The behavior of Gliclazide in solution and in the solid state: a case of organic compound presenting a solid‐solution structure

The structure of the hypoglycemic agent Gliclazide has been studied by ¹H, ¹³C, and ¹⁵N NMR in solution (CDCl₃ and DMSO‐d₆) and in the solid state. In the solid state, the compound crystallizes as an EZ isomer without dynamic properties. In CDCl₃ solution, the structure is still EZ but with a slow nitrogen inversion about the pyrrolidine nitrogen: two invertomers have been observed and characterized. In DMSO‐d₆, the rate is faster and only averaged signals were observed. GIAO calculated absolute shieldings were used to confirm the nature of the observed species. In the solid state, Gliclazide presents the phenomenon of solid‐solution with two disordered conformations present in the crystal at a 90:10 ratio. Copyright © 2009 John Wiley & Sons, Ltd.     

Disorder-dependent photoluminescence in Ba0.8Ca0.2TiO3 at room temperature

The photoluminescence (PL) emission in structurally disordered Ba_0.8Ca_0.2TiO₃ (BCT20) powders was observed at room temperature with laser excitation at lines 355 and 460 nm. The structural evolution perovskite-like titanate BCT20 powders prepared by a soft chemical processing at different annealing temperatures were accompanied by X-ray diffraction (XRD) and X-ray absorption near-edge structure (XANES). Intermediate oxycarbonate phase was identified and your influence with PL emission was discarding. BCT20 annealed at 500 °C displays intense PL emission. The results indicate relationship between broad PL band and order-disorder degree.     

An image adaptive, wavelet-based watermarking of digital images

In digital management, multimedia content and data can easily be used in an illegal way—being copied, modified and distributed again. Copyright protection, intellectual and material rights protection for authors, owners, buyers, distributors and the authenticity of content are crucial factors in solving an urgent and real problem. In such scenario digital watermark techniques are emerging as a valid solution. In this paper, we describe an algorithm—called WM2.0—for an invisible watermark: private, strong, wavelet-based and developed for digital images protection and authenticity. Using discrete wavelet transform (DWT) is motivated by good time-frequency features and well-matching with human visual system directives. These two combined elements are important in building an invisible and robust watermark. WM2.0 works on a dual scheme: watermark embedding and watermark detection. The watermark is embedded into high frequency DWT components of a specific sub-image and it is calculated in correlation with the image features and statistic properties. Watermark detection applies a re-synchronization between the original and watermarked image. The correlation between the watermarked DWT coefficients and the watermark signal is calculated according to the Neyman–Pearson statistic criterion. Experimentation on a large set of different images has shown to be resistant against geometric, filtering and StirMark attacks with a low rate of false alarm.     

A new approach to pre-processing digital image for wavelet-based watermark

The growth of the Internet has increased the phenomenon of digital piracy, in multimedia objects, like software, image, video, audio and text. Therefore it is strategic to individualize and to develop methods and numerical algorithms, which are stable and have low computational cost, that will allow us to find a solution to these problems. We describe a digital watermarking algorithm for color image protection and authenticity: robust, not blind, and wavelet-based. The use of Discrete Wavelet Transform is motivated by good time-frequency features and a good match with Human Visual System directives. These two combined elements are important for building an invisible and robust watermark. Moreover our algorithm can work with any image, thanks to the step of pre-processing of the image that includes resize techniques that adapt to the size of the original image for Wavelet transform. The watermark signal is calculated in correlation with the image features and statistic properties. In the detection step we apply a re-synchronization between the original and watermarked image according to the Neyman–Pearson statistic criterion. Experimentation on a large set of different images has been shown to be resistant against geometric, filtering, and StirMark attacks with a low rate of false alarm.