Flow is a mental state achieved by a person performing a task when there is a balance between skill and challenges, thereby evoking focused attention, full involvement, and enjoyment. For education applications, achieving a flow state may accelerate learning, retention and improve learning outcomes. Most current methods for assessing the extent of flow state use a self-reported questionnaire only when participants finish a learning activity. Alternatively, using objective real-time measurement such as electrophysiological techniques to assess the flow state, especially during a learning period, affords the possibility of timely adjustment of challenges to keep up with learners’ growing abilities. Such an adaptive approach may provide a more improved way to enter and sustain flow in learning activities. Previous studies also indicate that gamification is a proven way to induce the flow state, and virtual reality (VR) activities provide learners more immersive experience. The purpose of this preliminary study is to investigate an adaptive approach for assessing flow state during VR learning activities with electrophysiological measurement. This study used a NeuroSky EEG headset with a supervised machine learning algorithm to identify the features of attention, meditation, and eye blinks with flow state while performing VR learning activities. To evaluate the effects of the proposed approach, data collection was through an experiment with 10 UW students by assessing their flow state while playing VR educational games. The first version model trained by the three types of indicators is capable of determining if a learner performing VR learning activities enters the flow state. In turn, the result of this preliminary study serves as a metric for the follow-on research addressing the development of adaptive learning strategies to help each learner enter into a flow state and improve learning outcomes with the sustained flow state.

Past research has discovered a technique for photocatalyst synthesis utilizing never-dried cellulose fiber in order to produce non-agglomerating nanocrystals. This new technique allows the production of semiconductors in a flexible form. This form is useful in many areas, including in spacecraft technology, where solar panels must be kept in a compact form before deployment. One possible catalyst for this is gallium arsenide (GaAs), which holds many traits that are favorable to semiconductor devices in electronics. With a band gap that is receptive to visible light and many industry applications in radio frequency, light sensors and LEDs, gallium arsenide is an attractive candidate for storage in cellulose fiber. In particular, this storage method allows for the synthesis of long strands containing GaAs that are useful in solar cells. In order to form an electrostatic region necessary for electronic applications, a P-N junction must be formed within the compound. This is usually done by heating the compound and injecting ions into the structure so that P-N junctions are naturally formed. However, since the fibers are unable to be heated to the temperatures needed without compromising the storage, we instead propose that a magnetic field can be placed across the semiconductor during formation, with doping ions placed within the fiber so that the junctions naturally form within the GaAs structure. This new method for semiconductor production has the potential to offer a new strategy for electronic production that can exist in a flexible format.

Augmented reality is a promising field of study which aims to enhance our daily experiences by overlaying digital information into our view of the world, thus “augmenting” our perception of reality in useful ways. A key component of AR is computer vision which enables a computer to map and interpret the physical world. My proposed experiment will examine the effectiveness of several computer vision algorithms in balancing a pendulum attached to a movable cart. A camera, suspended by the pendulum, will collect observational data and relay this information to a microcontroller. Then I will utilize Hough Transform and Canny Edge Detection algorithms to recognize stable lines/edges in the environment, so a reference frame can be constructed in which to balance the pendulum. As the pendulum tips and the camera begins to fall, the system will compare the input frame to the reference frame and adjust the cart accordingly via servo motors. My research will compare several methods of video tracking and object recognition as well as SLAM (Simultaneous Localization and Mapping) as viable options to balance and orient systems with the real world. By designing a system that dynamically responds to its environment using visual cues, I aim to contribute to a critical component in the realization of emerging technologies such as autonomous vehicles, domestic robots and augmented reality systems.

Seismic reflection surveys use acoustic energy to image the structure beneath the seafloor by broadcasting broadband impulsive sound signals (called airguns) that reflect off the sea floor and are recorded through long arrays of hydrophones (called seismic streamers). During these surveys it is important to have a model to predict how much noise will be added to the ocean environment. The complexity of local geology, seafloor topography, and uncertainty in water properties makes it difficult to mathematically model acoustic propagation in ocean. However, ocean data like many other branches of science is experiencing an explosion in the amount of data collected and available for analysis. The goal of this research is to use previously recorded ocean data to (1) calculate the sound power levels generated by airguns during seismic reflection surveys and (2) use machine learning to create a predictive model of airgun noise considering various variables such as ocean depth and distance from the airgun. Several regression methods in Python were used and the method that resulted in the most accurate regressor was chosen. The data used in this experiment spanned a wide range of water depths from the continental shelf (∼40 m) to deep water (∼2600 m). In this presentation, the performance of a machine learning algorithm trained by the sound power levels calculated through one track line of this cruise is investigated.

