Pollution caused by oil spills does irreversible harm to marine biosystems. To find maritime oil spills, Synthetic Aperture Radar (SAR) has emerged as a crucial mean. How to accurately distinguish oil spill areas from other types of areas is a committed step in detecting oil spills. Owing to its capacity to extract multiscale features and its distinctive decoder, the Deeplabv3+ framework has been developed into an excellent deep learning model in field of picture segmentation. However, in some SAR pictures, there is a lack of clarity in the segmentation of oil film edges and incorrect segmentation of small areas. In order to solve these problems, an improved network, named ASA-DRNet, has been proposed. Firstly, a new structure which combines an axial self-attention module with ResNet-18 is proposed as the backbone of DeepLabv3+ encoder. Secondly, a atrous spatial pyramid pooling (ASPP) module is optimized to improve the network’s capacity of extracting multiscale features and to increase the speed of model calculation and finally merging low-level features of different resolutions to enhance the competence of network to extract edge information. The experiments show that ASA-DRNet obtains the better results compared to other neural network models.

This study describes an integrated approach for fetal heart beat detection from abdominal ECG signals. The signal is first processed by the first‐order difference operation, and then, the envelope of the differentiated signal is given by the Hilbert transform, which indicates both the maternal and fetal heart beats. After that, the non‐linear state‐space projections is applied to the envelope signal to remove the maternal heart beats, obtaining the fetal heart beats envelope. The validity and performance of the described method are confirmed through experiments on real ECG data. A comparison with the well‐known independent component analysis method has also been presented.

A new method of estimating the heart rate (HR) from electrocardiogram (ECG) signals, which accurately estimates the fundamental frequency of the heartbeat using the iterative Fourier interpolation algorithm, is presented. This frequency‐domain method has two advantages: (i) the Fourier interpolation algorithm could improve the estimation precision of the heartbeat fundamental frequency significantly without increasing the recording signal length, which could monitor the HR variability; (ii) the heartbeat fundamental frequency is low, usually between 1 and 2 Hz; thus, the required ECG recording can be acquired at a low sampling frequency, such as 20–30 Hz, in which the conventional R‐wave detection HR estimation method fails.

This paper introduces an integrated approach to estimate the fetal heart rate from the abdominal Electrocardiogram (ECG) signal. Empirical mode decomposition (EMD) can decompose the fetal ECG signal into a set of intrinsic mode functions, which can be used as the indicator of the occurrence for the fetal heartbeats. The decomposition basis functions are directly derived from the fetal signal under test, which make the detection process robust and adaptive. Multiple signal classification (MUSIC) is a high resolution algorithm for frequency estimation, which can be applied to the fetal heartbeats indicator sequence output from the preceding EMD, estimating the fetal heart rate in frequency domain without the heartbeat wave detection. Compared with the popular Independent component analysis (ICA) method, the proposed method has shown improved robustness and fidelity in estimation of the fetal heart rate during testing with real fetal ECG database from DaISy and PhysioNet.