6,650 research outputs found
Signal Detection for QPSK Based Cognitive Radio Systems using Support Vector Machines
Cognitive radio based network enables opportunistic dynamic spectrum access by sensing, adopting and utilizing the unused portion of licensed spectrum bands. Cognitive radio is intelligent enough to adapt the communication parameters of the unused licensed spectrum. Spectrum sensing is one of the most important tasks of the cognitive radio cycle. In this paper, the auto-correlation function kernel based Support Vector Machine (SVM) classifier along with Welch's Periodogram detector is successfully implemented for the detection of four QPSK (Quadrature Phase Shift Keying) based signals propagating through an AWGN (Additive White Gaussian Noise) channel. It is shown that the combination of statistical signal processing and machine learning concepts improve the spectrum sensing process and spectrum sensing is possible even at low Signal to Noise Ratio (SNR) values up to -50 dB
Towards Adapting ImageNet to Reality: Scalable Domain Adaptation with Implicit Low-rank Transformations
Images seen during test time are often not from the same distribution as
images used for learning. This problem, known as domain shift, occurs when
training classifiers from object-centric internet image databases and trying to
apply them directly to scene understanding tasks. The consequence is often
severe performance degradation and is one of the major barriers for the
application of classifiers in real-world systems. In this paper, we show how to
learn transform-based domain adaptation classifiers in a scalable manner. The
key idea is to exploit an implicit rank constraint, originated from a
max-margin domain adaptation formulation, to make optimization tractable.
Experiments show that the transformation between domains can be very
efficiently learned from data and easily applied to new categories. This begins
to bridge the gap between large-scale internet image collections and object
images captured in everyday life environments
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