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Wang Z, Wang H, Wang P, Shao Y. Robust Optical Physical Unclonable Function Based on Total Internal Reflection for Portable Authentication. ACS APPLIED MATERIALS & INTERFACES 2024; 16:27926-27935. [PMID: 38743936 DOI: 10.1021/acsami.4c03283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Physical unclonable functions (PUFs) utilize uncontrollable manufacturing randomness to yield cryptographic primitives. Currently, the fabrication of the most generally employed optical PUFs mainly depends on fluorescent, Raman, or plasmonic materials, which suffer inherent robustness issues. Herein, we construct an optical PUF with high environmental stability via total internal reflection (TIR-PUF) perturbed by randomly distributed polymer microspheres. The response image is transformed into encoded keys via an iterative binning procedure. The concentration of the polymer solution is optimized to debias the bit nonuniformity and maximize encoding capacity. The constructed TIR-PUF shows significantly high encoding capacity (2370) and markedly low total authentication error probability (1.614 × 10-23). The intra-Hamming distance is as low as 0.068, indicating the excellent readout reliability of TIR-PUF. The environmental stability of TIR-PUF has demonstrated promising results under a range of challenging conditions such as ultrasonic washing, high temperature, ultraviolet irradiation, and severe chemical environments. Moreover, the challenge-response pairs of our TIR-PUFs are demonstrated on an authentication system with low-power dissipation, lightweight components, and wireless imaging capture, rendering the possibility of portable authentication for practical applications.
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Affiliation(s)
- Zhiyuan Wang
- Laboratory of Thin Film Optics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, P. R. China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, P. R. China
- Key Laboratory of Materials for High-Power Laser, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, P. R. China
| | - Hu Wang
- Laboratory of Thin Film Optics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, P. R. China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, P. R. China
- Key Laboratory of Materials for High-Power Laser, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, P. R. China
| | - Pengxiang Wang
- Laboratory of Thin Film Optics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, P. R. China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, P. R. China
- Key Laboratory of Materials for High-Power Laser, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, P. R. China
| | - Yuchuan Shao
- Laboratory of Thin Film Optics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, P. R. China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, P. R. China
- Key Laboratory of Materials for High-Power Laser, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, P. R. China
- Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, P. R. China
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Zhang M. Consumer behavior analysis based on Internet of Things platform and the development of precision marketing strategy for fresh food e-commerce. PeerJ Comput Sci 2023; 9:e1531. [PMID: 37705616 PMCID: PMC10496010 DOI: 10.7717/peerj-cs.1531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 07/20/2023] [Indexed: 09/15/2023]
Abstract
The traditional approach to e-commerce marketing encounters challenges in effectively extracting and utilizing user data, as well as analyzing and targeting specific user segments. This manuscript aims to address these limitations by proposing the establishment of a consumer behavior analysis system based on an Internet of Things (IoT) platform. The system harnesses the potential of radio frequency identification devices (RFID) technology for product identification encoding, thus facilitating the monitoring of product sales processes. To categorize consumers, the system incorporates a k-means algorithm within its architectural framework. Furthermore, a similarity metric is employed to evaluate the gathered consumption information and refine the selection strategy for initial clustering centers. The proposed methodology is subjected to rigorous testing, revealing its effectiveness in resolving the issue of insufficient differentiation between customer categories after clustering. Across varying values of k, the average false recognition rate experiences a notable reduction of 20.6%. The system consistently demonstrates rapid throughput and minimal overall latency, boasting an impressive processing time of merely 2 ms, thereby signifying its exceptional concurrent processing capability. Through the implementation of the proposed system, the opportunity for further target market segmentation arises, enabling the establishment of core market positioning and the formulation of distinct and precise marketing strategies tailored to diverse consumer cohorts. This pioneering approach introduces an innovative and efficient methodology that e-commerce enterprises can embrace to amplify their marketing endeavors.
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Affiliation(s)
- Mengmeng Zhang
- The Department of Logistics and E-Commerce, Henan University of Animal Husbandry and Economy, Zhengzhou, Henan, China
- College of Business Administration College, University of the Cordilleras, Baguio, Baguio, Philippines
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