The Brillouin spectroscopy technique based on virtual image phased array (VIPA) is a high-resolution spectral detection technique. It offers advantages such as real-time monitoring and high sensitivity. Currently, this technique primarily relies on the mapping relationship between the free spectral range (FSR) of VIPA and the pixel to obtain frequency information. However, variations in the incident light angle can cause fluctuations in the FSR. Traditional measurement methods are based on the assumption of constant FSR and do not provide real-time calibration for FSR changes caused by variations in the incident angle, resulting in measurement errors. In this paper, we address this issue by analyzing the characteristics of Brillouin scattering and the interference principle of VIPA. We propose a real-time calibration method for FSR based on the Brillouin frequency shift of a single-mode fiber and a corresponding calculation method for pixel-to-frequency mapping. This method utilizes the Brillouin Stokes and anti-Stokes frequency shifts of a single-mode fiber as references to reduce frequency measurement errors caused by FSR fluctuations due to changes in the incident light angle. Experimental results demonstrate the rapid measurement of micro/nano fiber Brillouin frequency shift in different refractive index environments at a wavelength of 532 nm using this method.

This study proposes a dry ice particle spray cooling radiator for addressing the thermal management issues in high power semiconductor laser. The thermal flow field of the radiator is simulated using FLUENT software. The results indicate that the heat dissipation effect is optimal when the radiator has multi-outlet nozzles in the inlet and the cold fluid undergoes disturbances caused by the needle. With a dry ice flow velocity of 0.5 m/s, the stabilized temperature is measured to be 24.79 ℃. A comparison between experimental testing and simulation result reveals a maximum relative error of 8.3% and 8.8%, respectively. Moreover, in comparison to microchannel water cooling, dry ice cooling exhibits lower overall temperature of laser, higher electro-optical conversion efficiency, uniform temperature distribution along the width of the vertical bars, and reduced thermal stress-induced "smile" effects. These findings lay the foundation for the development of high power horizontal array semiconductor lasers with dry ice cooling.

Aiming at the problems of the image super-resolution reconstruction (SR) model based on convolutional neural network (CNN), such as insufficient feature extraction, a large number of parameters caused by too deep network, and the impact of redundant information on the final reconstruction performance of the network, this paper designs a lightweight densely connected image super-resolution network (LDCN). The network designs a multi-scale iterative feature extraction module (MIFEM), to achieve full extraction of multi-scale features in the case of lower parameters; according to the idea of residual shrinkage, a key information extraction module (KIEM) is constructed, which can remove more redundant information than the original module, so that the network can fully pay attention to the key information and the overall parameters of the module are reduced by 72%; finally, the feature transfer module (FTM) is introduced into the dense residual network, which further reduces the complexity of the model and solves the problem of deep model layers and large parameters. Experimental results show that LDCN outperforms mainstream models in both reconstruction performances and visual perceptions. On the four test sets, compared with the lightweight model MADNet, the PSNR is increased by 0.1 dB,0.11 dB,0.06 dB, and 0.26 dB, respectively, and the number of parameters is only 47.6% of MADNet.

With the rapid development of lidar and other sensing techniques, autonomous vehicles and mobile robotics are in the phase of real applications. But due to the poor ranging accuracy and detection range in foggy situation, the all-weather application of lidar has been limited. In this paper, the model of echo laser signals of targets in the fog is established according to the transmission and backscattering models. A combined attention mechanism network (CAMN) based on convolutional neural network (CNN) is proposed to identify the echo signal in the fog. The results of simulation and experiments show that CAMN can effectively remove the interference of fog on the detection of pulsed laser signal. The mean of absolute errors of the detection achieves 3.13 cm at the range of 10 m at the scattering rate of 30%. The detection range reaches 42 m, doubling or tripling the numbers of other approaches. The approach can effectively improve the ranging accuracy and detection range of lidar in foggy weather. It provides the basis for real applications of lidar.

