Phase-based displacement measurement on a straight edge using an optimal complex Gabor filter

Miao, Yinghao; Jeon, Jun Young; Kong, Yeseul; Park, Gyuhae

doi:10.1016/j.ymssp.2021.108224

Cited by 20 publications

(2 citation statements)

References 29 publications

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“…The Gram angle field is a mathematical tool based on Gabor transformations used to analyze the texture and structural features of images. This method was initially employed in signal processing and communication fields and later became widely used in texture analysis, feature extraction, and image recognition tasks, especially in biometric recognition and image processing fields [21,22]. The Gram angle field is a local feature extraction method that uses kernel functions of different scales and directions, enabling it to capture features of different spatial frequencies and directions in sample data.…”

Section: Relate Workmentioning

confidence: 99%

Device Identity Recognition Based on an Adaptive Environment for Intrinsic Security Fingerprints

Xi,

Zhang,

Zhang

et al. 2024

Electronics

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A device’s intrinsic security fingerprint, representing its physical characteristics, serves as a unique identifier for user devices and is highly regarded in the realms of device security and identity recognition. However, fluctuations in the environmental noise can introduce variations in the physical features of the device. To address this issue, this paper proposes an innovative method to enable the device’s intrinsic security fingerprint to adapt to environmental changes, aiming to improve the accuracy of the device’s intrinsic security fingerprint recognition in real-world physical environments. This paper initiates continuous data collection of device features in authentic noisy environments, recording the temporal changes in the device’s physical characteristics. The problem of unstable physical features is framed as a restricted statistical learning problem with a localized information structure. This paper employs an aggregated hypergraph neural network architecture to process the temporally changing physical features. This allows the system to acquire aggregated local state information from the interactive influences of adjacent sequential signals, forming an adaptive environment-enhanced device intrinsic security fingerprint recognition model. The proposed method enhances the accuracy and reliability of device intrinsic security fingerprint recognition in outdoor environments, thereby strengthening the overall security of terminal devices. Experimental results indicate that the method achieves a recognition accuracy of 98% in continuously changing environmental conditions, representing a crucial step in reinforcing the security of Internet of Things (IoT) devices when confronted with real-world challenges.

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Section: Relate Workmentioning

confidence: 99%

Device Identity Recognition Based on an Adaptive Environment for Intrinsic Security Fingerprints

Xi,

Zhang,

Zhang

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…To enhance accuracy, Cai et al [22] addressed the limitations of phase-based estimation and proposed a novel multi-view measurement method. Miao et al [23], by optimizing the Gabor filter parameters, introduced a robust phase-based displacement measurement technique to capture vibration responses.…”

Section: Introductionmentioning

confidence: 99%