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In modern warfare, active jamming can significantly impact the performance of radar systems. In the case of a low signal-to-noise ratio (SNR), suppressing radar jamming becomes more challenging, especially when dealing with mainlobe jamming. The traditional mix source separation (MSS) algorithm exhibits poor anti-jamming performance or may even fail in such scenarios. In the case of unknown prior information, to address this problem, this article integrates the concept of MSS with deep learning techniques to design a deep convolutional mix separation network based on the attention mechanism. End-to-end feature extraction, feature separation, and time-domain waveform recovery are performed on the received signal to separate the pure echo signal and jamming signal from the received signal. Finally, the effectiveness of the proposed method is verified through simulation. The results demonstrate that the proposed method can effectively suppress various types of mainlobe jamming under low SNR conditions. The correlation coefficient exceeds 0.9, indicating its superiority over traditional anti-jamming methods.

Grapevine is extensively grown for fresh table grapes, wine, and other processed products worldwide. DNA methylation levels are regulated by DNA methylation maintenance and DNA methylation removal involved in the grapevine growth. We comprehensively analyzed the transcriptome and metabolome of the ‘Kyoho’ fruit with or without demethylation and screened for a large number of differential genes and metabolites. Color, hardness, and aroma are the most obvious traits reflecting the ripening of grapes. We used gas chromatography–mass spectrometry and high-performance liquid chromatography to understand the changes in metabolites during ripening. We cloned many key genes selected by transcriptome analysis and found that intron retention was observed in VvCHS, VvDFR, and VvGST. The imbalance of methylation levels affects the alternative splicing of pre-mRNA, which makes the translation process abnormal and affects gene expression. In addition, analyzing promoters of some genes, such as proVvGST4and proVvUFGT, found that the promoters of these genes after demethylating were more difficult to methylate. Taken together, this study will provide new insights into comprehension of the molecular mechanism of methylation during ripening of grape berries. In addition, the study provides some genetic information to help guide our improvement, cultivation, and management of grapes in the future.

Soluble sugar and organic acids are key determinants of fruit organoleptic quality and directly affect the commodity value and economic returns of fruit crops. We performed whole-genome sequencing of the apple varieties Gala and Xiahongrou, along with their F1 hybrids, to construct a high-density bin map. Our quantitative genetic analysis pinpointed 53 quantitative trait loci (QTLs) related to 11 sugar and acid traits. We identified a candidate gene, MdNADP-ME, responsible for malate degradation, in a stable QTL on linkage group 15. Sequence analysis revealed an A/C SNP in the promoter region (MEp-799) that influences binding of the MdMYB2 transcription factor, thereby affecting MdNADP-MEexpression. In our study of various apple genotypes, this SNP has been demonstrated to be linked to malate and fructose levels. We also developed a dCAPS marker associated with fruit fructose content. These results substantiate the role of MdNADP-MEin maintaining the equilibrium between sugar and acid contents in apple fruits.

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. [ABSTRACT FROM AUTHOR]

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 ℓ0-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.

As an important part of the channel fading, large scale fading should be considered in downlink massive multiple-input multiple-output (MIMO) systems. This paper investigates the downlink massive MIMO system over a large scale fading channel, when the base station takes advantage of maximum-radio transmission (MRT) precoding. The cases when the base station has perfect and imperfect channel state information (CSI) are considered respectively. Specially, through the analysis of single user's ergodic achievable rate, some novel and approximate expressions for the spectral efficiency (SE) are derived. Based on the approximate SE, the effect of the channel estimation error is analyzed intuitively. In addition, the average SE of all the users with different large-scale fading parameters is carefully investigated. Simulations validate that all the theoretical results coincide with numerical results and the large scale fading factors have little influence on SE reduction resulted from channel estimation.

Minimum mean square error (MMSE) detection algorithm can achieve nearly optimal performance when the number of antennas at the base station (BS) is large enough compared to the number of users. But the traditional MMSE involves complicated matrix inversion. In this paper, we propose a modified MMSE algorithm which exploits the channel characteristics occurring in massive multiple-input multiple-output (MIMO) channels and the relaxation iteration (RI) method to avoid the matrix inversion. A proper initial solution is given to accelerate the convergence speed. In addition, we point out that the channel estimation scheme used in channel hardening-exploiting message passing (CHEMP) receiver is very appropriate for our proposed detection algorithm. Simulation results verify that the proposed algorithm can achieve very close performance of the traditional MMSE algorithm with a small number of iterations.

We consider the problem of nearly optimal energy efficiency in massive (Multiinput Multi-output) MIMO systems. Considering the correlated channel in practice, we derive the ergodic expression with zero-forcing precoding and analyze the simplified antennas selection method. Aiming at optimizing the energy efficiency, the closed form expressions of the nearly optimal number of transmit antennas and transmit power are given under the circuit consumption model. The joint solution of the number of transmit antennas and transmit power was replaced to only solve transmit power. Based on the expression only related with transmit power, we give an energy efficiency optimization algorithm. The simulation results show that the proposed algorithm can achieve nearly optimal energy efficiency with fast convergence speed.