Using Curvelet transform for watermarking based on amplitude modulation

In this paper, we propose a precise and robust watermarking scheme based on the technique called amplitude modulation. A watermark is embedded in a color image by modifying the pixel values in the blue channel. At the receiver, the watermark bits are retrieved using a prediction system, by a linear combination of nearby pixel values around the embedded pixels, and without having the original image. Because amplitude modulation is a spatial-domain watermarking method, it may not be robust enough, i.e., incapable of exact watermark retrieval. In order to enhance the bit retrieval, we apply a Gaussian mask to equalize the luminance intensity; we employ the pixel value replacing technique to enhance the prediction performance, and we use two additional bits as a geometrical reference. In addition, we demonstrate that choosing an improper location (like singularities) for watermarking will lead the prediction system to malfunction. In order to increase the robustness, we propose using the Curvelet transform to detect singularities such as lines and curves and to prevent the system from using these locations in an image for embedding the watermark bits. The experimental results indicate that our proposed method has a better performance in comparison with two other similar approaches, and in addition, it is robust against various geometrical and nongeometrical attacks as well as having a good imperceptibility.     

Blind channel equalization using chirp modulating signals

This paper addresses the problem of blind channel equalization in the context of digital communications. Recent results have shown that certain operations applied to the source signal at the transmitter help in the blind identification and equalization of the channel at the receiver. In this paper, the baseband data signal is multiplied with a chirp sequence. Exploiting certain structural properties arising from this operation, a batch-type algorithm is obtained for calculating the equalizer's coefficients. Conditions on the chirp sequence parameters are obtained that guarantee an equalization solution. A low-complexity adaptive algorithm is also proposed. Finally, extensive simulations, and comparisons with other well-known blind techniques, illustrate the excellent performance of this algorithm.     

Verocytotoxigenic Escherichia coli O157 in beef and sheep abattoirs in Ireland and characterisation of isolates by Pulsed-Field Gel Electrophoresis and Multi-Locus Variable Number of Tandem Repeat Analysis

This study aimed to investigate verocytotoxigenic Escherichia coli O157 in the largest beef and sheep slaughter plants in Ireland over a one-year period. Samples consisted of pooled rectal swabs (n = 407) and pooled carcass swabs (n = 407) from 5 animals belonging to the same herd or flock and minced meat (n = 91) from the same sampling date. E. coli O157 isolates were characterised using PCR for a range of genes, i.e. 16S, rfbE, fliC, vtx1, vtx2, eaeA and confirmed VTEC O157 isolates were tested for antimicrobial susceptibility and typed using Pulsed-Field Gel Electrophoresis (PFGE) and Multi-Locus Variable Number of Tandem Repeat Analysis (MLVA). VTEC O157 was isolated from 7.6% and 3.9% of bovine rectal and carcass swab samples and from 5.8% and 2.9% of ovine rectal and carcass swab samples respectively. None of the bovine minced meat samples (n = 77) and only one of the 14 ovine minced meat samples was positive for VTEC O157. Following PFGE and MLVA, cross contamination from faeces to carcasses was identified. While PFGE and MLVA identified the same clusters for highly related strains, MLVA discriminated better than PFGE in addition to being more rapid and less labour intensive. Results showed that cattle and sheep presented for slaughter in Ireland harbour VTEC O157, and although the levels entering the food chain are low, this should not be overlooked as possible sources of zoonotic infection; molecular typing was able to demonstrate relationships among strains and could be used to elucidate the sources of human infection.