Volume 20,Issue 1,2024 Table of Contents
2024, 20(1):1-6.
DOI: https://doi.org/10.1007/s11801-024-3064-y
Abstract:
The plasmonic properties of gold nanostructures composed of a disk outside a nonconcentric ring are numerically studied by the finite difference time domain (FDTD) method. Simulated results show that two Fano resonances are formed as a result of the coupling of the octupolar and quadrupolar modes of the ring with the dipolar mode of the disk. The reduction in structural symmetry causes a red shift of the Fano resonances and distinct changes in spectral lineshape by offsetting the center of the inner surface of the ring to different directions. The effects of several geometric parameters on the characteristics of Fano resonances are also discussed. In addition, the refractive index (RI) sensitivities for the two Fano resonances can be up to 581 nm/RIU and 780 nm/RIU with the corresponding figure of merits (FOMs) as large as 12.7 and 10.2, respectively. Such properties render the structures useful for potential applications in multi-wavelength sensors.
The plasmonic properties of gold nanostructures composed of a disk outside a nonconcentric ring are numerically studied by the finite difference time domain (FDTD) method. Simulated results show that two Fano resonances are formed as a result of the coupling of the octupolar and quadrupolar modes of the ring with the dipolar mode of the disk. The reduction in structural symmetry causes a red shift of the Fano resonances and distinct changes in spectral lineshape by offsetting the center of the inner surface of the ring to different directions. The effects of several geometric parameters on the characteristics of Fano resonances are also discussed. In addition, the refractive index (RI) sensitivities for the two Fano resonances can be up to 581 nm/RIU and 780 nm/RIU with the corresponding figure of merits (FOMs) as large as 12.7 and 10.2, respectively. Such properties render the structures useful for potential applications in multi-wavelength sensors.
2024, 20(1):7-11.
DOI: https://doi.org/10.1007/s11801-024-3084-7
Abstract:
A distributed fiber sensor was fabricated by splicing two single-mode fibers (SMFs) using the few-mode fiber (FMF) technique. A Brillouin optical time domain analysis (BOTDA) system was developed to measure the sensor's temperature and bending performance. Two-mode and four-mode step FMFs were combined to splice the few-mode segment. The results indicate that the temperature response coefficients of the few-mode segment are only slightly higher than those of the connected single-mode segment, measuring at 1.13 MHz/°C and 1.12 MHz/°C, respectively. The minimum bending radius for the sensor is 0.9 cm, and the four-mode bending response curve is superior to that of the two-mode one, proving that 4-SI-FMF offers better bending sensitivity.
A distributed fiber sensor was fabricated by splicing two single-mode fibers (SMFs) using the few-mode fiber (FMF) technique. A Brillouin optical time domain analysis (BOTDA) system was developed to measure the sensor's temperature and bending performance. Two-mode and four-mode step FMFs were combined to splice the few-mode segment. The results indicate that the temperature response coefficients of the few-mode segment are only slightly higher than those of the connected single-mode segment, measuring at 1.13 MHz/°C and 1.12 MHz/°C, respectively. The minimum bending radius for the sensor is 0.9 cm, and the four-mode bending response curve is superior to that of the two-mode one, proving that 4-SI-FMF offers better bending sensitivity.
2024, 20(1):12-17.
DOI: https://doi.org/10.1007/s11801-024-3051-3
Abstract:
This study investigates the fabrication process of Zn-diffused ridge waveguides in periodically poled magnesium-doped lithium niobate (PPMgO:LN). A controlled variable method is used to study the effects of diffusion temperature, diffusion time, ZnO film thickness, and barrier layer thickness on the surface domain depolarization and waveguide quality of PPMgO:LN. A special barrier layer is proposed that can automatically lift off from the sample surface, which increases the depth of Zn doping and reduces the surface loss of the waveguide. By optimizing the process parameters, we fabricate Zn-diffused PPMgO:LN ridge waveguides with a length of 22.80 mm and a period of 18.0 μm. The above waveguides can make a second harmonic generation (SHG) at 775 nm with an output power of 90.20 mW by a pump power of 741 mW at 1 550 nm. The corresponding conversion efficiency is 3.160%/W∙cm2, and the waveguide loss is approximately 0.81 dB/cm. These results demonstrate that high-efficiency devices can be obtained through the fabrication process described in this paper.
