Modulation recognition using artificial neural networks
Signal Processing
Wavelet applications in medicine
IEEE Spectrum
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
IEEE Computational Science & Engineering
Fingerprint Classification with Combinations of Support Vector Machines
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Comparative Exudate Classification Using Support Vector Machines and Neural Networks
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
A decision support system based on support vector machines for diagnosis of the heart valve diseases
Computers in Biology and Medicine
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
In this paper, the usage of an automatic decision system based on the combination of support vector machines (SVM), which recognizes the some analog modulation types, is introduced. Here, automatic analog modulation recognition performance of this system is compared with a multi-layer perceptrons (MLP) classifier. The discrete wavelet transform (DWT) and wavelet entropy (WE) methods are used for the extraction effective features from analog modulation signals in the feature extraction stages of both these SVM and MLP systems. In this study, some experiments were performed for finding the optimal C (cost) and @s (sigma) kernel parameters of SVM. The analog modulated signals used in this study are amplitude modulation (AM), double side band (DSB), upper single band (USB), lower single band (LSB), frequency modulation (FM), and phase modulation (PM). The performance of both SVM and MLP classifiers is evaluated by using total 3240 analog modulated signals. These test results show the effectiveness of the system proposed in this paper. The rate of correct classification is about 96.419753% for the sample analog modulated signals.