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Terahertz reflection imaging is considered as a potential diagnostic tool for the investigation of polymethacrylimide foam defects. Using terahertz time-domain spectroscopy (TDS) and detection methods based on terahertz spectroscopic analysis, the reflection imaging results of different thicknesses of polymethacrylimide foam with two kinds of detections (cracks and voids) are studied. The samples (Degussa Rohacell WF71) are planar slabs of polymethacrylimide foams with thicknesses of 35 mm, 60.5 mm and 10 mm. It is found that the same kinds of polymethacrylimide foam defects with different foam thicknesses have similar spectral characteristics, with marked differences only in the amplitude and phase of the reflected wave. In view of this, we focus our study on the defective spectral characteristics of one of the foams (35 mm thickness). The characteristics of void and crack defects are analyzed in the time domain, which is based mainly on the variation of the reflected waveform. In particular, the imaging and clear identification of voids of less than 2.4 mm in diameter, fine cracks (0.3 mm wide), and the quantification of defects can be readily achieved using the terahertz non-destructive testing technique described here.

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Biometric identification services have been applied to almost all aspects of life. However, how to securely and efficiently identify an individual in a huge biometric dataset is still very challenging. For one thing, biometric data is very sensitive and should be kept secure during the process of biometric identification. On the other hand, searching a biometric template in a large dataset can be very time-consuming, especially when some privacy-preserving measures are adopted. To address this problem, we propose an efficient and privacy-preserving biometric identification scheme based on the FITing-tree, iDistance, and a symmetric homomorphic encryption (SHE) scheme with two cloud servers. With our proposed scheme, the privacy of the user’s identification request and service provider’s dataset is guaranteed, while the computational costs of the cloud servers in searching the biometric dataset can be kept at an acceptable level. Detailed security analysis shows that the privacy of both the biometric dataset and biometric identification request is well protected during the identification service. In addition, we implement our proposed scheme and compare it to a previously reported M-Tree based privacy-preserving identification scheme in terms of computational and communication costs. Experimental results demonstrate that our proposed scheme is indeed efficient in terms of computational and communication costs while identifying a biometric template in a large dataset.