Reversible data hiding (RDH) is a technology that embeds secret data into a carrier where both the secret data and the carrier can be recovered without any data loss. Inspired by dual images technology, this article proposes to employ a high capacity RDH scheme that is based on turtle shell (TS). We start by constructing a newly designed TS-based reference matrix. Then, two meaningful shadows will be generated after hiding the secret data in the cover image with the reference matrix’s help. Meanwhile, the location conflict problem is solved. On the decoder side, when both shadows are gathered, the data extraction and image recovery can be accomplished using the orientation relationship between two stego pixels that are located at the same coordinates in the two shadows and the reference matrix. Moreover, we introduce a security enhancement technology that improves the security of data extraction. The experiment shows that compared with other state-of-the-art RDH schemes, a higher embedding capacity is achieved by this method, and a good visual quality is retained. Simultaneously, the proposed scheme is effective against attacks on pixel value difference histograms (PDH) and regular singular (RS) analysis.
A new kind of composite buffering material was made by filling the voids of honeycomb paperboard with polyurethane. Drop tests were performed to evaluate the dynamic energy absorption capacity of the material. Based on the tests results, we analyzed the mechanical behaviors of the material under different conditions and obtained the inherent influencing laws of some factors on the material's dynamic buffering performance. It was shown that the dynamic buffering performance varied directly with impact velocity, and inversely with the void diameter, thickness and buffeting area of the composite material. 相似文献
Being a new kind of nanomaterials, aromatic polyamide nanofibers (ANF) have been much highlighted in recent studies. We here demonstrate an isopropyl alcohol (IPA) accelerated chemical cleavage on poly (p-phenylene terephthalamide) chopped fibers, which provides an efficient preparation method of ANF. The comprehensive study on the processes accelerated by different alcohols revealed that the preparation time of ANF in the mixed medium of dimethyl sulfoxide (DMSO)-alcohol (20:1 in volume) was shorten to 45 min and 75 min for methanol (ethanol) and isopropanol, respectively. However, the nanofibers prepared in DMSO-IPA exhibited the minimum in axial and radial dimensions, providing the finest and most uniform diameter of 16 nm. The corresponding ANF films through vacuum assisted filtration also showed the highest tensile strength of 150 MPa, in comparison with those of the ANF films prepared using other alcohols, which were about 110 MPa. Furthermore, ANF/silicon hybrid films were prepared by the ionic ring-opening reaction followed by the alkoxysilane condensation and nanoparticle fabrication. By changing the organo functional groups in the alkoxysilane, the surface of the films were adjustable in a wide contact angle range from 56° (hydrophilic) to 150° (superhydrophobic), suggesting the amendable interfacial properties potential applicable to composite fabrication with most of the resin matrix. 相似文献
Aggregate similarity search, also known as aggregate nearest-neighbor (Ann) query, finds many useful applications in spatial and multimedia databases. Given a group Q of M query objects, it retrieves from a database the objects most similar to Q, where the similarity is an aggregation (e.g., \({{\mathrm{sum}}}\), \(\max \)) of the distances between each retrieved object p and all the objects in Q. In this paper, we propose an added flexibility to the query definition, where the similarity is an aggregation over the distances between p and any subset of \(\phi M\) objects in Q for some support\(0< \phi \le 1\). We call this new definition flexible aggregate similarity search and accordingly refer to a query as a flexible aggregate nearest-neighbor (Fann) query. We present algorithms for answering Fann queries exactly and approximately. Our approximation algorithms are especially appealing, which are simple, highly efficient, and work well in both low and high dimensions. They also return near-optimal answers with guaranteed constant-factor approximations in any dimensions. Extensive experiments on large real and synthetic datasets from 2 to 74 dimensions have demonstrated their superior efficiency and high quality. 相似文献