Prior research has shown that virtual reality (VR) has great promise as a science and engineering teaching tool at the primary and secondary school levels. Unfortunately there is little VR educational content for schools since the education marketplace is poorly funded. Our objective is to solve this problem by teaching middle school students how to build their own immersive 3D virtual worlds that can used as teaching aids for other students and schools. To investigate this approach we are working with Robert Eagle Staff Middle School in an after-school engineering club teaching world building tools and processes to 6th graders. We have organized the students into four teams to create VR games that teach STEM concepts including scale, gravity, light, and momentum. University of Washington engineering students are assigned to mentor these teams while serving as role models to the middle school students.The middle school students not only learn world building techniques but also understanding of these STEM concepts well enough to teach others. To create the VR games, we are instructing the students how to use Unity3D as a game engine. The virtual worlds will operate in real time on the HTC Vive VR headsets with high-end Intel-powered computers. In addition to the world building efforts student learn how to set up, configure and operate the technology within the VR workspace at the school. Our final product will be both the educational content designed by the students but also a prototype of the way to do it. We are documenting our findings in scholarly reports and a documentary film that can be used to inform schools across the nation of how to use virtual reality as a teaching tool and to build a repository of virtual worlds that can be exchanged across schools.

UW Solar is an interdisciplinary student-led organization committed to developing solar infrastructure projects at the University of Washington. Solar arrays on campus not only produces clean renewable energy, but also helps the UW meet its carbon neutrality goals of its Climate Action Plan while providing a sustainable return on investment. UW Solar is working with UW Sustainability to maximize solar capacity on campus in support of the first UW Sustainability Plan to achieve the goals of the UW Climate Action Plan. Therefore, UW Solar is proposing a multi-phase plan for solar development at UW Seattle which aims to maximize solar capacity while providing a positive return on investment for Capital projects. UW Solar assessed all buildings on campus, and has identified the top 30 projects to be developed over the next 30 years to meet the UW’s 2050 Carbon Neutrality Goals. UW Solar’s scope of work for the Campus Plan includes the evaluation of all buildings on campus for potential solar installations and ranking them based on a suite of criteria that takes into consideration the following: energy generated, financial value of energy generated, cost of installation, return on investment, identifying funding options, working with campus stakeholders to develop the solar plan and timeline, assisting with schematic design, selection of contractor, oversight of installations, monitoring of energy generated for research and education. The total solar capacity for the Seattle campus could reach 8,000,000 kWh/year, with an annual energy value savings of $600,000. UW Solar will continue evaluating buildings on campus and working with all stakeholders to decide the priority of solar installations over the next 30 years.

Dementia is a neurological condition that impairs memory, cognitive functions, and the performance of daily activities. It is estimated that 5.5 million Americans suffered from dementia in 2017. Currently, the predominant method used to treat persons with dementia is through pharmacological means. However, through a review of current research we have found evidence to suggest that cognitive behavior therapies (CBT) may be more beneficial to patients, with the potential to reverse some symptoms of dementia. Our study aims to improve CBT through virtual environments. Virtual Reality (VR) is not merely a visual platform but an immersive environment that stimulates the brain in the same way real environments do. Patients with dementia living nursing homes are rarely exposed to different environmental interactions or thought inducing stimuli, resulting in an escalated progression of their condition and a loss of “sense of self.” CBT, specifically reminiscence therapy, has shown promising results in improving cognitive functioning as well as morale. We hypothesize that if we use visual and audio equipment as well as tangible artifacts, we could create an immersive therapy to stimulate cognitive functions. We track certain markers like head and eye movement to track the progression of the disease as well. In collaboration with a UK based game development company we have identified a software with both therapeutic and a diagnostic capabilities. We have made connections with senior centers in the area to identify persons wishing to participate VR therapy sessions. This project will offer understanding of how implementing VR therapy can improve patients’ morale and cognitive capabilities, as well as exploring the use of VR as a diagnostic tool.

