In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to m...In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to machine learning techniques, the K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN). From a statistical and spectral analysis of signals, nine key differentiation features are extracted and used as input vectors for each trained model. The feature extraction is performed by using the Hilbert transform, the forward and inverse Fourier transforms. The experiments with the AMC Master dataset classify ten (10) types of analog and digital modulations. AM_DSB_FC, AM_DSB_SC, AM_USB, AM_LSB, FM, MPSK, 2PSK, MASK, 2ASK, MQAM are put forward in this article. For the simulation of the chosen model, signals are polluted by the Additive White Gaussian Noise (AWGN). The simulation results show that the best identification rate is the MLP neuronal method with 90.5% of accuracy after 10 dB signal-to-noise ratio value, with a shift of more than 15% from the k-nearest neighbors’ algorithm.展开更多
A computational model of analogical reasoning is presented, which divides analogical reasoning process into four subprocesses, i.e. reminding, elaboration, matching and transfer. For each subprocess, its role and the ...A computational model of analogical reasoning is presented, which divides analogical reasoning process into four subprocesses, i.e. reminding, elaboration, matching and transfer. For each subprocess, its role and the principles it follows are given. The model is discussed in detail, including salient feature-based reminding, relevance-directed elaboration, an improved matching model and a transfer model. And the advantages of this model are summarized based on the results of BHARS, which is an analogical reasoning system implemented by this model.展开更多
文摘In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to machine learning techniques, the K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN). From a statistical and spectral analysis of signals, nine key differentiation features are extracted and used as input vectors for each trained model. The feature extraction is performed by using the Hilbert transform, the forward and inverse Fourier transforms. The experiments with the AMC Master dataset classify ten (10) types of analog and digital modulations. AM_DSB_FC, AM_DSB_SC, AM_USB, AM_LSB, FM, MPSK, 2PSK, MASK, 2ASK, MQAM are put forward in this article. For the simulation of the chosen model, signals are polluted by the Additive White Gaussian Noise (AWGN). The simulation results show that the best identification rate is the MLP neuronal method with 90.5% of accuracy after 10 dB signal-to-noise ratio value, with a shift of more than 15% from the k-nearest neighbors’ algorithm.
基金Project supported by the National Natural Science Foundation of China and Key Project of Fundamental Research, Climbing Program of China.
文摘A computational model of analogical reasoning is presented, which divides analogical reasoning process into four subprocesses, i.e. reminding, elaboration, matching and transfer. For each subprocess, its role and the principles it follows are given. The model is discussed in detail, including salient feature-based reminding, relevance-directed elaboration, an improved matching model and a transfer model. And the advantages of this model are summarized based on the results of BHARS, which is an analogical reasoning system implemented by this model.
