14,981 research outputs found
The cross-frequency mediation mechanism of intracortical information transactions
In a seminal paper by von Stein and Sarnthein (2000), it was hypothesized
that "bottom-up" information processing of "content" elicits local, high
frequency (beta-gamma) oscillations, whereas "top-down" processing is
"contextual", characterized by large scale integration spanning distant
cortical regions, and implemented by slower frequency (theta-alpha)
oscillations. This corresponds to a mechanism of cortical information
transactions, where synchronization of beta-gamma oscillations between distant
cortical regions is mediated by widespread theta-alpha oscillations. It is the
aim of this paper to express this hypothesis quantitatively, in terms of a
model that will allow testing this type of information transaction mechanism.
The basic methodology used here corresponds to statistical mediation analysis,
originally developed by (Baron and Kenny 1986). We generalize the classical
mediator model to the case of multivariate complex-valued data, consisting of
the discrete Fourier transform coefficients of signals of electric neuronal
activity, at different frequencies, and at different cortical locations. The
"mediation effect" is quantified here in a novel way, as the product of "dual
frequency RV-coupling coefficients", that were introduced in (Pascual-Marqui et
al 2016, http://arxiv.org/abs/1603.05343). Relevant statistical procedures are
presented for testing the cross-frequency mediation mechanism in general, and
in particular for testing the von Stein & Sarnthein hypothesis.Comment: https://doi.org/10.1101/119362 licensed as CC-BY-NC-ND 4.0
International license: http://creativecommons.org/licenses/by-nc-nd/4.0
Gravitational Waveforms for Precessing, Quasicircular Compact Binaries with Multiple Scale Analysis: Small Spin Expansion
We obtain analytical gravitational waveforms in the frequency-domain for
precessing, quasi-circular compact binaries with small spins, applicable, for
example, to binary neutron star inspirals. We begin by calculating an analytic
solution to the precession equations, obtained by expanding in the
dimensionless spin parameters and using multiple-scale analysis to separate
timescales. We proceed by analytically computing the Fourier transform of
time-domain waveform through the stationary phase approximation. We show that
the latter is valid for systems with small spins. Finally, we show that these
waveforms have a high overlap with numerical waveforms obtained through direct
integration of the precession equations and discrete Fourier transformations.
The resulting, analytic waveform family is ideal for detection and parameter
estimation of gravitational waves emitted by inspiraling binary neutron stars
with ground-based detectors.Comment: 37 pages, 14 figures, final published versio
Spectral proper orthogonal decomposition
The identification of coherent structures from experimental or numerical data
is an essential task when conducting research in fluid dynamics. This typically
involves the construction of an empirical mode base that appropriately captures
the dominant flow structures. The most prominent candidates are the
energy-ranked proper orthogonal decomposition (POD) and the frequency ranked
Fourier decomposition and dynamic mode decomposition (DMD). However, these
methods fail when the relevant coherent structures occur at low energies or at
multiple frequencies, which is often the case. To overcome the deficit of these
"rigid" approaches, we propose a new method termed Spectral Proper Orthogonal
Decomposition (SPOD). It is based on classical POD and it can be applied to
spatially and temporally resolved data. The new method involves an additional
temporal constraint that enables a clear separation of phenomena that occur at
multiple frequencies and energies. SPOD allows for a continuous shifting from
the energetically optimal POD to the spectrally pure Fourier decomposition by
changing a single parameter. In this article, SPOD is motivated from
phenomenological considerations of the POD autocorrelation matrix and justified
from dynamical system theory. The new method is further applied to three sets
of PIV measurements of flows from very different engineering problems. We
consider the flow of a swirl-stabilized combustor, the wake of an airfoil with
a Gurney flap, and the flow field of the sweeping jet behind a fluidic
oscillator. For these examples, the commonly used methods fail to assign the
relevant coherent structures to single modes. The SPOD, however, achieves a
proper separation of spatially and temporally coherent structures, which are
either hidden in stochastic turbulent fluctuations or spread over a wide
frequency range
