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Intelligent Information System Lab.

Research

IoT Security and AI

Data Channel Characterization and Statistical Signal Processing

just like human biometrics, the physically unclonable functions in electronic devices offer a powerful tool for security and identification. All electronic devices contain physically unclonable functions (PUFs) that arise during the manufacturing process.

These PUFs are essentially unique, unpredictable variations in the physical characteristics of each device, making them an effective means of ensuring security and authenticity.Our research focuses on extracting and analyzing these distinctive attributes from various data channels within electronic devices.

For instance, static random access memory (SRAM) and ring oscillators (RO) can exhibit unique startup behaviors and frequency variations that serve as a digital "fingerprint" for each device. By using field-programmable gate arrays (FPGAs), we can systematically study and harness these characteristics for enhanced security applications.
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RF Sensing and wireless communication

  • Due to multi-path fading, the wireless channel exhibits unique responses that can be leveraged for both data communication and additional functionalities. Multi-path fading occurs when transmitted signals reflect off various surfaces and arrive at the receiver at slightly different times. This phenomenon creates a complex and rich channel response that can be exploited for sensing and communication.

    By harnessing the benefits of radio frequency (RF) signals, we can not only facilitate reliable data transmission but also perform advanced sensing tasks. RF signals can penetrate obstacles and reflect off surfaces, providing valuable information about the environment. This dual capability enables the simultaneous use of RF signals for traditional communication purposes and for applications such as xss-object detection, motion tracking, and environmental monitoring.
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