Independent component analysis (ICA) has found many uses in source separation in biomedical signals. We highlight a methodology and put forward an algorithm which allows single channel ICA to be performed on single ch...
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ISBN:
(纸本)9780863419348
Independent component analysis (ICA) has found many uses in source separation in biomedical signals. We highlight a methodology and put forward an algorithm which allows single channel ICA to be performed on single channel biomedical signal recordings. The algorithm uses a fast, deflationary approach to efficiently extract independent processes underlying the single channel recordings. We show that for processes which are reasonably spectrally disjoint the algorithm can separate out individual sources. We show examples of this using brain signal recordings and abdominal foetal recordings.
It is important to assess the effect of transmit power allocation schemes on the energy consumption on random cellular networks. The energy efficiency of 5G green cellular networks with average and water-filling power...
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It is important to assess the effect of transmit power allocation schemes on the energy consumption on random cellular networks. The energy efficiency of 5G green cellular networks with average and water-filling power allocation schemes is studied in this paper. Based on the proposed interference and achievable rate model,an energy efficiency model is proposed for MIMO random cellular networks. Furthermore, the energy efficiency with average and water-filling power allocation schemes are presented, respectively. Numerical results indicate that the maximum limits of energy efficiency are always there for MIMO random cellular networks with different intensity ratios of mobile stations(MSs) to base stations(BSs) and channel conditions. Compared with the average power allocation scheme, the water-filling scheme is shown to improve the energy efficiency of MIMO random cellular networks when channel state information(CSI) is attainable for both transmitters and receivers.
Parallel implementation of Markov Chain Monte Carlo (MCMC) algorithms for Bayesian inference has been effective but is usually restricted to the case where the dimension of the parameter vector is fixed. We propose an...
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作者：
Blumensath, T.Yaghoobi, M.Davies, M.E.IDCOM
Joint Research Institute for Signal and Image Processing Edinburgh University Mayfield Road Edinburgh EH9 3JL United Kingdom
Sparse signal approximations are approximations that use only a small number of elementary waveforms to describe a signal. In this paper we proof the convergence of an iterative hard thresholding algorithm and show, t...
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作者：
Blumensath, ThomasDavies, Mike E.IDCOM
Joint Research Institute for Signal and Image Processing University of Edinburgh Mayfield Road Edinburgh EH9 3JL United Kingdom
Matching Pursuit and orthogonal Matching Pursuit are greedy algorithms used to obtain sparse signal approximations. Orthogonal Matching Pursuit is known to offer better performance, but Matching Pursuit allows more ef...
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Cognitive radio (CR) has been considered as a promising technology to improve the spectrum utilization. In this paper we analyze the capacity of a CR network with average received interference power constraints. Under...
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In the multiple access channel, successive interference cancellation (SIC) can be used to achieve the boundary points of the capacity region. In this paper, we investigate the practical potential of SIC by employing a...
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ISBN:
(纸本)9783800730773
In the multiple access channel, successive interference cancellation (SIC) can be used to achieve the boundary points of the capacity region. In this paper, we investigate the practical potential of SIC by employing a set of practical moderate-blocksize Low-Density Parity-Check codes in the Multiple Access additive white Gaussian noise channel;in particular we consider binary modulation. The theoretically achievable points in the capacity region are compared with the practically achievable operating points at a low bit-error rate. It is shown that SIC makes available a higher transmission rate compared with simple separate detection that treats the signal of the other user as noise. This statement is also true for the use of binary modulation and a practical efficient implementation with channel codes of moderate blocksize.
The optimal solution to the problem of detecting, tracking and identifying multiple targets can be found through a direct generalisation of the Bayes filter to multi-object systems using Mahler's Finite Set Statis...
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ISBN:
(纸本)9780982443811
The optimal solution to the problem of detecting, tracking and identifying multiple targets can be found through a direct generalisation of the Bayes filter to multi-object systems using Mahler's Finite Set Statistics. Due to the inherent complexity of the multi-object Bayes filter, Mahler proposed to propagate the first-order multi-object moment density, known as the Probability Hypothesis Density (PHD), instead of the multi-object posterior. This was derived using the concept of the probability generating functional (***.) from point process theory. In this paper, I derive multi-object first-moment smoothers for forward-backward smoothing through a new formulation of the ***. smoother which takes advantage of the ***. Bayes update. This formulation permits the straightforward derivation of first-moment multi-object smoothers, including the PHD smoother.
Sparse signal models approximate signals using a small number of elements from a large set of vectors, called a dictionary. The success of such methods relies on the dictionary fitting the signal structure. Therefore,...
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Sparse signal models approximate signals using a small number of elements from a large set of vectors, called a dictionary. The success of such methods relies on the dictionary fitting the signal structure. Therefore, the dictionary has to be designed to fit the signal class of interest. This paper uses a general formulation that allows the dictionary to be learned form the data with some a priori information about the dictionary. In this formulation a universal cost function is proposed and practical algorithms are presented to minimize this cost under different constraints on the dictionary. The proposed methods are compared with previous approaches using synthetic and real data. Simulations highlight the advantages of the proposed methods over other currently available dictionary learning strategies. copyright by EURASIP.
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