This study investigates the fabrication process of Zn-diffused ridge waveguides in periodically poled magnesium-doped lithium niobate (PPMgO:LN). A controlled variable method is used to study the effects of diffusion temperature, diffusion time, ZnO film thickness, and barrier layer thickness on the surface domain depolarization and waveguide quality of PPMgO:LN. A special barrier layer is proposed that can automatically lift off from the sample surface, which increases the depth of Zn doping and reduces the surface loss of the waveguide. By optimizing the process parameters, we fabricate Zn-diffused PPMgO:LN ridge waveguides with a length of 22.80 mm and a period of 18.0 μm. The above waveguides can make a second harmonic generation (SHG) at 775 nm with an output power of 90.20 mW by a pump power of 741 mW at 1 550 nm. The corresponding conversion efficiency is 3.160%/W∙cm2, and the waveguide loss is approximately 0.81 dB/cm. These results demonstrate that high-efficiency devices can be obtained through the fabrication process described in this paper.
2024, 20(1):18-22.
DOI: https://doi.org/10.1007/s11801-024-3072-y
Abstract:
A multi-parametric sensor based on multi-mode microfiber (MMF) is proposed, utilizing the modal interference between HE11 mode and HE12 mode in the elongated multi-mode fiber to achieve the temperature and pressure measurement. In this paper, the simulation model of modal interference based on MMF is established and the mechanism of modal interference is analyzed. Using the different mechanisms of modal response in the fiber at different wavelengths, the temperature was inverted using the offset of wavelengths in the spectrum, and the pressure was measured using the change of light intensity. The independent measurement of temperature and pressure was achieved. The experimental results show that the sensor has a temperature sensitivity of 1.305 nm/°C. In the case of pressure sensing, the sensor shows a sensitivity of −0.163 dBm/g.
A multi-parametric sensor based on multi-mode microfiber (MMF) is proposed, utilizing the modal interference between HE11 mode and HE12 mode in the elongated multi-mode fiber to achieve the temperature and pressure measurement. In this paper, the simulation model of modal interference based on MMF is established and the mechanism of modal interference is analyzed. Using the different mechanisms of modal response in the fiber at different wavelengths, the temperature was inverted using the offset of wavelengths in the spectrum, and the pressure was measured using the change of light intensity. The independent measurement of temperature and pressure was achieved. The experimental results show that the sensor has a temperature sensitivity of 1.305 nm/°C. In the case of pressure sensing, the sensor shows a sensitivity of −0.163 dBm/g.
2024, 20(1):23-27.
DOI: https://doi.org/10.1007/s11801-024-3088-3
Abstract:
In this work, a new configuration of an all-optical nonlinear de-multiplexer gate based on two-dimensional (2D) photonic crystals (PhC) is proposed. The gate is considered in the double-ring resonator shaped structure of silicon rods. In order to have a more functional structure, some defect rods made of nonlinear materials were positioned in the structure. Considering the functionality of the structure, photonic band gap (PBG), field distribution and transmitted power spectra are investigated. Plane wave expansion and finite difference time domain (FDTD) methods are utilized for extracting the PBG and field distribution diagrams. The remarkable dimension, bit rate, maximum intensity and contrast ratio of 116.64 μm2, 3.125 Tbit/s, 97% and 40.2 dB are obtained, respectively, which make the gate an appropriate candidate for utilization in optical integrated circuits.
In this work, a new configuration of an all-optical nonlinear de-multiplexer gate based on two-dimensional (2D) photonic crystals (PhC) is proposed. The gate is considered in the double-ring resonator shaped structure of silicon rods. In order to have a more functional structure, some defect rods made of nonlinear materials were positioned in the structure. Considering the functionality of the structure, photonic band gap (PBG), field distribution and transmitted power spectra are investigated. Plane wave expansion and finite difference time domain (FDTD) methods are utilized for extracting the PBG and field distribution diagrams. The remarkable dimension, bit rate, maximum intensity and contrast ratio of 116.64 μm2, 3.125 Tbit/s, 97% and 40.2 dB are obtained, respectively, which make the gate an appropriate candidate for utilization in optical integrated circuits.
2024, 20(1):28-34.
