Tootfinder

Learning Safe Control via On-the-Fly Bandit Exploration
Alexandre Capone, Ryan Cosner, Aaaron Ames, Sandra Hirche
https://arxiv.org/abs/2506.10279 https://…

Learning Safe Control via On-the-Fly Bandit Exploration
Control tasks with safety requirements under high levels of model uncertainty are increasingly common. Machine learning techniques are frequently used to address such tasks, typically by leveraging model error bounds to specify robust constraint-based safety filters. However, if the learned model uncertainty is very high, the corresponding filters are potentially invalid, meaning no control input satisfies the constraints imposed by the safety filter. While most works address this issue by assu…

Evasion Under Blockchain Sanctions
Endong Liu, Mark Ryan, Liyi Zhou, Pascal Berrang
https://arxiv.org/abs/2507.11721 https://arxiv.or…

Evasion Under Blockchain Sanctions
Sanctioning blockchain addresses has become a common regulatory response to malicious activities. However, enforcement on permissionless blockchains remains challenging due to complex transaction flows and sophisticated fund-obfuscation techniques. Using cryptocurrency mixing tool Tornado Cash as a case study, we quantitatively assess the effectiveness of U.S. Office of Foreign Assets Control (OFAC) sanctions over a 957-day period, covering 6.79 million Ethereum blocks and 1.07 billion transact…

Adaptive Linearly Constrained Minimum Variance Volumetric Active Noise Control
Manan Mittal, Ryan M. Corey, Andrew C. Singer
https://arxiv.org/abs/2507.05657

Adaptive Linearly Constrained Minimum Variance Volumetric Active Noise Control
Traditional volumetric noise control typically relies on multipoint error minimization to suppress sound energy across a region, but offers limited flexibility in shaping spatial responses. This paper introduces a time-domain formulation for linearly constrained minimum variance active noise control (LCMV ANC) for spatial control filter design. We demonstrate how the LCMV ANC optimization framework allows system designers to prioritize noise reduction at specific spatial locations through strat…

Time-Varying Model Predictive Attitude Control for Magnetically Actuated Dual-Spin Satellites
Robert D. Halverson, Ryan J. Caverly
https://arxiv.org/abs/2506.07858

Time-Varying Model Predictive Attitude Control for Magnetically Actuated Dual-Spin Satellites
Attitude control hardware for small satellites is often limited in power and space availability given the importance of the science instruments they exist to transport. To mitigate this, a dual-spin stabilized satellite actuated via magnetic torque rods reduces the space and power required of the attitude control system, but may require advanced control policies. This paper explores the attitude control of a magnetically actuated dual-spin stabilized CubeSat with model predictive control using …

An In-situ Solid Fuel Ramjet Thrust Monitoring and Regulation Framework Using Neural Networks and Adaptive Control
Ryan DeBoskey, Parham Oveissi, Venkat Narayanaswamy, Ankit Goel
https://arxiv.org/abs/2506.08157

An In-situ Solid Fuel Ramjet Thrust Monitoring and Regulation Framework Using Neural Networks and Adaptive Control
Controlling the complex combustion dynamics within solid fuel ramjets (SFRJs) remains a critical challenge limiting deployment at scale. This paper proposes the use of a neural network model to process in-situ measurements for monitoring and regulating SFRJ thrust with a learning-based adaptive controller. A neural network is trained to estimate thrust from synthetic data generated by a feed-forward quasi-one-dimensional SFRJ model with variable inlet control. An online learning controller base…

Cloud-Based Interoperability in Residential Energy Systems
Darren Leniston, David Ryan, Ammar Malik, Jack Jackman, Terence O'Donnell
https://arxiv.org/abs/2506.05076

Cloud-Based Interoperability in Residential Energy Systems
As distributed energy resources (DERs) such as solar PV, batteries and electric vehicles become increasingly prevalent at the edge, maintaining grid stability requires advanced monitoring and control mechanisms. This paper presents a scalable smart grid gateway architecture that enables interoperability between Modbus-based inverters and IEEE 2030.5 cloud-based control systems. The proposed solution leverages Azure cloud services and edge-computing gateway devices to support dynamic configurati…

Learning to Explore: An In-Context Learning Approach for Pure Exploration
Alessio Russo, Ryan Welch, Aldo Pacchiano
https://arxiv.org/abs/2506.01876 https:…

Learning to Explore: An In-Context Learning Approach for Pure Exploration
In this work, we study the active sequential hypothesis testing problem, also known as pure exploration, where the goal is to actively control a data collection process to efficiently identify the correct hypothesis underlying a decision problem. While relevant across multiple domains, devising adaptive exploration strategies remains challenging, particularly due to difficulties in encoding appropriate inductive biases. Existing Reinforcement Learning (RL)-based methods often underperform when …

Securing Open RAN: A Survey of Cryptographic Challenges and Emerging Solutions for 5G
Ryan Barker, Fatemeh Afghah
https://arxiv.org/abs/2506.09418 https://…

Securing Open RAN: A Survey of Cryptographic Challenges and Emerging Solutions for 5G
The advent of Open Radio Access Networks (O-RAN) introduces modularity and flexibility into 5G deployments but also surfaces novel security challenges across disaggregated interfaces. This literature review synthesizes recent research across thirteen academic and industry sources, examining vulnerabilities such as cipher bidding-down attacks, partial encryption exposure on control/user planes, and performance trade-offs in securing O-RAN interfaces like E2 and O1. The paper surveys key cryptogr…

Optimizing information transmission in the canonical Wnt pathway
Olivier Witteveen, Samuel J. Rosen, Ryan S. Lach, Maxwell Z. Wilson, Marianne Bauer
https://arxiv.org/abs/2506.22633

Optimizing information transmission in the canonical Wnt pathway
Populations of cells regulate gene expression in response to external signals, but their ability to make reliable collective decisions is limited by both intrinsic noise in molecular signaling and variability between individual cells. In this work, we use optogenetic control of the canonical Wnt pathway as an example to study how reliably information about an external signal is transmitted to a population of cells, and determine an optimal encoding strategy to maximize information transmission …

Next Generation LLRF Control and Monitoring System for S-Band Linear Accelerators
Chao Liu, Ankur Dhar, Emma Snively, Mohamed Othman, Ryan Herbst, Emilio A. Nanni
https://arxiv.org/abs/2505.22876

Next Generation LLRF Control and Monitoring System for S-Band Linear Accelerators
The low-level RF (LLRF) systems for S-band linear accelerating structures are typically implemented with heterodyne base architectures. We have developed and characterized the next generation LLRF (NG-LLRF) based on the RF system-on-chip (RFSoC) for C-band accelerating structures, and the platform delivered the pulse-to-pulse fluctuation levels considerably better than the requirement of the targeted applications. The NG-LLRF system uses the direct RF sampling technique of the RFSoC, which sign…

High Precision RF Pulse Shaping with Direct RF Sampling for Future Linear Accelerators
Chao Liu, Ankur Dhar, Ryan Herbst, Emilio A. Nanni
https://arxiv.org/abs/2505.22872

High Precision RF Pulse Shaping with Direct RF Sampling for Future Linear Accelerators
In various of particle accelerator designs, amplitude and phase modulation methods are commonly applied to shape the RF pulses for implementing pulse compressors or compensating for the fluctuations introduced by the high-power RF components and beam loading effects. Phase modulations are typically implemented with additional phase shifters that require drive or control electronics. With our recent next-generation LLRF (NG-LLRF) platform developed based on direct RF sampling technology of RF sy…