According to optical diffraction limit, the photoresponsity of nanowire (NW)-based photodetector exponentially decreases when its NW diameter reduces to the range of deep subwavelength. In this paper, we demonstrate a photoresponse- enhanced method of the deep-subwavelength GaAs NW photodetector by using a plasmon-driven dipole antenna. Considering that the enhancement is extremely influenced by the shape and size of antenna, the structure of antenna is optimized by finite difference time domain (FDTD) solutions. The optimal structure of antenna optimizes the responsivity-enhanced factors to 1123.3 and 224.7 in NW photodetectors with NW diameters of 20 nm and 60 nm, respectively. This photoresponse-enhanced method is promising for easy-integration high-performance nanoscale photodetectors.

We propose and demonstrate a simple method to accomplish arbitrary free spectral range (FSR) control of optical frequency combs (OFC). The approach is based on the combination of an optical tapped delay line (OTDL) structure and a phase modulator (PM). The OTDL structure is utilized to multiply the FSR of input OFC, and the PM is used to divide the FSR of the consequent OFC. We illustrate that the proposed method allows one to divide or multiply the FSR of original OFC by any anticipated integer or fractional factor. Numerical simulation results match well with our theoretical analysis.

A dual-lens-integrated distributed feedback (DFB) laser based on hybrid integration for single-mode transmitter optical subassembly (TOSAs) is discussed in this paper. The alignment and fixing of the lenses are simple to manipulate and highly accurate, making it possible to achieve high-efficient optical coupling to single-mode fiber (SMF) without additional high-precision tools and fixing equipment. The capability for a low coupling loss of less than 3 dB between the laser and fiber was demonstrated. The fabricated TOSA module has clear opening eyes with minor time jitters at a bit rate of 25 Gbit/s. This hybrid integration is a low fabrication cost, compact, and low insertion loss method to manufacture TOSA for a 200 or 400 GbE optical transceiver in a high-speed optical network.

Bionic perception, especially temperature-aware, is one of the important issues in soft robots research. This paper presents a method to implant temperature sensor network into soft robot finger by using optical fiber gratings. For avoiding strain disturbance, a dedicated metal tube is designed to package and protect the optical fiber gratings. For implanting the sensors firmly, a solution using two kinds of adhesive is proposed. The prototype is calibrated in high precision temperature bath and then measured under different temperatures and different bending conditions, respectively. The experimental results are compared with electronic temperature sensor (PT100 thermal resistance), which verifies the accuracy and repeatability of the design. With the dedicated coating and adhesive solution, the proposed temperature sensor network is suitable for implanting into soft robots for the temperature-aware ability.

In order to obtain excellent slow-light performance, we propose a photonic crystal waveguide (PCW) that introduces extrinsic defect rods in the center row of a complete square lattice rotated 45° counterclockwise and the second row adjacent to it. The continuous cavities are used as a storage of electromagnetic energy and a speed reducer of light speed, used for slow optical transmission in PCWs. Then, the plane wave expansion method (PWE) is used to study the slow light transmission characteristics of the proposed structure, and the influence of the structure parameters on the slow light performance is analyzed. Finally, the bandwidth is obtained at 23.37 nm when the normalized delay bandwidth product (NDBP) reaches 0.40. In addition, considering the effect of material properties on slow light performance, NDBP is further optimized to 0.44, and the bandwidth reaches 27.63 nm. A simple but universal structure is designed to provide an important theoretical basis for further improving the storage capacity with high bandwidth and high NDBP slow light.

Enhancement of light absorption in two-dimensional (2D) single atomic layer materials is a significant issue for applications of 2D material-based optoelectronic devices. In this letter, the dual-band enhanced absorptions in monolayer transition-metal dichalcogenides (TMDs) are obtained based on the metmaterial nanostructures. The proposed nanostructure consists of a monolayer TMDs sandwiched between Ag nanodisks and a dielectric spacer on Ag substrate. The excitations of the surface plasmon polaritons (SPPs) and the magnetic dipole resonances contribute to the high absorption efficiency, and the resulting absorption enhancement can be separately tuned by simply adjusting the structural parameters such as the spacer thickness, the size and the period of the Ag nanodisks. In addition, a hybrid nanostructure consisting of different nanodisks can increase the absorption bandwidth. The calculated results may have some potential applications in photodetection and wavelength-selective photoluminescence.

