Publications

CLOUD-D RF: Cloud-based Distributed Radio Frequency Heterogeneous Spectrum Sensing (2024)

by Dylan Green, Caleb McIrvin, River Thaboun, Cora Wemlinger, Joseph Risi, Alyse Jones, Maymoonah Toubeh, and William Headley

In wireless communications, collaborative spectrum sensing is a process that leverages radio frequency (RF) data from multiple RF sensors to make more informed decisions and lower the overall risk of failure in distributed settings. However, most research in collaborative sensing focuses on homogeneous systems using identical sensors, which would not be the case in a real world wireless setting. Instead, due to differences…

Automatic Expansion of Metadata Standards for Historic Costume Collections (2024)

by Caleb McIrvin, Chreston Miller, Dina Smith-Glaviana, and Wen Nie Ng

We postulate that problems in cataloging efforts in the historical costuming domain can be mitigated through implementation of a standardized metadata schema. Existing metadata schemas that utilize controlled descriptive terminology for fashion artifacts, such as historic costume or dress, and items related to the process and product of dressing the body (Eicher and Evenson 2014), which include clothing, textiles, and …

Quantum state exclusion through offset measurement (2024)

by Caleb McIrvin, Ankith Mohan, and Jamie Sikora

The quantum state discrimination problem has Alice sending a quantum state to Bob who wins if he correctly identifies the state. The pretty good measurement, also known as the square root measurement, performs pretty well at this task. We study the version of this problem where Bob tries to lose with the greatest probability possible (which is harder than it sounds). We define the pretty bad measurement, which performs pretty…

RFRL Gym: A Reinforcement Learning Testbed for Cognitive Radio Applications

by Daniel Rosen, Illa Rochez, Caleb McIrvin, Joshua Lee, Kevin D’Alessandro, Max Wiecek, Nhan Hoang, Ramzy Saffarini, Sam Philips, Vanessa Jones, Will Ivey, Zavier Harris-Smart, Zavion Harris-Smart, Zayden Chin, Amos Johnson, Alyse M Jones, William C Headley

Abstract—Radio Frequency Reinforcement Learning (RFRL) is anticipated to be a widely applicable technology in the next generation of wireless communication systems, particularly 6G and next-gen military communications. Given this, our research is focused on developing a tool to promote the development of RFRL techniques that leverage spectrum sensing. In particular, the tool was designed to address two cognitive…

Comparative Study and Expansion of Metadata Standards for Historic Fashion Collections (2023)

by Dina Smith-Glaviana, Wen Nie Ng, Caleb McIrvin, Chreston Miller, and Julia Spencer

This research seeks to contribute to efforts to standardize metadata across the costume and fashion domain by adding new metadata elements and controlled vocabularies to Costume Core. Expanding the metadata schema could increase the searchability and discoverability of fashion collections. To expand Costume Core, we used vocabulary from pre-trained Natural Language Processing (NLP) models to identify potential new…

Simulating Noisy Quantum Circuits for Cryptographic Algorithms (2023)

by Sahay Harshvardhan, Sanil Jain, James E McClure, Caleb McIrvin, and Ngoc Quy Tran

The emergence of noisy intermediate-scale quantum (NISQ) computers has important consequences for cryptographic algorithms. It is theoretically well-established that key algorithms used in cybersecurity are vulnerable to quantum computers due to the fact that theoretical security guarantees, designed based on algorithmic complexity for classical computers, are not sufficient for quantum circuits. Many different quantum algorithms have …

Forecasting Network Traffic with Efficient Transformers (2022)

by Jude Canady, Caleb McIrvin, Blake Martin, Rebecca Wegznek and John O’Boyle

Computer networks are constantly being modified to adapt to ever-changing user demands. New devices are added and obsolete machines are exiled to storage closets to collect dust for eternity. If we want to understand these dynamic networks, monitor them effectively, and produce reliable predictions for them, we need to select machine learning models and algorithms that can adapt quickly to new network topologies and their…

Pre-Training Neural Networks is Not Always Beneficial (2022)

by Blake Martin, Caleb McIrvin, Rebecca Wegznek, John O’Boyle and Jude Canady

In the digital age, the security of digitized information is imperative to the operations of any business initiative. As vulnerabilities continue to be exploited for personal gain, and as cyber attacks continue to pose one of the largest global risks, network security remains crucial. Using NetFlow v9, a type of metadata derived from network traffic, we can train machine learning (ML) models to combat these security threats…