66,057 research outputs found
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
Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective
On metrics of density and power efficiency, neuromorphic technologies have
the potential to surpass mainstream computing technologies in tasks where
real-time functionality, adaptability, and autonomy are essential. While
algorithmic advances in neuromorphic computing are proceeding successfully, the
potential of memristors to improve neuromorphic computing have not yet born
fruit, primarily because they are often used as a drop-in replacement to
conventional memory. However, interdisciplinary approaches anchored in machine
learning theory suggest that multifactor plasticity rules matching neural and
synaptic dynamics to the device capabilities can take better advantage of
memristor dynamics and its stochasticity. Furthermore, such plasticity rules
generally show much higher performance than that of classical Spike Time
Dependent Plasticity (STDP) rules. This chapter reviews the recent development
in learning with spiking neural network models and their possible
implementation with memristor-based hardware
An OFDM Signal Identification Method for Wireless Communications Systems
Distinction of OFDM signals from single carrier signals is highly important
for adaptive receiver algorithms and signal identification applications. OFDM
signals exhibit Gaussian characteristics in time domain and fourth order
cumulants of Gaussian distributed signals vanish in contrary to the cumulants
of other signals. Thus fourth order cumulants can be utilized for OFDM signal
identification. In this paper, first, formulations of the estimates of the
fourth order cumulants for OFDM signals are provided. Then it is shown these
estimates are affected significantly from the wireless channel impairments,
frequency offset, phase offset and sampling mismatch. To overcome these
problems, a general chi-square constant false alarm rate Gaussianity test which
employs estimates of cumulants and their covariances is adapted to the specific
case of wireless OFDM signals. Estimation of the covariance matrix of the
fourth order cumulants are greatly simplified peculiar to the OFDM signals. A
measurement setup is developed to analyze the performance of the identification
method and for comparison purposes. A parametric measurement analysis is
provided depending on modulation order, signal to noise ratio, number of
symbols, and degree of freedom of the underlying test. The proposed method
outperforms statistical tests which are based on fixed thresholds or empirical
values, while a priori information requirement and complexity of the proposed
method are lower than the coherent identification techniques
Multi-modal dictionary learning for image separation with application in art investigation
In support of art investigation, we propose a new source separation method
that unmixes a single X-ray scan acquired from double-sided paintings. In this
problem, the X-ray signals to be separated have similar morphological
characteristics, which brings previous source separation methods to their
limits. Our solution is to use photographs taken from the front and back-side
of the panel to drive the separation process. The crux of our approach relies
on the coupling of the two imaging modalities (photographs and X-rays) using a
novel coupled dictionary learning framework able to capture both common and
disparate features across the modalities using parsimonious representations;
the common component models features shared by the multi-modal images, whereas
the innovation component captures modality-specific information. As such, our
model enables the formulation of appropriately regularized convex optimization
procedures that lead to the accurate separation of the X-rays. Our dictionary
learning framework can be tailored both to a single- and a multi-scale
framework, with the latter leading to a significant performance improvement.
Moreover, to improve further on the visual quality of the separated images, we
propose to train coupled dictionaries that ignore certain parts of the painting
corresponding to craquelure. Experimentation on synthetic and real data - taken
from digital acquisition of the Ghent Altarpiece (1432) - confirms the
superiority of our method against the state-of-the-art morphological component
analysis technique that uses either fixed or trained dictionaries to perform
image separation.Comment: submitted to IEEE Transactions on Images Processin
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