scholarly journals Emitter Identification of Digital Modulation Transmitter Based on Nonlinearity and Modulation Distortion of Power Amplifier

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4362
Author(s):  
Yue Chen ◽  
Xiang Chen ◽  
Yingke Lei
Keyword(s):  
Power Amplifier ◽  
Recognition Accuracy ◽  
Test Signal ◽  
Digital Modulation ◽  
Practical Applications ◽  
Wireless Communication Network ◽  
Reconstructed Signal ◽  
Modulation Parameter ◽  
Modulation Signal ◽  
Training Signal

Specific transmitter identification (SEI) is a technology that uses a received signal to identify to which individual radiation source the transmitted signal belongs. It can complete the identification of the signal transmitter in a non-cooperative scenario. Therefore, there are broad application prospects in the field of wireless-communication-network security, spectral resource management, and military battlefield-target communication countermeasures. This article demodulates and reconstructs a digital modulation signal to obtain a signal without modulator distortion and power-amplifier nonlinearity. Comparing the reconstructed signal with the actual received signal, the coefficient representation of the nonlinearity of the power amplifier and the distortion of the modulator can be obtained, and these coefficients can be used as the fingerprint characteristics of different transmitters through a convolutional neural network (CNN) to complete the identification of specific transmitters. The existing SEI strategy for changing the modulation parameters of a test signal is to mix part of the test signal with the training signal so that the classifier can learn the signal of which the modulation parameter was changed. This method is still data-oriented and cannot process signals for which the classifier has not been trained. It has certain limitations in practical applications. We compared the fingerprint features extracted by the method in this study with the fingerprint features extracted by the bispectral method. When SNR < 20 dB, the recognition accuracy of the bispectral method dropped rapidly. The method in this paper still achieved 86% recognition accuracy when SNR = 0 dB. When the carrier frequency of the test signal was changed, the bispectral feature failed, and the proposed method could still achieve a recognition accuracy of about 70%. When changing the test-signal baud rate, the proposed method could still achieve a classification accuracy rate of more than 70% for four different individual radiation sources when SNR = 0 dB.

Improvement of Carrier Power to Third-Order Intermodulation Distortion Power Ratio for a Power Amplifier at 5.25 GHz using LINC Method

2018 ◽  
Vol 12 (1) ◽  
pp. 34-40
Author(s):  
Said Elkhaldi ◽  
Naima Amar Touhami ◽  
Mohamed Aghoutane ◽  
Taj-eddin Elhamadi
Keyword(s):  
Power Amplifier ◽  
Artificial Neural Network Model ◽  
Power Ratio ◽  
Linear Amplification ◽  
Third Order ◽  
Gaas Mesfet ◽  
Constant Envelope ◽  
Modulation Signal ◽  
Low Efficiency ◽  
Nonlinear Components

Introduction:This paper focuses on improving the power amplifier linearity for wireless communications. The use of a single branch of a power amplifier can produce high distortion with low efficiency.Method:In this paper, the Linear Amplification with Nonlinear Components (LINC) technique is used to improve the linearity and efficiency of the power amplifier. The LINC technique is based on converting the envelope modulation signal into two constant envelope phase-modulated baseband signals. After amplification and combining the resulting signals, the required linear output signal is obtained. To validate the proposed approach, LINC technique is used for linearizing an amplifier based on a GaAs MESFET (described by an artificial neural network Model).Conclusion:Good results have been achieved, and an improvement of about 40.80 dBc and 47.50 dBc respectively is obtained for the Δlower C/I and Δupper C/I at 5.25 GHz.

scholarly journals Electromyogram-based hand gesture recognition robust to various arm postures

2018 ◽  
Vol 14 (7) ◽  
pp. 155014771879075 ◽  
Author(s):  
Kiwon Rhee ◽  
Hyun-Chool Shin
Keyword(s):  
Gesture Recognition ◽  
Recognition Accuracy ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Hand Gestures ◽  
Practical Applications ◽  
Electrode Positioning ◽  
Recognition Technique

