To investigate the effect of Zn2+ ions on the luminescence properties of rare earth (RE) doped calcium titanate materials, CaTiO3: Yb3+/Er3+/Zn2+ nanocubes with uniform size were prepared by solvothermal method in this paper. They were respectively characterized by X-ray diffractometer (XRD), scanning electron microscope (SEM), photoluminescence (PL) spectroscopy and their fluorescence lifetimes. The results show that the average size of the nanocubes is about 550 nm×650 nm×850 nm with good upconversion luminescence (UCL) properties. Under 980 nm laser excitation, the effects of the ratio between activator Er3+ and sensitizer Yb3+ and Zn2+ doping on the upconversion fluorescence properties were investigated, and the optimal ion ratio was obtained. The results of steady-state spectra show that the strongest fluorescence intensity of CaTiO3: Yb3+/Er3+ was obtained with the addition of 10 mol% Zn2+ at a Yb3+/Er3+ molar doping ratio of 3:0.3, which was attributed to the crystal field asymmetry generated by the introduction of Zn2+ ions. The energy transfer and upconversion mechanism between Yb3+ and Er3+ ions in CaTiO3 nanocubes were investigated by analyzing the upconversion fluorescence kinetics.

In this paper, we investigate the quantum fluctuations of subharmonic reflected field from a triple-resonant degenerate optical parametric amplifier (OPA) inside coupled optical resonators, which is driven by the squeezed beam at signal frequency. By controlling the relative phase between the pump beam and the injected signal beam, we can see the quantum fluctuation in the phase direction and amplitude direction due to the parametric down-conversion process in the cavity. Thus, the phase sensitive operation of the compression field is realized due to the quantum interference between the harmonic field of the down converter of OPA and the inner field of the coupled optical resonator. We verified the quantum coherent phenomena of OPA in coupled optical resonators and phase-sensitive manipulations of quantum entanglement for quantum information processing. We realized the electromagnetically induced transparency-like (EIT-like) effect and the optical parameter conversion process at the same time in one optical device. These properties can favor higher manipulation precision and control efficiency, which is more suitable for the integration of quantum-on-chip systems, laying a foundation for the practical application of quantum information.

We obtain the enhanced stimulated Brillouin scattering (SBS) by adding the optimized Bragg grating to an As2S3 chalcogenide half suspended-core rectangle waveguide. The half suspended-core waveguide grating is characterized by the period of 344.67 nm and the refractive index modulation depth of 0.000 1. Through simulation experiments, the obtained Brillouin gain is 58.5 dB and the 3 dB bandwidth can reduce to 7.8 MHz. The half suspended-core waveguide structure can decrease the size of the chip while the periodic structure can enhance the slow light effect, so we have improved the integration of the waveguide and enhanced SBS by combining these two advantages.

This paper provides an attempt to utilize machine learning algorithm, explicitly random-forest algorithm, to optimize the performance of dye sensitized solar cells (DSSCs) in terms of conversion efficiency. The optimization is implemented with respect to both the mesoporous TiO2 active layer thickness and porosity. Herein, the porosity impact is reflected to the model as a variation in the effective refractive index and dye absorption. Database set has been established using our data in the literature as well as numerical data extracted from our numerical model. The random-forest model is used for model regression, prediction, and optimization, reaching 99.87% accuracy. Perfect agreement with experimental data was observed, with 4.17% conversion efficiency.

In an optical camera communication (OCC) system, multi-level modulation is essential for data rate enhancement with the finite frame rate of the receiving camera constraint, where the K-means algorithm is widely used as a thresholding scheme. The result of K-means clustering is sensitive to initial cluster centers. In this paper, we propose a multi-level modulation scheme utilizing the pilot-aided K-means (PAK) algorithm. PAK algorithm innovates in both obtaining the state of the stripes propagated through the optical channel under different environments and overcoming the susceptibility of K-means. Our scheme could prompt data rate and improve the performance of OCC. Finally, non-orthogonal multiple access (NOMA) pattern is designed based on the proposed scheme to achieve multiplex communications.