Electroencephalograms (EEG’s) are a common form of measuring electrical activity in the brain. Electrical activity from the brain directly correlates to neuronal activity, providing information that can be used for disease diagnosis or research on brain mechanisms. EEG recordings are limited in practicality and efficiency in some circumstances due to poor spatial resolution, vulnerability to noise, and consistency. Subdermal electrodes are unable to capture deep brain activity, which can only be obtained through surgically implanted intracranial electrodes. As a result, for patients in a condition where surgery is not optimal, the subdermal EEG offers little to no information in terms of deep brain activity. Through previous work, we have shown that applying 1050 Hz pulsed focused ultrasound (pFU) resulted in EEG readings above normal physiological frequencies. Natural brain activity frequencies can be derived from the amplitude demodulation of the 1050 Hz signal. The ultrasound is amplifying the electrical activity in the region it is focused upon, allowing the EEG to record these signals. In this study, we attempted to increase the capabilities and specificity of EEG noninvasively through coupling it with pFU. We continued our previous work by applying pFU transcranially to the left visual cortex of a C57BL/6J mouse brain while stimulating the right eye with light. The observed frequencies collected by EEG were analyzed for neuronal stimulation in addition to sensory stimulation elicited from the ultrasound. A technological breakthrough in this field may someday lead to the ability to measure brain activity non-invasively from anywhere in the brain.

There has been a research effort in exploring RFID (Radio-frequency identification) interfaces. RFID is a form of wireless communication that uses electromagnetic coupling in the radio frequencies to uniquely identify an object, person or animal. By expanding on previous work in paper and RFID technologies, we are able to create new interactive capabilities from paper and RFID. During my time with the Ubiquitous Computing Lab, my goal is to find a technique using capacitive sensing, conductive ink, and RFID tags in order to create an interactive touch surface. An RFID antenna can read an RFID tag and give information pertaining to that tag, for example, tag ID, time, a tag’s doppler shift, tag’s signal strength and more. My research has been exploring the relationship among these variables and creating a quantitative model that can map for continuous interactions. With such a model, one can make that surface customizable for a variety of interactions. 2D Sense strives to enable 2-dimensional touch interactions on paper by applying signal processing techniques towards existing RFID technology.

UW's electricity bill is around $1MM per month, making it Seattle City Light's second-largest customer. Solar power represents one way that UW can reduce load on the city's power grid. In 2017, UW Housing and Food Services completed installation of four solar panel arrays to its buildings on campus. How have those panels performed, and has UW benefited from their presence? To gain insights into the effect of these installations, we developed the UW Solar Power Monitor to collect and present data about solar power usage within these buildings. The data was analyzed, interpreted, and integrated into the dashboard. Our hope is that the tools we have developed will enable UW administrators to make informed decisions about power infrastructure on campus, educate the public and promote awareness about solar projects on campus, facilitate the study and analysis of solar power, and encourage increased investment in solar infrastructure at the University of Washington.

Insights from the study of biosonar (echolocation) in mammals such as bats have applications in developing coordinated autonomous manoeuvres in a variety of environments. However, limited research has studied bat echolocation and flight behaviour in natural habitats, which involves multiple bats competing for a shared prey resource. In a confined space, bats have been observed to emit calls with lower intensity, shorter duration, and broader bandwidth compared to when echolocating in the open. Bats foraging in the wild also experience more complex echo-acoustic scenes created by sounds of all sources in the landscape, such as calls from other bats and echoes from vegetation and obstacles in the environment. Evidence from previous field studies suggest that solo bats engage in stereotypic flight path behaviour to minimise the cost of sensory processing required for spatial orientation, making those resources available for prey localisation. Other studies have observed leader-follower behaviour in pairs of bats, where they coordinate manoeuvres by copying the others’ trajectory through active echolocation. We propose to investigate flight and echolocation behaviour of an even larger number of simultaneously foraging bats over the Central Pond in the Union Bay Natural Area. This 74-acre natural area on the shore of Lake Washington is a prime location for a natural observatory right on the UW campus. We are currently developing an ultrasonic microphone array that is a non-cost-prohibitive alternative to proprietary wildlife recording devices, and we will start data collection when local bats local emerge from winter hibernation. We plan to develop fully automated algorithms for tracking bats and estimating their echolocation directional aim during foraging. These data will serve as a basis for deriving computational principles of coordinated flight and sensing across multiple agents, which can inspire new technology for autonomous vehicles in the air, on the ground, or underwater.