In the recognition of electromyogram-based hand gestures, the recognition accuracy may be degraded during the actual stage of practical applications for various reasons such as electrode positioning bias and different subjects. Besides these, the change in electromyogram signals due to different arm postures even for identical hand gestures is also an important issue. We propose an electromyogram-based hand gesture recognition technique robust to diverse arm postures. The proposed method uses both the signals of the accelerometer and electromyogram simultaneously to recognize correct hand gestures even for various arm postures. For the recognition of hand gestures, the electromyogram signals are statistically modeled considering the arm postures. In the experiments, we compared the cases that took into account the arm postures with the cases that disregarded the arm postures for the recognition of hand gestures. In the cases in which varied arm postures were disregarded, the recognition accuracy for correct hand gestures was 54.1%, whereas the cases using the method proposed in this study showed an 85.7% average recognition accuracy for hand gestures, an improvement of more than 31.6%. In this study, accelerometer and electromyogram signals were used simultaneously, which compensated the effect of different arm postures on the electromyogram signals and therefore improved the recognition accuracy of hand gestures.

Learn the Temporal-Spatial Feature of sEMG via Dual-Flow Network

2019 ◽  
Vol 16 (04) ◽  
pp. 1941004 ◽  
Author(s):  
Runze Tong ◽  
Yue Zhang ◽  
Hongfeng Chen ◽  
Honghai Liu
Keyword(s):  
Gesture Recognition ◽  
Short Term Memory ◽  
Recognition Accuracy ◽  
Human Machine Interaction ◽  
Flow Network ◽  
Spatial Feature ◽  
Practical Applications ◽  
Long Short Term Memory ◽  
Machine Interaction ◽  
Semg Signals

Surface electromyography (sEMG) signals have been widely used in human–machine interaction, providing more nature control expedience for external devices. However, due to the instability of sEMG, it is hard to extract consistent sEMG patterns for motion recognition. This paper proposes a dual-flow network to extract the temporal-spatial feature of sEMG for gesture recognition. The proposed network model uses convolutional neural network (CNN) and long short-term memory methods (LSTM) to, respectively, extract the spatial feature and temporal feature of sEMG, simultaneously. These features extracted by CNN and LSTM are merged into temporal-spatial feature to form an end-to-end network. A dataset was constructed for testing the performance of the network. In this database, the average recognition accuracy by using our dual-flow model reached 78.31%, which was improved by 6.69% compared to the baseline CNN (71.67%). In addition, NinaPro DB1 is also used to evaluate the proposed methods, receiving 1.86% higher recognition accuracy than the baseline CNN classifier. It is believed that the proposed dual-flow network owns the merit in extracting stable sEMG feature for gesture recognition, and can be further applied into practical applications.

Digital Modulation Recognition in Different Environments

2014 ◽  
Vol 1049-1050 ◽  
pp. 2084-2087 ◽  
Author(s):  
Rong Li
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Automatic Recognition ◽  
Modulation Recognition ◽  
Signal To Noise ◽  
Digital Modulation ◽  
Multipath Interference ◽  
Modulation Signal ◽  
Simulation Results ◽  
Recognition Success

For the using of multi-modulation, the precondition of receiving and demodulating signal is to determine the type of the modulation, so automatic recognition of modulation signal has significant influence on the analysis of the signals. In this paper, digital modulation recognition is studied respectively in different environment of White Gaussian Noise (WGN), stationary interference and multipath interference. The simulation results show that the recognition success rate is the highest in stationary interference environment and the lowest in multipath interference environment with the same signal to noise ratio (SNR).

Fault Diagnosis of Pump Based on a Hybrid HMM/SVM Model

2011 ◽  
Vol 188 ◽  
pp. 629-635
Author(s):  
Xia Yue ◽  
Chun Liang Zhang ◽  
Jian Li ◽  
H.Y. Zhu
Keyword(s):  
Fault Diagnosis ◽  
Hybrid Model ◽  
Large Scale ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Double Layers ◽  
Support Vector ◽  
Practical Applications ◽  
Average Recognition Rate ◽  
Svm Model

