Physiological signals are high-dimensional time series of great practical values in medical and healthcare applications. However, previous works on its classification fail to obtain promising results due the intractable data characteristics severe label sparsity issues. In this paper, we try address these challenges by proposing a more effective interpretable scheme tailored for physiological s...

Schizophrenia is a severe mental disorder associated with wide spectrum of cognitive and neurophysiological dysfunctions. Early diagnosis still difficult based on the manifestation disorder. In this study, we have evaluated whether machine learning techniques can help in schizophrenia, proposed processing pipeline order to obtain classifiers schizophrenia resting state EEG data. We computed wel...

In a non-cooperative communication environment, automatic modulation classification (AMC) is an essential technology for analyzing signals and classifying different kinds of signal before they are demodulated. Deep learning (DL)-based AMC has been proposed as efficient method achieving high performance. However, most current DL-AMC methods have limited generalization capabilities under varying ...

Automatic modulation classification (AMC) aims to automatically identify the type of a detected signal in an intelligent wireless receiver, such as software-defined radio (SDR). Recently, deep learning-based methods convolutional neural networks have been applied AMC, showing high-accuracy performance. However, earlier studies do not consider various degradations that can possibly occur during ...

Ultrasonic signal classification in nondestructive testing is of great significance for the detection defects. The current methods have mainly utilized low-level handcrafted features based on traditional processing approaches, such as Fourier transform, wavelet transform and like, to interpret information carried by signals classification. This paper proposes an automatic method via a convoluti...

Wireless sensor networks (WSNs) are a number of sensitive nodes senses physical phenomenon at the position their deployment then sends information to base station take appropriate operation. used in many applications such track military targets, discover fires, study natural phenomena as earthquakes, humidity, heat, etc. The spread large areas and it is difficult locate them manually because th...

Deep learning methods achieve great success in many areas due to their powerful feature extraction capabilities and end-to-end training mechanism, recently they are also introduced for radio signal modulation classification. In this paper, we propose a novel deep framework called SigNet, where signal-to-matrix (S2M) operator is adopted convert the original into square matrix first co-trained wi...