Raven is a research prototype surgical robot that helps a surgeon perform surgery remotely. The purpose of my project is to develop a program to keep the surgical camera automatically focusing on the current surgical location. I wrote a GUI (graphical user interface) to perform object tracking and eye detection through the programing language Python with the OpenCV library. I created a ROS (robot operating system) node to program Raven. I expect the result will make Raven a more convenient tool for performing surgery remotely in the future.

Modeling how electromagnetic waves scatter from a distribution of rough plates poses many applications. Certain systems may be easy to approximate with planar geometry, but use of numerical field solvers to determine the radiated fields could take a long time for nontrivial structures. We propose an iteration on existing formulas based on the Kirchhoff approximation. This iteration will consider the case of multiple rough, finite-sized rectangular plates. We developed software to determine the scattering of waves off of a distribution of rough plates, of arbitrary position and orientation between a transmitter and receiver. The method considers each plate individually, calculating the coherent and incoherent scattered fields. Provided all plates and the transmitter and receiver are sufficiently spaced, we calculate the total fields by summing the result from each individual plate. We verified this method via comparison to numerical data. This model can then be used for determining the radiated fields from unwanted reflectors in urban environments such as buildings or car rooftops. The software could be additionally used to optimize a distribution of reflective plates to achieve a desired radiation pattern.

During the Space Race, NASA calculated lunar rocket trajectories by using Euler’s Method to estimate the differential equation obtained by numerically integrating Newton’s Laws of Motion. At the time, computing power was scarce, costly, and primitive. The objective of this project is to develop an interactive MATLAB application that leverages modern computing architecture to accurately and efficiently approximate lunar rocket trajectories based on multiple variables relating to a rocket's journey to the Moon using the Runge-Kutta method. The Runge-Kutta method is a more accurate and sophisticated version of Euler’s method as it uses a multi-stage algorithm that involves slope calculations at discrete time values. This project will bring valuable input to the feasibility of using such numerical methods in approximating lunar trajectories to the industry. The rocket emulated in the program will be based on NASA's Saturn V rocket. Our application will simulate three phases of travel to the moon: achieving Earth orbit, lunar transfer orbit, and lunar orbit. It will consider variables such as time, distance, velocity, acceleration, position, rocket thrust, exhaust velocity, mass flow rate, and aerodynamic drag. The application will allow users to adjust various parameters in real time and see results graphically.

The field of robotics attempts to replicate human intelligence by harnessing vast amounts of information, and will heavily influence next generation technologies. Of course, in order to operate in an intelligent manner, our robots must be equipped with sensors that can provide informative data. By endowing our robots which such sensors, we can enable them to assist humans that are dependent on others for completing every day tasks, such as opening doors, picking up fallen objects, and pulling wheelchairs. Up until recently, many robots required their sensors to be wired to their main computers, potentially impeding their movement and/or limiting the types of sensors that they can employ. Our work, in particular, focuses on pre-touch sensors, which are sensors mounted to the robot's fingers that allow it to sense an object prior to making contact. This project redeveloped the pre-touch sensing system such that data from up to five sensors is wirelessly transferred to the robot's main computer. There are five micro USB ports connecting to the five fingertip sensors, all of which are controlled by the main USB hub. Vocore, a single-board computer, is incorporated, and this Linux based mini-computer is an essential component for sending the sensor data via Wi-Fi to any selected computer. Additionally, the elimination of wires makes it much easier to reconfigure the form factor of the pre-touch sensing system. As a result, these versatile sensors could be applied to any robot in the world.