Volume 18,Issue 5,2022 Table of Contents
2022, 18(5):257-262.
DOI: https://doi.org/10.1007/s11801-022-1162-2
Abstract:
The effects of the stabilizing agent on the structural and luminescence properties of cadmium sulfide (CdS) nanocrystals have been investigated. Samples were prepared by chemical precipitation method using sodium hexametaphosphate (SHMP) and polyvinyl pyrrolidone (PVP). The structural and optical properties have been studied by X-ray diffraction (XRD), ultraviolet-visible (UV-Vis) absorption, photoluminescence (PL) and Fourier transform infrared (FTIR) spectroscopy. XRD patterns confirm the presence of cubic zinc blend crystal structure with space group F-43m. The results show the variation in crystallite size with change in surfactant. Blue shift in absorption edge as compared to bulk CdS is found. PL results represent broad and very intense red emission with smaller particle size due to modification of surface emitting states with surfactant.
The effects of the stabilizing agent on the structural and luminescence properties of cadmium sulfide (CdS) nanocrystals have been investigated. Samples were prepared by chemical precipitation method using sodium hexametaphosphate (SHMP) and polyvinyl pyrrolidone (PVP). The structural and optical properties have been studied by X-ray diffraction (XRD), ultraviolet-visible (UV-Vis) absorption, photoluminescence (PL) and Fourier transform infrared (FTIR) spectroscopy. XRD patterns confirm the presence of cubic zinc blend crystal structure with space group F-43m. The results show the variation in crystallite size with change in surfactant. Blue shift in absorption edge as compared to bulk CdS is found. PL results represent broad and very intense red emission with smaller particle size due to modification of surface emitting states with surfactant.
2022, 18(5):263-268.
DOI: https://doi.org/10.1007/s11801-022-2061-2
Abstract:
A real-time label-free lateral offset spliced coreless fiber (CF) Mach-Zehnder interferometer (MZI) biosensor functionalized with hydrophobin Grifola frondosa I (HGFI) was proposed for the detection of cytokine tumour necrosis factor alpha (TNF-α). The nanolayer self-assembled on the optical fiber surfaces by HGFI rendered the immobilization of probe TNF-α antibody and recognition of antigen TNF-α. Trifluoroacetic acid was utilized to remove the HGFI layer from the glass surface, which was validated by field emission scanning electron microscopy (FESEM) and water contact angle (WCA). Results demonstrated that the processes of HGFI modification, antibody immobilization and specific antibody detection can be monitored in real time. The proposed biosensor exhibited good specificity, repeatability and low detection limit for TNF-α, extending its application in inflammation and disease monitoring.
A real-time label-free lateral offset spliced coreless fiber (CF) Mach-Zehnder interferometer (MZI) biosensor functionalized with hydrophobin Grifola frondosa I (HGFI) was proposed for the detection of cytokine tumour necrosis factor alpha (TNF-α). The nanolayer self-assembled on the optical fiber surfaces by HGFI rendered the immobilization of probe TNF-α antibody and recognition of antigen TNF-α. Trifluoroacetic acid was utilized to remove the HGFI layer from the glass surface, which was validated by field emission scanning electron microscopy (FESEM) and water contact angle (WCA). Results demonstrated that the processes of HGFI modification, antibody immobilization and specific antibody detection can be monitored in real time. The proposed biosensor exhibited good specificity, repeatability and low detection limit for TNF-α, extending its application in inflammation and disease monitoring.
2022, 18(5):269-275.
DOI: https://doi.org/10.1007/s11801-022-1135-5
Abstract:
A novel magnetic field sensor was proposed based on the grapefruit optical fiber with the magnetic fluid injected into the six air holes. The sensor utilizes the Mach-Zehnder interference (MZI). The light is transmitted through the single-mode fiber to the fusion splice point and is divided into two parts. When the light passes through the cladding air hole with magnetic fluid, it interferes with the core light, and the change of the interference light is related with the change of the magnetic field. The sensor cavity length is 6 cm. It can be obtained from the experimental results that the magnetic field sensitivity reaches up to 2.243 nm/Oe with the range from 0 to 2.28 Oe. The sensor has the advantages of easy fabrication, easy installation and low cost. The findings provide new ideas for the study of ocean wind electromagnetic fields.
