RESUMO
The spin torque nano-oscillator (STNO) is a very promising candidate for next generation telecommunication systems due to its small size ~100 nm and high output frequency range. However, it still suffers low output power, usually smaller than µW, and very high phase noise. Also, the modulation method for the STNO should be further developed. The frequency modulation and amplitude modulation method for STNO can be easily applied because of the non-linear nature of STNO, yet it is very rare to see the proposal of a phase modulation method. In this work, we propose a robust phase shift keying modulation method for STNO. Its feasibility is demonstrated with both theoretical and numerical analysis, and its robustness is investigated under room temperature thermal noise. It is shown that our proposed phase modulation method can tune the phase arbitrarily, while the modulation speed can be as fast as 10 ns at room temperature. Comparing with the other phase modulation method, our approach has advantages of larger phase tuning range and stronger robustness against thermal noise.
RESUMO
Spin transfer nano-oscillators (STNOs) are a new type of radio frequency (RF) oscillators that utilize the current-induced magnetization precession in a magnetic tunnel junction device to generate high frequency microwave signal. Since both the frequency and the amplitude of STNOs can be tuned by changing the current, they are potentially used for amplitude shift keying and frequency shift keying modulation without the need for an RF mixer, which leads to compact RF components. In this letter, a novel strategy is proposed to modulate the frequency and the amplitude by memristor-controlled spin nano-oscillators, whereby the STNO is responsible for microwave emitting and memristor serves as a current regulator which further modulates the frequency and amplitude. In addition, the I-V curves show that a multilevel resistance behavior can also be achieved in the same architecture.
RESUMO
To handle imbalanced datasets in machine learning or deep learning models, some studies suggest sampling techniques to generate virtual examples of minority classes to improve the models' prediction accuracy. However, for kernel-based support vector machines (SVM), some sampling methods suggest generating synthetic examples in an original data space rather than in a high-dimensional feature space. This may be ineffective in improving SVM classification for imbalanced datasets. To address this problem, we propose a novel hybrid sampling technique termed modified mega-trend-diffusion-extreme learning machine (MMTD-ELM) to effectively move the SVM decision boundary toward a region of the majority class. By this movement, the prediction of SVM for minority class examples can be improved. The proposed method combines α-cut fuzzy number method for screening representative examples of majority class and MMTD method for creating new examples of the minority class. Furthermore, we construct a bagging ELM model to monitor the similarity between new examples and original data. In this paper, four datasets are used to test the efficiency of the proposed MMTD-ELM method in imbalanced data prediction. Additionally, we deployed two SVM models to compare prediction performance of the proposed MMTD-ELM method with three state-of-the-art sampling techniques in terms of geometric mean (G-mean), F-measure (F1), index of balanced accuracy (IBA) and area under curve (AUC) metrics. Furthermore, paired t-test is used to elucidate whether the suggested method has statistically significant differences from the other sampling techniques in terms of the four evaluation metrics. The experimental results demonstrated that the proposed method achieves the best average values in terms of G-mean, F1, IBA and AUC. Overall, the suggested MMTD-ELM method outperforms these sampling methods for imbalanced datasets.