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1. Physical Visualization Design

   https://doi.org/10.1145/3318464.3384711  

   Ramjit, Lana; Kong, Zhaoning; Netravali, Ravi; Wu, Eugene (January 2020, SIGMOD Demo)

   null (Ed.)
2. Physical Cyclic Animations

   https://doi.org/10.1145/3606938  

   Jia, Shiyang; Wang, Stephanie; Li, Tzu-Mao; Chern, Albert (August 2023, Proceedings of the ACM on Computer Graphics and Interactive Techniques)

   We address the problem of synthesizing physical animations that can loop seamlessly. We formulate a variational approach by deriving a physical law in a periodic time domain. The trajectory of the animation is represented as a parametric closed curve, and the physical law corresponds to minimizing the bending energy of the curve. Compared to traditional keyframe animation approaches, our formulation is constraint-free, which allows us to apply a standard Gauss--Newton solver. We further propose a fast projection method to efficiently generate an initial guess close to the desired animation. Our method can handle a variety of physical cyclic animations, including clothes, soft bodies with collisions, and N-body systems. 

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3. Guaranteed Physical Security with Restart-Based Design for Cyber-Physical Systems

   https://doi.org/10.1109/ICCPS.2018.00010  

   Abdi, Fardin; Chen, Chien-Ying; Hasan, Monowar; Liu, Songran; Mohan, Sibin; Caccamo, Marco (April 2018, ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS))
4. QuickProbe: Quick Physical Prototyping-in-Context Using Physical Scaffolds in Digital Environments

   https://doi.org/10.1115/DETC2022-91023  

   Raina, Abhijeet Singh; Vyas, Shantanu; Ebert, Matthew; Krishnamurthy, Vinayak R. (November 2022, ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Abstract In this paper, we introduce a novel prototyping workflow, QuickProbe, that enables a user to create quick-and-dirty prototypes taking direct inspiration from existing physical objects. Our workflow is inspired by the notion of prototyping-in-context using physical scaffolds in digital environments. To achieve this we introduce a simple kinesthetic-geometric curve representation wherein we integrated the geometric representation of the curve with the virtual kinesthetic feedback. We test the efficacy of this kinesthetic-geometric curve representation through a qualitative user study conducted with ten participants. In this study, users were asked to generate wire-frame curve networks on top of the physical shapes by sampling multiple control points along the surface. We conducted two different sets of experiments in this work. In the first set of experiments, users were tasked with tracing the physical shape of the object. In the second set of experiments, the goal was to explore different artistic designs that the user could draw using the physical scaffolding of the shapes. Through our user studies, we showed the variety of designs that the users were able to create. We also evaluated the similarities and differences we observed between the two different sets of experiments. We further discuss the user feedback and the possible design scenarios where our QuickProbe workflow can be used. 

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5. DefRec: Establishing Physical FunctionVirtualization to Disrupt Reconnaissance of PowerGrids’ Cyber-Physical Infrastructures

   https://doi.org/ndss.2020.24365  

   Lin, Hui; Zhuang, Jianing; Hu, Yih-Chun; Zhou, Huayu (January 2020, The Proceedings of 2020 Network and Distributed System Security Symposium (NDSS))

   Reconnaissance is critical for adversaries to prepare attacks causing physical damage in industrial control systems (ICS) like smart power grids. Disrupting reconnaissance is challenging. The state-of-the-art moving target defense (MTD) techniques based on mimicking and simulating system behaviors do not consider the physical infrastructure of power grids and can be easily identified. To overcome these challenges, we propose physical function virtualization (PFV) that “hooks” network interactions with real physical devices and uses these real devices to build lightweight virtual nodes that follow the actual implementation of network stacks, system invariants, and physical state variations in the real devices. On top of PFV, we propose DefRec, a defense mechanism that significantly increases the effort required for an adversary to infer the knowledge of power grids’ cyber-physical infrastructures. By randomizing communications and crafting decoy data for virtual nodes, DefRec can mislead adversaries into designing damage-free attacks. We implement PFV and DefRec in the ONOS network operating system and evaluate them in a cyber-physical testbed, using real devices from different vendors and HP physical switches to simulate six power grids. The experimental results show that with negligible overhead, PFV can accurately follow the behavior of real devices. DefRec can delay adversaries’ reconnaissance for more than 100 years by adding a number of virtual nodes less than or equal to 20% of the number of real devices. 

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