Volume 20,Issue 8,2024 Table of Contents
2024, 20(8):449-453.
DOI: https://doi.org/10.1007/s11801-024-3194-2
Abstract:
This paper investigates the impedance spectroscopy of monocrystalline silicon solar cells (MSSC) and dye-sensitized solar cells (DSSC) using solar cell dynamic equivalent resistor-capacitance (RC) and capacitor sum capacitor (CSC) circuits. Firstly, these circuits effectively represent the dynamic behavior of MSSC and DSSC. Secondly, the measurement method significantly impacts the accuracy of impedance measurements in the high-frequency region. Finally, the series resistance affects the distance between the left end of the impedance spectroscopy and the origin, while the parallel resistance influences the size of the impedance spectroscopy. In the CSC circuit, the relative magnitudes of dielectric relaxation capacitance and chemical capacitance affect the number and position of arc in the impedance spectroscopy. The value of the dielectric relaxation capacitor determines the number of impedance spectroscopy arcs. These conclusions provide guidelines for improving the accuracy of solar cell impedance measurements.
This paper investigates the impedance spectroscopy of monocrystalline silicon solar cells (MSSC) and dye-sensitized solar cells (DSSC) using solar cell dynamic equivalent resistor-capacitance (RC) and capacitor sum capacitor (CSC) circuits. Firstly, these circuits effectively represent the dynamic behavior of MSSC and DSSC. Secondly, the measurement method significantly impacts the accuracy of impedance measurements in the high-frequency region. Finally, the series resistance affects the distance between the left end of the impedance spectroscopy and the origin, while the parallel resistance influences the size of the impedance spectroscopy. In the CSC circuit, the relative magnitudes of dielectric relaxation capacitance and chemical capacitance affect the number and position of arc in the impedance spectroscopy. The value of the dielectric relaxation capacitor determines the number of impedance spectroscopy arcs. These conclusions provide guidelines for improving the accuracy of solar cell impedance measurements.
2024, 20(8):454-459.
DOI: https://doi.org/10.1007/s11801-024-3162-x
Abstract:
This paper presents the simulation and implementation of a reconfigurable pixel that serves both data acquisition and energy harvesting purposes. The main topic focuses on switching between the two operating modes of the photodiode:photoconductive and photovoltaic modes. This proposed model can be used to design novel optical sensors with energy harvesting capability, such as position sensitive device (PSD) and complementary metal oxide semiconductor (CMOS) image sensors, which can extend the battery lifetime of the whole optical system. Thus, we can overcome power supply problems like wiring and changing batteries frequently, especially in hard-to-reach places like space (cube satellites) or even underwater wireless optical communication (UWOC). The proposed pixel architecture offers the advantage of a minimalistic design with only four transistors. Nevertheless, it does come with a drawback in the form of higher noise levels. The simulation was achieved using MATLAB, and the implementation was performed using the programmable system-on-chip (PSoC) microcontroller. The results showed that the functionality of the dual-function pixel is correct, and the scheduling of both energy harvesting and signal sensing functions was successfully achieved.
This paper presents the simulation and implementation of a reconfigurable pixel that serves both data acquisition and energy harvesting purposes. The main topic focuses on switching between the two operating modes of the photodiode:photoconductive and photovoltaic modes. This proposed model can be used to design novel optical sensors with energy harvesting capability, such as position sensitive device (PSD) and complementary metal oxide semiconductor (CMOS) image sensors, which can extend the battery lifetime of the whole optical system. Thus, we can overcome power supply problems like wiring and changing batteries frequently, especially in hard-to-reach places like space (cube satellites) or even underwater wireless optical communication (UWOC). The proposed pixel architecture offers the advantage of a minimalistic design with only four transistors. Nevertheless, it does come with a drawback in the form of higher noise levels. The simulation was achieved using MATLAB, and the implementation was performed using the programmable system-on-chip (PSoC) microcontroller. The results showed that the functionality of the dual-function pixel is correct, and the scheduling of both energy harvesting and signal sensing functions was successfully achieved.
2024, 20(8):460-471.
