Abstract:This paper proposes a method that combines etching, masking, and magnetron sputtering techniques to prepare a ring-shaped silver mirror structure on the end face of a standard multimode optical fiber. By utilizing the capillary effect to fill a capillary glass tube with polydimethylsiloxane (PDMS), a structure consisting of a PDMS cavity and an air cavity is made, resulting in the achievement of a fiber optic dual Fabry–Perot (F–P) cavity temperature sensor with a ring-shaped silver mirror structure. When the external temperature changes, both the cavity length and the refractive index of PDMS change, causing variations in the intensity of interference light. The annular silver mirror utilizes its high reflectivity to allow more light to enter the receiving end, resulting in a more pronounced change in photon count. Within the range of 25–80 °C, sensitivity of 150.74 cps/°C and linearity of 0.998 have been achieved. Our optical fiber temperature sensor has demonstrated cost-effectiveness, wide range, and high stability in temperature detection through multiple repeated experiments.

Abstract: In this paper, we propose and numerically characterize the optical characteristics of a high-sensitivity dual-polarization four-channel surface plasmon resonance (SPR) refractive index sensor with wide detection range and high linearity based on photonic crystal fiber (PCF). Four light transmission channels are constructed by using big air holes. The small rectangular air hole in the center of the fiber core is used to add asymmetry. We use gold plasma material to achieve better sensing performance. A thin film of TiO2 is placed on the PCF surface, which assists gold in the adhesion on PCF. The maximum wavelength sensitivity of the sensor can reach 20 000 nm/RIU in both x and y polarization modes. While the maximum wavelength resolution of the sensor is 5×10-6 RIU. The average wavelength sensitivity reached 15 900 nm/RIU and 15 800 nm/RIU in x and y polarization modes. The sensor features R2=0.996 9/R2=0.996 6 high linear performance in x and y polarization modes and the maximum figure of merit (FOM) values of 509.13 RIU-1/489.63 RIU-1 in x and y polarization modes. Besides, the sensor’s detection range is 1.26–1.36, which is a wide refractive index detection range.

Abstract:Cationic doping is considered to be an effective way to improve the efficiency of Cu2ZnSn(S,Se)4 (CZTSSe) photovoltaic materials. To improve the quality of the absorption layer of the crystal film, CZTSSe film was doped with some Cd2+ ions to change the ratio of Cd and Zn in the precursor solution. X-ray diffraction (XRD), Raman, scanning electron microscope (SEM), ultraviolet-visible (UV-Vis) spectroscopy and other results showed that Cd successfully replaced Zn in the crystal lattice during post-selenization. The formation of harmful Zn-related defects was reduced, the grain size and crystallinity of the films were significantly increased, and the photoelectric properties of the films such as crystal quality and structural morphology were improved. As the Cd/(Cd+Zn) ratio increased from 0 to 0.35, the band gap of CZCTSSe decreased to 1.02 eV. When Cd/(Cd+Zn)=0.25, the crystallinity and grain size reached the best value, and the film surface was smooth and dense, which inhibited the formation of the second phase on the surface of CZCTSSe. The theoretical basis and experimental results showed that proper Cd doping can promote the growth of grain, so that compact CZCTSSe films with larger grain sizes and fewer holes could be prepared.

Abstract:In this paper, a scheme for generating sinc-shaped optical Nyquist pulses based on external modulation is proposed. First, five flat optical frequency comb (OFC) lines are generated by a dual-parallel Mach–Zehnder modulator (DP-MZM) for optical carrier phase cancellation interference. Then, the phase-locked OFC is split into two paths, one of which is transmitted to a single-drive Mach–Zehnder modulator (SD-MZM) for the modulation of the even-order side-band suppression, and the other is used to remodulate the signal in order to obtain equally spaced comb lines. Eventually, equal frequency spaced phase-locked 15-line OFCs are generated and extremely narrow over-zero width Nyquist pulses are realized at 2.5 GHz, 5 GHz, 10 GHz and 20 GHz. The root-mean-square error (RMSE) is calculated for the generated Nyquist pulses which enables the verification of the signal quality.

Abstract:Aiming at the poor performance of the parity check (PC) aided adaptive successive cancellation list (PC-ASCL) decoding algorithm because the PC code in the polar code can only verify odd errors, an optimized parity check (OPC) code which can verify all odd errors as well as the half even errors is proposed. The OPC code is used to improve the PC-ASCL decoding algorithm, thus an OPC aided ASCL (OPC-ASCL) decoding algorithm is proposed. In the coding stage, the algorithm divides the information sequence into multiple segments, and places an OPC code at the end of each segment to verify the current information sequence, and places a cyclic redundancy check code at the end of the entire information sequence to verify the entire information sequence. In the decoding stage, the algorithm uses the OPC-ASCL decoder to decode. Simulation results show that compared to the PC-ASCL decoding algorithm, the OPC-ASCL decoding algorithm can reduce the complexity and obtain the certain performance gain.

