89 research outputs found
Parallel Algorithm and Dynamic Exponent for Diffusion-limited Aggregation
A parallel algorithm for ``diffusion-limited aggregation'' (DLA) is described
and analyzed from the perspective of computational complexity. The dynamic
exponent z of the algorithm is defined with respect to the probabilistic
parallel random-access machine (PRAM) model of parallel computation according
to , where L is the cluster size, T is the running time, and the
algorithm uses a number of processors polynomial in L\@. It is argued that
z=D-D_2/2, where D is the fractal dimension and D_2 is the second generalized
dimension. Simulations of DLA are carried out to measure D_2 and to test
scaling assumptions employed in the complexity analysis of the parallel
algorithm. It is plausible that the parallel algorithm attains the minimum
possible value of the dynamic exponent in which case z characterizes the
intrinsic history dependence of DLA.Comment: 24 pages Revtex and 2 figures. A major improvement to the algorithm
and smaller dynamic exponent in this versio
Breakdown of Landau Theory in Overdoped Cuprates near the Onset of Superconductivity
We use the functional renormalization group to analyze the temperature
dependence of the quasi-particle scattering rates in the two-dimensional
Hubbard model below half-filling. Using a band structure appropriate to
overdoped Tl2Ba2CuO(6+x) we find a strongly angle dependent term linearly
dependent on temperature which derives from an increasing scattering vertex as
the energy scale is lowered. This behavior agrees with recent experiments and
confirms earlier conclusions on the origin of the breakdown of the Landau Fermi
liquid near the onset of superconductivity.Comment: 4 pages, 5 figures, typos correcte
Optimally Controlling the Timing of Energy Transfer in Elastic Joints: Experimental Validation of the Bi-Stiffness Actuation Concept
Elastic actuation taps into elastic elements' energy storage for dynamic
motions beyond rigid actuation. While Series Elastic Actuators (SEA) and
Variable Stiffness Actuators (VSA) are highly sophisticated, they do not fully
provide control over energy transfer timing. To overcome this problem on the
basic system level, the Bi-Stiffness Actuation (BSA) concept was recently
proposed. Theoretically, it allows for full link decoupling, while
simultaneously being able to lock the spring in the drive train via a
switch-and-hold mechanism. Thus, the user would be in full control of the
potential energy storage and release timing. In this work, we introduce an
initial proof-of-concept of Bi-Stiffness-Actuation in the form of a 1-DoF
physical prototype, which is implemented using a modular testbed. We present a
hybrid system model, as well as the mechatronic implementation of the actuator.
We corroborate the feasibility of the concept by conducting a series of
hardware experiments using an open-loop control signal obtained by trajectory
optimization. Here, we compare the performance of the prototype with a
comparable SEA implementation. We show that BSA outperforms SEA 1) in terms of
maximum velocity at low final times and 2) in terms of the movement strategy
itself: The clutch mechanism allows the BSA to generate consistent launch
sequences while the SEA has to rely on lengthy and possibly dangerous
oscillatory swing-up motions. Furthermore, we demonstrate that providing full
control authority over the energy transfer timing and link decoupling allows
the user to synchronously release both elastic joint and gravitational energy.
This facilitates the optimal exploitation of elastic and gravitational
potentials in a synergistic manner.Comment: 8 pages, 9 figures. Submitted to IEEE Robotics and Automation Letter
Multifractal Dimensions for Branched Growth
A recently proposed theory for diffusion-limited aggregation (DLA), which
models this system as a random branched growth process, is reviewed. Like DLA,
this process is stochastic, and ensemble averaging is needed in order to define
multifractal dimensions. In an earlier work [T. C. Halsey and M. Leibig, Phys.
