One way to obtain plasma is by using a Direct Current (DC) discharge. Plasma is an ionized gas, meaning the separation of positive ions and electrons in a gas. There are three main variables when it comes to a DC discharge configuration. A gas forms into a plasma in an isolated space of low pressure between 2 electrodes, a cathode and an anode. Voltage must constantly be applied across the cathode and the anode to maintain the plasma. The initial voltage needed to initiate the separation of electrons and protons in a gas to produce a plasma is called the breakdown voltage. Our study investigates the configuration of a DC discharge plasma and the correlation between electrode separation, breakdown voltage, and pressures in a DC discharge environment. We constructed an environment consisting of long oval glass tube housing an anode and cathode on each side. A vacuum pump is attached to the glass container to extract air to reduce pressure in our glass tube. To maintain an ideal pressure, we established a concealed air tube connected to our glass tube with a fine adjust valve to let air into our glass tube at the same rate as our vacuum pump extraction resulting in a stable low pressure in our experimental configuration. We designed and conducted a series of tests to investigate the properties of a DC plasma formation. Moreover, we wanted to establish evidence of the Paschen Curve, which relates the breakdown voltage and the product of electrode distance and pressure in DC discharge. We experimentally determined the optimum pressure and electrode separation distance product for plasma breakdown in air and Argon gas. DC plasmas can be utilized as sputter sources to deposit thin films for solar panels; characterizing the breakdown voltage is significant at low pressures and short spacing to control the sputtering rate.

In 2020, during a KING 5 interview, a local student from “AAPI Against Hate” discussed how this is the “first time” Asian American, Native Hawaiian, and Pacific Islanders (AANHPIs) have fought back. While we congratulate and appreciate her empowerment, we are dismayed by her ignorance. In 2022, a group of concerned multicultural UW students from American Ethnic Studies came together with students from other disciplines to create a comic novella, KingDOOM: How Protestors Saved Seattle’s Chinatown, focusing on the AAPI protests from 50 years ago against the Kingdome encroachment that led to the creation of many Chinatown International District (CID) agencies. In 2022, we successfully applied for a city grant to publish our comic book. Yet, as we honor the 50th Anniversary of the protestors, we were shocked by the dual announcement that the light rail is demolishing a part of the CID, followed by news that the King County Council will be expanding a mega-shelter to be built adjacent to our community – when there are already 20 shelters within walking distance of the CID. Meanwhile, we are still feeling the impact of the pandemic, anti-Asian Hate, vandalism, business closures, racism, and xenophobia. We were compelled to expand our book honoring the 70’s protestors to our current fight, demonstrating solidarity of the past with the present. As we photographed, researched, interviewed, and participated in current-day protests, we witnessed former seventies activists united with the elderly and young adults, marching to public meetings, and attending rallies, press conferences, and workshops. While there is a temporary moratorium on the light rail and the mega-shelter expansion, we feel that we must end our narrative here, even though we acknowledge that our struggle continues. We have been working since September 2022 and our project will be completed by June 2023.

Automated digital watermarking is an excellent technique for prevention of online digital data from theft and piracy. With current advances in internet, network and social media technologies, protecting online data theft and piracy to preserve brand ownership remains a top priority. A watermark is a pattern inserted into a digital image, audio or video file which identifies a file’s copyright information. Common types of signals to watermark are text, digital images, audio music clips and videos. Digital images are stored as arrays/matrices on computers which allows matrix-algebra based methods for image processing. We have applied mathematical modeling techniques using singular value decomposition (SVD) for digital watermarking of visual data. We wrote Python-based codes to digitize images and embedded copyright information into visual images using SVD based methods. We find that the embedded watermark is tamper resistant and the watermark could be retrieved from manipulated greyscale images subject to rotation and compression distortions. Our preliminary studies using greyscale images suggest that SVD based digital watermarking methods are robust and can be used to verify and authenticate data ownership. Digital watermarking can be used to prevent copyright infringement and data theft online. This project integrates advanced application of linear-algebra based mathematical methods with Python based programming to provide solutions to real-world problems of current interest.

Mathematical modeling and simulations of the gait and pose of mobile robots find important applications for mobile robot design for process automation, industrial applications, and deriving algorithms for walking styles. Here, we have used Webot robotic simulations and mathematical modeling methods to study, analyze and interpret the gait and pose of six-legged (hexapod) and four-legged (quadruped) robots that mimic dog-like movements. Hexapod and quadruped mobile robots are perfect for deployment for outer space exploration as these mobile robots can traverse uneven terrain and use artificial intelligence to plan their safe foothold positions to navigate their environment. We have analyzed the simulated gait and pose of the hexapod robot using rigid body inverse kinematics and symmetry analysis. The hexapod mantis insect-shaped robot motion in the simulations can be expressed as linear combinations of rigid translational and rotational motion. The hexapod robot moves using an alternating tripod-like gait wherein three legs move at a time while the other three remain stationary. The hexapod robot has greater dynamic stability for uneven terrain and can move more legs as compared to a quadruped robot. This research project serves as an elegant platform for applications of simulations and mathematical methods for studying the gait and stability of legged mobile robots that are relevant for the safety design of computer-controlled walking robots for radioactive waste management, space exploration, and for the design of mobile robots working in nuclear power stations and finding other industrial applications.

As the field of astrophysics continues to grow, the quantity of data to analyze is constantly expanding. With projects like the James Webb Space Telescope each sending back hundreds of gigabytes of data every day, Artificial Intelligence (AI) technologies is needed to assist manual analytical techniques in processing these volumes of information. One of the most apparent tasks for AI in astrophysics is image categorization – identifying what sort of astronomical object a certain body is. If a machine could categorize these bodie in significantly less time than a person, it would free tens of thousands of human hours every year. I created a Machine Learning program using a Deep Neural Network (DNN) implemented in Keras and TensorFlow capable of classifying astronomical images based on photometric data. Built from scratch, it utilizes existing labeled images to “learn” how astronomical bodies differ in appearance and assign them a category. The value of automated classification of astronomical phenomena cannot be understated. DNN allows the model to find unique identifiers in images humans often cannot spot, leading to often-more reliable predictions, recognizing possible discoveries in far less time, and freeing astronomers to undertake higher-cognition tasks only humans can accomplish. As the model is continuouly improved, it will be able to make increasingly accurate classifications and be of ever-growing value.

The existential risk of being unable to control a super-intelligent agent is called the Control Problem. Philosophers argue that an intelligence explosion and the creation of a singularity are inevitable, likening it to a ticking bomb. This fear is also present within the media, with rogue robots and singularities being frequent tropes for science fiction. However, the catastrophizing of sentient computers is not new. When first invented, academics and citizens speculated the computer was a precursor to supernatural thinking machines. Even in the mid-20th century, scientists believed sentient computers were right around the corner. This belief led to widespread computer phobia- the general public was afraid of what they thought were sentient gadgets and their implications. As familiarity with computers grew, along with a redefinition of what qualifies as human intelligence, this fear dwindled, and the public viewed computers as mere tools. Once again, due to the innovation of neural networks, we are experiencing a resurgence of phobia, reviving the belief that computers are supernatural thinking machines. This literature review will compare recent and historical philosophical arguments to current psychology and computer science. I expect to find similarities between the 1950s and present phobia and logical dissonance between the application of computer science and philosophical arguments. By confronting a potentially baseless fear, we can correct and alleviate the issues caused by irrationality and identify policies separate from sentience but still necessary to safeguard against non-sentient AI.