68,640 research outputs found
Collaborative spectrum sensing optimisation algorithms for cognitive radio networks
The main challenge for a cognitive radio is to detect the existence of primary users reliably in order to minimise the interference to licensed communications. Hence, spectrum sensing is a most important requirement of a cognitive radio. However, due to the channel uncertainties, local observations are not reliable and collaboration among users is required. Selection of fusion rule at a common receiver has a direct impact on the overall spectrum sensing performance. In this paper, optimisation of collaborative spectrum sensing in terms of optimum decision fusion is studied for hard and soft decision combining. It is concluded that for optimum fusion, the fusion centre must incorporate signal-to-noise ratio values of cognitive users and the channel conditions. A genetic algorithm-based weighted optimisation strategy is presented for the case of soft decision combining. Numerical results show that the proposed optimised collaborative spectrum sensing schemes give better spectrum sensing performance
Graviton production with 2 jets at the LHC in large extra dimensions
We study Kaluza-Klein (KK) graviton production in the large extra dimensions
model via 2 jets plus missing transverse momentum signatures at the LHC. We
make predictions for both the signal and the dominant Zjj and Wjj backgrounds,
where we introduce missing P_T-dependent jet selection cuts that ensure the
smallness of the 2-jet rate over the 1-jet rate. With the same jet selection
cuts, the distributions of the two jets and their correlation with the missing
transverse momentum provide additional evidence for the production of an
invisible massive object.Comment: 8 pages, 10 figures, 1 table; Version to be printed in JHE
On the Achievable Rates of Decentralized Equalization in Massive MU-MIMO Systems
Massive multi-user (MU) multiple-input multiple-output (MIMO) promises
significant gains in spectral efficiency compared to traditional, small-scale
MIMO technology. Linear equalization algorithms, such as zero forcing (ZF) or
minimum mean-square error (MMSE)-based methods, typically rely on centralized
processing at the base station (BS), which results in (i) excessively high
interconnect and chip input/output data rates, and (ii) high computational
complexity. In this paper, we investigate the achievable rates of decentralized
equalization that mitigates both of these issues. We consider two distinct BS
architectures that partition the antenna array into clusters, each associated
with independent radio-frequency chains and signal processing hardware, and the
results of each cluster are fused in a feedforward network. For both
architectures, we consider ZF, MMSE, and a novel, non-linear equalization
algorithm that builds upon approximate message passing (AMP), and we
theoretically analyze the achievable rates of these methods. Our results
demonstrate that decentralized equalization with our AMP-based methods incurs
no or only a negligible loss in terms of achievable rates compared to that of
centralized solutions.Comment: Will be presented at the 2017 IEEE International Symposium on
Information Theor
THE TOOLS AND MONTE CARLO WORKING GROUP Summary Report from the Les Houches 2009 Workshop on TeV Colliders
This is the summary and introduction to the proceedings contributions for the
Les Houches 2009 "Tools and Monte Carlo" working group.Comment: 144 Pages. Workshop site
http://wwwlapp.in2p3.fr/conferences/LesHouches/Houches2009/ . Conveners were
Butterworth, Maltoni, Moortgat, Richardson, Schumann and Skand
Developement of real time diagnostics and feedback algorithms for JET in view of the next step
Real time control of many plasma parameters will be an essential aspect in
the development of reliable high performance operation of Next Step Tokamaks.
The main prerequisites for any feedback scheme are the precise real-time
determination of the quantities to be controlled, requiring top quality and
highly reliable diagnostics, and the availability of robust control algorithms.
A new set of real time diagnostics was recently implemented on JET to prove the
feasibility of determining, with high accuracy and time resolution, the most
important plasma quantities. With regard to feedback algorithms, new
model–based controllers were developed to allow a more robust control of
several plasma parameters. Both diagnostics and algorithms were successfully
used in several experiments, ranging from H-mode plasmas to configuration with
ITBs. Since elaboration of computationally heavy measurements is often
required, significant attention was devoted to non-algorithmic methods like
Digital or Cellular Neural/Nonlinear Networks. The real time hardware and
software adopted architectures are also described with particular attention to
their relevance to ITER.Comment: 12th International Congress on Plasma Physics, 25-29 October 2004,
Nice (France
TagBook: A Semantic Video Representation without Supervision for Event Detection
We consider the problem of event detection in video for scenarios where only
few, or even zero examples are available for training. For this challenging
setting, the prevailing solutions in the literature rely on a semantic video
representation obtained from thousands of pre-trained concept detectors.
Different from existing work, we propose a new semantic video representation
that is based on freely available social tagged videos only, without the need
for training any intermediate concept detectors. We introduce a simple
algorithm that propagates tags from a video's nearest neighbors, similar in
spirit to the ones used for image retrieval, but redesign it for video event
detection by including video source set refinement and varying the video tag
assignment. We call our approach TagBook and study its construction,
descriptiveness and detection performance on the TRECVID 2013 and 2014
multimedia event detection datasets and the Columbia Consumer Video dataset.
Despite its simple nature, the proposed TagBook video representation is
remarkably effective for few-example and zero-example event detection, even
outperforming very recent state-of-the-art alternatives building on supervised
representations.Comment: accepted for publication as a regular paper in the IEEE Transactions
on Multimedi
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