The goal of relational triple extraction is to extract knowledge-rich relational triples from unstructured text. Although the previous methods obtain considerable performance, there are still some problems, such as error propagation, the overlapping triple problem, and suboptimal subject–object alignment. To address the shortcomings above, in this paper, we decompose this task into three subtasks from a fresh perspective: entity extraction, subject–object alignment and relation judgement, as well as propose a novel bi-directional translating decoding model (BTDM). Specifically, a bidirectional translating decoding structure is designed to perform entity extraction and subject–object alignment, which decodes entity pairs from both forward and backward extraction. The bidirectional structure effectively mitigates the error propagation problem and aligns the subject–object pairs. The translating decoding approach handles the overlapping triple problem. Finally, a (entity pair, relation) bipartite graph is designed to achieve practical relationship judgement. Experiments show that our model outperforms previous methods and achieves state-of-the-art performance on NYT and WebNLG. We achieved F1-scores of 92.7% and 93.8% on the two datasets. Meanwhile, in various complementary experiments on complex scenarios, our model demonstrates consistent performance gain in various complex scenarios.

In this paper, we present a novel signal sorting method aimed at reducing the impact of interference and noise while achieving blind detection and accurate sorting of a variable-speed frequency-hopping communication system. To achieve this, we combine spectrogram analysis with an innovative sorting approach. First, we generate the spectrogram of the received signal, and then employ a morphology filter to effectively eliminate noise and sweep frequency interference from the spectrogram. Subsequently, we identify and mark connected domains in the spectrogram, from which we extract the duration data to create a dataset specifically for separating fixed-frequency interference. Furthermore, we propose a specialized time alignment algorithm designed to accommodate the unique characteristics of variable-speed frequency-hopping signals, enabling precise sorting of variable-speed frequency-hopping signals. Through rigorous comparative evaluations against existing algorithms, we demonstrate that our proposed approach provides superior accuracy by offering a clearer representation of the time–frequency situation of the received signals. The proposed method provides a high correct sorting probability which is equal to 0.8 when signal-to-noise ratio is 0 dB and reaches 1 when signal-to-noise ratio reaches over 12 dB. In comparison, the correct sorting probability of the comparison algorithm is far inferior to the proposed algorithm.

This paper considers the problem of estimating the parameters of a frequency-hopping signal under non-cooperative conditions. To make the estimation of different parameters independently of each other, a compressed domain frequency-hopping signal parameter estimation algorithm based on the improved atomic dictionary is proposed. By segmenting and compressive sampling the received signal, the center frequency of each signal segment is estimated using the maximum dot product. The signal segments are processed with central frequency variation using the improved atomic dictionary to accurately estimate the hopping time. We highlight that one superiority of the proposed algorithm is that high-resolution center frequency estimation can be directly obtained without reconstructing the frequency-hopping signal. Additionally, another superiority of the proposed algorithm is that hopping time estimation has nothing to do with center frequency estimation. Numerical results show that the proposed algorithm can achieve superior performance compared with the competing method.