Browsing by Author "Zhang, Jingtao"

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    Optical cryptography with biometrics for multi-depth objects
    Yan, Aimin; Wei, Yang; Hu, Zhijuan; Zhang, Jingtao; Tsang, Peter Wai Ming; Poon, Ting-Chung (Springer Nature, 2017-10-11)
    We propose an optical cryptosystem for encrypting images of multi-depth objects based on the combination of optical heterodyne technique and fingerprint keys. Optical heterodyning requires two optical beams to be mixed. For encryption, each optical beam is modulated by an optical mask containing either the fingerprint of the person who is sending, or receiving the image. The pair of optical masks are taken as the encryption keys. Subsequently, the two beams are used to scan over a multidepth 3-D object to obtain an encrypted hologram. During the decryption process, each sectional image of the 3-D object is recovered by convolving its encrypted hologram (through numerical computation) with the encrypted hologram of a pinhole image that is positioned at the same depth as the sectional image. Our proposed method has three major advantages. First, the lost-key situation can be avoided with the use of fingerprints as the encryption keys. Second, the method can be applied to encrypt 3-D images for subsequent decrypted sectional images. Third, since optical heterodyning scanning is employed to encrypt a 3-D object, the optical system is incoherent, resulting in negligible amount of speckle noise upon decryption. To the best of our knowledge, this is the first time optical cryptography of 3-D object images has been demonstrated in an incoherent optical system with biometric keys.
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    A Uniform Error Bound for Stochastic Kriging: Properties and Implications on Simulation Experimental Design
    Chen, Xi; Zhang, Yutong; Xie, Guangrui; Zhang, Jingtao (ACM, 2024-08)
    In this work, we propose a method to construct a uniform error bound for the SK predictor. In investigating the asymptotic properties of the proposed uniform error bound, we examine the convergence rate of SK's predictive variance under the supremum norm in both fixed and random design settings. Our analyses reveal that the large-sample properties of SK prediction depend on the design-point set determined by the design-point sampling scheme and the budget allocation scheme adopted. Appropriately controlling the order of noise variances through budget allocation is crucial for achieving a desirable convergence rate of SK's approximation error, as quantified by the uniform error bound, and for maintaining SK's numerical stability. Moreover, we investigate the impact of noise variance estimation on the uniform error bound's performance theoretically and numerically. Through comprehensive numerical evaluations, we demonstrate the superiority of the proposed uniform bound to the Bonferroni correction-based simultaneous confidence interval under various experimental settings.