To solve these problems, such as large amount of calculation, lack of ability of generalization and poor detection performance, a light-weight night driving infrared image target detection algorithm is proposed in this paper. The algorithm first utilizes the Ghost structure as the backbone network to reduce the amount of model calculation. Then, the bidirectional feature gramid network (BIFPN) structure and coordinate attention (CA) mechanism are introduced in the neck to improve the model detection effect. Finally, the Focal-EIOU and Mish functions are used as the loss function and activation function of the algorithm to improve the convergence speed and regression accuracy. The experimental results show that the improved algorithm has significantly improved compared with YOLOv3-tiny in all aspects. Compared with YOLOv5, the accuracy has increased to 88.9%, the model volume has been reduced by 24.09%, the number of parameters has been reduced by 25.07%, and the amount of calculation has been reduced by 28.48%, the detection accuracy is improved in the two categories of person and bicycle. A balance between detection accuracy and model complexity is achieved.

Object detection plays a crucial role in practical applications such as robotics and autonomous driving. In these scenarios, real-time execution of object detection tasks on resource-constrained platforms is essential, demanding highly on parameters and detection speed of object detection algorithm, realizing lightweight and efficient object detection algorithms. However, traditional convolutional neural networks (CNNs) with complex network structures and high computational requirements are not suitable for deployment on mobile devices. To address these challenges, this paper proposes a one-stage lightweight object detection algorithm, named CYM-Net, based on point cloud data. The CYM-Net model cleverly integrates the design principles of MobileNetV3′s bneck module and YOLOv4′s object detection concept while improving the feature pyramid, resulting in a significant reduction in model parameters. The CYM-Net model is trained and validated on the KITTI dataset. Experimental results demonstrate that the CYM-Net model outperforms other methods in both bird′s-eye view and 3D detection tasks, and it also exhibits superior detection speed. This research provides an efficient and lightweight solution for object detection in fields like robotics and autonomous driving.

For the strain-temperature cross-sensitivity problem of fiber Bragg grating (FBG) sensor, a temperature compensation algorithm based on Elman neural network with particle swarm optimization (PSO) is proposed. Firstly, based on the principles of fluid mechanics and FBG sensing, a probe-type FBG flow-temperature composite measurement sensor is designed and the flow-temperature composite sensing mechanism is analyzed; then, a flow-temperature composite measurement experimental platform is built, measurement data are obtained, and error analysis is performed; finally, the optimal number of implied layers and the optimal combination of functions are obtained using the PSO-optimized Elman neural network, the flow maximum error and the mean error of the FBG sensor are 0.086 m3/h and 0.002 7 m3/h, in the flow range of 2 m3/h—30 m3/h after FBG sensor is compensated, the maximum error and mean square error of temperature are 0.084 ℃ and 0.001 7 ℃, respectively. The experimental results show that the sensor can realize the composite measurement of fluid flow and temperature in the pipeline, and the combination of the PSO-Elman algorithm can effectively reduce the error caused by strain-temperature cross-sensitivity and significantly improve the measurement performance of the sensor.

In order to meet the requirements of low phase noise and high stability multi-carrier light source for terahertz communication over fiber, a high flat tunable optical frequency comb (OFC) signal generation scheme based on an electro-absorption modulator (EAM), an intensity modulator (IM), and an EAM cascade is presented. In the scheme, based on the primary EAM, the optical frequency comb signal is generated, the number of combs is further increased by IM, and the flatness is optimized by adjusting the drive voltage of the second EAM. The simulation results show that this scheme can generate a 21 line optical frequency comb signal with a maximum bandwidth of 800 GHz, with a flatness of 0.52 dB. Subsequently, the impact of the operating parameters of key components in the scheme on their performance is analyzed. To further verify the communication performance of generating optical frequency comb signals, the performance of single or multi-channel transmission of 256-ary quadrature orthogonal amplitude modulation (256QAM) signals is analyzed through simulation under three conditions of back to back (BTB),10 km fiber, and 15 m wireless. The results indicate that the bit error rate (BER) in each of the above cases is lower than the forward error correction coding decision threshold of 3.8×10-3.

