"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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[12] Human motion identification for rehabilitation exercise assessment of knee osteoarthritis
[13] FPGA-based Automatic Modulation Recognition System for Small Satellite Communication Systems
[14] System and method for signal emitter identification using higher-order cumulants
[15] Automatic digital modulation recognition based on stacked sparse autoencoder
[16] Development of wavelet transforms to predict methane in chili using the electronic nose
[17] Cumulant based maximum likelihood classification for overlapped signals
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[19] Supervised Radar Signal Classification
[20] Online segmentation with multi-layer SVM for knee osteoarthritis rehabilitation monitoring
[21] OFDMA system identification using cyclic autocorrelation function: A software defined radio testbed
[22] An SNR estimation based adaptive hierarchical modulation classification method to recognize M-ary QAM and M-ary PSK signals
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[23] Sensor Data Classification for Renal Dysfunction Patients Using Support Vector Machine
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[24] Classificação automática de modulação baseada em aprendizagem discriminativa
[25] Deep Convolutional Neural Networks as a Method to Classify Rotating Objects based on Monostatic Radar Cross Section
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[26] A Novel Modulation Classification Approach Using
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[27] Recognition of QAM Signals with Low SNR Using a Combined Threshold Algorithm
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[29] A Novel Modulation Classification Approach Using Gabor Filter Network
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[30] Modulation Recognition of MFSK Signals Based on Multifractal Spectrum
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[31] Digital Modulation Classification in Cognitive Radio Using Hybrid Particle Swarm Optimization Algorithm-support Vector Machines:
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[32] An overview of feature-based methods for digital modulation classification
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[33] Specific Emitter Identification Based on Transient Energy Trajectory
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[34] Automatic modulation classification of digital modulations in presence of HF noise
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[35] Classification of Multi-User Chirp Modulation Signals Using Wavelet Higher-Order-Statistics Features and Artificial Intelligence Techniques
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[36] Automatic Modulation Classification Using Grey Relational Analysis
[37] Classification of multi-user chirp modulation signals using higher order cumulant features and four types of classifiers
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