An efficient algorithm to detect palindromes in DNA sequences using periodicity transform
Signal Processing - Special section: Advances in signal processing-assisted cross-layer designs
ENIGMA - Enhanced interactive general movement assessment
Expert Systems with Applications: An International Journal
A Sparse Decomposition Method for Periodic Signal Mixtures
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
A hybrid technique for the periodicity characterization of genomic sequence data
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Characterization of BitTorrent swarms and their distribution in the Internet
Computer Networks: The International Journal of Computer and Telecommunications Networking
International Journal of Bioinformatics Research and Applications
Computing and evaluating the body laughter index
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
Computational method for high resolution spectral analysis of fractionated atrial electrograms
Computers in Biology and Medicine
| Hi-index | 35.69 |
This paper presents a method of detecting periodicities in data that exploits a series of projections onto “periodic subspaces”. The algorithm finds its own set of nonorthogonal basis elements (based on the data), rather than assuming a fixed predetermined basis as in the Fourier, Gabor, and wavelet transforms. A major strength of the approach is that it is linear-in-period rather than linear-in-frequency or linear-in-scale. The algorithm is derived and analyzed, and its output is compared to that of the Fourier transform in a number of examples. One application is the finding and grouping of rhythms in a musical score, another is the separation of periodic waveforms with overlapping spectra, and a third is the finding of patterns in astronomical data. Examples demonstrate both the strengths and weaknesses of the method