Free Energy of Anisotropic Strangeon Stars

Can pulsar-like compact objects release further huge free energy besides the kinematic energy of rotation?This is actually relevant to the equation of state of cold supra-nuclear matter,which is still under hot debate.Enormous energy is surely needed to understand various observations,such asγ-ray bursts,fast radio bursts and softγ-ray repeaters.In this paper,the elastic/gravitational free energy of solid strangeon stars is revisited for strangeon stars,with two anisotropic models to calculate in general relativity.It is found that huge free energy(>10^(46)erg)could be released via starquakes,given an extremely small anisotropy((p_(t)-p_(r))/p_(r)~10^(-4),with pt/pr the tangential/radial pressure),implying that pulsar-like stars could have great potential of free energy release without extremely strong magnetic fields in the solid strangeon star model.

Incremental Learning of Radio Modulation Classification Based on Sample Recall

Radio modulation classification has always been an important technology in the field of communications.The difficulty of incremental learning in radio modulation classification is that learning new tasks will lead to catastrophic forgetting of old tasks.In this paper,we propose a sample memory and recall framework for incremental learning of radio modulation classification.For data with different signal-to-noise ratios,we use a partial memory strategy by selecting appropriate samples for memorizing.We compare the performance of our proposed method with three baselines through a large number of simulation experiments.Results show that our method achieves far higher classification accuracy than finetuning method and feature extraction method.Furthermore,it performs closely to joint training method which uses all old data in terms of classification accuracy which validates the effectiveness of our method against catastrophic forgetting.

Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios

Spectrum sensing is a key technology for cognitive radios.We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification.We normalize the received signal power to overcome the effects of noise power uncertainty.We train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new signals.We also use transfer learning strategies to improve the performance for real-world signals.Extensive experiments are conducted to evaluate the performance of this method.The simulation results show that the proposed method performs better than two traditional spectrum sensing methods,i.e.,maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based method.In addition,the experimental results of the new untrained signal types show that our method can adapt to the detection of these new signals.Furthermore,the real-world signal detection experiment results show that the detection performance can be further improved by transfer learning.Finally,experiments under colored noise show that our proposed method has superior detection performance under colored noise,while the traditional methods have a significant performance degradation,which further validate the superiority of our method.