scholarly journals Underwater Communication Signal Recognition Using Sequence Convolutional Network

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Yan Wang ◽  
Yiheng Jin ◽  
Hao Zhang ◽  
Qian Lu ◽  
Conghui Cao ◽  
...  
Keyword(s):  
Signal Recognition ◽  
Underwater Communication ◽  
Convolutional Network ◽  
Communication Signal

Smart Sensor for the Underwater Communication Signal

2021 ◽  
Author(s):  
Ashish Bagwari ◽  
Jyotshana Bagwari ◽  
G. S. Tomar
Keyword(s):  
Smart Sensor ◽  
Underwater Communication ◽  
Communication Signal

scholarly journals Research on signal modulation based on machine learning intelligent algorithm and computer automatic identification

2021 ◽  
Vol 2083 (4) ◽  
pp. 042092
Author(s):  
Zixi Li
Keyword(s):  
Neural Network ◽  
Signal To Noise Ratio ◽  
Feature Representation ◽  
Automatic Identification ◽  
Signal Recognition ◽  
Modulation Recognition ◽  
Data Set ◽  
Communication Signals ◽  
Communication Signal ◽  
Automatic Modulation Recognition

Abstract In the process of communication, modulation signal recognition and classification are an important part of non-cooperative communication. Automatic modulation recognition technology of communication signals based on feature extraction and pattern recognition is a key research object in the radio field. The use of neural network can achieve automatic recognition of a variety of modulation signals and achieve good results. In this method, the received signal is preprocessed to obtain the complex baseband signal including in-phase component and orthogonal component. As the data set of the input convolution neural network model, the signal further optimizes the traditional method of manual extraction of expert features for communication signal recognition, which has great limitations and low accuracy under low signal-to-noise ratio, and the simulation results are verified. The results show that the proposed method has stronger feature representation ability and competitiveness in automatic modulation recognition, and is helpful to promote the application of deep learning in the field of automatic modulation recognition.

Underwater non-cooperative communication signal recognition with deep learning

2017 ◽  
Vol 142 (4) ◽  
pp. 2732-2732 ◽  
Author(s):  
Cheng LI ◽  
Qiming Zhou ◽  
Xiao Han ◽  
Jingwei Yin ◽  
Mengqi Shao
Keyword(s):  
Deep Learning ◽  
Cooperative Communication ◽  
Signal Recognition ◽  
Communication Signal

Methodology of ECG Interpretation in the AVA Program

1990 ◽  
Vol 29 (04) ◽  
pp. 337-340 ◽  
Author(s):  
H. A. Pipberger ◽  
H. V. Pipberger ◽  
C. D. McManus
Keyword(s):  
Right Atrial ◽  
Signal Recognition ◽  
Multivariate Statistical ◽  
Reference Base ◽  
T Wave ◽  
Electrolyte Disturbances ◽  
Ecg Criteria ◽  
Wall Injury ◽  
Ecg Leads ◽  
Diagnostic Module

AbstractThe AVA program combines a thirty-year history with an approach that remains innovative; namely: multivariate statistical analysis on orthogonal ECG leads. Its diagnostic reference base includes only diagnoses independently verified by non-ECG criteria. The diagnostic module assesses probabilities of nine alternative disease categories, based on QRS-T parameters; or four other categories in case of conduction defects. Probabilities of left or right atrial overload are also computed. The program also recognizes wall injury, T-wave abnormalities, electrolyte disturbances, myocardial ischemia, and makes differential diagnoses between strain and digitalis effects. An arrhythmia classification module can generate any of 40 rhythm statements. Signal recognition is based on the spatial velocity function. The program has been translated to a microcomputer version.

Adaptive identification methods of nonlinear random electrical signal

2020 ◽  
Vol 64 (1-4) ◽  
pp. 431-438
Author(s):  
Jian Liu ◽  
Lihui Wang ◽  
Zhengqi Tian
Keyword(s):  
Wavelet Transform ◽  
Electric Vehicle ◽  
Signal To Noise Ratio ◽  
Sudden Change ◽  
Electrical Signal ◽  
Signal Recognition ◽  
State Variable ◽  
Signal Characteristics ◽  
The Matrix ◽  
Kalman Filter Algorithm

The nonlinearity of the electric vehicle DC charging equipment and the complexity of the charging environment lead to the complex and changeable DC charging signal of the electric vehicle. It is urgent to study the distortion signal recognition method suitable for the electric vehicle DC charging. Focusing on the characteristics of fundamental and ripple in DC charging signal, the Kalman filter algorithm is used to establish the matrix model, and the state variable method is introduced into the filter algorithm to track the parameter state, and the amplitude and phase of the fundamental waves and each secondary ripple are identified; In view of the time-varying characteristics of the unsteady and abrupt signal in the DC charging signal, the stratification and threshold parameters of the wavelet transform are corrected, and a multi-resolution method is established to identify and separate the unsteady and abrupt signals. Identification method of DC charging distortion signal of electric vehicle based on Kalman/modified wavelet transform is used to decompose and identify the signal characteristics of the whole charging process. Experiment results demonstrate that the algorithm can accurately identify ripple, sudden change and unsteady wave during charging. It has higher signal to noise ratio and lower mean root mean square error.

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