A novel magnetic field sensor was proposed based on the grapefruit optical fiber with the magnetic fluid injected into the six air holes. The sensor utilizes the Mach-Zehnder interference (MZI). The light is transmitted through the single-mode fiber to the fusion splice point and is divided into two parts. When the light passes through the cladding air hole with magnetic fluid, it interferes with the core light, and the change of the interference light is related with the change of the magnetic field. The sensor cavity length is 6 cm. It can be obtained from the experimental results that the magnetic field sensitivity reaches up to 2.243 nm/Oe with the range from 0 to 2.28 Oe. The sensor has the advantages of easy fabrication, easy installation and low cost. The findings provide new ideas for the study of ocean wind electromagnetic fields.
2022, 18(5):276-282.
DOI: https://doi.org/10.1007/s11801-022-1144-4
Abstract:
Numerical simulation has been performed to improve the performance of Cu2ZnSnS4 (CZTS) solar cells by replacing CdS with Zn1–xSnxO buffer layer. The influences of thickness, donor concentration and defect density of buffer layers on the performance of CZTS solar cells were investigated. It has been found that Zn1–xSnxO buffer layer for Sn content of 0.20 is better for CZTS solar cell. A higher efficiency can be achieved with thinner buffer layer. The optimized solar cell demonstrated a maximum power conversion efficiency of 13%.
Numerical simulation has been performed to improve the performance of Cu2ZnSnS4 (CZTS) solar cells by replacing CdS with Zn1–xSnxO buffer layer. The influences of thickness, donor concentration and defect density of buffer layers on the performance of CZTS solar cells were investigated. It has been found that Zn1–xSnxO buffer layer for Sn content of 0.20 is better for CZTS solar cell. A higher efficiency can be achieved with thinner buffer layer. The optimized solar cell demonstrated a maximum power conversion efficiency of 13%.
2022, 18(5):283-287.
DOI: https://doi.org/10.1007/s11801-022-1032-y
Abstract:
We reported the formation of diffraction ring patterns induced by transmitted Gaussian laser beam through 8-hydroxyquinolin-1-ium 4-aminobenzenesulfonate (8HQABS) solution as absorbing medium. Theoretical model depending on the Fresnel-Kirchhoff diffraction (FKD) theory was used to generate diffraction rings with respect to the sample position. Analysis of the generated diffraction rings shows a reasonable agreement between the features of the experimental and numerical diffraction rings. Our results are considered to be useful to understand the way of propagation and interaction of laser beam with liquid absorbing medium.
We reported the formation of diffraction ring patterns induced by transmitted Gaussian laser beam through 8-hydroxyquinolin-1-ium 4-aminobenzenesulfonate (8HQABS) solution as absorbing medium. Theoretical model depending on the Fresnel-Kirchhoff diffraction (FKD) theory was used to generate diffraction rings with respect to the sample position. Analysis of the generated diffraction rings shows a reasonable agreement between the features of the experimental and numerical diffraction rings. Our results are considered to be useful to understand the way of propagation and interaction of laser beam with liquid absorbing medium.
6 BER performance analysis of an OFDM-based visible light communication system using DHT post-coding
2022, 18(5):288-292.
DOI: https://doi.org/10.1007/s11801-022-1160-4
Abstract:
The theoretical analysis of the bit error rate (BER) performance in a discrete Hartley transform (DHT) post-coded orthogonal frequency division multiplexing (OFDM) visible light communication (VLC) system over an additive white Gaussian noise (AWGN) channel is presented. The theoretical analysis results show that the proposed DHT post-coding scheme does not degrade the BER performance of the post-coded OFDM VLC systems. The analysis result is confirmed by our simulation results for random bit source. In addition, the peak-to-average power ratio (PAPR) of the DHT post-coded OFDM is evaluated by simulation. The simulation results show that DHT post-coding can greatly reduce of the PAPR of the optical OFDM system.
