56 research outputs found
Fast and reliable detection of incumbent users in cognitive radios
Fast and reliable Spectrum Sensing (SS) plays a crucial role in the cognitive radio (CR) technology in order to prevent unwanted interference to the primary users (PU) and to reliably and quickly detect the white spaces in the spectrum for opportunistic access by the secondary users (SU). Spectrum Sensing must often be performed in the absence of information such as PU signaling scheme, noise level and channel fading coefficients. While these parameters can be estimated in the SU, estimation errors significantly deteriorates the performance of SS techniques. In this thesis, we introduce and evaluate the performance of two novel blind spectrum sensing algorithms which do not rely on knowledge of these parameters. The first is a SS technique for signaling schemes which introduce controlled intersymbol interference in the transmitter. The second is for cases when the receiver of the SU is equipped with a multiantenna system. This approach exploits the path correlation among the signals received at different antennas. Next we analyze the performance of Spectrum Monitoring (SM), an new technique which allows the SU to detect the presence of the PU using its own receiver statistics. In contrast to SS, with SM, the SU does not need to interrupt its own transmission in order to detect the presence of the PU. We carefully construct the decision statistics for SM and evaluate its performance. The performance of a hybrid SM/SS system shows a significant improvement over SS alone
Image Enhancement and Noise Reduction Using Modified Delay-Multiply-and-Sum Beamformer: Application to Medical Photoacoustic Imaging
Photoacoustic imaging (PAI) is an emerging biomedical imaging modality
capable of providing both high contrast and high resolution of optical and
UltraSound (US) imaging. When a short duration laser pulse illuminates the
tissue as a target of imaging, tissue induces US waves and detected waves can
be used to reconstruct optical absorption distribution. Since receiving part of
PA consists of US waves, a large number of beamforming algorithms in US imaging
can be applied on PA imaging. Delay-and-Sum (DAS) is the most common
beamforming algorithm in US imaging. However, make use of DAS beamformer leads
to low resolution images and large scale of off-axis signals contribution. To
address these problems a new paradigm namely Delay-Multiply-and-Sum (DMAS),
which was used as a reconstruction algorithm in confocal microwave imaging for
breast cancer detection, was introduced for US imaging. Consequently, DMAS was
used in PA imaging systems and it was shown this algorithm results in
resolution enhancement and sidelobe degrading. However, in presence of high
level of noise the reconstructed image still suffers from high contribution of
noise. In this paper, a modified version of DMAS beamforming algorithm is
proposed based on DAS inside DMAS formula expansion. The quantitative and
qualitative results show that proposed method results in more noise reduction
and resolution enhancement in expense of contrast degrading. For the
simulation, two-point target, along with lateral variation in two depths of
imaging are employed and it is evaluated under high level of noise in imaging
medium. Proposed algorithm in compare to DMAS, results in reduction of lateral
valley for about 19 dB followed by more distinguished two-point target.
Moreover, levels of sidelobe are reduced for about 25 dB.Comment: This paper was accepted and presented at Iranian Conference on
Electrical Engineering (ICEE) 201
Eigenspace-Based Minimum Variance Combined with Delay Multiply and Sum Beamformer: Application to Linear-Array Photoacoustic Imaging
In Photoacoustic imaging, Delay-and-Sum (DAS) algorithm is the most commonly
used beamformer. However, it leads to a low resolution and high level of
sidelobes. Delay-Multiply-and-Sum (DMAS) was introduced to provide lower
sidelobes compared to DAS. In this paper, to improve the resolution and
sidelobes of DMAS, a novel beamformer is introduced using Eigenspace-Based
Minimum Variance (EIBMV) method combined with DMAS, namely EIBMV-DMAS. It is
shown that expanding the DMAS algebra leads to several terms which can be
interpreted as DAS. Using the EIBMV adaptive beamforming instead of the
existing DAS (inside the DMAS algebra expansion) is proposed to improve the
image quality. EIBMV-DMAS is evaluated numerically and experimentally. It is
shown that EIBMV-DMAS outperforms DAS, DMAS and EIBMV in terms of resolution
and sidelobes. In particular, at the depth of 11 mm of the experimental images,
EIBMV-DMAS results in about 113 dB and 50 dB sidelobe reduction, compared to
DMAS and EIBMV, respectively. At the depth of 7 mm, for the experimental
images, the quantitative results indicate that EIBMV-DMAS leads to improvement
in Signal-to-Noise Ratio (SNR) of about 75% and 34%, compared to DMAS and
EIBMV, respectively.Comment: arXiv admin note: substantial text overlap with arXiv:1709.0796
Photoacoustic Imaging using Combination of Eigenspace-Based Minimum Variance and Delay-Multiply-and-Sum Beamformers: Simulation Study
Delay and Sum (DAS), as the most common beamforming algorithm in
Photoacoustic Imaging (PAI), having a simple implementation, results in a
low-quality image. Delay Multiply and Sum (DMAS) was introduced to improve the
quality of the reconstructed images using DAS. However, the resolution
improvement is now well enough compared to high resolution adaptive
reconstruction methods such as Eigenspace- Based Minimum Variance (EIBMV). We
proposed to integrate the EIBMV inside the DMAS formula by replacing the
existing DAS algebra inside the expansion of DMAS, called EIBMV-DMAS. It is
shown that EIBMV-DMAS outperforms DMAS in the terms of levels of sidelobes and
width of mainlobe significantly. For instance, at the depth of 35 mm,
EIBMV-DMAS outperforms DMAS and EIBMV in the term of sidelobes for about 108
dB, 98 dB and 44 dB compared to DAS, DMAS, and EIBMV, respectively. The
quantitative comparison has been conducted using Full-Width-Half-Maximum (FWHM)
and Signal-to-Noise Ratio (SNR), and it was shown that EIBMV-DMAS reduces the
FWHM about 1.65 mm and improves the SNR about 15 dB, compared to DMAS.Comment: Submitted in 24th Iranian Conference on Biomedical Engineering (ICBME
2017
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