DOI: https://doi.org/10.1007/s11801-024-3164-8
Abstract:
Mid-infrared wavelength switchable and dual-wavelength random laser output has many potential applications. A polarization-maintaining random fiber Bragg grating (PMRFBG) array based on the photonic localization effect of longitudinal invariant transverse disorder in fiber structure is proposed, which can be used as random feedback of dual-wavelength and wavelength switchable output of random fiber laser (RFL). The random fiber Bragg grating (RFBG) array was designed on the panda-type polarization-maintaining fiber (PMF), and the two center wavelengths were 2 151.60 nm and 2 152.22 nm, respectively. The RFBG array was designed on the bow tie-type PMF, and the two center wavelengths were obtained, which were 2 153.08 nm and 2 153.96 nm, respectively. The RFBG array with a center wavelength of 2 139.27 nm was designed on single-mode fiber (SMF). The length of individual fiber Bragg grating (FBG) and PMRFBG, the refractive index modulation depth, the number of cascaded gratings, and the distance between gratings have different effects on the full width at half maximum (FWHM) and reflectance of the RFBG and PMRFBG array, but not on the central wavelength, as obtained by simulation using the transmission matrix method. The designed PMRFBG array provides theoretical support for the design of the feedback mechanism of RFL.
Mid-infrared wavelength switchable and dual-wavelength random laser output has many potential applications. A polarization-maintaining random fiber Bragg grating (PMRFBG) array based on the photonic localization effect of longitudinal invariant transverse disorder in fiber structure is proposed, which can be used as random feedback of dual-wavelength and wavelength switchable output of random fiber laser (RFL). The random fiber Bragg grating (RFBG) array was designed on the panda-type polarization-maintaining fiber (PMF), and the two center wavelengths were 2 151.60 nm and 2 152.22 nm, respectively. The RFBG array was designed on the bow tie-type PMF, and the two center wavelengths were obtained, which were 2 153.08 nm and 2 153.96 nm, respectively. The RFBG array with a center wavelength of 2 139.27 nm was designed on single-mode fiber (SMF). The length of individual fiber Bragg grating (FBG) and PMRFBG, the refractive index modulation depth, the number of cascaded gratings, and the distance between gratings have different effects on the full width at half maximum (FWHM) and reflectance of the RFBG and PMRFBG array, but not on the central wavelength, as obtained by simulation using the transmission matrix method. The designed PMRFBG array provides theoretical support for the design of the feedback mechanism of RFL.
2024, 20(8):472-476.
DOI: https://doi.org/10.1007/s11801-024-3222-2
Abstract:
An algorithm that can implement particle shape recognition and size measurement in hybrid particle field is proposed. Based on the defocused images obtained by the interferometric particle imaging (IPI) system, shape recognition of particles can be realized through ResNet50. Two-dimensional (2D) Fourier transform and 2D autocorrelation transform are used to obtain the size of spherical and non-spherical particles, respectively. The shape and size of the particle in hybrid particle field are determined. Numerical simulation and experiment results suggest that the method has good accuracy in measuring the particle size in hybrid particle field.
An algorithm that can implement particle shape recognition and size measurement in hybrid particle field is proposed. Based on the defocused images obtained by the interferometric particle imaging (IPI) system, shape recognition of particles can be realized through ResNet50. Two-dimensional (2D) Fourier transform and 2D autocorrelation transform are used to obtain the size of spherical and non-spherical particles, respectively. The shape and size of the particle in hybrid particle field are determined. Numerical simulation and experiment results suggest that the method has good accuracy in measuring the particle size in hybrid particle field.
2024, 20(8):477-482.
DOI: https://doi.org/10.1007/s11801-024-3249-4
Abstract:
A temperature and refractive index sensor based on fiber Bragg grating (FBG) end surface cascade open Fabry-Pérot (FP) cavity has been designed and demonstrated experimentally. The open FP cavity has been fabricated on the end face of an FBG by dislocation fusion in this work, the open FP cavity could be used for refractive index sensing, and the temperature is measured by the FBG. The working principle of the sensor and the method of improving the sensitivity are analyzed by theoretical simulation. The refractive index sensitivity of the sensor is 1 108.4 nm/RIU, while the maximum fluctuation of the sensor stability experiment detection is 0.005 nm. The results show that it has satisfactory characteristics. The sensor is a compact all-fiber structure, so it has potential applications in the field of temperature refractive index sensing, such as biomedical and capacitor electrolyte detection.
