Shichen Zhang (Michigan State University), Qijun Wang (Michigan State University), Maolin Gan (Michigan State University), Zhichao Cao (Michigan State University), Huacheng Zeng (Michigan State University)

This paper aims to design and implement a radio device capable of detecting a person's handwriting through a wall. Although there is extensive research on radio frequency (RF) based human activity recognition, this task is particularly challenging due to the textit{through-wall} requirement and the textit{tiny-scale} handwriting movements. To address these challenges, we present RadSee---a 6 GHz frequency modulated continuous wave (FMCW) radar system designed for detecting handwriting content behind a wall. RadSee is realized through a joint hardware and software design. On the hardware side, RadSee features a 6 GHz FMCW radar device equipped with two custom-designed, high-gain patch antennas. These two antennas provide a sufficient link power budget, allowing RadSee to "see'' through most walls with a small transmission power. On the software side, RadSee extracts effective phase features corresponding to the writer's hand movements and employs a bidirectional LSTM (BiLSTM) model with an attention mechanism to classify handwriting letters. As a result, RadSee can detect millimeter-level handwriting movements and recognize most letters based on their unique phase patterns. Additionally, it is resilient to interference from other moving objects and in-band radio devices. We have built a prototype of RadSee and evaluated its performance in various scenarios. Extensive experimental results demonstrate that RadSee achieves 75% letter recognition accuracy when victims write 62 random letters, and 87% word recognition accuracy when they write articles.

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BULKHEAD: Secure, Scalable, and Efficient Kernel Compartmentalization with PKS

Yinggang Guo (State Key Laboratory for Novel Software Technology, Nanjing University; University of Minnesota), Zicheng Wang (State Key Laboratory for Novel Software Technology, Nanjing University), Weiheng Bai (University of Minnesota), Qingkai Zeng (State Key Laboratory for Novel Software Technology, Nanjing University), Kangjie Lu (University of Minnesota)

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Ghidra: Is Newer Always Better?

Jonathan Crussell (Sandia National Laboratories)

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Securing BGP ASAP: ASPA and other Post-ROV Defenses

Justin Furuness (University of Connecticut), Cameron Morris (University of Connecticut), Reynaldo Morillo (University of Connecticut), Arvind Kasiliya (University of Connecticut), Bing Wang (University of Connecticut), Amir Herzberg (University of Connecticut)

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