53,220 research outputs found
Matched Metrics and Channels
The most common decision criteria for decoding are maximum likelihood
decoding and nearest neighbor decoding. It is well-known that maximum
likelihood decoding coincides with nearest neighbor decoding with respect to
the Hamming metric on the binary symmetric channel. In this work we study
channels and metrics for which those two criteria do and do not coincide for
general codes
On the Impact of Antenna Topologies for Massive MIMO Systems
Approximate expressions for the spatial correlation of cylindrical and
uniform rectangular arrays (URA) are derived using measured distributions of
angles of departure (AOD) for both the azimuth and zenith domains. We examine
massive multiple-input-multiple-output (MIMO) convergence properties of the
correlated channels by considering a number of convergence metrics. The
per-user matched filter (MF) signal-to-interference-plus-noise ratio (SINR)
performance and convergence rate, to respective limiting values, of the two
antenna topologies is also explored.Comment: 6 pages, 6 figure
A General Formula for the Mismatch Capacity
The fundamental limits of channels with mismatched decoding are addressed. A
general formula is established for the mismatch capacity of a general channel,
defined as a sequence of conditional distributions with a general decoding
metrics sequence. We deduce an identity between the Verd\'{u}-Han general
channel capacity formula, and the mismatch capacity formula applied to Maximum
Likelihood decoding metric. Further, several upper bounds on the capacity are
provided, and a simpler expression for a lower bound is derived for the case of
a non-negative decoding metric. The general formula is specialized to the case
of finite input and output alphabet channels with a type-dependent metric. The
closely related problem of threshold mismatched decoding is also studied, and a
general expression for the threshold mismatch capacity is obtained. As an
example of threshold mismatch capacity, we state a general expression for the
erasures-only capacity of the finite input and output alphabet channel. We
observe that for every channel there exists a (matched) threshold decoder which
is capacity achieving. Additionally, necessary and sufficient conditions are
stated for a channel to have a strong converse. Csisz\'{a}r and Narayan's
conjecture is proved for bounded metrics, providing a positive answer to the
open problem introduced in [1], i.e., that the "product-space" improvement of
the lower random coding bound, , is indeed the mismatch
capacity of the discrete memoryless channel . We conclude by presenting an
identity between the threshold capacity and in the DMC
case
Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis
Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is
an early predictor of Parkinson's disease. This study proposes a
fully-automated framework for RBD detection consisting of automated sleep
staging followed by RBD identification. Analysis was assessed using a limited
polysomnography montage from 53 participants with RBD and 53 age-matched
healthy controls. Sleep stage classification was achieved using a Random Forest
(RF) classifier and 156 features extracted from electroencephalogram (EEG),
electrooculogram (EOG) and electromyogram (EMG) channels. For RBD detection, a
RF classifier was trained combining established techniques to quantify muscle
atonia with additional features that incorporate sleep architecture and the EMG
fractal exponent. Automated multi-state sleep staging achieved a 0.62 Cohen's
Kappa score. RBD detection accuracy improved by 10% to 96% (compared to
individual established metrics) when using manually annotated sleep staging.
Accuracy remained high (92%) when using automated sleep staging. This study
outperforms established metrics and demonstrates that incorporating sleep
architecture and sleep stage transitions can benefit RBD detection. This study
also achieved automated sleep staging with a level of accuracy comparable to
manual annotation. This study validates a tractable, fully-automated, and
sensitive pipeline for RBD identification that could be translated to wearable
take-home technology.Comment: 20 pages, 3 figure
Convolutional neural networks: a magic bullet for gravitational-wave detection?
In the last few years, machine learning techniques, in particular
convolutional neural networks, have been investigated as a method to replace or
complement traditional matched filtering techniques that are used to detect the
gravitational-wave signature of merging black holes. However, to date, these
methods have not yet been successfully applied to the analysis of long
stretches of data recorded by the Advanced LIGO and Virgo gravitational-wave
observatories. In this work, we critically examine the use of convolutional
neural networks as a tool to search for merging black holes. We identify the
strengths and limitations of this approach, highlight some common pitfalls in
translating between machine learning and gravitational-wave astronomy, and
discuss the interdisciplinary challenges. In particular, we explain in detail
why convolutional neural networks alone cannot be used to claim a statistically
significant gravitational-wave detection. However, we demonstrate how they can
still be used to rapidly flag the times of potential signals in the data for a
more detailed follow-up. Our convolutional neural network architecture as well
as the proposed performance metrics are better suited for this task than a
standard binary classifications scheme. A detailed evaluation of our approach
on Advanced LIGO data demonstrates the potential of such systems as trigger
generators. Finally, we sound a note of caution by constructing adversarial
examples, which showcase interesting "failure modes" of our model, where inputs
with no visible resemblance to real gravitational-wave signals are identified
as such by the network with high confidence.Comment: First two authors contributed equally; appeared at Phys. Rev.
Methods for Reducing False Alarms in Searches for Compact Binary Coalescences in LIGO Data
The LIGO detectors are sensitive to a variety of noise transients of
non-astrophysical origin. Instrumental glitches and environmental disturbances
increase the false alarm rate in the searches for gravitational waves. Using
times already identified when the interferometers produced data of questionable
quality, or when the channels that monitor the interferometer indicated
non-stationarity, we have developed techniques to safely and effectively veto
false triggers from the compact binary coalescences (CBCs) search pipeline
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