A hybrid support vector machine (SVM) and hidden Markov model (HMM) model was introduced into the fault diagnosis of pump. This model had double layers: the first layer used HMM to classify preliminarily in order to get the coverage of possible faults; the second layer utilized this information to activate the corresponding SVMs for improving the recognition accuracy. The structure of this hybrid model was clear and feasible. Especially the model had the potential of large-scale multiclass application in fault diagnosis because of its good scalability. The recognition experiments of 26 statuses on the ZLH600-2 pump showed that the recognition capability of this model was sound in multiclass problems. The recognition rate of one bearing eccentricity increased from SVM’s 84.42% to 89.61% while the average recognition rate of hybrid model reached 95.05%. Although some goals while model constructed did not be fully realized, this model was still very good in practical applications.

scholarly journals A Novel Subspace Alignment-Based Interference Suppression Method for the Transfer Caused by Different Sample Carriers in Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4846 ◽  
Author(s):  
Zhifang Liang ◽  
Fengchun Tian ◽  
Ci Zhang ◽  
Liu Yang
Keyword(s):  
Wound Infection ◽  
Electronic Nose ◽  
Recognition Accuracy ◽  
Interference Suppression ◽  
Laboratory Data ◽  
Clinical Samples ◽  
Culture Solution ◽  
Practical Applications ◽  
Whole Space ◽  
Subspace Alignment

A medical electronic nose (e-nose) with 31 gas sensors is used for wound infection detection by analyzing the bacterial metabolites. In practical applications, the prediction accuracy drops dramatically when the prediction model established by laboratory data is directly used in human clinical samples. This is a key issue for medical e-nose which should be more worthy of attention. The host (carrier) of bacteria can be the culture solution, the animal wound, or the human wound. As well, the bacterial culture solution or animals (such as: mice, rabbits, etc.) obtained easily are usually used as experimental subjects to collect sufficient sensor array data to establish the robust predictive model, but it brings another serious interference problem at the same time. Different carriers have different background interferences, therefore the distribution of data collected under different carriers is different, which will make a certain impact on the recognition accuracy in the detection of human wound infection. This type of interference problem is called “transfer caused by different sample carriers”. In this paper, a novel subspace alignment-based interference suppression (SAIS) method with domain correction capability is proposed to solve this interference problem. The subspace is the part of space whose dimension is smaller than the whole space, and it has some specific properties. In this method, first the subspaces of different data domains are gotten, and then one subspace is aligned to another subspace, thereby the problem of different distributions between two domains is solved. From experimental results, it can be found that the recognition accuracy of the infected rat samples increases from 29.18% (there is no interference suppression) to 82.55% (interference suppress by SAIS).

scholarly journals Early Fault Diagnosis for Planetary Gearbox Based Wavelet Packet Energy and Modulation Signal Bispectrum Analysis

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2908 ◽  
Author(s):  
Junchao Guo ◽  
Zhanqun Shi ◽  
Haiyang Li ◽  
Dong Zhen ◽  
Fengshou Gu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
High Energy ◽  
Rotating Machinery ◽  
Economic Losses ◽  
Mechanical Transmission ◽  
Planetary Gearbox ◽  
Time Frequency ◽  
Reconstructed Signal ◽  
Modulation Signal

The planetary gearbox is at the heart of most rotating machinery. The premature failure and subsequent downtime of a planetary gearbox not only seriously affects the reliability and safety of the entire rotating machinery but also results in severe accidents and economic losses in industrial applications. It is an important and challenging task to accurately detect failures in a planetary gearbox at an early stage to ensure the safety and reliability of the mechanical transmission system. In this paper, a novel method based on wavelet packet energy (WPE) and modulation signal bispectrum (MSB) analysis is proposed for planetary gearbox early fault diagnostics. First, the vibration signal is decomposed into different time-frequency subspaces using wavelet packet decomposition (WPD). The WPE is calculated in each time-frequency subspace. Secondly, the relatively high energy vectors are selected from a WPE matrix to obtain a reconstructed signal. The reconstructed signal is then subjected to MSB analysis to obtain the fault characteristic frequency for fault diagnosis of the planetary gearbox. The validity of the proposed method is carried out through analyzing the vibration signals of the test planetary gearbox in two fault cases. One fault is a chipped sun gear tooth and the other is an inner-race fault in the planet gear bearing. The results show that the proposed method is feasible and effective for early fault diagnosis in planetary gearboxes.

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