36,383 research outputs found
Analysis of Spectrum Occupancy Using Machine Learning Algorithms
In this paper, we analyze the spectrum occupancy using different machine
learning techniques. Both supervised techniques (naive Bayesian classifier
(NBC), decision trees (DT), support vector machine (SVM), linear regression
(LR)) and unsupervised algorithm (hidden markov model (HMM)) are studied to
find the best technique with the highest classification accuracy (CA). A
detailed comparison of the supervised and unsupervised algorithms in terms of
the computational time and classification accuracy is performed. The classified
occupancy status is further utilized to evaluate the probability of secondary
user outage for the future time slots, which can be used by system designers to
define spectrum allocation and spectrum sharing policies. Numerical results
show that SVM is the best algorithm among all the supervised and unsupervised
classifiers. Based on this, we proposed a new SVM algorithm by combining it
with fire fly algorithm (FFA), which is shown to outperform all other
algorithms.Comment: 21 pages, 6 figure
Exploring the spectroscopic diversity of type Ia supernovae with DRACULA: a machine learning approach
The existence of multiple subclasses of type Ia supernovae (SNeIa) has been
the subject of great debate in the last decade. One major challenge inevitably
met when trying to infer the existence of one or more subclasses is the time
consuming, and subjective, process of subclass definition. In this work, we
show how machine learning tools facilitate identification of subtypes of SNeIa
through the establishment of a hierarchical group structure in the continuous
space of spectral diversity formed by these objects. Using Deep Learning, we
were capable of performing such identification in a 4 dimensional feature space
(+1 for time evolution), while the standard Principal Component Analysis barely
achieves similar results using 15 principal components. This is evidence that
the progenitor system and the explosion mechanism can be described by a small
number of initial physical parameters. As a proof of concept, we show that our
results are in close agreement with a previously suggested classification
scheme and that our proposed method can grasp the main spectral features behind
the definition of such subtypes. This allows the confirmation of the velocity
of lines as a first order effect in the determination of SNIa subtypes,
followed by 91bg-like events. Given the expected data deluge in the forthcoming
years, our proposed approach is essential to allow a quick and statistically
coherent identification of SNeIa subtypes (and outliers). All tools used in
this work were made publicly available in the Python package Dimensionality
Reduction And Clustering for Unsupervised Learning in Astronomy (DRACULA) and
can be found within COINtoolbox (https://github.com/COINtoolbox/DRACULA).Comment: 16 pages, 12 figures, accepted for publication in MNRA
Semi-Supervised Radio Signal Identification
Radio emitter recognition in dense multi-user environments is an important
tool for optimizing spectrum utilization, identifying and minimizing
interference, and enforcing spectrum policy. Radio data is readily available
and easy to obtain from an antenna, but labeled and curated data is often
scarce making supervised learning strategies difficult and time consuming in
practice. We demonstrate that semi-supervised learning techniques can be used
to scale learning beyond supervised datasets, allowing for discerning and
recalling new radio signals by using sparse signal representations based on
both unsupervised and supervised methods for nonlinear feature learning and
clustering methods
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