Rev. A46, 7793 (1992)], annealed average dimensions were computed for this
model. In this paper, we compute the quenched average dimensions, which are
expected to apply to typical members of the ensemble. We develop a perturbative
expansion for the average of the logarithm of the multifractal partition
function; the leading and sub-leading divergent terms in this expansion are
then resummed to all orders. The result is that in the limit where the number
of particles n -> \infty, the quenched and annealed dimensions are {\it
identical}; however, the attainment of this limit requires enormous values of
n. At smaller, more realistic values of n, the apparent quenched dimensions
differ from the annealed dimensions. We interpret these results to mean that
while multifractality as an ensemble property of random branched growth (and
hence of DLA) is quite robust, it subtly fails for typical members of the
ensemble.Comment: 82 pages, 24 included figures in 16 files, 1 included tabl
Long-range correlation and multifractality in Bach's Inventions pitches
We show that it can be considered some of Bach pitches series as a stochastic
process with scaling behavior. Using multifractal deterend fluctuation analysis
(MF-DFA) method, frequency series of Bach pitches have been analyzed. In this
view we find same second moment exponents (after double profiling) in ranges
(1.7-1.8) in his works. Comparing MF-DFA results of original series to those
for shuffled and surrogate series we can distinguish multifractality due to
long-range correlations and a broad probability density function. Finally we
determine the scaling exponents and singularity spectrum. We conclude fat tail
has more effect in its multifractality nature than long-range correlations.Comment: 18 page, 6 figures, to appear in JSTA
Topological self-similarity on the random binary-tree model
Asymptotic analysis on some statistical properties of the random binary-tree
model is developed. We quantify a hierarchical structure of branching patterns
based on the Horton-Strahler analysis. We introduce a transformation of a
binary tree, and derive a recursive equation about branch orders. As an
application of the analysis, topological self-similarity and its generalization
is proved in an asymptotic sense. Also, some important examples are presented
Characterization of Sleep Stages by Correlations of Heartbeat Increments
We study correlation properties of the magnitude and the sign of the
increments in the time intervals between successive heartbeats during light
sleep, deep sleep, and REM sleep using the detrended fluctuation analysis
method. We find short-range anticorrelations in the sign time series, which are
strong during deep sleep, weaker during light sleep and even weaker during REM
sleep. In contrast, we find long-range positive correlations in the magnitude
time series, which are strong during REM sleep and weaker during light sleep.
We observe uncorrelated behavior for the magnitude during deep sleep. Since the
magnitude series relates to the nonlinear properties of the original time
series, while the signs series relates to the linear properties, our findings
suggest that the nonlinear properties of the heartbeat dynamics are more
pronounced during REM sleep. Thus, the sign and the magnitude series provide
information which is useful in distinguishing between the sleep stages.Comment: 7 pages, 4 figures, revte
Effect of Trends on Detrended Fluctuation Analysis
Detrended fluctuation analysis (DFA) is a scaling analysis method used to
estimate long-range power-law correlation exponents in noisy signals. Many
noisy signals in real systems display trends, so that the scaling results
obtained from the DFA method become difficult to analyze. We systematically
study the effects of three types of trends -- linear, periodic, and power-law
trends, and offer examples where these trends are likely to occur in real data.
We compare the difference between the scaling results for artificially
generated correlated noise and correlated noise with a trend, and study how
trends lead to the appearance of crossovers in the scaling behavior. We find
that crossovers result from the competition between the scaling of the noise
and the ``apparent'' scaling of the trend. We study how the characteristics of
these crossovers depend on (i) the slope of the linear trend; (ii) the
amplitude and period of the periodic trend; (iii) the amplitude and power of
the power-law trend and (iv) the length as well as the correlation properties
of the noise. Surprisingly, we find that the crossovers in the scaling of noisy
signals with trends also follow scaling laws -- i.e. long-range power-law
dependence of the position of the crossover on the parameters of the trends. We
show that the DFA result of noise with a trend can be exactly determined by the
superposition of the separate results of the DFA on the noise and on the trend,
assuming that the noise and the trend are not correlated. If this superposition
rule is not followed, this is an indication that the noise and the superimposed
trend are not independent, so that removing the trend could lead to changes in
the correlation properties of the noise.Comment: 20 pages, 16 figure
Effect of nonstationarities on detrended fluctuation analysis
Detrended fluctuation analysis (DFA) is a scaling analysis method used to
quantify long-range power-law correlations in signals. Many physical and
biological signals are ``noisy'', heterogeneous and exhibit different types of
nonstationarities, which can affect the correlation properties of these
signals. We systematically study the effects of three types of
nonstationarities often encountered in real data. Specifically, we consider
nonstationary sequences formed in three ways: (i) stitching together segments
of data obtained from discontinuous experimental recordings, or removing some
noisy and unreliable parts from continuous recordings and stitching together
the remaining parts -- a ``cutting'' procedure commonly used in preparing data
prior to signal analysis; (ii) adding to a signal with known correlations a
tunable concentration of random outliers or spikes with different amplitude,
and (iii) generating a signal comprised of segments with different properties
-- e.g. different standard deviations or different correlation exponents. We
compare the difference between the scaling results obtained for stationary
correlated signals and correlated signals with these three types of
nonstationarities.Comment: 17 pages, 10 figures, corrected some typos, added one referenc
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