8,219 research outputs found
Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks
Cognitive radio has been widely considered as one of the prominent solutions
to tackle the spectrum scarcity. While the majority of existing research has
focused on single-band cognitive radio, multiband cognitive radio represents
great promises towards implementing efficient cognitive networks compared to
single-based networks. Multiband cognitive radio networks (MB-CRNs) are
expected to significantly enhance the network's throughput and provide better
channel maintenance by reducing handoff frequency. Nevertheless, the wideband
front-end and the multiband spectrum access impose a number of challenges yet
to overcome. This paper provides an in-depth analysis on the recent
advancements in multiband spectrum sensing techniques, their limitations, and
possible future directions to improve them. We study cooperative communications
for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also
investigate several limits and tradeoffs of various design parameters for
MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that
differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE
Journal, Special Issue on Future Radio Spectrum Access, March 201
Time-scale analysis of abrupt changes corrupted by multiplicative noise
Multiplicative Abrupt Changes (ACs) have been considered in many applications. These applications include image processing (speckle) and random communication models (fading). Previous authors have shown that the Continuous Wavelet Transform (CWT) has good detection properties for ACs in additive noise. This work applies the CWT to AC detection in multiplicative noise. CWT translation invariance allows to define an AC signature. The problem then becomes signature detection in the time-scale domain. A second-order contrast criterion is defined as a measure of detection performance. This criterion depends upon the first- and second-order moments of the multiplicative process's CWT. An optimal wavelet (maximizing the contrast) is derived for an ideal step in white multiplicative noise. This wavelet is asymptotically optimal for smooth changes and can be approximated for small AC amplitudes by the Haar wavelet. Linear and quadratic suboptimal signature-based detectors are also studied. Closed-form threshold expressions are given as functions of the false alarm probability for three of the detectors. Detection performance is characterized using Receiver Operating Characteristic (ROC) curves computed from Monte-Carlo simulations
Scale Invariant Interest Points with Shearlets
Shearlets are a relatively new directional multi-scale framework for signal
analysis, which have been shown effective to enhance signal discontinuities
such as edges and corners at multiple scales. In this work we address the
problem of detecting and describing blob-like features in the shearlets
framework. We derive a measure which is very effective for blob detection and
closely related to the Laplacian of Gaussian. We demonstrate the measure
satisfies the perfect scale invariance property in the continuous case. In the
discrete setting, we derive algorithms for blob detection and keypoint
description. Finally, we provide qualitative justifications of our findings as
well as a quantitative evaluation on benchmark data. We also report an
experimental evidence that our method is very suitable to deal with compressed
and noisy images, thanks to the sparsity property of shearlets
Digital mammography, cancer screening: Factors important for image compression
The use of digital mammography for breast cancer screening poses several novel problems such as development of digital sensors, computer assisted diagnosis (CAD) methods for image noise suppression, enhancement, and pattern recognition, compression algorithms for image storage, transmission, and remote diagnosis. X-ray digital mammography using novel direct digital detection schemes or film digitizers results in large data sets and, therefore, image compression methods will play a significant role in the image processing and analysis by CAD techniques. In view of the extensive compression required, the relative merit of 'virtually lossless' versus lossy methods should be determined. A brief overview is presented here of the developments of digital sensors, CAD, and compression methods currently proposed and tested for mammography. The objective of the NCI/NASA Working Group on Digital Mammography is to stimulate the interest of the image processing and compression scientific community for this medical application and identify possible dual use technologies within the NASA centers
Principled Design and Implementation of Steerable Detectors
We provide a complete pipeline for the detection of patterns of interest in
an image. In our approach, the patterns are assumed to be adequately modeled by
a known template, and are located at unknown position and orientation. We
propose a continuous-domain additive image model, where the analyzed image is
the sum of the template and an isotropic background signal with self-similar
isotropic power-spectrum. The method is able to learn an optimal steerable
filter fulfilling the SNR criterion based on one single template and background
pair, that therefore strongly responds to the template, while optimally
decoupling from the background model. The proposed filter then allows for a
fast detection process, with the unknown orientation estimation through the use
of steerability properties. In practice, the implementation requires to
discretize the continuous-domain formulation on polar grids, which is performed
using radial B-splines. We demonstrate the practical usefulness of our method
on a variety of template approximation and pattern detection experiments
A Wavelet-Based Algorithm for the Spatial Analysis of Poisson Data
Wavelets are scaleable, oscillatory functions that deviate from zero only
within a limited spatial regime and have average value zero. In addition to
their use as source characterizers, wavelet functions are rapidly gaining
currency within the source detection field. Wavelet-based source detection
involves the correlation of scaled wavelet functions with binned,
two-dimensional image data. If the chosen wavelet function exhibits the
property of vanishing moments, significantly non-zero correlation coefficients
will be observed only where there are high-order variations in the data; e.g.,
they will be observed in the vicinity of sources.
In this paper, we describe the mission-independent, wavelet-based source
detection algorithm WAVDETECT, part of the CIAO software package. Aspects of
our algorithm include: (1) the computation of local, exposure-corrected
normalized (i.e. flat-fielded) background maps; (2) the correction for exposure
variations within the field-of-view; (3) its applicability within the
low-counts regime, as it does not require a minimum number of background counts
per pixel for the accurate computation of source detection thresholds; (4) the
generation of a source list in a manner that does not depend upon a detailed
knowledge of the point spread function (PSF) shape; and (5) error analysis.
These features make our algorithm considerably more general than previous
methods developed for the analysis of X-ray image data, especially in the low
count regime. We demonstrate the algorithm's robustness by applying it to
various images.Comment: Accepted for publication in Ap. J. Supp. (v. 138 Jan. 2002). 61
pages, 23 figures, expands to 3.8 Mb. Abstract abridged for astro-ph
submissio
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