"Signal Classification Method Based on Support Vector Machine and High-Order Cumulants"
written by Xin ZHOU, Ying WU, Bin YANG,
published by Wireless Sensor Network, Vol.2 No.1, 2010
has been cited by the following article(s):
  • Google Scholar
  • CrossRef
[1] Neuroimaging Subjective Labeling Dichotomization and Class Imbalance Alleviation
2019
[2] Wireless Signal Classification Based on High-Order Cumulants and Machine Learning
2018
[3] 페이딩 환경에서의 딥러닝 기반 고성능 자동 변조분류 기법
2018
[4] A method for recognition and classification for hybrid signals based on Deep Convolutional Neural Network
2018
[5] Automatic Modulation Classification of Overlapped Sources Using Multi-Gene Genetic Programming With Structural Risk Minimization Principle
2018
[6] Polar Feature Based Deep Architectures for Automatic Modulation Classification Considering Channel Fading
2018
[7] INFORME DE PROYECTO INTEGRADOR
2018
[8] Classifiers Accuracy Improvement Based on Missing Data Imputation
Journal of Artificial Intelligence and Soft Computing Research, 2018
[9] An Information Retrieval Approach for Robust Prediction of Road Surface States
Sensors, 2017
[10] Automatic modulation classification of overlapped sources using multiple cumulants
2017
[11] Robust Automatic Modulation Classification Technique for Fading Channels via Deep Neural Network
Entropy, 2017
[12] Human motion identification for rehabilitation exercise assessment of knee osteoarthritis
2017
[13] FPGA-based Automatic Modulation Recognition System for Small Satellite Communication Systems
2017
[14] System and method for signal emitter identification using higher-order cumulants
2017
[15] Automatic digital modulation recognition based on stacked sparse autoencoder
2017
[16] Development of wavelet transforms to predict methane in chili using the electronic nose
2017
[17] Cumulant based maximum likelihood classification for overlapped signals
Electronics Letters, 2016
[18] 딥러닝 기술을 이용한 디지털 변조타입 자동 인식 기술
The Journal of Korean Institute of Information Technology, 2016
[19] Supervised Radar Signal Classification
2016
[20] Online segmentation with multi-layer SVM for knee osteoarthritis rehabilitation monitoring
2016
[21] OFDMA system identification using cyclic autocorrelation function: A software defined radio testbed
2016
[22] An SNR estimation based adaptive hierarchical modulation classification method to recognize M-ary QAM and M-ary PSK signals
Signal Processing, Communication and Networking (ICSCN), 2015 3rd International Conference on, 2015
[23] Sensor Data Classification for Renal Dysfunction Patients Using Support Vector Machine
Journal of Medical and Biological Engineering, 2015
[24] Classificação automática de modulação baseada em aprendizagem discriminativa
2015
[25] Deep Convolutional Neural Networks as a Method to Classify Rotating Objects based on Monostatic Radar Cross Section
IET Research Journals, 2015
[26] A Novel Modulation Classification Approach Using
The Scientific World Journal. Hindawi Publishing Corporation, 2014
[27] Recognition of QAM Signals with Low SNR Using a Combined Threshold Algorithm
IETE Journal of Research, 2014
[28] Parameter selection for SVM in automatic modulation classification of analog and digital signals
Telecommunications Symposium (ITS), 2014 International. IEEE, 2014
[29] A Novel Modulation Classification Approach Using Gabor Filter Network
The Scientific World Journal. Hindawi Publishing Corporation, 2014
[30] Modulation Recognition of MFSK Signals Based on Multifractal Spectrum
Wireless personal communications. Springer Berlin Heidelberg, 2013
[31] Digital Modulation Classification in Cognitive Radio Using Hybrid Particle Swarm Optimization Algorithm-support Vector Machines:
Journal of Computational Information Systems, 2013
[32] An overview of feature-based methods for digital modulation classification
Communications, Signal Processing, and their Applications (ICCSPA), 2013 1st International Conference on. IEEE, 2013
[33] Specific Emitter Identification Based on Transient Energy Trajectory
Progress In Electromagnetics Research C, 2013
[34] Automatic modulation classification of digital modulations in presence of HF noise
EURASIP Journal on Advances in Signal Processing. Springer Berlin Heidelberg, 2012
[35] Classification of Multi-User Chirp Modulation Signals Using Wavelet Higher-Order-Statistics Features and Artificial Intelligence Techniques
Int'l J. of Communications, Network and System Sciences, 2012
[36] Automatic Modulation Classification Using Grey Relational Analysis
2011
[37] Classification of multi-user chirp modulation signals using higher order cumulant features and four types of classifiers
Radio Science Conference (NRSC), 2011 28th National. IEEE, 2011