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Synchronization of hyperchaotic circuits using a one-dimensional signal: robustness analysis
"... Abstract: – In this paper an approach to achieve synchronization of hyperchaotic circuits using a proper onedimensional signal is presented. The robustness of the proposed synchronization technique to parameter mismatch and to the presence of channel noise is investigated. The approach is effective ..."
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Abstract: – In this paper an approach to achieve synchronization of hyperchaotic circuits using a proper onedimensional signal is presented. The robustness of the proposed synchronization technique to parameter mismatch and to the presence of channel noise is investigated. The approach
Transcription Factor Binding Site Detection by One-Dimensional Signal
, 2003
"... Introduction The prediction of the regulation of expression of the genes detected in mammalian genomes involves, amongst other things, efficient detection of transcription factor binding site sequence motifs. As is the case for detecting protein-coding regions in genomic DNA, this will probably req ..."
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relative to each other in the motif consensus sequence. Multi-dimensional Fourier analysis has long been used for the detection of the 3-base periodicities representing protein-coding regions in DNA [1], with the DNA sequence coded numerically in 3 or 4 dimensions. This multidimensionality would make
Persistent Homology meets Statistical Inference – A Case Study: Detecting Modes of One-Dimensional Signals∗
, 2014
"... We investigate the problem of estimating persistent homology of noisy one dimensional signals. We relate this to the problem of estimating the number of modes (i.e., local maxima) – a well known question in statistical inference – and we show how to do so without presmoothing the data. To this end, ..."
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We investigate the problem of estimating persistent homology of noisy one dimensional signals. We relate this to the problem of estimating the number of modes (i.e., local maxima) – a well known question in statistical inference – and we show how to do so without presmoothing the data. To this end
2.1 One-Dimensional Signal Processing 2.1.1 Continuous and Discrete One-Dimensional Functions
"... We can’t hope to cover all the important details of one- and twodimensional signal processing in one chapter. For those who have already seen this material, we hope this chapter will serve as a refresher. For those readers who haven’t had prior exposure to signal and image processing, we hope that t ..."
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We can’t hope to cover all the important details of one- and twodimensional signal processing in one chapter. For those who have already seen this material, we hope this chapter will serve as a refresher. For those readers who haven’t had prior exposure to signal and image processing, we hope
VCG-Kelly mechanisms for allocation of divisible goods: Adapting VCG mechanisms to one-dimensional signals
- in Proc. of CISS
, 2006
"... Abstract — The VCG-Kelly mechanism is proposed, which is obtained by composing the communication efficient, onedimensional signaling idea of Kelly with the VCG mechanism, providing efficient allocation for strategic buyers at Nash equilibrium points. It is shown that the revenue to the seller can be ..."
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Cited by 43 (0 self)
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be maximized or minimized using a particular one-dimensional family of surrogate valuation functions. Index Terms— I.
4. TITLE AND SUBTITLE Comparison Study of Unequal Error Protection Methods for One-Dimensional Signal Constellations
, 1995
"... Approved for public release; distribution unlimited. ..."
ral ssBioMed CentNonlinear Biomedical Physics Open AcceMethodology Extracting complexity waveforms from one-dimensional signals
"... Background: Nonlinear methods provide a direct way of estimating complexity of one-dimensional sampled signals through calculation of Higuchi's fractal dimension (1<FD<2). In most cases the signal is treated as being characterized by one value of FD and consequently analyzed as one epoch ..."
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Background: Nonlinear methods provide a direct way of estimating complexity of one-dimensional sampled signals through calculation of Higuchi's fractal dimension (1<FD<2). In most cases the signal is treated as being characterized by one value of FD and consequently analyzed as one epoch
Shiftable Multi-scale Transforms
, 1992
"... Orthogonal wavelet transforms have recently become a popular representation for multiscale signal and image analysis. One of the major drawbacks of these representations is their lack of translation invariance: the content of wavelet subbands is unstable under translations of the input signal. Wavel ..."
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Cited by 562 (36 self)
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in more than one domain. Two examples of jointly shiftable transforms are designed and implemented: a one-dimensional tran...
Singularity Detection And Processing With Wavelets
- IEEE Transactions on Information Theory
, 1992
"... Most of a signal information is often found in irregular structures and transient phenomena. We review the mathematical characterization of singularities with Lipschitz exponents. The main theorems that estimate local Lipschitz exponents of functions, from the evolution across scales of their wavele ..."
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Cited by 595 (13 self)
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study separately. We show that the size of the oscillations can be measured from the wavelet transform local maxima. It has been shown that one and two-dimensional signals can be reconstructed from the local maxima of their wavelet transform [14]. As an application, we develop an algorithm that removes
Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression
, 1988
"... A three-layered neural network is described for transforming two-dimensional discrete signals into generalized nonorthogonal 2-D “Gabor” representations for image analysis, segmentation, and compression. These transforms are conjoint spatial/spectral representations [lo], [15], which provide a comp ..."
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Cited by 478 (8 self)
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A three-layered neural network is described for transforming two-dimensional discrete signals into generalized nonorthogonal 2-D “Gabor” representations for image analysis, segmentation, and compression. These transforms are conjoint spatial/spectral representations [lo], [15], which provide a
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