<![CDATA[ IET Signal Processing - new TOC ]]>
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TOC Alert for Publication# 4159607 2016June 23<![CDATA[Low-complexity robust adaptive beamforming algorithms exploiting shrinkage for mismatch estimation]]>105429438608<![CDATA[QR-based robust diffusion strategies for wireless sensor networks using minimum-Wilcoxon-norm]]>105439448960<![CDATA[Criterion for non-existence of limit cycles in 2D state-space digital filters described by the Fornasini–Marchesini second model with finite wordlength non-linearities]]>105449456285<![CDATA[System approximations based on Meixner-like models]]>105457464478<![CDATA[Discrete blind reconstruction method for multi-coset sampling]]>105465470408<![CDATA[Distributed fast channel allocation in cognitive wireless sensor networks]]>105471477492<![CDATA[Evolutionary clustering framework based on distance matrix for arbitrary-shaped data sets]]>105478485601<![CDATA[TDOA/FDOA estimation method based on dechirp]]>105486492351<![CDATA[Novel <italic>N</italic>-scan GM-PHD-based approach for multi-target tracking]]>N-scan approach which employs the weight history of targets to improve the performance of the GM-PHD-based methods. They propose to assign a label, a weight history and a binary confidence indicator to each Gaussian component and propagate them in time. Then, they explain a novel N-scan state extraction algorithm to estimate the target states based on their histories in the N last steps. To study the efficiency of the proposed N-scan approach, it is applied on the GM-PHD filter as well as its several recent variants. The experimental results provided for various uncertainties show the effectiveness of the method.]]>1054935031763<![CDATA[Iterative sequential Monte Carlo algorithm for motif discovery]]>1) deals with the case of one motif instance of fixed length in each nucleotide sequence. Furthermore, the proposed ISMC algorithm is extended to deal with more complex situations including unique motif of unknown length in Algorithm 2, unique motif with unknown abundance in Algorithm 3 (see Fig. 2) and multiple motifs. Experimental results over both synthetic and real datasets show that the proposed ISMC algorithm outperforms five other widely used motif discovery algorithms in terms of nucleotide and site-level sensitivity, nucleotide and site-level positive prediction value, nucleotide-level performance coefficient, nucleotide-level correlation coefficient and site-level average site performance.]]>105504513593<![CDATA[Bayesian compressive sensing for primary user detection]]>105514523942<![CDATA[Improved semi-blind spectrum sensing for cognitive radio with locally optimum detection]]>105524531599<![CDATA[Distributed binary majority voting via exponential distribution]]>F + 1 in the presence of F adversarial nodes. Thus, the proposed algorithm is more robust compared with the previous works which are vulnerable to the existence of adversarial nodes.]]>105532542551<![CDATA[Combinatorial optimisation for pulse position modulation-ultra wideband signal detection based on compressed sensing and analogue-to-information converter]]>l_{2}- and l_{p}-norms, respectively. l_{p}-norm (0 < p < 1), which is a non-convex function, has stricter restriction on sparseness than l_{1}-norm. Meanwhile, the steepest descent method is adopted for l_{p}-norm optimisation, which can rapidly converge to objective values. Proposed method has more comprehensive restriction than greedy algorithm and convex optimisation, while maintain low complexity in computation as greedy algorithm. Numerical experiments demonstrate the validity of proposed method.]]>105543548356<![CDATA[Iteratively reweighted correlation analysis method for robust parameter identification of multiple-input multiple-output discrete-time systems]]>t-noises. The iterative method achieves good robustness and high efficiency by the combination of multivariable correlation analysis and t-distribution based M-estimators. The appropriate updating weights are able to enter into the sample cross-correlation function, so that the heavy tails are lowered, and the impact of outliers is weakened to the greatest extent. Based on the robust finite impulse response models, the identification procedure is then to reconstruct the noise-free estimates to identify the parameters of an MIMO system. The theoretical discussions and simulation results demonstrate that the proposed method works well.]]>105549556256<![CDATA[Computing the proximity operator of the ℓ<sub><italic>p</italic></sub> norm with 0 < <italic>p</italic> < 1]]>p norm of 0 ≤ p ≤ 1 requires the availability of the proximity operator of the ℓ_{p} norm. The proximity operators of the ℓ_{0} and ℓ_{1} norms are the well-known hard- and soft-thresholding estimators, respectively. In this study, the authors give a complete study on the properties of the proximity operator of the ℓ_{p} norm. Based on these properties, explicit formulas of the proximity operators of the ℓ_{1/2} norm and ℓ_{2/3} norm are derived with simple proofs; for other values of p, an iterative Newton's method is developed to compute the proximity operator of the ℓ_{p} norm by fully exploring the available proximity operators of the ℓ_{0}, ℓ_{1/2}, ℓ_{2/3}, and ℓ_{1} norms. As applications, the proximity operator of the ℓ_{p} norm with 0 ≤ p ≤ 1 is applied to the ℓ_{p}-regularisation for compressive sensing and image restoration.]]>105557565491<![CDATA[Reconstruction algorithm using exact tree projection for tree-structured compressive sensing]]>105566573601