We designed a clamping device to study lateral photovoltaic effect (LPE) in Ni-SiO2-Si structure with bias due to the appropriate barrier height. The LPE has a prominent sensitivity and linearity with 532 nm wavelength laser. The tran-sient response time is 450 μs and the relaxation time is 2 250 μs in the Ni-SiO2-Si structure without bias. The LPE sen-sitivity has a significant improvement with bias. The transient response time is 6 μs and the relaxion time is 47 μs with ?7 V bias, not only improving the LPE sensitivity, but also increasing the response speed with bias. The research shows that the Schottky barrier structure can improve the sensitivity and linearity of LPE with bias effectively, and thus it can be used in position sensitive sensors.

A switchable and tunable multi-wavelength Brillouin erbium-doped fiber laser (MWBEFL) is designed and experi- mentally demonstrated. A Sagnac loop filter is employed as the switcher to obtain the double Brillouin frequency shift (BFS) of 0.172 nm (~20 GHz) and the quadruple BFS of 0.35 nm (~40 GHz). The working principles of the proposed laser are theoretically analyzed. The experimental results show that up to 8 Stokes lines with a wavelength interval of 0.172 nm can be obtained. When the Sagnac loop filter is used, two different output spectra with wavelength interval of 0.35 nm are obtained by adjusting the polarization controller (PC) and the optical signal-to-noise ratio (OSNR) is greater than 33 dB. By adjusting the Brillouin pump (BP) wavelength to investigate the tunability of the fiber laser, the output of 2—5 laser channels can be realized corresponding to 20 nm wavelength range. This approach is simple and can be employed for the microwave generation of other frequency ranges subject to the filtering shift of the Sagnac loop.

Since first synthesized in 2011, MXenes have attracted extensive attention in many scientific fields as a new two-dimensional (2D) material because of the unique physical and chemical properties. Over the past decade, in particular, MXenes have obtained numerous exciting achievements in the field of terahertz applications. In this review, we first briefly introduce the MXene materials, such as the basic structure and main fabrication processes of MXenes. Then, we summarize the recent applications of MXene materials in various terahertz research areas, including terahertz modulation, terahertz absorption, terahertz shielding, terahertz communication, terahertz detection and terahertz generation, in which the representative results are presented. Finally, we give an outlook on the future research directions of MXene materials and their potential applications.

The overlapping frequency domain equalization (O-FDE) in digital signal processing (DSP) is frequently employed to provide dispersion compensation in long-distance coherent fiber optical communications. However, the change in overlapping symbol length that occurs during the processing of the O-FDE algorithm will typically be influenced by the decision and zero filling of the last subblock, which is harmful to the robustness of the O-FDE algorithm. In this study, with a thorough robustness analysis on changing overlapping symbol length, we present a novel method for de- cision and zero filling of the last subblock and examine the correspondingly resulting error vector magnitude (EVM) and symbol error ratio (SER) under different values of optical signal-to-noise ratio (OSNR), chromatic dispersion, and overlapped symbol lengths.

In order to achieve high accuracy measurement for non-landing vehicle-mounted photoelectric theodolite (NVPT) with continuous zoom optical system, a theoretical model of pointing error is presented. Starting with the working mode, the error source of the whole closed loop which affects the pointing error is analyzed. The measurement equation is derived using a coordinate transformation. The pointing error model is then obtained with the help of Monte Carlo method. The error of an NVPT prototype is measured, showing that the pointing error can be predicted accurately. A high precision NVPT with continuous zoom optical system is successfully designed and analyzed, with an accuracy of 11.9″ at azimuth and 10.9″ at elevation, when the optical focal length is set between 1 000 mm and 4 000 mm.

The robustness against noise, outliers, and corruption is a crucial issue in image feature extraction. To address this concern, this paper proposes a discriminative low-rank embedding image feature extraction algorithm. Firstly, to en- hance the discriminative power of the extracted features, a discriminative term is introduced using label information, obtaining global discriminative information and learning an optimal projection matrix for data dimensionality reduc- tion. Secondly, manifold constraints are incorporated, unifying low-rank embedding and manifold constraints into a single framework to capture the geometric structure of local manifolds while considering both local and global infor- mation. Finally, test samples are projected into a lower-dimensional space for classification. Experimental results demonstrate that the proposed method achieves classification accuracies of 95.62%, 95.22%, 86.38%, and 86.54% on the ORL, CMUPIE, AR, and COIL20 datasets, respectively, outperforming dimensionality reduction-based image fea- ture extraction algorithms.

Most of existing methods exhibit poor performance in detecting forged images due to the small size of tampered areas and the limited pixel difference between untampered and tampered regions. To alleviate the above problem, a dou- ble-branch tampered image detection based on multi-scale features is proposed. Firstly, we introduce a fusion module based on attention mechanism in the first branch to enhance the network's sensitivity towards tampered regions. Sec- ondly, we construct a second branch specifically designed for detection, aiming to identify subtle differences between tampered and untampered areas by utilizing rich edge information from shallow features as guidance. Compared to the existing methods on the public benchmark datasets CASIA1.0, Columbia and NIST16, the values ofF-score reached 0.766, 0.900 and 0.930 on those datasets, respectively. The experimental results show that our method could signifi- cantly improve the accuracy on detecting the tampered area.

Within the fields of underwater robotics and ocean information processing, computer vision-based underwater target detection is an important area of research. Underwater target detection is made more difficult by a number of problems with underwater imagery, such as low contrast, color distortion, fuzzy texture features, and noise interference, which are caused by the limitations of the unique underwater imaging environment. In order to solve the above challenges, this paper proposes a multi-color space residual you only look once (MCR-YOLO) model for underwater target detec- tion. First, the RGB image is converted into YCbCr space, and the brightness channel Y is used to extract the non-color features of color-biased images based on improved ResNet50. Then, the output features of three scales are combined between adjacent scales to exchange information. At the same time, the image features integrated with low-frequency information are obtained via the low-frequency feature extraction branch and the three-channel RGB image, and the features from the three scales of the two branches are fused at the corresponding scales. Finally, mul- ti-scale fusion and target detection are accomplished utilizing the path aggregation network (PANet) framework. Ex- periments on relevant datasets demonstrate that the method can improve feature extraction of critical targets in under- water environments and achieve good detection accuracy.