Volume 19,Issue 10,2023 Table of Contents
2023, 19(10):577-582.
DOI: https://doi.org/10.1007/s11801-023-3032-y
Abstract:
This study reports the performance of an InAs/GaSb type-II superlattices (T2SLs) detector with nBn structure for mid-wavelength infrared (MWIR) detection. An electronic band structure of M barrier is calculated using 8-band k.p method, and the nBn structure is designed with the M barrier. The detector is prepared by wet etching, which is simple in manufacturing process. X-ray diffraction (XRD) and atomic force microscope (AFM) characteristics indicate that the detector material has good crystal quality and surface morphology. The saturation bias of the spectral response measurements at 77 K is 300 mV, and the device is promising to work at a temperature of 140 K. Energy gap of T2SLs versus temperature is fitted by the Varshni curve, and zero temperature bandgap Eg(0), empirical coefficients α and β are extracted. A dark current density of 3.2×10-5 A/cm2 and differential resistance area (RA) product of 1.0×104 Ω.cm2 are measured at 77 K. The dominant mechanism of dark current at different temperature ranges is analyzed. The device with a 50% cutoff wavelength of 4.68 mm exhibits a responsivity of 0.6 A/W, a topside illuminated quantum efficiency of 20% without antireflection coating (ARC), and a detectivity of 9.17×1011 cm.Hz1/2/W at 77 K and 0.3 V.
This study reports the performance of an InAs/GaSb type-II superlattices (T2SLs) detector with nBn structure for mid-wavelength infrared (MWIR) detection. An electronic band structure of M barrier is calculated using 8-band k.p method, and the nBn structure is designed with the M barrier. The detector is prepared by wet etching, which is simple in manufacturing process. X-ray diffraction (XRD) and atomic force microscope (AFM) characteristics indicate that the detector material has good crystal quality and surface morphology. The saturation bias of the spectral response measurements at 77 K is 300 mV, and the device is promising to work at a temperature of 140 K. Energy gap of T2SLs versus temperature is fitted by the Varshni curve, and zero temperature bandgap Eg(0), empirical coefficients α and β are extracted. A dark current density of 3.2×10-5 A/cm2 and differential resistance area (RA) product of 1.0×104 Ω.cm2 are measured at 77 K. The dominant mechanism of dark current at different temperature ranges is analyzed. The device with a 50% cutoff wavelength of 4.68 mm exhibits a responsivity of 0.6 A/W, a topside illuminated quantum efficiency of 20% without antireflection coating (ARC), and a detectivity of 9.17×1011 cm.Hz1/2/W at 77 K and 0.3 V.
2023, 19(10):583-586.
DOI: https://doi.org/10.1007/s11801-023-2209-8
Abstract:
The sensitivity of a Kretschmann surface plasmon resonance (SPR) sensor was analyzed. The Kretschmann setup had multiple layers, a BK7 prism, silver, barium titanate (BaTiO3), titanium dioxide (TiO2), and graphene. The BaTiO3 and TiO2 coatings were sandwiched between two silver layers. The sensitivity of 260°/RIU has been achieved. The graphene layers are added to the configuration to improve sensitivity and as a bio-compatibility agent. This configuration can be used for biochemical sensors.
The sensitivity of a Kretschmann surface plasmon resonance (SPR) sensor was analyzed. The Kretschmann setup had multiple layers, a BK7 prism, silver, barium titanate (BaTiO3), titanium dioxide (TiO2), and graphene. The BaTiO3 and TiO2 coatings were sandwiched between two silver layers. The sensitivity of 260°/RIU has been achieved. The graphene layers are added to the configuration to improve sensitivity and as a bio-compatibility agent. This configuration can be used for biochemical sensors.
2023, 19(10):587-592.
