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A switchable triple-wavelength erbium-doped fiber laser employing a linear cavity incorporating a spatial mode beating filter was proposed and demonstrated. The filter formed by the incorporation of a section of multimode fiber into a single-mode fiber loop mirror was used as a comb to select lasing wavelengths. By using a polarization controller and spatial hole-burning effect, this laser could switch to output single wavelength, dual wavelength and triple wavelength in the C- plus L-bands at room temperature. The laser, with an optical signal-to-noise ratio of more than 45 dB and wavelength spacing of 2 nm, had good power uniformity and stability. [ABSTRACT FROM AUTHOR]

We investigate on a multi-wavelength operation erbium-doped fiber laser Q-switched by a graphene-based saturable absorber. Stable pulses were generated with the widths from 6.9 to 1.5 µs, energies from 40.4 to 130.2 nJ and repetition rates from 68.32 to 132.9 kHz, when the pump power increased from 142.32 to 441.86 mW. A fiber Bragg grating with five reflective peaks was inserted into the fiber ring through an optical circulator, resulting in a stable output of five-lasing-wavelength output. The laser can perform as a low-cost and easy-built all fiber light source, and has potential applications in the fields where pulses at multi-wavelength operation are needed, i.e., temperature or strain fiber sensors. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has been used for electromyographic (EMG) signal recognition and achieved high accuracy for multiple classification tasks. However, implementation in resource-constrained prostheses and human-computer interaction devices remains challenging. To overcome these problems, this paper implemented a low-power system for EMG gesture and force level recognition using Zynq architecture. Firstly, a lightweight network model structure was proposed by Ultra-lightweight depth separable convolution (UL-DSC) and channel attention-global average pooling (CA-GAP) to reduce the computational complexity while maintaining accuracy. A wearable EMG acquisition device for real-time data acquisition was subsequently developed with size of 36mm×28mm×4mm. Finally, a highly parallelized dedicated hardware accelerator architecture was designed for inference computation. 18 gestures were tested, including force levels from 22 healthy subjects. The results indicate that the average accuracy rate was 94.92% for a model with 5.0k parameters and a size of 0.026MB. Specifically, the average recognition accuracy for static and force-level gestures was 98.47% and 89.92%, respectively. The proposed hardware accelerator architecture was deployed with 8-bit precision, a single-frame signal inference time of 41.9μs, a power consumption of 0.317W, and a data throughput of 78.6 GOP/s.

Congenitally corrected transposition of the great arteries (CTGA) is a rare congenital heart disease. In patients with functional CTGA with circumflex artery occlusion and mitral regurgitation (MR), the right ventricle functions as the left ventricle. Coronary artery bypass grafting with mitral valve replacement is an effective treatment for CTGA with concomitant myocardial infarction (MI) and MR.