Abstract
Digital images are increasingly being used as steganographic covers for secret communication. The Least Significant Bit (LSB) encoding is one of the most widely used methods for embedding a message in a digital image. However, the direct application of LSB encoding is vulnerable to steganalysis. For example, RS steganalysis is very efficient in detecting the presence of a message in a digital image and to estimate its approximate size. This paper presents a method robust to RS steganalysis, that makes the presence of a message unnoticeable. The method is based on the application of reversible histogram transformation functions to the image, before and after embedding the secret message. The method was tested on 4 greyscale images, with messages of 10%, 30% and 90% of the maximum embedding size. The proposed method proved to be effective in eluding RS steganalysis for all cases tested.

Abstract
Among the different materials studied for ferroelectric applications, SrBi2Ta2O9 (SBT) and SrBi2Nb2O9 (SBN) have deserved a particular interest for the production of non-volatile random access memories (NVFRAMs) due to their fatigue endurance propel-ties. However, the high temperatures required for obtaining these Aurivillius compounds in a straightforward way make necessary to search alternative procedures to synthesize them. In this work the approach of using SBT seeds to improve the synthesis of SBT thin films by a sol-gel procedure is studied and their effects on the thin film properties are evaluated. XRD and SEM analysis of the obtained seeded and unseeded thin films, annealed at different temperatures, show that the use of SBT seeds lowers the crystallization temperature of the perovskite phase and affects the thin film microstructure inducing the development of well defined and more elongated grains. Dielectric and ferroelectric properties are also improved by the seeding procedure. The characteristics of the seeded films annealed at 720 degrees C for 20 min show that the present technique has the advantage of requiring smoother annealing conditions than currently reported methods for producing films with comparable characteristics.

Abstract
Reconfigurable computing has already confirmed a significant potential for accelerating certain computing tasks. However, the most successful applications relied on user expertise to design a specific architecture implemented by the hardware structures of the reconfigurable computing device. Hence, one of the most challenging issues is to map, efficiently and automatically, computations (described in software programming languages) to reconfigurable computing devices. This paper presents CHIADO, a research project aiming a compiler framework to map efficiently software programs to reconfigurable computing platforms, especially the ones based on FPGA (Field-Programmable Gate Array) devices. The framework is also intended to support research of new optimization techniques. The project, based on our previous work on compiling Java bytecodes to FPGAs, focuses on high-performance solutions, schemes to estimate the impact of some transformations supported by the compiler (partial/full loop unrolling), and schemes to take advantage of dynamic reconfiguration (e.g., temporal partitioning). This paper gives an overview about the CHIADO project, shows the framework, and enumerates the main project goals.

Abstract
Image classification is often used to extract information from multi-spectral satellite images. Unsupervised methods can produce results well adjusted to the data, but that are usually difficult to assess. The purpose of this work was to evaluate the Xu internal similarity index ability to estimate the natural number of classes in multi-spectral satellite images. The performance of the index was initially tested with data produced synthetically. Four Landsat TM image sections were then used to evaluate the index. The test images were classified into a large number of classes, using the unsupervised algorithm ISODATA, which were subsequently structured hierarchically. The Xu index was used to identify the optimum partition for each test image. The results were analysed in the context of the land cover types expected for each location.

Abstract
The von Neumann-style architectures have been tremendously well succeeded by taking advantage of the Moore's law. It is now understood that, it will be very difficult to meet the supercomputing demands of the future computing systems with this style of microprocessor architectures. Most nowadays applications require high-performance for processing data streams. Being dataflow computing a natural paradigm to process data streams, architectures based on dataflow principles are emerging as a way to meet the supercomputing demands. Data-driven arrays, introduced in the 80's, are examples of such architectures. They devised a scalable and effective fashion to directly support the dataflow model of computation and have been revived by a number of reconfigurable architectures (e.g., KressArray, WaveScalar, and XPP). Those coarse-grained reconfigurable architectures with dataflow semantics depict interesting achievements with respect to performance and programming methodologies, when compared to other computing platforms. This paper presents the most interesting data-driven array architectures. Trends and open issues related to a number of properties at architectural level and to compilation techniques are enumerated and discussed. A number of features are illustrated, especially the support for hardware virtualization, speculative configuration, and software pipelining. Examples using the PACT XPP reconfigurable array are shown. Those examples include the ADPCM decoder, from the MediaBench repository, and LeeDCT, an optimized DCT algorithm.

Abstract