55,424 research outputs found
Gravitational waves from Sco X-1: A comparison of search methods and prospects for detection with advanced detectors
The low-mass X-ray binary Scorpius X-1 (Sco X-1) is potentially the most
luminous source of continuous gravitational-wave radiation for interferometers
such as LIGO and Virgo. For low-mass X-ray binaries this radiation would be
sustained by active accretion of matter from its binary companion. With the
Advanced Detector Era fast approaching, work is underway to develop an array of
robust tools for maximizing the science and detection potential of Sco X-1. We
describe the plans and progress of a project designed to compare the numerous
independent search algorithms currently available. We employ a mock-data
challenge in which the search pipelines are tested for their relative
proficiencies in parameter estimation, computational efficiency, robust- ness,
and most importantly, search sensitivity. The mock-data challenge data contains
an ensemble of 50 Scorpius X-1 (Sco X-1) type signals, simulated within a
frequency band of 50-1500 Hz. Simulated detector noise was generated assuming
the expected best strain sensitivity of Advanced LIGO and Advanced VIRGO ( Hz). A distribution of signal amplitudes was then
chosen so as to allow a useful comparison of search methodologies. A factor of
2 in strain separates the quietest detected signal, at
strain, from the torque-balance limit at a spin frequency of 300 Hz, although
this limit could range from (25 Hz) to (750 Hz) depending on the unknown frequency of Sco X-1. With future
improvements to the search algorithms and using advanced detector data, our
expectations for probing below the theoretical torque-balance strain limit are
optimistic.Comment: 33 pages, 11 figure
Finding Structural Information of RF Power Amplifiers using an Orthogonal Non-Parametric Kernel Smoothing Estimator
A non-parametric technique for modeling the behavior of power amplifiers is
presented. The proposed technique relies on the principles of density
estimation using the kernel method and is suited for use in power amplifier
modeling. The proposed methodology transforms the input domain into an
orthogonal memory domain. In this domain, non-parametric static functions are
discovered using the kernel estimator. These orthogonal, non-parametric
functions can be fitted with any desired mathematical structure, thus
facilitating its implementation. Furthermore, due to the orthogonality, the
non-parametric functions can be analyzed and discarded individually, which
simplifies pruning basis functions and provides a tradeoff between complexity
and performance. The results show that the methodology can be employed to model
power amplifiers, therein yielding error performance similar to
state-of-the-art parametric models. Furthermore, a parameter-efficient model
structure with 6 coefficients was derived for a Doherty power amplifier,
therein significantly reducing the deployment's computational complexity.
Finally, the methodology can also be well exploited in digital linearization
techniques.Comment: Matlab sample code (15 MB):
https://dl.dropboxusercontent.com/u/106958743/SampleMatlabKernel.zi
Nonlinear system-identification of the filling phase of a wet-clutch system
The work presented illustrates how the choice of input perturbation signal and experimental design improves the derived model of a nonlinear system, in particular the dynamics of a wet-clutch system. The relationship between the applied input current signal and resulting output pressure in the filling phase of the clutch is established based on bandlimited periodic signals applied at different current operating points and signals approximating the desired filling current signal. A polynomial nonlinear state space model is estimated and validated over a range of measurements and yields better fits over a linear model, while the performance of either model depends on the perturbation signal used for model estimation
A Bayesian method for detecting stellar flares
We present a Bayesian-odds-ratio-based algorithm for detecting stellar flares
in light curve data. We assume flares are described by a model in which there
is a rapid rise with a half-Gaussian profile, followed by an exponential decay.
Our signal model also contains a polynomial background model. This is required
to fit underlying light curve variations that are expected in the data, which
could otherwise partially mimic a flare. We characterise the false alarm
probability and efficiency of this method and compare it with a simpler
thresholding method based on that used in Walkowicz et al (2011). We find our
method has a significant increase in detection efficiency for low
signal-to-noise ratio (S/N) flares. For a conservative false alarm probability
our method can detect 95% of flares with S/N less than ~20, as compared to S/N
of ~25 for the simpler method. As an example we have applied our method to a
selection of stars in Kepler Quarter 1 data. The method finds 687 flaring stars
with a total of 1873 flares after vetos have been applied. For these flares we
have characterised their durations and and signal-to-noise ratios.Comment: Accepted for MNRAS. The code used for the analysis can be found at
https://github.com/BayesFlare/bayesflare/releases/tag/v1.0.
Optimal control of ankle joint moment: Toward unsupported standing in paraplegia
This paper considers part of the problem of how to provide unsupported standing for paraplegics by feedback control. In this work our overall objective is to stabilize the subject by stimulation only of his ankle joints while the other joints are braced, Here, we investigate the problem of ankle joint moment control. The ankle plantarflexion muscles are first identified with pseudorandom binary sequence (PRBS) signals, periodic sinusoidal signals, and twitches. The muscle is modeled in Hammerstein form as a static recruitment nonlinearity followed by a linear transfer function. A linear-quadratic-Gaussian (LQG)-optimal controller design procedure for ankle joint moment was proposed based on the polynomial equation formulation, The approach was verified by experiments in the special Wobbler apparatus with a neurologically intact subject, and these experimental results are reported. The controller structure is formulated in such a way that there are only two scalar design parameters, each of which has a clear physical interpretation. This facilitates fast controller synthesis and tuning in the laboratory environment. Experimental results show the effects of the controller tuning parameters: the control weighting and the observer response time, which determine closed-loop properties. Using these two parameters the tradeoff between disturbance rejection and measurement noise sensitivity can be straightforwardly balanced while maintaining a desired speed of tracking. The experimentally measured reference tracking, disturbance rejection, and noise sensitivity are good and agree with theoretical expectations
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