A large-range switchable RF signal generator is demonstrated using a triple-wavelength fiber laser with uneven-frequency-spacing. Due to the birefringence characteristics of the triple-wavelength fiber laser, switchable dual-wavelength operation can be obtained by adjusting a polarization controller. Therefore, we can achieve a stable RF signals at microwave or millimeter-wave band.

Slotted wireless ad hoc networks are drawing more and more attention because of their advantage of QoS (Quality of Service) support for multimedia applications owing to their collision-free packet transmission. Time slot assignment is an unavoidable and important problem in such networks. The existing time slot assignment methods have in general a drawback of limited available bandwidth due to their local assignment optimization without the consideration of directions of the radio wave transmission of wireless links along the routes in such networks. A new time slot assignment is proposed in this paper in order to overcome this drawback. The proposed assignment is different from the existing methods in the following aspects: a) consideration of link directions during time slot assignment; b) largest bandwidth to be achieved; c) feasibility in resource limited ad hoc networks because of its fast assignment. Moreover, the effectiveness of the proposal is confirmed by some simulation results.

Nonprimitive non-narrow-sense BCH codes have been studied by many scholars. In this paper, we utilize nonprimitive non-narrow-sense BCH codes to construct a family of asymmetric quantum codes and two families of quantum convolutional codes. Most quantum codes constructed in this paper are different from the ones in the literature. Moreover, some quantum codes constructed in this paper have good parameters compared with the ones in the literature.

Objective: Detection of Electrocardiogram (ECG) characteristic points can provide critical diagnostic information about heart diseases. We proposed a novel feature extraction and machine learning scheme for automatic detection of ECG characteristic points. Methods: A new feature, termed as randomly selected wavelet transform (RSWT) feature, was devised to represent ECG characteristic points. A random forest classifier was adapted to infer the characteristic points position with high sensitivity and precision. Results: Compared with other state-of-the-art algorithms' testing results on QT database, our detection results of RSWT scheme showed comparable performance (similar sensitivity, precision, and detection error for each characteristic point). RSWT testing on MIT-BIH database also demonstrated promising cross-database performance. Conclusion: A novel RSWT feature and a new detection scheme was fabricated for ECG characteristic points. The RSWT demonstrated a robust and trustworthy feature for representing ECG morphologies. Significance: With the effectiveness of the proposed RSWT feature we presented a novel machine learning based scheme to automatically detect all types of ECG characteristic points at a time. Furthermore, it showed that our algorithm achieved better performance than other reported machine learning based methods.