Volume 20,Issue 7,2024 Table of Contents
2024, 20(7):385-392.
DOI: https://doi.org/10.1007/s11801-024-3185-3
Abstract:
This study delves into the feasibility of using amorphous silicon photocells as photosensitive units for retinal prostheses. Firstly, theoretical simulations coupled with experimental results demonstrated its strong light absorption and quantum efficiency within the 300—800 nm range. Subsequently, measurements on its visual sensitivity properties were conducted. The findings revealed that under photopic vision conditions, the photocells could provide the stimulating current required for the human retinal nerve cells. Finally, the visual spectral sensitivity curve of the amorphous silicon photocells was assessed, and the results indicated that the spectral sensitivity curve of the amorphous silicon photocells closely mirrors the visual function curve of the human eye under photopic conditions, demonstrating a response to light across various wavelengths.
This study delves into the feasibility of using amorphous silicon photocells as photosensitive units for retinal prostheses. Firstly, theoretical simulations coupled with experimental results demonstrated its strong light absorption and quantum efficiency within the 300—800 nm range. Subsequently, measurements on its visual sensitivity properties were conducted. The findings revealed that under photopic vision conditions, the photocells could provide the stimulating current required for the human retinal nerve cells. Finally, the visual spectral sensitivity curve of the amorphous silicon photocells was assessed, and the results indicated that the spectral sensitivity curve of the amorphous silicon photocells closely mirrors the visual function curve of the human eye under photopic conditions, demonstrating a response to light across various wavelengths.
2024, 20(7):393-399.
DOI: https://doi.org/10.1007/s11801-024-3229-8
Abstract:
In this paper, we propose a photonic crystal fiber (PCF) sensor based on the surface plasmonic resonance (SPR) effect for simultaneous temperature and refractive index (RI) measurement. The coupling characteristics and sensing performance of the sensor are analyzed using the full vector finite element method (FEM). The sensor provides two channels for independent measurement of RI and temperature. When operating independently, channel I supports y-polarized light with a sensitivity of up to 7 000 nm/RIU for detecting RI, while channel II supports x-polarized light with a sensitivity of up to 16 nm/°C for detecting temperature. Additionally, we investigate the influence of gold layer thickness on the sensing performance to optimize the sensor.
In this paper, we propose a photonic crystal fiber (PCF) sensor based on the surface plasmonic resonance (SPR) effect for simultaneous temperature and refractive index (RI) measurement. The coupling characteristics and sensing performance of the sensor are analyzed using the full vector finite element method (FEM). The sensor provides two channels for independent measurement of RI and temperature. When operating independently, channel I supports y-polarized light with a sensitivity of up to 7 000 nm/RIU for detecting RI, while channel II supports x-polarized light with a sensitivity of up to 16 nm/°C for detecting temperature. Additionally, we investigate the influence of gold layer thickness on the sensing performance to optimize the sensor.
Huda Adnan Zain
,
Malathy Batumalay
,
Md Ashadi Md Johari
,
Hazli Rafis Abdul Rahim
,
Sulaiman Wadi Harun
2024, 20(7):400-405.
DOI: https://doi.org/10.1007/s11801-024-3107-4
Abstract:
Graphene oxide (GO) is a 2D coating material used to improve fiber optics sensors’ response to relative humidity. Microbottle resonators (MBRs) have garnered more attention as sensing media structures. An MBR with a 190 µm diameter was coated with GO. Then, tapered fiber light coupling was used to investigate the relative humidity sensing performance in the range of 35—70%RH at 25 °C. The MBR showed a higher Q factor before and after GO coating. The sensitivity of 0.115 dB/%RH was recorded with the 190 µm GO-coated MBR sample compared to a sensitivity of 0.022 dB/%RH for the uncoated MBR sample. These results show that the MBR can be used in fiber optic sensing applications for environmental sensing.
Graphene oxide (GO) is a 2D coating material used to improve fiber optics sensors’ response to relative humidity. Microbottle resonators (MBRs) have garnered more attention as sensing media structures. An MBR with a 190 µm diameter was coated with GO. Then, tapered fiber light coupling was used to investigate the relative humidity sensing performance in the range of 35—70%RH at 25 °C. The MBR showed a higher Q factor before and after GO coating. The sensitivity of 0.115 dB/%RH was recorded with the 190 µm GO-coated MBR sample compared to a sensitivity of 0.022 dB/%RH for the uncoated MBR sample. These results show that the MBR can be used in fiber optic sensing applications for environmental sensing.
2024, 20(7):406-411.
DOI: https://doi.org/10.1007/s11801-024-3138-x
Abstract:
We developed a plasmonic refractive index (RI) with a metal-dielectric-metal (MDM) structure that utilizes two Persian Orsi windows-like separated cavities with a high figure of merit (FoM) and ultrasensitivity. The simulated and optimized Ag-air-Ag MDM sensor for surface plasmon resonance (SPR) offers high RI sensitivity (SRI) and the ability to detect blood plasma concentration (BPC). The results verified that structural parameters have an effect on SRI, full width at half maximum (FWHM), FoM, sensitivity of blood plasma (Sp) for right and left peaks, whose values are 1 345.45 nm.RIU−1, 32 nm, 42.04 RIU−1, 0.26 and 0.19 nm.L.g-1, respectively. The proposed design opens a new horizon in sensor development.