A temperature and refractive index sensor based on fiber Bragg grating (FBG) end surface cascade open Fabry-Pérot (FP) cavity has been designed and demonstrated experimentally. The open FP cavity has been fabricated on the end face of an FBG by dislocation fusion in this work, the open FP cavity could be used for refractive index sensing, and the temperature is measured by the FBG. The working principle of the sensor and the method of improving the sensitivity are analyzed by theoretical simulation. The refractive index sensitivity of the sensor is 1 108.4 nm/RIU, while the maximum fluctuation of the sensor stability experiment detection is 0.005 nm. The results show that it has satisfactory characteristics. The sensor is a compact all-fiber structure, so it has potential applications in the field of temperature refractive index sensing, such as biomedical and capacitor electrolyte detection.
2024, 20(8):483-489.
DOI: https://doi.org/10.1007/s11801-024-3172-8
Abstract:
Feature extraction of point clouds is a fundamental component of three-dimensional (3D) vision tasks. While existing feature extraction networks primarily focus on enhancing the geometric perception abilities of networks and overlook the crucial role played by coordinates. For instance, though two airplane wings share the same shape, it demands distinct feature representations due to their differing positions. In this paper, we introduce a novel module called position aware module (PAM) to leverage the coordinate features of points for positional encoding, and integrating this encoding into the feature extraction network to provide essential positional context. Furthermore, we embed PAM into the PointNet++ framework, and design a novel feature extraction network, named PointNetV3. To validate the effectiveness of PointNetV3, we conducted comprehensive experiments including classification, object tracking and object detection on point cloud. The results of remarkable improvement in three tasks demonstrate the exceptional performance achieved by PointNetV3 in point cloud processing.
Feature extraction of point clouds is a fundamental component of three-dimensional (3D) vision tasks. While existing feature extraction networks primarily focus on enhancing the geometric perception abilities of networks and overlook the crucial role played by coordinates. For instance, though two airplane wings share the same shape, it demands distinct feature representations due to their differing positions. In this paper, we introduce a novel module called position aware module (PAM) to leverage the coordinate features of points for positional encoding, and integrating this encoding into the feature extraction network to provide essential positional context. Furthermore, we embed PAM into the PointNet++ framework, and design a novel feature extraction network, named PointNetV3. To validate the effectiveness of PointNetV3, we conducted comprehensive experiments including classification, object tracking and object detection on point cloud. The results of remarkable improvement in three tasks demonstrate the exceptional performance achieved by PointNetV3 in point cloud processing.
2024, 20(8):490-496.
DOI: https://doi.org/10.1007/s11801-024-3218-y
Abstract:
The traditional transmission line detection has the problems of low efficiency. To improve the performance, this paper proposes an improved you only look once version 5 (YOLOv5) transmission line foreign object detection algorithm. First, efficient channel attention (ECA) module is introduced in the backbone network for focusing the target features and improving the feature extraction capability of the network. Secondly, bilinear interpolation upsampling is introduced in the neck network to improve the model detection accuracy. Finally, by integrating the efficient intersection over union (EIoU) loss function and Soft non-maximum suppression (Soft NMS) algorithm, the convergence speed of the model is accelerated while the detection effect of the model is enhanced. Relative to the original algorithm, the improved algorithm reduces the number of parameters by 16.4%, increases the mean average precision (mAP)@0.5 by 3.9%, mAP@0.5:0.95 by 6.3%, and increases the detection speed to 55.3 frames per second (FPS). The improved algorithm is able to improve the performance of the foreign object detection in transmission lines effectively.
