Wen-wen Tung

The Madden–Julian Oscillation Observed during the TOGA COARE IOP: Global View

Abstract During the TOGA COARE Intensive Observing Period (November 1992–February 1993), two pronounced Madden–Julian oscillation (MJO) events associated with super cloud clusters and westerly wind bursts were observed. This paper presents a global view of the MJOs including the origin of the super clusters in the Indian Ocean, their migration into the Maritime Continent and the TOGA COARE large-scale soundings array (LSA) in the western equatorial Pacific, and their rapid decay over cold water of the eastern Pacific. The structure and evolution of the MJO are examined with emphasis on the coupling between large-scale motion and convection. Because of differences in propagation speeds, the positions of maximum zonal wind perturbations relative to deep convection undergo systematic changes during the travel of the MJO. However, the centers of deep convection always coincide with those of large-scale ascent. The super cloud cluster accompanies a wide area of warm air in the upper troposphere. Over the warm ...

Denoising Nonlinear Time Series by Adaptive Filtering and Wavelet Shrinkage: A Comparison

Time series measured in real world is often nonlinear, even chaotic. To effectively extract desired information from measured time series, it is important to preprocess data to reduce noise. In this Letter, we propose an adaptive denoising algorithm. Using chaotic Lorenz data and calculating root-mean-square-error, Lyapunov exponent, and correlation dimension, we show that our adaptive algorithm more effectively reduces noise in the chaotic Lorenz system than wavelet denoising with three different thresholding choices. We further analyze an electroencephalogram (EEG) signal in sleep apnea and show that the adaptive algorithm again more effectively reduces the Electrocardiogram (ECG) and other types of noise contaminated in EEG than wavelet approaches.

Facilitating Joint Chaos and Fractal Analysis of Biosignals through Nonlinear Adaptive Filtering

Background Chaos and random fractal theories are among the most important for fully characterizing nonlinear dynamics of complicated multiscale biosignals. Chaos analysis requires that signals be relatively noise-free and stationary, while fractal analysis demands signals to be non-rhythmic and scale-free. Methodology/Principal Findings To facilitate joint chaos and fractal analysis of biosignals, we present an adaptive algorithm, which: (1) can readily remove nonstationarities from the signal, (2) can more effectively reduce noise in the signals than linear filters, wavelet denoising, and chaos-based noise reduction techniques; (3) can readily decompose a multiscale biosignal into a series of intrinsically bandlimited functions; and (4) offers a new formulation of fractal and multifractal analysis that is better than existing methods when a biosignal contains a strong oscillatory component. Conclusions The presented approach is a valuable, versatile tool for the analysis of various types of biological signals. Its effectiveness is demonstrated by offering new important insights into brainwave dynamics and the very high accuracy in automatically detecting epileptic seizures from EEG signals.

Convective Momentum Transport Observed during the TOGA COARE IOP. Part I: General Features

Abstract The momentum budget residual X = (X, Y) is estimated with objectively analyzed soundings taken during the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) intense observing period (IOP; November 1992–February 1993) to study the effects of convective momentum transport (CMT) over the western Pacific warm pool. The time series of X and Y exhibit multiscale temporal behavior, showing modulations by the Madden–Julian oscillation (MJO) and other disturbances. The power spectra of X, Y, and ITBB (an index of convective activity) are remarkably similar, showing peaks near 10, 4–5, and 2 days, and at the diurnal period, suggesting a link between deep cumulus convection and the acceleration–deceleration of the large-scale horizontal motion, via CMT, which is being modulated by various atmospheric disturbances. The temporal behavior of X and Y can be described as fractals from 1/4 to ∼20 and from 1/4 to ∼16 days, respectively. Their fractal characteristics are ...

Detection of low observable targets within sea clutter by structure function based multifractal analysis

Sea clutter is the backscattered returns from a patch of the sea surface illuminated by a radar pulse. Robust detection of targets within sea clutter may strengthen coastal security, improve navigation safety, and help environmental monitoring. However, no simple and reliable methods for detecting targets within sea clutter have been proposed. We introduce the structure function based multifractal theory to analyze 392 sea clutter datasets measured under various sea and weather conditions. It is found that sea clutter data exhibit multifractal behaviors in the time scale range of about 0.01 s to a few seconds, especially for data with targets. The fractal and multifractal features of sea clutter enable us to develop a simple and effective method to detect targets within sea clutter. It is shown that the method achieves very high detection accuracy. It is further shown that in the time scale range of 0.01 s to a few seconds, sea clutter data is weakly nonstationary. The nonstationarity may explain why modeling using distributions such as Weibull, log-normal, K, and compound-Gaussian only offers limited understanding of the physics of sea clutter and is not very effective in detecting targets within sea clutter.

