39,117 research outputs found
An Efficient Spectral Leakage Filtering for IEEE 802.11af in TV White Space
Orthogonal frequency division multiplexing (OFDM) has been widely adopted for
modern wireless standards and become a key enabling technology for cognitive
radios. However, one of its main drawbacks is significant spectral leakage due
to the accumulation of multiple sinc-shaped subcarriers. In this paper, we
present a novel pulse shaping scheme for efficient spectral leakage suppression
in OFDM based physical layer of IEEE 802.11af standard. With conventional pulse
shaping filters such as a raised-cosine filter, vestigial symmetry can be used
to reduce spectral leakage very effectively. However, these pulse shaping
filters require long guard interval, i.e., cyclic prefix in an OFDM system, to
avoid inter-symbol interference (ISI), resulting in a loss of spectral
efficiency. The proposed pulse shaping method based on asymmetric pulse shaping
achieves better spectral leakage suppression and decreases ISI caused by
filtering as compared to conventional pulse shaping filters
Scale Space Smoothing, Image Feature Extraction and Bessel Filters
The Green function of Mumford-Shah functional in the absence of discontinuities is known to be a modified Bessel function of the second kind and zero degree. Such a Bessel function is regularized here and used as a filter for feature extraction. It is demonstrated in this paper that a Bessel filter does not follow the scale space smoothing property of bounded linear filters such as Gaussian filters. The features extracted by the Bessel filter are therefore scale invariant. Edges, blobs, and junctions are features considered here to show that the extracted features remain unchanged by varying the scale of a Bessel filter. The scale invariance property of Bessel filters for edges is analytically proved here. We conjecture that Bessel filters also enjoy this scale invariance property for other kinds of features. The experimental results presente
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
Bilateral Filter: Graph Spectral Interpretation and Extensions
In this paper we study the bilateral filter proposed by Tomasi and Manduchi,
as a spectral domain transform defined on a weighted graph. The nodes of this
graph represent the pixels in the image and a graph signal defined on the nodes
represents the intensity values. Edge weights in the graph correspond to the
bilateral filter coefficients and hence are data adaptive. Spectrum of a graph
is defined in terms of the eigenvalues and eigenvectors of the graph Laplacian
matrix. We use this spectral interpretation to generalize the bilateral filter
and propose more flexible and application specific spectral designs of
bilateral-like filters. We show that these spectral filters can be implemented
with k-iterative bilateral filtering operations and do not require expensive
diagonalization of the Laplacian matrix
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