A hybrid fractal video compression method

Fractal image compression is a relatively recent image compression method. Its extension to a sequence of motion images is important in video compression applications. There are two basic fractal compression methods, namely the cube-based and the frame-based methods, being commonly used in the industry. However there are advantages and disadvantages in both methods. This paper proposes a hybrid algorithm highlighting the advantages of the two methods in order to produce a good compression algorithm for video industry. Experimental results show the hybrid algorithm improves the compression ratio and the quality of decompressed images.

Adaptive partition and hybrid method in fractal video compression

Fractal image compression is a relatively recent image compression method, which is simple to use and often leads to a high compression ratio. These advantages make it suitable for the situation of a single encoding and many decoding, as required in video on demand, archive compression, etc. There are two fundamental fractal compression methods, namely, the cube-based and the frame-based methods, being commonly studied. However, there are advantages and disadvantages in both methods. This paper gives an extension of the fundamental compression methods based on the concept of adaptive partition. Experimental results show that the algorithms based on adaptive partition may obtain a much higher compression ratio compared to algorithms based on fixed partition while maintaining the quality of decompressed images.

A hybrid Laplace transform/finite difference boundary element method for diffusion problems

The solution process for diffusion problems usually involves the time development separately from the space solution. A finite difference algorithm in time requires a sequential time development in which all previous values must be determined prior to the current value. The Stehfest Laplace transform algorithm, however, allows time solutions without the knowledge of prior values. It is of interest to be able to develop a time-domain decomposition suitable for implementation in a parallel environment. One such possibility is to use the Laplace transform to develop coarse-grained solutions which act as the initial values for a set of fine-grained solutions. The independence of the Laplace transform solutions means that we do indeed have a time-domain decomposition process. Any suitable time solver can be used for the fine-grained solution. To illustrate the technique we shall use an Euler solver in time together with the dual reciprocity boundary element method for the space solution

A move toward hybrid interregionalism in Asia

The chapter provides a critical overview of European engagement with Asia, and reviews the contemporary economic cooperation and trade relations between the two regions. The chapter concludes with a critical assessment of the nature of EU interregionalism and speculates on the influence and actorness of the European Union in Asia.

Two-hybrid analysis of the interaction between the fission yeast proteins Sal3 and Cdc25

Cdc25 is a mitosis triggering phosphatase in Schizosaccharomyces pombe, and is transported in to the nucleus during G2 phase by the importin-β protein Sal3. Cdc25 triggers mitosis and cell division by dephosphorylating tyrosine 15 of Cdc2. In sal3 mutants, Cdc25 is not transported into the nucleus and the cells halt in G2. The purpose of this study is to use a two-hybrid system to determine the nature of the relationship between Sal3 and Cdc25. Previous research has failed to detect any interaction between the two proteins, but specific modifications were made to the two-hybrid system in this study including the separation of Sal3 into its two binding domains, the addition of fluorescent tags to the fusion protein, and the reversal of plasmids in the fusion proteins. Unique PCR primers were successfully designed, based on a multiple alignment of Sal3 and its homologues, to separate Sal3 into its two domains.

A Hybrid Forward Algorithm for RBF Neural Network Construction

This paper proposes a novel hybrid forward algorithm (HFA) for the construction of radial basis function (RBF) neural networks with tunable nodes. The main objective is to efficiently and effectively produce a parsimonious RBF neural network that generalizes well. In this study, it is achieved through simultaneous network structure determination and parameter optimization on the continuous parameter space. This is a mixed integer hard problem and the proposed HFA tackles this problem using an integrated analytic framework, leading to significantly improved network performance and reduced memory usage for the network construction. The computational complexity analysis confirms the efficiency of the proposed algorithm, and the simulation results demonstrate its effectiveness