"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):
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[10] Automatic Modulation Classification Using Compressive Convolutional Neural Network
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[14] INFORME DE PROYECTO INTEGRADOR
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[16] Automatic Modulation Classification of Overlapped Sources Using Multi-Gene Genetic Programming With Structural Risk Minimization Principle
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[19] Classifiers Accuracy Improvement Based on Missing Data Imputation
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[20] System and method for signal emitter identification using higher-order cumulants
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[21] FPGA-based Automatic Modulation Recognition System for Small Satellite Communication Systems
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[22] Human motion identification for rehabilitation exercise assessment of knee osteoarthritis
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[25] An Information Retrieval Approach for Robust Prediction of Road Surface States
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[26] Development of wavelet transforms to predict methane in chili using the electronic nose
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[27] Automatic digital modulation recognition based on stacked sparse autoencoder
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[28] 딥러닝 기술을 이용한 디지털 변조타입 자동 인식 기술
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[29] OFDMA system identification using cyclic autocorrelation function: A software defined radio testbed
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[30] Online segmentation with multi-layer SVM for knee osteoarthritis rehabilitation monitoring
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[31] Supervised Radar Signal Classification
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[32] Cumulant based maximum likelihood classification for overlapped signals
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[33] Sensor Data Classification for Renal Dysfunction Patients Using Support Vector Machine
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[34] An SNR estimation based adaptive hierarchical modulation classification method to recognize M-ary QAM and M-ary PSK signals
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[35] Classificação automática de modulação baseada em aprendizagem discriminativa
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[36] AUTOMATIC MODULATION CLASSIFICATION USING FEATURE BASED APPROACH
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[37] Deep Convolutional Neural Networks as a Method to Classify Rotating Objects based on Monostatic Radar Cross Section
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[38] Otomatik modülasyon tanıma için etkili bir algoritma
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[39] Recognition of QAM Signals with Low SNR Using a Combined Threshold Algorithm
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[40] A Novel Modulation Classification Approach Using
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[42] A Novel Modulation Classification Approach Using Gabor Filter Network
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[43] Digital Modulation Classification in Cognitive Radio Using Hybrid Particle Swarm Optimization Algorithm-support Vector Machines:
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[44] Specific Emitter Identification Based on Transient Energy Trajectory
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[45] An overview of feature-based methods for digital modulation classification
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[46] Modulation Recognition of MFSK Signals Based on Multifractal Spectrum
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[47] Classification of Multi-User Chirp Modulation Signals Using Wavelet Higher-Order-Statistics Features and Artificial Intelligence Techniques
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[49] Classification of multi-user chirp modulation signals using higher order cumulant features and four types of classifiers
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[50] Automatic Modulation Classification Using Grey Relational Analysis
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[51] Classificaçao Automática de Modulaçao Baseada em Aprendizagem Discriminativa