removal of artifacts from bio-signals is a necessary step before automatic processing and obtaining clinical information. recently, many applications of empirical mode decomposition (emd) on biomedical researches have been presented that artifact reduction from bio-signals is one of them. emd separates a time series into finite numbers of its individual oscillations, which are called intrinsic ...

Binary Decision Diagrams (BDDs) are the state-of-the-art data structure in VLSI CAD. Since their size largely depends on the chosen variable ordering, dynamic variable reordering methods, like sifting, often have to be applied while the BDD for a given circuit is constructed. Usually sifting is called each time a given node limit is reached and it is therefore called frequently during the const...

This study introduces a new approach based on Bidimensional Empirical Mode Decomposition (BEMD) to extract texture features at multiple scales or spatial frequencies. Moreover, it can resolve the intrawave frequency modulation provided the frequency modulation. This decomposition, obtained by the bidimensional sifting process, plays an important role in the characterization of regions in textur...

This paper presents two different approaches in color image decomposition domain with Bidimensional Empirical Mode Decomposition (BEMD). The first approach applies the BEMD on each channel separately and the second is based on interpolation of each channel in the sifting process. The comparison of two approaches shows the same performance of each approach in terms of visual quality, but they do...

Multi-valued decision diagrams (MDDs) are a generalization of binary decision diagrams (BDDs). They are suitable for several applications in synthesis and verification of integrated circuits since often, functions with multi-valued input variables can be represented efficiently by MDDs. Their sizes counted in number of nodes vary from linear to exponential dependent on the variable ordering use...

This paper presents two proposed approaches to color image decomposition with Bidimensional Empirical Mode Decomposition (BEMD) technique. The first one applies the BEMD on each channel separately and the second is based on interpolation of each channel in the sifting process. The application of the two methods shows the same performance of each approach in terms of PSNR and visual quality, but...

Discrete functions are now commonly represented by binary (BDD) and multiple-valued (MDD) decision diagrams. Sifting is an effective heuristic technique which applies adjacent variable interchanges to find a good variable ordering to reduce the size of a BDD or MDD. Linear sifting is an extension of BDD sifting where XOR operations involving adjacent variable pairs augment adjacent variable int...