The direct measurement operation of plasma jet temperature is complicated and the particles types are easily disturbed, in this paper, the temperature characteristics of atmospheric discharge plasma jets are analyzed based on emission spectroscopy at different powers. The plasma emission spectra at different discharge powers are diagnosed by a pin-canister corona discharge device with air as the medium at a atmospheric pressure of 0.2 MPa. The vibration temperature and electron temperature of particles are calculated by Boltzmann slope method and bispectral intensity method, and the molecular rotational temperature is fitted by LIFBASE software. The results show that with the increase of power from 500 W to 1 000 W, the relative intensity of emission spectrum increases with power, and the types of active particles also increase, the vibration temperature of particles increase from 5 200 K to 7 000 K, and the electron excitation temperature increases from 16 700 K to 17 200 K, the fitting molecular rotation temperature is between 300 and 550 K. It is concluded that the discharge power directly affects the plasma jet temperature and particle type, and this study can provide reference for the application of plasma surface treatment.

To address the issue of high computational complexity in coding units (CU) partitioning for versatile video coding (VVC) intra-frame coding, this paper proposes a CU fast partitioning algorithm based on DenseNet+FPN (feature pyramid network). The algorithm significantly reduces the encoding complexity of VVC, resulting in reduced encoding time. Firstly, a CU classification algorithm based on texture complexity is proposed to evaluate the texture complexity of CU blocks. Secondly, a network model based on DenseNet+FPN is introduced, utilizing multi-scale information to optimize CU partitioning to adapt to encoding requirements in various scales. Lastly, a novel adaptive quality-complexity balanced loss function is designed to balance encoding quality and computational complexity. Extensive experimental analysis is conducted for the proposed algorithm, and the results demonstrate that compared to VVC test model (VTM) 10.0, the average encoding time of the proposed algorithm is reduced by 44.268%, while the bjntegaard delta bit rate (BDBR) only increases by 0.94%.

The existing reversible data hiding in encrypted image (RDHEI) algorithm has the problems of poor utilization rate of pixels, and small embedding capacity. Addressing such issues, an RDHEI algorithm based on adjacency difference and block classification (ADBC) scheme is proposed. Firstly, the spatial correlation of the original image is fully utilised to obtain the adjacency difference image by calculation, and the initial block classification operation is realised according to the maximum value of the image block; secondly, the median edge predictor is used to predict the pixels for the non-embeddable image blocks in the initial classification to complete the second block classification; then, the image encryption is executed; finally, the auxiliary data and secret data are embedded in the encrypted image by bit substitution. The experimental results show that the average embedding rate (ER) of algorithm in this paper is over 0.06 bpp,0.01 bpp and 0.15 bpp higher on the BOSS base, BOWS-2 and UCID datasets respectively compared with the existing algorithms.

Cataract is a severe ophthalmic disease that significantly affects human visual function. To precisely assess the grading of visual acuity afflicted by cataracts, we propose a cataract visual acuity grading algorithm based on the efficient channel attention technique. This algorithm first employs the contrast limited adaptive histogram equalization (CLAHE) technique to preprocess fundus images, enhancing critical features such as blood vessels, optic disc, and macula. Subsequently, the efficient channel attention (ECA) mechanism is fused with a deep residual network to focus on fundus tissues and lesion areas relevant to visual acuity grading. To address the challenge of imbalanced dataset of fundus images, a focal loss (FL) function is introduced as the guiding objective for optimization, biasing the model towards patients with severe visual acuity. The algorithm is experimented with clinical data, achieving accuracies of 98.3%,90.5%, and 92.1% for normal vision, moderate vision cataract, and low vision cataract, respectively. The experimental results demonstrate that the proposed algorithm exhibits excellent performance in cataract visual acuity grading.