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Fully cognitive transceiver for High Frequency (HF) applications
Ionospheric conditions are variable in nature and can cause destructive interference to transmissions made in the High Frequency (HF) band, which ranges from 3-30 MHz. This poses a problem as the HF band is a critical frequency range for various applications (i.e. emergency, military). To manage these dynamic conditions, intelligent techniques should be implemented at the transmitter and receiver to properly maintain reliable communications. In this paper, we present work deriving components of a cognitive HF transceiver with agents called cognitive engines (CEs) operating at the transmitter and receiver. At the transmitter, cognition is employed to determine the combination of modulation and coding techniques that maximize throughput. At the receiver, cognition is implemented to derive the best parameters for equalization (i.e. tap length, step size, filter type, etc.) Results are presented showing that the individual components are able to satisfy their objectives. A discussion is also provided surveying recent research efforts pertaining to the development of cognitive methods for the Automatic Link Establishment (ALE) protocol, a common networking methodology for HF stations.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]
Closed-loop separation control over a sharp edge ramp using Genetic Programming
We experimentally perform open and closed-loop control of a separating
turbulent boundary layer downstream from a sharp edge ramp. The turbulent
boundary layer just above the separation point has a Reynolds number
based on momentum thickness. The goal of the
control is to mitigate separation and early re-attachment. The forcing employs
a spanwise array of active vortex generators. The flow state is monitored with
skin-friction sensors downstream of the actuators. The feedback control law is
obtained using model-free genetic programming control (GPC) (Gautier et al.
2015). The resulting flow is assessed using the momentum coefficient, pressure
distribution and skin friction over the ramp and stereo PIV. The PIV yields
vector field statistics, e.g. shear layer growth, the backflow area and vortex
region. GPC is benchmarked against the best periodic forcing. While open-loop
control achieves separation reduction by locking-on the shedding mode, GPC
gives rise to similar benefits by accelerating the shear layer growth.
Moreover, GPC uses less actuation energy.Comment: 24 pages, 24 figures, submitted to Experiments in Fluid
Multi-fidelity data fusion through parameter space reduction with applications to automotive engineering
Multi-fidelity models are of great importance due to their capability of
fusing information coming from different simulations and sensors. Gaussian
processes are employed for nonparametric regression in a Bayesian setting. They
generalize linear regression embedding the inputs in a latent manifold inside
an infinite-dimensional reproducing kernel Hilbert space. We can augment the
inputs with the observations of low-fidelity models in order to learn a more
expressive latent manifold and thus increment the model's accuracy. This can be
realized recursively with a chain of Gaussian processes with incrementally
higher fidelity. We would like to extend these multi-fidelity model
realizations to case studies affected by a high-dimensional input space but
with low intrinsic dimensionality. In these cases physical supported or purely
numerical low-order models are still affected by the curse of dimensionality
when queried for responses. When the model's gradient information is provided,
the existence of an active subspace, or a nonlinear transformation of the input
parameter space, can be exploited to design low-fidelity response surfaces and
thus enable Gaussian process multi-fidelity regression, without the need to
perform new simulations. This is particularly useful in the case of data
scarcity. In this work, we present a new multi-fidelity approach involving
active subspaces and nonlinear level-set learning method. We test the proposed
numerical method on two different high-dimensional benchmarks, and on a more
complex car aerodynamics problem. We show how a low intrinsic dimensionality
bias can increase the accuracy of Gaussian process response surfaces.Comment: arXiv admin note: substantial text overlap with arXiv:2010.0834
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