The theoretical analysis of the bit error rate (BER) performance in a discrete Hartley transform (DHT) post-coded orthogonal frequency division multiplexing (OFDM) visible light communication (VLC) system over an additive white Gaussian noise (AWGN) channel is presented. The theoretical analysis results show that the proposed DHT post-coding scheme does not degrade the BER performance of the post-coded OFDM VLC systems. The analysis result is confirmed by our simulation results for random bit source. In addition, the peak-to-average power ratio (PAPR) of the DHT post-coded OFDM is evaluated by simulation. The simulation results show that DHT post-coding can greatly reduce of the PAPR of the optical OFDM system.
2022, 18(5):293-299.
DOI: https://doi.org/10.1007/s11801-022-2055-0
Abstract:
Existing multi-view three-dimensional (3D) reconstruction methods can only capture single type of feature from input view, failing to obtain fine-grained semantics for reconstructing the complex shapes. They rarely explore the semantic association between input views, leading to a rough 3D shape. To address these challenges, we propose a semantics-aware transformer (SATF) for 3D reconstruction. It is composed of two parallel view transformer encoders and a point cloud transformer decoder, and takes two red, green and blue (RGB) images as input and outputs a dense point cloud with richer details. Each view transformer encoder can learn a multi-level feature, facilitating characterizing fine-grained semantics from input view. The point cloud transformer decoder explores a semantically-associated feature by aligning the semantics of two input views, which describes the semantic association between views. Furthermore, it can generate a sparse point cloud using the semantically-associated feature. At last, the decoder enriches the sparse point cloud for producing a dense point cloud with richer details. Extensive experiments on the ShapeNet dataset show that our SATF outperforms the state-of-the-art methods.
Existing multi-view three-dimensional (3D) reconstruction methods can only capture single type of feature from input view, failing to obtain fine-grained semantics for reconstructing the complex shapes. They rarely explore the semantic association between input views, leading to a rough 3D shape. To address these challenges, we propose a semantics-aware transformer (SATF) for 3D reconstruction. It is composed of two parallel view transformer encoders and a point cloud transformer decoder, and takes two red, green and blue (RGB) images as input and outputs a dense point cloud with richer details. Each view transformer encoder can learn a multi-level feature, facilitating characterizing fine-grained semantics from input view. The point cloud transformer decoder explores a semantically-associated feature by aligning the semantics of two input views, which describes the semantic association between views. Furthermore, it can generate a sparse point cloud using the semantically-associated feature. At last, the decoder enriches the sparse point cloud for producing a dense point cloud with richer details. Extensive experiments on the ShapeNet dataset show that our SATF outperforms the state-of-the-art methods.
2022, 18(5):300-306.
DOI: https://doi.org/10.1007/s11801-022-1148-0
Abstract:
A geo-localization method is proposed for military and civilian applications, which is used when no global navigation satellite system (GNSS) information is available. The open graphics library (OpenGL) is used to build a three-dimensional geographic model of the test area using digital elevation model (DEM) data, and the skyline can thus be extracted with the model to form a database. Then, MultiSkip DeepLab (MS-DeepLab), a fully convolutional semantic segmentation network with multiple skip structures, is proposed to extract the skyline from the query image. Finally, a matching model based on convolutional neural network (CNN) feature is adopted to calculate the similarity between the skyline features of the query image and the DEM database to realize automatic geo-localization. The experiments are conducted at a 202.6 km2 test site in north-eastern Changsha, China. 50 test points are selected to verify the effectiveness of the proposed method, and an excellent result with an average positioning error of 49.29 m is obtained.