Abstract:An improved algorithm for traffic sign detection based on YOLOv8 is proposed. Firstly, YOLOv8n is used as the base model of the network, the inverted residual mobile block and exponential moving average (iRMB_EMA) attention mechanism is used to improve the model’s ability to perceive small targets, which reduces the leakage detection phenomenon of the model, convolution (Conv) is upgraded to receptive-field attention convolution (RFAConv), which improves the model’s ability to deal with details and complexity in the image, the idea of adaptive spatial feature fusion (ASFF) is introduced in the detection head, and the small target detection layer, a four-head detection head is designed to improve the model’s ability to detect small targets, solves the case of feature loss due to cross-scale fusion, and use of the Inner-minimum points distance intersection over union (MPDIoU) loss function provides a more accurate loss metric by calculating the distance of key points between the predicted and true frames. The experimental results of this algorithm on the public dataset CCTSDB show that the improved model mean average precision (mAP) reaches 82.6%, which is 4% higher than the YOLOv8n. The experimental results of dataset TT100k show that the mAP reaches 84.5%, which is 7.1% higher than the YOLOv8n. This algorithm effectively improves the problem of detail perception and leakage of the model in the detection of small targets, and has a significant detection effect compared to other algorithms.

Abstract:Due to the property of infrared aerial imagery, the local prior is sufficient especially for low-subrate block compressive sensing (BCS) reconstruction of infrared aerial images, while its complexity is much lower than nonlocal prior. The typical low-subrates can effectively improve the BCS transmission efficiency and reduce the burden of transmitter hardware. Therefore, this paper proposes a low-subrate sparse reconstruction algorithm with threshold-adaptive denoising and basis learning (TDBL), which adopts both split Bregman iteration (SBI) and adaptive threshold to implement the model-based BCS reconstruction for infrared aerial imagery. The experimental results show that as compared with the state-of-the-art algorithms, the proposed algorithm can obtain better recovery quality and less runtime on both HIT-UAV and M200-XT2DroneVehicle datasets.

Abstract:The current infrared image pedestrian detectors have problems with high rates of false positives and false negatives. To solve these problems, we proposed an improved anchor-free fully convolutional one-stage object detection (FCOS) algorithm. Firstly, we introduced the channel attention module squeeze excitation (SE)-Block in the FCOS backbone network, which was used to learn how to model the relative importance between different feature channels, and to achieve the weight recalibration of the features extracted from the convolution neural network, and improve the weight values that are more important for pedestrian target detection. Secondly, soft non-maximum suppression (Soft-NMS) replaced the conventional NMS within the algorithm’s post-processing phase, which was used to reduce the probability of missed detection for occluded pedestrians. The experimental results show that our improved FCOS algorithm improves the average precision (AP) by 6.71% on the original dataset and 7.97% on the augmented KAIST pedestrian dataset compared with the original FCOS algorithm. Our improvements effectively meet the real-time requirements and there is no significant decrease in speed compared with the original FCOS algorithm, and decreased the false positives and false negatives for infrared image pedestrian detection.

Abstract:Imaging-based phenotypic screening uses cellular phenotypes to describe the drug performance, which generally focuses on single cellular feature, lacking of a comprehensive characterization. Here, we propose a high content phenotypic screening method based on expansion microscopy (ExM) assisted cell painting, which enables multi-channel imaging with approximately 50 nm resolution. As a demonstration, we applied this method to a phenotypic screening involving five drugs. The morphological attributes of three subcellular structures were summarized to consist a “fingerprint” describing the drug effect. The proposed method can provide comprehensive and detailed clues for drug evaluation, enriching the content of phenotypic screening.

Abstract:To enhance the tracking stability of DeepOCSORT, this paper proposes a novel multi-sensor data fusion-based multi-object tracking (MOT) method. Specifically, we build upon the DeepOCSORT foundation and additionally integrate target velocity information directly measured by light detection and ranging (LiDAR). The introduction of this velocity information is conducted from three perspectives. Firstly, during data association, a penalty term is constructed based on the differences in target velocities to constrain generating matches with consistent velocities. Secondly, use LiDAR velocity for initialization and online updating of the velocity state within the tracker, making tracking predictions more stable. Thirdly, control the degree of dependence on velocity information by adjusting the process noise covariance matrix. Evaluation results on the KITTI dataset demonstrate that compared to the original DeepOCSORT, the proposed improved multi-source heterogeneous information fusion method significantly enhances tracking performance, with maximum improvements of 3.35, 3.26, and 3.71 on the higher order tracking accuracy (HOTA), multi-object tracking accuracy (MOTA), and interaction detection F1 score (IDF1) metrics, respectively. This study provides an effective approach to building a more stable and accurate MOT system.

Abstract:The purpose of this paper is to investigate the interaction between dispersed and elongated bubbles in horizontal slug flow utilizing the laser-induced fluorescence method. A segmentation method based on the fuzzy C-mean (FCM) algorithm is proposed to effectively separate elongated bubbles from the liquid phase, and an extreme value extraction method is developed to calculate the number of dispersed bubbles in front of the nose of elongated bubbles. Moreover, the velocity offsets of elongated bubbles with and without dispersed bubbles are calculated separately based on contour extraction. In addition, the effects of dispersed bubbles on the fluctuating offsets of the nose tip position and velocity of elongated bubbles are statistically investigated under different flow velocities. The experimental results show that the increase of the gas-liquid flow velocity exacerbates the radial deviation of the nose tip and the fluctuation of axial velocity.