We developed a plasmonic refractive index (RI) with a metal-dielectric-metal (MDM) structure that utilizes two Persian Orsi windows-like separated cavities with a high figure of merit (FoM) and ultrasensitivity. The simulated and optimized Ag-air-Ag MDM sensor for surface plasmon resonance (SPR) offers high RI sensitivity (SRI) and the ability to detect blood plasma concentration (BPC). The results verified that structural parameters have an effect on SRI, full width at half maximum (FWHM), FoM, sensitivity of blood plasma (Sp) for right and left peaks, whose values are 1 345.45 nm.RIU−1, 32 nm, 42.04 RIU−1, 0.26 and 0.19 nm.L.g-1, respectively. The proposed design opens a new horizon in sensor development.
5 Tuning the optoelectronic properties for small organic compounds as organic solar cell applications
2024, 20(7):412-417.
DOI: https://doi.org/10.1007/s11801-024-3165-7
Abstract:
Recently, organic solar cells have attracted the attention of many researchers owing to flexibility, low cost, light weight and large-area applications, and significant improvement in the power conversion efficiency (PCE). In this work, we designed chains from organic compounds as donors and replaced the core unit in each series with a variety of acceptors in order to enhance their optical and electronic characterization and performance for power conversion efficiencies in organic solar cells. We utilized density functional theory (DFT) and time-dependent density functional theory (TD-DFT) to investigate the geometry optimization by using the Gaussian 09 program. Both electronic and optical properties were determined, which involve the lowest unoccupied molecular orbital (LUMO) and the highest occupied molecular orbital (HOMO) levels, the band gap energy, maximum absorption wavelength (λmax), open circuit voltage (Voc), and light harvesting efficiency (LHE). Our evaluation denotes that the small compounds suggested are predicted to exhibit the best performance compared to the first series (SC1), like a lower band gap energy, a lower HOMO energy level, a greater absorption range, and a larger PCE.
Recently, organic solar cells have attracted the attention of many researchers owing to flexibility, low cost, light weight and large-area applications, and significant improvement in the power conversion efficiency (PCE). In this work, we designed chains from organic compounds as donors and replaced the core unit in each series with a variety of acceptors in order to enhance their optical and electronic characterization and performance for power conversion efficiencies in organic solar cells. We utilized density functional theory (DFT) and time-dependent density functional theory (TD-DFT) to investigate the geometry optimization by using the Gaussian 09 program. Both electronic and optical properties were determined, which involve the lowest unoccupied molecular orbital (LUMO) and the highest occupied molecular orbital (HOMO) levels, the band gap energy, maximum absorption wavelength (λmax), open circuit voltage (Voc), and light harvesting efficiency (LHE). Our evaluation denotes that the small compounds suggested are predicted to exhibit the best performance compared to the first series (SC1), like a lower band gap energy, a lower HOMO energy level, a greater absorption range, and a larger PCE.
2024, 20(7):418-423.
DOI: https://doi.org/10.1007/s11801-024-3214-2
Abstract:
In order to reduce the number of redundant candidate codewords generated by the fast successive cancellation list (FSCL) decoding algorithm for polar codes, a simplified FSCL decoding algorithm based on critical sets (CS-FSCL) of polar codes is proposed. The algorithm utilizes the number of information bits belonging to the CS in the special nodes, such as Rate-1 node, repetition (REP) node and single-parity-check (SPC) node, to constrain the number of the path splitting and avoid the generation of unnecessary candidate codewords, and thus the latency and computational complexity are reduced. Besides, the algorithm only flips the bits corresponding to the smaller log-likelihood ratio (LLR) values to generate the sub-maximum likelihood (sub-ML) decoding codewords and ensure the decoding performance. Simulation results show that for polar codes with the code length of 1 024, the code rates of 1/4, 1/2 and 3/4, the proposed CS-FSCL algorithm, compared with the conventional FSCL decoding algorithm, can achieve the same decoding performance, but reduce the latency and computational complexity at different list sizes. Specifically, under the list size of L=8, the code rates of R=1/2 and R=1/4, the latency is reduced by 33% and 13% and the computational complexity is reduced by 55% and 50%, respectively.