DOI: https://doi.org/10.1007/s11801-024-2171-0
Abstract:
When the photovoltaic (PV) system is generating PV power, the partial shading (PS) condition will cause multiple peaks in the power-voltage curve, and changes in light intensity and ambient temperature will cause the curve to shift. Traditional maximum power point tracking (MPPT) methods, such as the incremental conductance (INC) method, have the problem of being trapped in the local optimal solution. Biomimetic optimization algorithms, such as particle swarm optimization (PSO), have problems with oscillation and low tracking efficiency near the global maximum power point (GMPP). As a result, a hybrid algorithm CS-INC based on the cuckoo search (CS) algorithm and the perturb and observe (P&O) approach is proposed in this study. The light intensity remains constant, the light intensity changes in steps, and the partial shade scenario are simulated. Simulation results show that the MPPT improves accuracy, speed, and stability.
When the photovoltaic (PV) system is generating PV power, the partial shading (PS) condition will cause multiple peaks in the power-voltage curve, and changes in light intensity and ambient temperature will cause the curve to shift. Traditional maximum power point tracking (MPPT) methods, such as the incremental conductance (INC) method, have the problem of being trapped in the local optimal solution. Biomimetic optimization algorithms, such as particle swarm optimization (PSO), have problems with oscillation and low tracking efficiency near the global maximum power point (GMPP). As a result, a hybrid algorithm CS-INC based on the cuckoo search (CS) algorithm and the perturb and observe (P&O) approach is proposed in this study. The light intensity remains constant, the light intensity changes in steps, and the partial shade scenario are simulated. Simulation results show that the MPPT improves accuracy, speed, and stability.
2024, 20(1):35-41.
DOI: https://doi.org/10.1007/s11801-024-3069-6
Abstract:
To solve the problems of pulse broadening and channel fading caused by atmospheric scattering and turbulence, multiple-input multiple-output (MIMO) technology is a valid way. A wireless ultraviolet (UV) MIMO channel estimation approach based on deep learning is provided in this paper. The deep learning is used to convert the channel estimation into the image processing. By combining convolutional neural network (CNN) and attention mechanism (AM), the learning model is designed to extract the depth features of channel state information (CSI). The simulation results show that the approach proposed in this paper can perform channel estimation effectively for UV MIMO communication and can better suppress the fading caused by scattering and turbulence in the MIMO scattering channel.
To solve the problems of pulse broadening and channel fading caused by atmospheric scattering and turbulence, multiple-input multiple-output (MIMO) technology is a valid way. A wireless ultraviolet (UV) MIMO channel estimation approach based on deep learning is provided in this paper. The deep learning is used to convert the channel estimation into the image processing. By combining convolutional neural network (CNN) and attention mechanism (AM), the learning model is designed to extract the depth features of channel state information (CSI). The simulation results show that the approach proposed in this paper can perform channel estimation effectively for UV MIMO communication and can better suppress the fading caused by scattering and turbulence in the MIMO scattering channel.
2024, 20(1):42-47.
DOI: https://doi.org/10.1007/s11801-024-3082-9
Abstract:
This paper focuses on the standard chemical oxygen demand (COD) liquid and studies the impact of pH, nitrite nitrogen, nitrate nitrogen, heavy metals, salinity, and other factors on fluorescence intensity and fluorescence peak positions during the detection of COD in water using fluorescence spectrometry. The influence mechanisms of different environmental factors on fluorescence spectra are also analyzed. Results indicate that pH value affects the fluorescence emission wavelength (Em), resulting in a red shift from 1.5 to 7.2, and a blue shift from 7.2 to 12.3. Nitrate nitrogen can react with organic matter in water to form nitro compounds, leading to a decrease in fluorescence intensity. Salinity has a negligible effect on T1 peak but a relatively large effect on T2 peak. Heavy metal ion concentration has a significant impact on T2 peak, while T1 peak position shifts with an increase in heavy metal ions. This study aims to explore the factors that can impact the detection of COD in water using three-dimensional fluorescence spectrometry, providing references to improve accuracy and practicability for COD detection based on three-dimensional fluorescence spectrometry.
