Precision bottom tracking is a core step in the data processing of side scan sonar (SSS), which is of critical value for the quality of the final SSS production. Currently, the automatic precision SSS bottom tracking remains challenging due to the complex noise caused by the measuring environment; especially, the existing methods did not fully exploit the temporal correlations or depended on the hand-crafted setting. Therefore, we proposed a novel temporal fusion-based 1-D sequence semantic segmentation model, TFSSM–1–D, to fuse the temporal correlation features and perform automatic precision SSS bottom tracking. The TFSSM–1–D uses the deep learning (DL) encoder–decoder model for mapping inputs to 1-D semantic label outputs, with the aid of preprocess and temporal fusion modules, to improve the accuracy and robustness. Among them, preprocess module is used to introduce the prior knowledge of bilateral symmetry, which alleviates the defect of long-distance features correlation caused by the inductive bias of convolution neural network (CNN). The temporal fusion consists of the point-wise temporal fusion module (PTFM), and the bi-directional attention propagation module (BAPM) guides the model to explicitly fuse the temporal variation features on different scales. The experimental results demonstrate the effectiveness of TFSSM–1–D, and its mean port offset error (MPOE) reaches 2.7058 on the testing set without downsampling, which is 40% lower than the previous DL based model with single ping input, and other evaluation metrics have also significantly improved. The inference on unseen data shows that TFSSM–1–D can achieve precision bottom tracking with good noise immunity.

The bottom tracking is to confirm the first bottom return signal in each ping to divide the data into the seabed area and the water column area, which is an essential part of side-scan sonar (SSS) data processing. More than that, SSS is usually affected by water flow or suspended solids, resulting in serious noises, which make the bottom tracking more difficult and complex. Some existing methods follow ideas such as abnormal point detection or filtering, resulting in unsatisfactory bottom tracking effects. Inspired by the semantic segmentation mission in deep learning, we innovatively transform the bottom tracking into a binary-class semantic segmentation mission on 1-D SSS data. Following this idea, we propose a semantic segmentation-based bottom tracking method and modify two image semantic segmentation models, SegNet and U-Net, so that they can achieve bottom tracking on 1-D SSS data. Benefiting from the large receptive field brought by convolution and down-sampling, our method has made significant progress in accuracy and antinoise performance compared with the existing method. According to the experimental result, our method has obvious improvement compared with the existing method on the testing set; not only that, our method also performs better on the unused data.

Graph query services (GQS) are widely used today to interactively answer graph traversal queries on large-scale graph data. Existing graph query engines focus largely on optimizing the latency of a single query. This ignores significant challenges posed by GQS, including fine-grained control and scheduling during query execution, as well as performance isolation and load balancing in various levels from across user to intra-query. To tackle these control and scheduling challenges, we propose a novel scoped dataflow for modeling graph traversal queries, which explicitly exposes concurrent execution and control of any subquery to the finest granularity. We implemented Banyan, an engine based on the scoped dataflow model for GQS. Banyan focuses on scaling up the performance on a single machine, and provides the ability to easily scale out. Extensive experiments on multiple benchmarks show that Banyan improves performance by up to three orders of magnitude over state-of-the-art graph query engines, while providing performance isolation and load balancing.