In order to reduce the number of redundant candidate codewords generated by the fast successive cancellation list (FSCL) decoding algorithm for polar codes, a simplified FSCL decoding algorithm based on critical sets (CS-FSCL) of polar codes is proposed. The algorithm utilizes the number of information bits belonging to the CS in the special nodes, such as Rate-1 node, repetition (REP) node and single-parity-check (SPC) node, to constrain the number of the path splitting and avoid the generation of unnecessary candidate codewords, and thus the latency and computational complexity are reduced. Besides, the algorithm only flips the bits corresponding to the smaller log-likelihood ratio (LLR) values to generate the sub-maximum likelihood (sub-ML) decoding codewords and ensure the decoding performance. Simulation results show that for polar codes with the code length of 1 024, the code rates of 1/4, 1/2 and 3/4, the proposed CS-FSCL algorithm, compared with the conventional FSCL decoding algorithm, can achieve the same decoding performance, but reduce the latency and computational complexity at different list sizes. Specifically, under the list size of L=8, the code rates of R=1/2 and R=1/4, the latency is reduced by 33% and 13% and the computational complexity is reduced by 55% and 50%, respectively.
2024, 20(7):424-429.
DOI: https://doi.org/10.1007/s11801-024-3181-7
Abstract:
Detecting small objects on highways is a novel research topic. Due to the small pixel of objects on highways, traditional detectors have difficulty in capturing discriminative features. Additionally, the imbalance of feature fusion methods and the inconsistency between classification and regression tasks lead to poor detection performance on highways. In this paper, we propose a balance feature fusion and task-specific encoding network to address these issues. Specifically, we design a balance feature pyramid network (FPN) to integrate the importance of each layer of feature maps and construct long-range dependencies among them, thereby making the features more discriminative. In addition, we present task-specific decoupled head, which utilizes task-specific encoding to moderate the imbalance between the classification and regression tasks. As demonstrated by extensive experiments and visualizations, our method obtains outstanding detection performance on small object detection on highways (HSOD) dataset and AI-TOD dataset.
Detecting small objects on highways is a novel research topic. Due to the small pixel of objects on highways, traditional detectors have difficulty in capturing discriminative features. Additionally, the imbalance of feature fusion methods and the inconsistency between classification and regression tasks lead to poor detection performance on highways. In this paper, we propose a balance feature fusion and task-specific encoding network to address these issues. Specifically, we design a balance feature pyramid network (FPN) to integrate the importance of each layer of feature maps and construct long-range dependencies among them, thereby making the features more discriminative. In addition, we present task-specific decoupled head, which utilizes task-specific encoding to moderate the imbalance between the classification and regression tasks. As demonstrated by extensive experiments and visualizations, our method obtains outstanding detection performance on small object detection on highways (HSOD) dataset and AI-TOD dataset.
2024, 20(7):430-436.
DOI: https://doi.org/10.1007/s11801-024-4053-x
Abstract:
Aiming at the problem of low detection accuracy of occluded pedestrian in traffic environments, this paper proposes a key points and visible part fusion network for occluded pedestrian detection. The proposed algorithm constructs two attention modules by introducing human key points and the bounding box of visible parts respectively, which suppresses the occluded parts in the channel features and spatial features of pedestrian features respectively. Experimental results on CityPersons and Caltech datasets demonstrate the effectiveness of the proposed algorithm. The missing rate (MR) is reduced to 40.78 on the Heavy subset of the CityPersons dataset and surpasses many outstanding methods.
Aiming at the problem of low detection accuracy of occluded pedestrian in traffic environments, this paper proposes a key points and visible part fusion network for occluded pedestrian detection. The proposed algorithm constructs two attention modules by introducing human key points and the bounding box of visible parts respectively, which suppresses the occluded parts in the channel features and spatial features of pedestrian features respectively. Experimental results on CityPersons and Caltech datasets demonstrate the effectiveness of the proposed algorithm. The missing rate (MR) is reduced to 40.78 on the Heavy subset of the CityPersons dataset and surpasses many outstanding methods.
2024, 20(7):437-448.
DOI: https://doi.org/10.1007/s11801-024-3155-9
Abstract:
This paper proposes a method for learning background restoration for infrared small target detection, employing a local sparse dictionary alongside an equalized structural texture representation. The method is specifically designed for the detection of small infrared targets, accommodating various levels of brightness, spatial size, and intensity. Our proposed model intelligently combines global low-rankness and local sparsity to estimate the rank of the background tensor, leveraging spatial and structural information to overcome the limitations posed by insufficient detailed texture knowledge. Subsequently, a structural texture representation, combining local gradient maps and local intensity maps, is applied to emphasize small objects. By comparing our method with nine advanced and representative approaches and quantifying the comparison using various metrics, the experimental results indicate that our proposed method has achieved favorable outcomes in both quantitative assessments and visual results.
This paper proposes a method for learning background restoration for infrared small target detection, employing a local sparse dictionary alongside an equalized structural texture representation. The method is specifically designed for the detection of small infrared targets, accommodating various levels of brightness, spatial size, and intensity. Our proposed model intelligently combines global low-rankness and local sparsity to estimate the rank of the background tensor, leveraging spatial and structural information to overcome the limitations posed by insufficient detailed texture knowledge. Subsequently, a structural texture representation, combining local gradient maps and local intensity maps, is applied to emphasize small objects. By comparing our method with nine advanced and representative approaches and quantifying the comparison using various metrics, the experimental results indicate that our proposed method has achieved favorable outcomes in both quantitative assessments and visual results.
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