This paper focuses on the standard chemical oxygen demand (COD) liquid and studies the impact of pH, nitrite nitrogen, nitrate nitrogen, heavy metals, salinity, and other factors on fluorescence intensity and fluorescence peak positions during the detection of COD in water using fluorescence spectrometry. The influence mechanisms of different environmental factors on fluorescence spectra are also analyzed. Results indicate that pH value affects the fluorescence emission wavelength (Em), resulting in a red shift from 1.5 to 7.2, and a blue shift from 7.2 to 12.3. Nitrate nitrogen can react with organic matter in water to form nitro compounds, leading to a decrease in fluorescence intensity. Salinity has a negligible effect on T1 peak but a relatively large effect on T2 peak. Heavy metal ion concentration has a significant impact on T2 peak, while T1 peak position shifts with an increase in heavy metal ions. This study aims to explore the factors that can impact the detection of COD in water using three-dimensional fluorescence spectrometry, providing references to improve accuracy and practicability for COD detection based on three-dimensional fluorescence spectrometry.
2024, 20(1):48-57.
DOI: https://doi.org/10.1007/s11801-024-3050-4
Abstract:
Recent advancements in artificial intelligence (AI) have shown promising potential for the automated screening and grading of cataracts. However, the different types of visual impairment caused by cataracts exhibit similar phenotypes, posing significant challenges for accurately assessing the severity of visual impairment. To address this issue, we propose a dense convolution combined with attention mechanism and multi-level classifier (DAMC_Net) for visual impairment grading. First, the double-attention mechanism is utilized to enable the DAMC_Net to focus on lesions-related regions. Then, a hierarchical multi-level classifier is constructed to enhance the recognition ability in distinguishing the severities of visual impairment, while maintaining a better screening rate for normal samples. In addition, a cost-sensitive method is applied to address the problem of higher false-negative rate caused by the imbalanced dataset. Experimental results demonstrated that the DAMC_Net outperformed ResNet50 and dense convolutional network 121 (DenseNet121) models, with sensitivity improvements of 6.0% and 3.4% on the category of mild visual impairment caused by cataracts (MVICC), and 2.1% and 4.3% on the category of moderate to severe visual impairment caused by cataracts (MSVICC), respectively. The comparable performance on two external test datasets was achieved, further verifying the effectiveness and generalizability of the DAMC_Net.
Recent advancements in artificial intelligence (AI) have shown promising potential for the automated screening and grading of cataracts. However, the different types of visual impairment caused by cataracts exhibit similar phenotypes, posing significant challenges for accurately assessing the severity of visual impairment. To address this issue, we propose a dense convolution combined with attention mechanism and multi-level classifier (DAMC_Net) for visual impairment grading. First, the double-attention mechanism is utilized to enable the DAMC_Net to focus on lesions-related regions. Then, a hierarchical multi-level classifier is constructed to enhance the recognition ability in distinguishing the severities of visual impairment, while maintaining a better screening rate for normal samples. In addition, a cost-sensitive method is applied to address the problem of higher false-negative rate caused by the imbalanced dataset. Experimental results demonstrated that the DAMC_Net outperformed ResNet50 and dense convolutional network 121 (DenseNet121) models, with sensitivity improvements of 6.0% and 3.4% on the category of mild visual impairment caused by cataracts (MVICC), and 2.1% and 4.3% on the category of moderate to severe visual impairment caused by cataracts (MSVICC), respectively. The comparable performance on two external test datasets was achieved, further verifying the effectiveness and generalizability of the DAMC_Net.
2024, 20(1):58-64.
DOI: https://doi.org/10.1007/s11801-024-3083-8
Abstract:
Aero-optic imaging deviation research is carried out for infrared-guided vehicle with cone-head side window, with a focus on the propagation characteristics of light in an aero-optic flow field. When the light entering the aero-optic flow field from the free-stream should be close to the normal, numerous data indicate that the light is refracted away from the normal. This paper divides the aero-optic flow field into two parts and uses the gas density distribution in the aero-optic flow field to propose the hypothesis that there are two modes of refraction when light propagates through the flow field. The results show that light propagates from the optically denser medium to the optically thinner medium after passing through the shock wave and eventually leads to refraction away from the normal when the light enters the aero-optic flow field.
Aero-optic imaging deviation research is carried out for infrared-guided vehicle with cone-head side window, with a focus on the propagation characteristics of light in an aero-optic flow field. When the light entering the aero-optic flow field from the free-stream should be close to the normal, numerous data indicate that the light is refracted away from the normal. This paper divides the aero-optic flow field into two parts and uses the gas density distribution in the aero-optic flow field to propose the hypothesis that there are two modes of refraction when light propagates through the flow field. The results show that light propagates from the optically denser medium to the optically thinner medium after passing through the shock wave and eventually leads to refraction away from the normal when the light enters the aero-optic flow field.
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