Stochastic to deterministic crossover of fractal dimension for a Langevin equation

Using algorithms of Higuchi and of Grassberger and Procaccia, we study numerically how fractal dimensions cross over from finite-dimensional Brownian noise at short time scales to finite values of deterministic chaos at longer time scales for data generated from a Langevin equation that has a strange attractor in the limit of zero noise. Our results suggest that the crossover occurs at such short time scales that there is little chance of finite-dimensional Brownian noise being incorrectly identified as deterministic chaos.Comment: 12 pages including 3 figures, RevTex and epsf. To appear Phys. Rev. E, April, 199

Don't bleach chaotic data

A common first step in time series signal analysis involves digitally filtering the data to remove linear correlations. The residual data is spectrally white (it is ``bleached''), but in principle retains the nonlinear structure of the original time series. It is well known that simple linear autocorrelation can give rise to spurious results in algorithms for estimating nonlinear invariants, such as fractal dimension and Lyapunov exponents. In theory, bleached data avoids these pitfalls. But in practice, bleaching obscures the underlying deterministic structure of a low-dimensional chaotic process. This appears to be a property of the chaos itself, since nonchaotic data are not similarly affected. The adverse effects of bleaching are demonstrated in a series of numerical experiments on known chaotic data. Some theoretical aspects are also discussed.Comment: 12 dense pages (82K) of ordinary LaTeX; uses macro psfig.tex for inclusion of figures in text; figures are uufile'd into a single file of size 306K; the final dvips'd postscript file is about 1.3mb Replaced 9/30/93 to incorporate final changes in the proofs and to make the LaTeX more portable; the paper will appear in CHAOS 4 (Dec, 1993

NACHOS, a CubeSat-Based High-Resolution UV-Visible Hyperspectral Imager for Remote Sensing of Trace Gases: System Overview, Science Objectives, and Preliminary Results

The Nano-satellite Atmospheric Chemistry Hyperspectral Observation System (NACHOS) is a high-throughput (f/2.9), high spectral resolution (1.3 nm optical, 0.57 nm sampling) hyperspectral imager covering the 300-500 nm spectral region with 350 spectral bands. The combined 1.5U instrument payload and 1.5U spacecraft bus comprise a 3U CubeSat. Spectroscopically similar to NASA’s Ozone Monitoring Instrument (OMI), which provides wide-field coverage at ~20 km spatial resolution, NACHOS offers complementary targeted measurements at far higher spatial resolution of ~0.4 km/pixel from 500 km altitude over its 15 ̊ across-track field of view. NACHOS incorporates highly streamlined onboard gas-retrieval algorithms, alleviating the need to routinely downlink massive hyperspectral data cubes. This paper discusses the instrument design, requirements leading to it, preliminary results, and science goals, including monitoring NO2 as a proxy for anthropogenic greenhouse gases, low-level degassing of SO2 and halogen oxides at pre-eruptive volcanoes, and formaldehyde from wildfires. Aiming for an eventual many-satellite constellation providing both high spatial resolution and frequent target revisits, the current NACHOS project is launching two CubeSats, the first already launched to the International Space Station aboard the NG-17 Cygnus vehicle on February 19, 2022 and awaiting deployment to its final orbit in June, and the second launching June 29, 2022

Alcator C-Mod: research in support of ITER and steps beyond

This paper presents an overview of recent highlights from research on Alcator C-Mod. Significant progress has been made across all research areas over the last two years, with particular emphasis on divertor physics and power handling, plasma–material interaction studies, edge localized mode-suppressed pedestal dynamics, core transport and turbulence, and RF heating and current drive utilizing ion cyclotron and lower hybrid tools. Specific results of particular relevance to ITER include: inner wall SOL transport studies that have led, together with results from other experiments, to the change of the detailed shape of the inner wall in ITER; runaway electron studies showing that the critical electric field required for runaway generation is much higher than predicted from collisional theory; core tungsten impurity transport studies reveal that tungsten accumulation is naturally avoided in typical C-Mod conditions.United States. Department of Energy (DE-FC02-99ER54512-CMOD)United States. Department of Energy (DE-AC02-09CH11466)United States. Department of Energy (DE-FG02-96ER-54373)United States. Department of Energy (DE-FG02-94ER54235

Approximate entropy detects the effect of a secondary cognitive task on postural control in healthy young adults: a methodological report

<p>Abstract</p> <p>Background</p> <p>Biomechanical measures of postural stability, while generally useful in neuroscience and physical rehabilitation research, may be limited in their ability to detect more subtle influences of attention on postural control. Approximate entropy (ApEn), a regularity statistic from nonlinear dynamics, recently has demonstrated relatively good measurement precision and shown promise for detecting subtle change in postural control after cerebral concussion. Our purpose was to further explore the responsiveness of ApEn by using it to evaluate the immediate, short-term effect of secondary cognitive task performance on postural control in healthy, young adults.</p> <p>Methods</p> <p>Thirty healthy, young adults performed a modified version of the Sensory Organization Test featuring single (posture only) and dual (posture plus cognitive) task trials. ApEn values, root mean square (RMS) displacement, and equilibrium scores (ES) were calculated from anterior-posterior (AP) and medial-lateral (ML) center of pressure (COP) component time series. For each sensory condition, we compared the ability of the postural control parameters to detect an effect of cognitive task performance.</p> <p>Results</p> <p>COP AP time series generally became more random (higher ApEn value) during dual task performance, resulting in a main effect of cognitive task (p = 0.004). In contrast, there was no significant effect of cognitive task for ApEn values of COP ML time series, RMS displacement (AP or ML) or ES.</p> <p>Conclusion</p> <p>During dual task performance, ApEn revealed a change in the randomness of COP oscillations that occurred in a variety of sensory conditions, independent of changes in the amplitude of COP oscillations. The finding expands current support for the potential of ApEn to detect subtle changes in postural control. Implications for future studies of attention in neuroscience and physical rehabilitation are discussed.</p