Convective Momentum Transport Observed during the TOGA COARE IOP. Part II: Case Studies

Abstract Convective momentum transport (CMT) associated with the Madden–Julian oscillation (MJO), tropical waves, squall and nonsquall mesoscale convective systems (MCSs), and the diurnal cycle is studied by examining the momentum budget residual X = (X, Y) deduced from the objectively analyzed in situ observations during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) intensive observing period (IOP; November 1992–February 1993). Using wavelet transform, time evolution of signals of these disturbances in the time series of |X| and ITBB (an index for deep convection), averaged over the intensive flux array (IFA), is analyzed. Signals of disturbances with periods ≥1 day in |X| generally evolve in phase with those in ITBB. During the convective phase of MJO, signals in both |X| and ITBB with shorter periods are also enhanced. Frequency distribution of IFA-mean E = −v · X in the troposphere is examined. The mean E is positive, that is, kinetic energy (K) transfe...

Complexity measures of brain wave dynamics

To understand the nature of brain dynamics as well as to develop novel methods for the diagnosis of brain pathologies, recently, a number of complexity measures from information theory, chaos theory, and random fractal theory have been applied to analyze the EEG data. These measures are crucial in quantifying the key notions of neurodynamics, including determinism, stochasticity, causation, and correlations. Finding and understanding the relations among these complexity measures is thus an important issue. However, this is a difficult task, since the foundations of information theory, chaos theory, and random fractal theory are very different. To gain significant insights into this issue, we carry out a comprehensive comparison study of major complexity measures for EEG signals. We find that the variations of commonly used complexity measures with time are either similar or reciprocal. While many of these relations are difficult to explain intuitively, all of them can be readily understood by relating these measures to the values of a multiscale complexity measure, the scale-dependent Lyapunov exponent, at specific scales. We further discuss how better indicators for epileptic seizures can be constructed.

Characterizing heart rate variability by scale-dependent Lyapunov exponent

Previous studies on heart rate variability (HRV) using chaos theory, fractal scaling analysis, and many other methods, while fruitful in many aspects, have produced much confusion in the literature. Especially the issue of whether normal HRV is chaotic or stochastic remains highly controversial. Here, we employ a new multiscale complexity measure, the scale-dependent Lyapunov exponent (SDLE), to characterize HRV. SDLE has been shown to readily characterize major models of complex time series including deterministic chaos, noisy chaos, stochastic oscillations, random 1/f processes, random Levy processes, and complex time series with multiple scaling behaviors. Here we use SDLE to characterize the relative importance of nonlinear, chaotic, and stochastic dynamics in HRV of healthy, congestive heart failure, and atrial fibrillation subjects. We show that while HRV data of all these three types are mostly stochastic, the stochasticity is different among the three groups.

Multiscale Analysis of Heart Rate Variability: A Comparison of Different Complexity Measures

Heart rate variability (HRV) is an important dynamical variable of the cardiovascular function. There have been numerous efforts to determine whether HRV dynamics are chaotic or random, and whether certain complexity measures are capable of distinguishing healthy subjects from patients with certain cardiac disease. In this study, we employ a new multiscale complexity measure, the scale-dependent Lyapunov exponent (SDLE), to characterize the relative importance of nonlinear, chaotic, and stochastic dynamics in HRV of healthy, congestive heart failure (CHF), and atrial fibrillation subjects. We show that while HRV data of all these three types are mostly stochastic, the stochasticity is different among the three groups. Furthermore, we show that for the purpose of distinguishing healthy subjects from patients with CHF, features derived from SDLE are more effective than other complexity measures such as the Hurst parameter, the sample entropy, and the multiscale entropy.

Inertia and memory in ambiguous visual perception

Perceptual multistability during ambiguous visual perception is an important clue to neural dynamics. We examined perceptual switching during ambiguous depth perception using a Necker cube stimulus, and also during binocular rivalry. Analysis of perceptual switching time series using variance–sample size analysis, spectral analysis and time series shuffling shows that switching times behave as a 1/f noise and possess very long range correlations. The long memory feature contrasts sharply with the traditional satiation models of multistability, where the memory is not incorporated, as well as with recently published models of multistability and neural processing, where memory is excluded. On the other hand, the long memory feature favors the concept of “dynamic core” or coalition of neurons, where neurons form transient coalitions. Perceptual switching then corresponds to replacement of one coalition of neurons by another. The inertia and memory measures the stability of a coalition: a strong and stable coalition has to be won over by another similarly strong and stable coalition, resulting in long switching times. The complicated transient dynamics of competing coalitions of neurons may be addressable using a combination of functional imaging, measurement of frequency-tagged magnetoencephalography and frequency-tagged encephalography, simultaneous recordings of groups of neurons in many areas of the brain, and concepts from statistical mechanics and nonlinear dynamics theory.