This paper is contributed to the boundary effect problem of the empirical mode decomposition algorithm, which results in a serious distortion in the EMD sifting process. An adaptive method for processing boundary effect in the empirical mode decomposition sifting process is presented, which has exploited the local timeor spatialscales and the waveform or texture characteristics near boundary of...

The National Institute of Standards and Technology (NIST) high-speed quantum key distribution (QKD) system was designed to include custom hardware to support the generation and management of gigabit data streams. As our photonics improved our software sifting algorithm couldn’t keep up with the amount of data generated. To eliminate this problem we implemented the sifting algorithm into our pro...

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Ordered Binary Decision Diagrams (OBDDs) are so far the most eecient representation scheme for manipulation of Boolean functions in computer-aided design and veriication of digital systems. The concise-ness of an OBDD representation depends on the choice of the variable ordering, and the minimization problem that can be formulated as the problem of nding an ordering that minimizse the OBDD size...

Binary Decision Diagrams (BDDs) are the state-of-the-art data structure in VLSI CAD, especially in sequential verification tasks, like state-space exploration and image computation. Since their size largely depends on the chosen variable ordering, dynamic variable reordering methods, like sifting, often have to be applied while the BDD is constructed. Usually sifting is called each time a given...

A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the p...