The traditional transmission line detection has the problems of low efficiency. To improve the performance, this paper proposes an improved you only look once version 5 (YOLOv5) transmission line foreign object detection algorithm. First, efficient channel attention (ECA) module is introduced in the backbone network for focusing the target features and improving the feature extraction capability of the network. Secondly, bilinear interpolation upsampling is introduced in the neck network to improve the model detection accuracy. Finally, by integrating the efficient intersection over union (EIoU) loss function and Soft non-maximum suppression (Soft NMS) algorithm, the convergence speed of the model is accelerated while the detection effect of the model is enhanced. Relative to the original algorithm, the improved algorithm reduces the number of parameters by 16.4%, increases the mean average precision (mAP)@0.5 by 3.9%, mAP@0.5:0.95 by 6.3%, and increases the detection speed to 55.3 frames per second (FPS). The improved algorithm is able to improve the performance of the foreign object detection in transmission lines effectively.
2024, 20(8):497-504.
DOI: https://doi.org/10.1007/s11801-024-3255-6
Abstract:
Leaking data through screen-shooting has become the main way of modern leaks. Digital watermarking technology can trace the leaker through the watermark information after the data is leaked. The current screen-shooting watermarking scheme can resist part of the distortion in the screen-shooting process, but it faces two problems. On the one hand, the watermark capacity is small. On the other hand, when the shot watermarked image is incomplete, high watermark extraction accuracy cannot be guaranteed. Based on the above problems, we propose a resistant to incomplete shooting watermarking (RiSw) scheme. Specifically, we design a set of codecs that can embed binary images as watermarks into carrier images and extract them, which not only ensures good visual effects of watermarked image, but also greatly increases watermark capacity. To resist incomplete shooting, we propose an incomplete shooting layer to simulate the situation of incomplete shooting in the screen-shooting process. Robustness to incomplete shooting can be achieved through end-to-end training. Extensive experiments show that the scheme proposed in this paper has superior performance. Even if the watermarked image lacks 50% pixels, it can still maintain a stable extraction accuracy.
Leaking data through screen-shooting has become the main way of modern leaks. Digital watermarking technology can trace the leaker through the watermark information after the data is leaked. The current screen-shooting watermarking scheme can resist part of the distortion in the screen-shooting process, but it faces two problems. On the one hand, the watermark capacity is small. On the other hand, when the shot watermarked image is incomplete, high watermark extraction accuracy cannot be guaranteed. Based on the above problems, we propose a resistant to incomplete shooting watermarking (RiSw) scheme. Specifically, we design a set of codecs that can embed binary images as watermarks into carrier images and extract them, which not only ensures good visual effects of watermarked image, but also greatly increases watermark capacity. To resist incomplete shooting, we propose an incomplete shooting layer to simulate the situation of incomplete shooting in the screen-shooting process. Robustness to incomplete shooting can be achieved through end-to-end training. Extensive experiments show that the scheme proposed in this paper has superior performance. Even if the watermarked image lacks 50% pixels, it can still maintain a stable extraction accuracy.
2024, 20(8):505-511.
DOI: https://doi.org/10.1007/s11801-024-4139-5
Abstract:
With the deepening of neural network research, object detection has been developed rapidly in recent years, and video object detection methods have gradually attracted the attention of scholars, especially frameworks including multiple object tracking and detection. Most current works prefer to build the paradigm for multiple object tracking and detection by multi-task learning. Different with others, a multi-level temporal feature fusion structure is proposed in this paper to improve the performance of framework by utilizing the constraint of video temporal consistency. For training the temporal network end-to-end, a feature exchange training strategy is put forward for training the temporal feature fusion structure efficiently. The proposed method is tested on several acknowledged benchmarks, and encouraging results are obtained compared with the famous joint detection and tracking framework. The ablation experiment answers the problem of a good position for temporal feature fusion.
With the deepening of neural network research, object detection has been developed rapidly in recent years, and video object detection methods have gradually attracted the attention of scholars, especially frameworks including multiple object tracking and detection. Most current works prefer to build the paradigm for multiple object tracking and detection by multi-task learning. Different with others, a multi-level temporal feature fusion structure is proposed in this paper to improve the performance of framework by utilizing the constraint of video temporal consistency. For training the temporal network end-to-end, a feature exchange training strategy is put forward for training the temporal feature fusion structure efficiently. The proposed method is tested on several acknowledged benchmarks, and encouraging results are obtained compared with the famous joint detection and tracking framework. The ablation experiment answers the problem of a good position for temporal feature fusion.
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