Performance of non-line-of-sight (NLOS) ultraviolet (UV) communication is closely related with the system geometry, the communication range, and the atmospheric parameters. In this paper, we implement a full numerical analysis of the relations of path loss of NLOS UV communication with these factors using the Mie scattering theory and the Monte-Carlo method. In the numerical simulations, the actual polydisperse aerosol systems are used as the transmission medium. Since for the actual aerosol systems the atmosphere conditions may be similar within a short period, the path loss may be exclusively determined by the atmosphere visibility. Hence, we build a relation between the path loss of the communication channel and the atmosphere visibility. Simulation results reveal that for a relatively small communication range, the path loss increases with the visibility. On the other hand, low elevation of the transceiver may reduce the path loss. Our simulation results are useful for the evaluation of performance of the real NLOS UV communication systems.

In this paper, a non-conductive droplet driven by electrowetting (EW) with planar electrode in surrounding fluid is studied. COMSOL is employed to simulate the evolution of droplet shrinkage and the relative experimental setup is established to monitor the evolution of contact angle and height at different voltages. The droplet contracts inward and the corresponding contact angle/height increase when voltage increases. When the voltage ranges from 50 V to 140 V, the variation of the relative contact angles and height reach up to 118.78° and 3.194 mm, respectively. The system of silicon oil and surrounding liquid propylene glycol (PG) acts as a positive lens, whose focal length varies from 87.153 mm to 42.963 mm.

To solve the problems of small signal intensity and low signal-to-noise ratio (SNR) in single-ended Brillouin optical time domain analysis (BOTDA) system based on Fresnel reflection, we propose and experimentally demonstrate a pulse coding single-ended BOTDA system, which can improve the SNR and temperature measurement accuracy. A single-ended BOTDA temperature sensing system using single pulse and pulse coding is designed, and the Brillouin time domain signal and Brillouin frequency shift under different pulse coding bits are measured. The experimental results show that the fluctuations of Brillouin power and Brillouin frequency shift are gradually decreased with the increase of pulse coding bits, and the SNR under 32 bit Golay coding offers a 6.18 dB improvement with respect to traditional single pulse system. And the temperature measurement accuracy under 32 bit coding over a 9.35-km-long sensing fiber can be accurately measured as 1.59 °C, while providing a 1.73 °C enhancement when compared to single pulse system.

Unsupervised image-to-image translation is a challenging task for computer vision. The goal of image translation is to learn a mapping between two domains, without corresponding image pairs. Many previous works only focused on image-level translation but ignored image features processing, which led to a certain semantics loss, such as the changes of the background of the generated image, partial transformation, and so on. In this work, we propose a method of image-to-image translation based on generative adversarial nets (GANs). We use autoencoder structure to extract image features in the generator and add semantic consistency loss on extracted features to maintain the semantic consistency of the generated image. Self-attention mechanism at the end of generator is used to obtain long-distance dependency in image. At the same time, as expanding the convolution receptive field, the quality of the generated image is enhanced. Quantitative experiment shows that our method significantly outperforms previous works. Especially on images with obvious foreground, our model shows an impressive improvement.

To enhance the optical computation's utilization efficiency, we develop an optimization method for optical convolution kernel in the optoelectronic hybrid convolution neural network (OHCNN). To comply with the actual calculation process, the convolution kernel is expanded from single-channel to two-channel, containing positive and negative weights. The Fashion-MNIST dataset is used to test the network architecture's accuracy, and the accuracy is improved by 7.5% with the optimized optical convolution kernel. The energy efficiency ratio (EER) of two-channel network is 46.7% higher than that of the single-channel network, and it is 2.53 times of that of traditional electronic products.

Deep generative prior (DGP) is recently proposed for image restoration and manipulation, obtaining compelling results for recovering missing semantics. In this paper, we exploit a general solution for single image deblurring using DGP as the image prior. To this end, two aspects of this object are investigated. One is modeling the process of latent image degradation, corresponding to the estimation of blur kernels in conventional deblurring methods. In this regard, a Reblur2Deblur network is proposed and trained on large-scale datasets. In this way, the proposed structure can simulate the degradation of latent sharp images. The other is encouraging deblurring results faithful to the content of latent images, and matching the appearance of blurry observations. As the generative adversarial network (GAN)-based methods often result in mismatched reconstruction, a deblurring framework with the relaxation strategy is implemented to tackle this problem. The pre-trained GAN and pre-trained ReblurNet are allowed to be fine-tuned on the fly in a self-supervised manner. Finally, we demonstrate empirically that the proposed model can perform favorably against the state-of-the-art methods.