728 research outputs found
Robust CS reconstruction based on appropriate minimization norm
Noise robust compressive sensing algorithm is considered. This algorithm
allows an efficient signal reconstruction in the presence of different types of
noise due to the possibility to change minimization norm. For instance, the
commonly used l1 and l2 norms, provide good results in case of Laplace and
Gaussian noise. However, when the signal is corrupted by Cauchy or Cubic
Gaussian noise, these norms fail to provide accurate reconstruction. Therefore,
in order to achieve accurate reconstruction, the application of l3 minimization
norm is analyzed. The efficiency of algorithm will be demonstrated on examples
Compressive sensing based velocity estimation in video data
This paper considers the use of compressive sensing based algorithms for
velocity estimation of moving vehicles. The procedure is based on sparse
reconstruction algorithms combined with time-frequency analysis applied to
video data. This algorithm provides an accurate estimation of object's velocity
even in the case of a very reduced number of available video frames. The
influence of crucial parameters is analysed for different types of moving
vehicles.Comment: 4 pages, 5 figure
Adaptive-Rate Compressive Sensing Using Side Information
We provide two novel adaptive-rate compressive sensing (CS) strategies for
sparse, time-varying signals using side information. Our first method utilizes
extra cross-validation measurements, and the second one exploits extra
low-resolution measurements. Unlike the majority of current CS techniques, we
do not assume that we know an upper bound on the number of significant
coefficients that comprise the images in the video sequence. Instead, we use
the side information to predict the number of significant coefficients in the
signal at the next time instant. For each image in the video sequence, our
techniques specify a fixed number of spatially-multiplexed CS measurements to
acquire, and adjust this quantity from image to image. Our strategies are
developed in the specific context of background subtraction for surveillance
video, and we experimentally validate the proposed methods on real video
sequences
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
Compressed sensing MRI using masked DCT and DFT measurements
This paper presents modification of the TwIST algorithm for Compressive
Sensing MRI images reconstruction. Compressive Sensing is new approach in
signal processing whose basic idea is recovering signal form small set of
available samples. The application of the Compressive Sensing in biomedical
imaging has found great importance. It allows significant lowering of the
acquisition time, and therefore, save the patient from the negative impact of
the MR apparatus. TwIST is commonly used algorithm for 2D signals
reconstruction using Compressive Sensing principle. It is based on the Total
Variation minimization. Standard version of the TwIST uses masked 2D Discrete
Fourier Transform coefficients as Compressive Sensing measurements. In this
paper, different masks and different transformation domains for coefficients
selection are tested. Certain percent of the measurements is used from the
mask, as well as small number of coefficients outside the mask. Comparative
analysis using 2D DFT and 2D DCT coefficients, with different mask shapes is
performed. The theory is proved with experimental results
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