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With the rapid development of information transfer technology and the influence of complex electromagnetic environment, the signal components in communication systems are becoming more and more complex, and the spectrum congestion is further aggravated, which seriously affects the performance of communication systems. Single-channel blind source separation technology is an effective solution, but the existing algorithms are deficient in terms of estimation accuracy, time delay and computational complexity. Based on the powerful feature extraction capability and timing signal processing capability of deep learning, this paper proposes an end-to-end time-domain blind separation convolutional network structure, including three parts: encoder, separation module and decoder. The attention mechanism module is also introduced into the encoding layer of the network to enhance the deep feature extraction ability of the network, highlight the influence of important timing features of communication signals, further improve the separation performance and enhance the practicality of the network. The experimental results show that the algorithm has superior performance and can effectively separate the mixed communication signal of various modulation types, and it can also extract own-side communication signals from high-power masked signals. Compared with the existing algorithms, the network in this paper has high separation accuracy and low computational complexity, which breaks the bottleneck of traditional single-channel blind separation algorithms in complex communication electromagnetic environment. [ABSTRACT FROM AUTHOR]

In this paper, a novel method is introduced to diagnose the cardiac arrhythmias. Automatic diagnosis of cardiac arrhythmias is crucial for successful treatment of heart diseases, and nowadays, machine learning is used to facilitate this purpose. For accurate classification of arrhythmias, it is vital to extract some appropriate features which distinguish different classes. In this research, a deep convolutional neural network is used to extract the features. Since the heart rates of various patients are very different, arrhythmias of a specific class will be placed in different locations of feature space. To reduce the intra-class variations, each patient's heart rate is mapped with a dedicated function to increase its similarity to the heart rate of a training patient. The proposed patient-specific mapping decreases the intra-class variation and significantly increases the classification accuracy of cardiac arrhythmias. The calculated accuracy is about 91.08%, which shows the better performance compared to other similar works. [ABSTRACT FROM AUTHOR]

Denoising electrocardiogram (ECG) signals is a challenge because of the unavoidable artifacts. In this paper, an adaptive and effective signed error normalized least mean square algorithm (SE-NLMS) is proposed to improve denoising and reduce the mean square error (MSE). The performance of the SE-NLMS algorithm with unfolding technique (SE-UNLMS) has been compared with that of the normalized least mean square algorithm with unfolding structure (UNLMS) as well as the least mean square algorithm with unfolding structure (ULMS). SE-NLMS has the advantage of NLMS, namely an adaptive convergence rate and reduced computational complexity. We have implemented all three algorithms, namely LMS, NLMS and SE-NLMS, using an unfolding architecture that offers minimum delay and is suitable for real-time applications. Performance metrics such as an MSE of 0.0062 and a signal-to-noise ratio (SNR) improvement of about 10.2% are achieved through the proposed method of SE-UNLMS compared to other denoising methods. Additionally, exploration has been carried out for the application of denoised ECG signals under various arrhythmia conditions. The time-domain features were extracted from various denoised ECG signals and classified with support vector machines (SVM) to yield a good classification accuracy of 96.5%. The SE-UNLMS denoising method is demonstrated to be a valuable implementation for the detection and diagnosis of arrhythmias in real-world medical situations via this classification process. For real-time validation, the proposed method SE-UNLMS was implemented on a field-programmable gate array (FPGA) using Spartan 6 VPTB-20. The FPGA implementation highlights the feasibility of the proposed SE-UNLMS for real-time ECG monitoring applications. [ABSTRACT FROM AUTHOR]