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With the rapid development of network technology, concerns pertaining to the enhancement of security and protection against violations of digital images have become critical over the past decade. In this paper, an image copy detection scheme based on the Inception convolutional neural network (CNN) model in deep learning is proposed. The image dataset is transferred by a number of image processing manipulations and the feature values in images are automatically extracted for learning and detecting the suspected unauthorized digital images. The experimental results show that the proposed scheme takes on an extraordinary role in the process of detecting duplicated images with rotation, scaling, and other content manipulations. Moreover, the mechanism of detecting duplicate images via a convolutional neural network model with different combinations of original images and manipulated images can improve the accuracy and efficiency of image copy detection compared with existing schemes.

Based on the investigation of spatial variations of impact-echo signals for different source and receiver locations, a simple multisensor data fusion strategy is proposed to increase the accuracy of nondestructive evaluation of concrete structures using the impact-echo test. The data fusion strategy fuses the ratios between spectral amplitudes of the delamination and concrete bottom echo signals (D/B ratios) from multiple source-receiver arrays. The fused D/B ratios demonstrate different characteristics for test locations above the delamination, above the sound concrete, and across the delamination boundary. These characteristics can be used to accurately locate the delamination and its boundaries without increasing testing time. The applicability of the multisensor data fusion for impact-echo testing is validated using both numerical simulation and experimental testings.

Air-coupled ultrasonic non-destructive testing (NDT) provides an ideal solution for rapid scanning of large specimens. Unfortunately, despite decades of research, many challenges remain to render air-coupled ultrasonic methods a broadly effective sensing modality for high impedance materials due to low energy transmission between air and the solids being inspected. In this paper, we present experimental results and theoretical analysis of an electrical spark source focused by an ellipsoidal reflector. This acoustic source, which generates a short duration, high amplitude signal in air, is of high interest for air-coupled NDT for high impedance materials and has been shown to excite wave motion in concrete without contact. Theoretical modeling using weak shock theory and the KZK equation is used to predict the temporal and spatial features of the pressure field in the region of the geometric focus. We also present a series of experimental studies that characterize the spark generated acoustic wave in both free-field and the focused conditions. The bandwidth and directivity of the focused spark source are shown to be adjustable by changing the spark gap size and the reflector geometry. Finally, experimental results from three reflectors made of different material and geometries are presented.