Ventilatory Chaos Is Impaired in Carotid Atherosclerosis

Ventilatory chaos is strongly linked to the activity of central pattern generators, alone or influenced by respiratory or cardiovascular afferents. We hypothesized that carotid atherosclerosis should alter ventilatory chaos through baroreflex and autonomic nervous system dysfunctions. Chaotic dynamics of inspiratory flow was prospectively evaluated in 75 subjects undergoing carotid ultrasonography: 27 with severe carotid stenosis (>70%), 23 with moderate stenosis (<70%), and 25 controls. Chaos was characterized by the noise titration method, the correlation dimension and the largest Lyapunov exponent. Baroreflex sensitivity was estimated in the frequency domain. In the control group, 92% of the time series exhibit nonlinear deterministic chaos with positive noise limit, whereas only 68% had a positive noise limit value in the stenoses groups. Ventilatory chaos was impaired in the groups with carotid stenoses, with significant parallel decrease in the noise limit value, correlation dimension and largest Lyapunov exponent, as compared to controls. In multiple regression models, the percentage of carotid stenosis was the best in predicting the correlation dimension (p<0.001, adjusted R2: 0.35) and largest Lyapunov exponent (p<0.001, adjusted R2: 0.6). Baroreflex sensitivity also predicted the correlation dimension values (p = 0.05), and the LLE (p = 0.08). Plaque removal after carotid surgery reversed the loss of ventilatory complexity. To conclude, ventilatory chaos is impaired in carotid atherosclerosis. These findings depend on the severity of the stenosis, its localization, plaque surface and morphology features, and is independently associated with baroreflex sensitivity reduction. These findings should help to understand the determinants of ventilatory complexity and breathing control in pathological conditions

Uncorrelated versus independent elliptically-contoured distributions for anomalous change detection in hyperspectral imagery

The detection of actual changes in a pair of images is confounded by the inadvertent but pervasive differences that inevitably arise whenever two pictures are taken of the same scene, but at different times and under different conditions. These differences include effects due to illumination, calibration, misregistration, etc. If the actual changes are assumed to be rare, then one can 'learn' what the pervasive differences are, and can identify the deviations from this pattern as the anomalous changes. A recently proposed framework for anomalous change detection recasts the problem as one of binary classification between pixel pairs in the data and pixel pairs that are independently chosen from the two images. When an elliptically-contoured (EC) distribution is assumed for the data, then analytical expressions can be derived for the measure of anomalousness of change. However, these expression are only available for a limited class of EC distributions. By replacing independent pixel pairs with uncorrelated pixel pairs, an approximate solution can be found for a much broader class of EC distributions. The performance of this approximation is investigated analytically and empirically, and includes experiments comparing the detection of real changes in real data

Resampling approach for anomaly detection in multispectral images

We propose a novel approach for identifying the 'most unusual' samples in a data set, based on a resampling of data attributes. The resampling produces a 'background class' and then binary classification is used to distinguish the original training set from the background. Those in the training set that are most like the background (i e, most unlike the rest of the training set) are considered anomalous. Although by their nature, anomalies do not permit a positive definition (if I knew what they were, I wouldn't call them anomalies), one can make 'negative definitions' (I can say what does not qualify as an interesting anomaly). By choosing different resampling schemes, one can identify different kinds of anomalies. For multispectral images, anomalous pixels correspond to locations on the ground with unusual spectral signatures or, depending on how feature sets are constructed, unusual spatial textures

Automated Image Registration (AIR) of MTI Imagery

This paper describes an algorithm for the registration of imagery collected by the Multispectral Thermal Imager (MTI). The Automated Image Registration (AIR) algorithm is entirely image-based and is implemented in an automated fashion, which avoids any requirement for human interaction. The AIR method differs from the 'direct georeferencing' method used to create our standard coregistered product since explicit information about the satellite's trajectory and the sensor geometry are not required. The AIR method makes use of a maximum cross-correlation (MCC) algorithm, which is applied locally about numerous points within any two images being compared. The MCC method is used to determine the row and column translations required to register the bands of imagery collected by a single SCA (band-to-band registration), and the raw and column translations required to regisler the imagery collected by the three SCAs for any individual band (SCA-to-SCA registration). Of particular note is the use of reciprocity and a weighted least squares approach to obtaining the band-to-band registration shifts. Reciprocity is enforced by using the MCC method to determine the row and column translations between all pair-wise combinations of bands. This information is then used in a weighted least squares approach to determine the optimum shift values between an arbitrarily selected reference band and the other 15 bands. The individual steps of the AIR methodology, and the results of registering MTI imagery through use of this algorithm, are described