous situations, assist overworked humans, and improve our quality of life by completing chores. However, current bipedal robots haven’t matched the performance of humans and are still impractical for commercial use.

One of the Agile and Adaptive Robotics Lab’s goals is to create a humanoid robot whose anatomy is similar to the human body. If this can be accomplished, we can have a functioning model of the human body that we can adjust to improve both humanoid robots’ functions and the functionality of our own human bodies. This specific project looks …

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

Maseeh Summer Undergraduate Research Experience

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 …

Material Parameter Estimation Of Thin Wafers With Terahertz Time-Domain Spectroscopy, Kirk R. Jungles

Maseeh Summer Undergraduate Research Experience

Terahertz Time Domain Spectroscopy(THz TDS) is a spectroscopic technique that can be implemented to perform non destructive material parameter extraction on a variety of materials. Accuracy of these material parameters is often limited by statistical variation between measurements and insufficient knowledge of the thickness of the slabs being measured.

The goal of this project was to develop an in house procedure that would allow us to perform THz TDS on thin wafers using an up to date signal processing algorithm that would provide accurate predictions for the thickness of the wafers, reliable estimations of the wafer’s material parameters, and demonstration …

Memcapacitive Reservoir Computing Architectures, Dat Tien Tran

Dissertations and Theses

In this thesis, I propose novel brain-inspired and energy-efficient computing systems. Designing such systems has been the forefront goal of neuromorphic scientists over the last few decades. The results from my research show that it is possible to design such systems with emerging nanoscale memcapacitive devices.

Technological development has advanced greatly over the years with the conventional von Neumann architecture. The current architectures and materials, however, will inevitably reach their physical limitations. While conventional computing systems have achieved great performances in general tasks, they are often not power-efficient in performing tasks with large input data, such as natural image recognition …

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

Maseeh Summer Undergraduate Research Experience

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 …

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 …

The Applications Of Grid Cells In Computer Vision, Keaton Kraiger

Maseeh Summer Undergraduate Research Experience

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 …

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 …

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

Maseeh Summer Undergraduate Research Experience

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 …

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

Complex Systems 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 …

Biomimetic Design And Construction Of A Bipedal Walking Robot, Alexander Gabriel Steele

Dissertations and Theses

Human balance and locomotion control is highly complex and not well understood. To understand how the nervous system controls balance and locomotion works, we test how the body responds to controlled perturbations, the results are analyzed, and control models are developed. However, to recreate this system of control there is a need for a robot with human-like kinematics. Unfortunately, such a robotic testbed does not exist despite the numerous applications such a design would have in mobile robotics, healthcare, and prosthetics.

This thesis presents a robotic testbed model of human lower legs. By using MRI and CT scans, I designed …

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

Maseeh Summer Undergraduate Research Experience

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

Maseeh Summer Undergraduate Research Experience

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

Maseeh Summer Undergraduate Research Experience

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

Maseeh Summer Undergraduate Research Experience

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

Maseeh Summer Undergraduate Research Experience

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 …

Combining Algorithms For More General Ai, Mark Robert Musil

Maseeh Summer Undergraduate Research Experience

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 …

Learning In Bio-Molecular Computing Systems, Lauren Braun

Maseeh Summer Undergraduate Research Experience

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, …

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

Maseeh Summer Undergraduate Research Experience

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 …

Radiation Source Localization By Using Backpropagation Neural Network, Jian Meng, Christof Teuscher, Walt Woods

Student Research Symposium

The most difficult part of the radiation localization is that we cannot use the traditional acoustic localization method to determine where the radiation source is. It’s mainly because the electromagnetic waves are totally different with the sound wave. From the expression of the radioactive intensity, we can tell that the intensity of radiation not only depend on the distance from the radiation but also related to the type of the nuclide. In general, the relationship between the intensity and the distance satisfy the inverse-square law, which is a non-linear relationship. In other words, if we can use the measurement and …

Biochemical Reservoir Computing, Hoang Nguyen, Christof Teuscher

Student Research Symposium

Reservoir computing is an emerging machine learning paradigm. Compared to traditional feedforward neural networks, the reservoir can be unstructured and recurrent and only the output layer is trained. Reservoirs can be built with various types of physical components, yet, biochemical building blocks have not been widely used. This project focuses on designing and testing a reservoir computer (RC) based on chemical reaction network (CRN). We simulated high-level CRNs in MATLAB and their complex chemical dynamics were observed over time. A CRN constructed by a network of coupled deoxyribozyme oscillators was chosen for the final RC model. The inputs of the …

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

Maseeh Summer Undergraduate Research Experience

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 …

Silicon Compilation And Test For Dataflow Implementations In Gasp And Click, Swetha Mettala Gilla

Dissertations and Theses

Many modern computer systems are distributed over space. Well-known examples are the Internet of Things and IBM's TrueNorth for deep learning applications. At the Asynchronous Research Center (ARC) at Portland State University we build distributed hardware systems using self-timed computation and delay-insensitive communication. Where appropriate, self-timed hardware operations can reduce average and peak power, energy, latency, and electromagnetic interference. Alternatively, self-timed operations can increase throughput, tolerance to delay variations, scalability, and manufacturability.

The design of complex hardware systems requires design automation and support for test, debug, and product characterization.

This thesis focuses on design compilation and test support for dataflow …

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 …

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

Maseeh Summer Undergraduate Research Experience

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 …

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 …