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Although blind source separation of convolutive mixtures can be efficiently solved in the frequency domain, the problem of permutation ambiguity must be solved. Thus, this paper proposes an improved permutation algorithm. Firstly, the improved algorithm uses energy correlation of the separated signal to sort the signals of each frequency bin. Then, the reliability of the sorting of the signals on the frequency bin will be judged according to the threshold. The unreliable frequency bins will be corrected in time so that we can obtain the separated signal accurately, eventually. This algorithm effectively reduces sorting errors and error propagation, thereby improving the separation effect. BSS experiments are performed on the voice signals and radar signals under different convolutive mixing models. The simulation results show that the improved algorithm has better separation performance and higher robustness than the traditional permutation algorithms, which is reflected by the increasing signal to interference ratio of separated signals.

An original algorithm for underdetermined mixing matrix estimation is proposed in this paper, which can estimate the mixing matrix effectively when the number of source signals is unknown. Firstly, a new single source point (SSP) detection algorithm based on transform matrix is proposed, which can effectively detect single source time-frequency (TF) points by using the characteristics of complex ratio and improve the sparsity of source signals. In view of the fact that the number of source signals is unknown, a novel estimation algorithm based on element sorting is proposed, which can significantly improve the estimation accuracy of the number of source signals. Finally, the mixing matrix is estimated by using the cluster center obtained by agglomerative hierarchical clustering (AHC) algorithm. The simulation results show that the proposed algorithm can improve the estimation accuracy of the number of source signals and underdetermined mixing matrix obviously, and the algorithm has higher robustness compared with other algorithms.

Mediator is a conserved multiprotein complex important for transcription by RNA polymerase II (Pol II). Arabidopsis Mediator subunit MED18 regulates flowering, hormone signaling and plant immunity. Here we report that Arabidopsis MED18 interacted with NUCLEAR RNA POLYMERASE D2a (NRPD2a), the second largest subunit of the nuclear Pol IV and V, which function in RNA-directed DNA methylation and epigenetic regulation of gene expression. Mutants for both MED18 and NRPD2a were compromised in resistance to necrotrophic fungal pathogen Botrytis cinerea. Mutants for NRPD1a, the largest subunit of Pol IV, were also compromised in resistance to Botrytis, supporting a critical role of Pol IV and V in plant defense against Botrytis. Increased Botrytis susceptibility of both the med18 and nrpd2a mutants were associated with reduced accumulation of reactive oxygen species, which are known to promote resistance to Botrytis. Both the basal and pathogen-induced levels of salicylic acid and jasmonic acid were also significantly altered in the med18 and nrpd2a mutants. Transcriptome profiling found that MED18 and NRPD2a affected both unique and overlapping sets of genes in a broad spectrum of biological processes and pathways that influence plant–pathogen interaction. The genes altered in expression in the med18 and nrpd2a mutants include disease resistance proteins, salicylic acid and jasmonic acid signaling and responses, which are known to affect resistance to necrotrophic pathogens. The novel interaction between subunits of Mediator and plant-specific RNA polymerases provides a new mechanism for epigenetic regulation of resistance and expression of defense-related genes in plant immunity.

Under the circumstances that source signals are sufficiently sparse, an algorithm based on density measurement for blind estimation of the underdetermined mixing matrix is proposed in this paper. The proposed algorithm can estimate the number of source signals and the mixing matrix of the transmission channel simultaneously without any prior information. There are mainly three steps, including the preprocessing of observed samples, reservation of high-density samples, and estimation of the mixing matrix. Compared with the existing algorithms such as fuzzy clustering algorithm and probability density-based algorithm, the proposed algorithm does not require many iterations, which improves the efficiency. Simulation results show that the proposed algorithm has obvious advantages in the aspects of estimation accuracy of the mixing matrix as well as computational complexity and robustness.

In order to achieve accurate recovery signals under the underdetermined circumstance in a comparatively short time, an algorithm based on plane pursuit (PP) is proposed. The proposed algorithm selects the atoms according to the correlation between received signals and hyper planes, which are composed by column vectors of the mixing matrix, and uses these atoms to recover source signals. Simulation results demonstrate that the PP algorithm has low complexity and higher accuracy as compared with basic pursuit (BP), orthogonal matching pursuit (OMP), and adaptive sparsity matching pursuit (ASMP) algorithms.

We propose a novel source recovery algorithm for underdetermined blind source separation, which can result in better accuracy and lower computational cost. On the basis of the model of underdetermined blind source separation, the artificial neural network with single-layer perceptron is introduced into the proposed algorithm. Source signals are regarded as the weight vector of single-layer perceptron, and approximate l 0 -norm is taken into account for output error decision rule of the perceptron, which leads to the sparse recovery. Then the procedure of source recovery is adjusting the weight vector of the perceptron. What's more, the optimal learning factor is calculated and a descent sequence of smoothed parameter is used during iteration, which improves the performance and significantly decreases computational complexity of the proposed algorithm. The simulation results reveal that the algorithm proposed can recover the source signal with high precision, while it requires lower computational cost.