A geo-localization method is proposed for military and civilian applications, which is used when no global navigation satellite system (GNSS) information is available. The open graphics library (OpenGL) is used to build a three-dimensional geographic model of the test area using digital elevation model (DEM) data, and the skyline can thus be extracted with the model to form a database. Then, MultiSkip DeepLab (MS-DeepLab), a fully convolutional semantic segmentation network with multiple skip structures, is proposed to extract the skyline from the query image. Finally, a matching model based on convolutional neural network (CNN) feature is adopted to calculate the similarity between the skyline features of the query image and the DEM database to realize automatic geo-localization. The experiments are conducted at a 202.6 km2 test site in north-eastern Changsha, China. 50 test points are selected to verify the effectiveness of the proposed method, and an excellent result with an average positioning error of 49.29 m is obtained.
2022, 18(5):307-312.
DOI: https://doi.org/10.1007/s11801-022-1157-z
Abstract:
Studies have shown that deep neural networks (DNNs) are vulnerable to adversarial examples (AEs) that induce incorrect behaviors. To defend these AEs, various detection techniques have been developed. However, most of them only appear to be effective against specific AEs and cannot generalize well to different AEs. We propose a new detection method against AEs based on the maximum channel of saliency maps (MCSM). The proposed method can alter the structure of adversarial perturbations and preserve the statistical properties of images at the same time. We conduct a complete evaluation on AEs generated by 6 prominent adversarial attacks on the ImageNet large scale visual recognition challenge (ILSVRC) 2012 validation sets. The experimental results show that our method performs well on detecting various AEs.
Studies have shown that deep neural networks (DNNs) are vulnerable to adversarial examples (AEs) that induce incorrect behaviors. To defend these AEs, various detection techniques have been developed. However, most of them only appear to be effective against specific AEs and cannot generalize well to different AEs. We propose a new detection method against AEs based on the maximum channel of saliency maps (MCSM). The proposed method can alter the structure of adversarial perturbations and preserve the statistical properties of images at the same time. We conduct a complete evaluation on AEs generated by 6 prominent adversarial attacks on the ImageNet large scale visual recognition challenge (ILSVRC) 2012 validation sets. The experimental results show that our method performs well on detecting various AEs.
2022, 18(5):313-320.
DOI: https://doi.org/10.1007/s11801-022-2015-8
Abstract:
Three-dimensional human pose estimation (3D HPE) has broad application prospects in the fields of trajectory prediction, posture tracking and action analysis. However, the frequent self-occlusions and the substantial depth ambiguity in two-dimensional (2D) representations hinder the further improvement of accuracy. In this paper, we propose a novel video-based human body geometric aware network to mitigate the above problems. Our network can implicitly be aware of the geometric constraints of the human body by capturing spatial and temporal context information from 2D skeleton data. Specifically, a novel skeleton attention (SA) mechanism is proposed to model geometric context dependencies among different body joints, thereby improving the spatial feature representation ability of the network. To enhance the temporal consistency, a novel multilayer perceptron (MLP)-Mixer based structure is exploited to comprehensively learn temporal context information from input sequences. We conduct experiments on publicly available challenging datasets to evaluate the proposed approach. The results outperform the previous best approach by 0.5 mm in the Human3.6m dataset. It also demonstrates significant improvements in HumanEva-I dataset.
Three-dimensional human pose estimation (3D HPE) has broad application prospects in the fields of trajectory prediction, posture tracking and action analysis. However, the frequent self-occlusions and the substantial depth ambiguity in two-dimensional (2D) representations hinder the further improvement of accuracy. In this paper, we propose a novel video-based human body geometric aware network to mitigate the above problems. Our network can implicitly be aware of the geometric constraints of the human body by capturing spatial and temporal context information from 2D skeleton data. Specifically, a novel skeleton attention (SA) mechanism is proposed to model geometric context dependencies among different body joints, thereby improving the spatial feature representation ability of the network. To enhance the temporal consistency, a novel multilayer perceptron (MLP)-Mixer based structure is exploited to comprehensively learn temporal context information from input sequences. We conduct experiments on publicly available challenging datasets to evaluate the proposed approach. The results outperform the previous best approach by 0.5 mm in the Human3.6m dataset. It also demonstrates significant improvements in HumanEva-I dataset.
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