A Novel Adaptive Spectrum Noise Cancellation Approach for Enhancing Heartbeat Rate Monitoring in a Wearable Device
This paper presents a novel approach, Adaptive Spectrum Noise Cancellation (ASNC), for motion artifacts removal in Photoplethysmography (PPG) signals measured by an optical biosensor to obtain clean PPG waveforms for heartbeat rate calculation. One challenge faced by this optical sensing method is the inevitable noise induced by movement when the user is in motion, especially when the motion frequency is very close to the target heartbeat rate. The proposed ASNC utilizes the onboard accelerometer and gyroscope sensors to detect and remove the artifacts adaptively, thus obtaining accurate heartbeat rate measurement while in motion. The ASNC algorithm makes use of a commonly accepted spectrum analysis approaches in medical digital signal processing, discrete cosine transform, to carry out frequency domain analysis. Results obtained by the proposed ASNC have been compared to the classic algorithms, the adaptive threshold peak detection and adaptive noise cancellation. The mean (standard deviation) absolute error and mean relative error of heartbeat rate calculated by ASNC is 0.33 (0.57) beats·min-1 and 0.65%, by adaptive threshold peak detection algorithm is 2.29 (2.21) beats·min-1 and 8.38%, by adaptive noise cancellation algorithm is 1.70 (1.50) beats·min-1 and 2.02%. While all algorithms performed well with both simulated PPG data and clean PPG data collected from our Verity device in situations free of motion artifacts, ASNC provided better accuracy when motion artifacts increase, especially when motion frequency is very close to the heartbeat rate
Blind deconvolution of medical ultrasound images: parametric inverse filtering approach
©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2007.910179The problem of reconstruction of ultrasound images by means of blind deconvolution has long been recognized as one of the central problems in medical ultrasound imaging. In this paper, this problem is addressed via proposing a blind deconvolution method which is innovative in several ways. In particular, the method is based on parametric inverse filtering, whose parameters are optimized using two-stage processing. At the first stage, some partial information on the point spread function is recovered. Subsequently, this information is used to explicitly constrain the spectral shape of the inverse filter. From this perspective, the proposed methodology can be viewed as a ldquohybridizationrdquo of two standard strategies in blind deconvolution, which are based on either concurrent or successive estimation of the point spread function and the image of interest. Moreover, evidence is provided that the ldquohybridrdquo approach can outperform the standard ones in a number of important practical cases. Additionally, the present study introduces a different approach to parameterizing the inverse filter. Specifically, we propose to model the inverse transfer function as a member of a principal shift-invariant subspace. It is shown that such a parameterization results in considerably more stable reconstructions as compared to standard parameterization methods. Finally, it is shown how the inverse filters designed in this way can be used to deconvolve the images in a nonblind manner so as to further improve their quality. The usefulness and practicability of all the introduced innovations are proven in a series of both in silico and in vivo experiments. Finally, it is shown that the proposed deconvolution algorithms are capable of improving the resolution of ultrasound images by factors of 2.24 or 6.52 (as judged by the autocorrelation criterion) depending on the type of regularization method used
Statistics of the seasonal cycle of the 1951-2000 surface temperature records in Italy
We present an analysis of seasonal cycle of the last 50 years of records of
surface temperature in Italy. We consider two data sets which synthesize the
surface temperature fields of Northern and Southern Italy. Such data sets
consist of records of daily maximum and minimum temperature. We compute the
best estimate of the seasonal cycle of the variables considered by adopting the
cyclograms' technique. We observe that in general the minimum temperature cycle
lags behind the maximum temperature cycle, and that the cycles of the Southern
Italy temperatures records lag behind the corresponding cycles referring to
Northern Italy. All seasonal cycles lag considerably behind the solar cycle.
The amplitude and phase of the seasonal cycles do not show any statistically
significant trend in the time interval considered.Comment: 30 pages, 6 figures, submitted to IJ
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