Modal decomposition technology is an effective method to study the mode characteristics of laser beam in few-mode fibers. However, certain types of eigenmodes in the fiber can cause focal shift and affect the accuracy of modal decomposition. This article focuses on the influence of the focal shift of Laguerre-Gaussian mode and Linear Polarization mode on modal decomposition, and the research is based on correlation filter and the optimization algorithm of focal shift. The two-step ABCD algorithm is used to simulate and analyze the focal shift phenomenon of the two kinds of eigenmodes and the error influence of focal shift on the mode decomposition; Meanwhile, an iterative algorithm based on Fresnel diffraction is proposed to numerically calculate the light field distribution in focal plane to avoid the influence of focal shift errors. The focal shift analysis and its optimization algorithm make the modal decomposition technology be applicable to engineering applications.

A highly reliable wavelength division multiplexing passive optical network architecture for the fifth generation (5G) applications is designed by combining a tree topology with a dual-fiber ring. While the tree topology ensures the transmission quality of the network, the dual-fiber ring topology allows one to achieve flexible switching between the nodes, which aims to provide fault protection and network reliability. The signal transmission under the normal and three types of protection modes are analyzed. The performance analysis verifies the feasibility of the proposed architecture.

As virtual networks services emerge increasingly with higher diversification, the issue of spectrum fragments presents great challenge to the elastic optical networks (EON), especially under heaven services burdens. Aimed to solve this problem, this article proposes a dynamic fragments awareness based virtual network mapping (DFA-VNM) strategy of elastic optical network. In this proposed approach, the dynamic fragments awareness model of it is established, which takes available bandwidth demand and spectrum fragment degree into consideration. Moreover, the dynamic fragments awareness based virtual network mapping strategy makes full advantage of real-time fragments awareness result to conduct virtual network service mapping operation with less fragments and lower blocking rate. Testing results show that the proposed approach is able to improved services supporting ability of EON.

Aiming at the problem of the loss of 3D point cloud, due to the occlusion of the field of view in the 3D measurement process, a measurement scheme based on line-structured light with dual-camera is given. In addition, in the line-structured light measurement technology, the traditional light plane calibration is more complicated, and the three-dimensional measurement accuracy is relatively low. For this reason, this paper used the binocular polar line constraint to calibrate the physical parameters of the light plane. Experimental results show that the dual-camera measurement system can obtain high-precision global measurement results. The maximum measurement error is 0.091 51 mm, and the average measurement error is 0.076 05 mm. Compared with the traditional binocular matching method and the traditional laser triangulation method, this method can deal with the problem of field occlusion more effectively, thereby reducing the loss of measurement information in the measurement process.

Saliency ship detection has received increasing attention due to its important applications in maritime field in recent years. Up to now, numerous studies on saliency detection have been done based on traditional methods and deep learning methods. But these previous research works are still not competent enough in detecting ship targets with complex backgrounds and noises. In this letter, we propose a deep attention mechanism method for more accurate and faster maritime salient ship detection. We optimize the initial ship saliency map by using a feature attention module to focus on salient objects. We reduce and improve the convolution kernel in refinement residual module to enhance the detection efficiency. In addition, Leaky ReLU is selected as the activation function to increase the non-linear capability of the method. Experiment results show that, the proposed method could obtain outstanding performance in salient ship detection in complex sea background.

K-nearest neighbor (KNN) has yielded excellent performance in physiological signals based on emotion recognition. But there are still some issues: the majority vote only by the nearest neighbors is too simple to deal with complex (like skewed) class distribution; features with the same contribution to the similarity will degrade the classification accuracy; samples in boundaries between classes are easily misclassified when k is larger. Therefore, we propose an improved KNN algorithm called WB-KNN, which takes into account the weight (both features and classification) and boundaries between classes. Firstly, a novel weighting method based on the distance and farthest neighbors named WDF is proposed to weight the classification, which improves the voting accuracy by making the nearer neighbors contribute more to the classification and using the farthest neighbors to reduce the weight of non-target class. Secondly, feature weight is introduced into the distance formula, so that the significant features contribute more to the similarity than noisy or irrelevant features. Thirdly, a voting classifier is adopted in order to overcome the weakness of KNN in boundaries between classes by combining different classifiers. Results of WB-KNN algorithm are encouraging compared with the traditional KNN and other classification algorithms on the physiological dataset with a skewed class distribution. Classification accuracy for 29 participants achieves 94.219 2% for the recognition of four emotions.