DOI: https://doi.org/10.1007/s11801-023-2221-z
Abstract:
Recently, the hole transport layer-free planar perovskite solar cells (HTL-free PSCs) have attracted intense attention. However, the poor absorption of light in the wavelengths longer than 800 nm is an important challenge in all configurations of PSCs. In this study, the HTL-free PSC with a gold rectangular grating at back contact is proposed. In order to improve the performance of the solar cell, effects of grating dimensions and periodicity on the absorption of the active layer are numerically investigated. In the improved condition, an absorption enhancement of 25% in the range of 300—1 400 nm is obtained compared with the flat electrode-based structure. These improvements are attributed to the coupling of light to surface plasmon polariton (SPP) modes. Also, the electrical simulation results of the improved solar cell demonstrated short-circuit current density and power conversion efficiency of 27.72 mA/cm2 and 18%, respectively.
Recently, the hole transport layer-free planar perovskite solar cells (HTL-free PSCs) have attracted intense attention. However, the poor absorption of light in the wavelengths longer than 800 nm is an important challenge in all configurations of PSCs. In this study, the HTL-free PSC with a gold rectangular grating at back contact is proposed. In order to improve the performance of the solar cell, effects of grating dimensions and periodicity on the absorption of the active layer are numerically investigated. In the improved condition, an absorption enhancement of 25% in the range of 300—1 400 nm is obtained compared with the flat electrode-based structure. These improvements are attributed to the coupling of light to surface plasmon polariton (SPP) modes. Also, the electrical simulation results of the improved solar cell demonstrated short-circuit current density and power conversion efficiency of 27.72 mA/cm2 and 18%, respectively.
2023, 19(10):593-598.
DOI: https://doi.org/10.1007/s11801-023-3033-x
Abstract:
With the continuous development of optical communication and the increase in data transmission volume, optical transport network (OTN) has become the focus of research in next-generation transmission networks. In the process of data transmission, errors caused by noise often occur, resulting in an increase in the bit error rate (BER) and a decrease in the performance of the optical communication system. Therefore, we use forward error correction (FEC) technology in OTN for error control to improve the transmission efficiency of signals in OTN and reduce the BER. Standard FEC technology uses RS(255,239) code. On this basis, since the performance of low density parity check (LDPC) code is close to the Shannon limit, we propose a method of cascading RS code and LDPC code. Applying this improved FEC technology to OTN, the simulation results show that the improved FEC technology has a reduced BER compared with the standard FEC technology. When the BER is at the 10-3 level, the performance is improved by about 1.7 dB.
With the continuous development of optical communication and the increase in data transmission volume, optical transport network (OTN) has become the focus of research in next-generation transmission networks. In the process of data transmission, errors caused by noise often occur, resulting in an increase in the bit error rate (BER) and a decrease in the performance of the optical communication system. Therefore, we use forward error correction (FEC) technology in OTN for error control to improve the transmission efficiency of signals in OTN and reduce the BER. Standard FEC technology uses RS(255,239) code. On this basis, since the performance of low density parity check (LDPC) code is close to the Shannon limit, we propose a method of cascading RS code and LDPC code. Applying this improved FEC technology to OTN, the simulation results show that the improved FEC technology has a reduced BER compared with the standard FEC technology. When the BER is at the 10-3 level, the performance is improved by about 1.7 dB.
2023, 19(10):599-604.
DOI: https://doi.org/10.1007/s11801-023-3038-5
Abstract:
For space-based optical detection systems, there is usually a difference between actual on-orbit operational performance and designed requirements based on fixed scenarios. To assess the availability of space-based optical detection systems in different backgrounds, the radiation characteristics of aerial targets have been simulated using body radiation and atmospheric transmission models. The background radiation characteristics were also statistically analyzed. Then, for the parameters of the fixed space-based optical detection system, the signal-to-clutter and availability were evaluated under different conditions. A linear relationship between the radiation intensity and the flight height of the target was obtained. For a space-based optical detection system, the analytical availability model was constructed. Finally, multiple groups of data under different simulation conditions were used to validate the universality and reliability of the model. This availability model could significantly reduce the time required to predict the availability of the space-based optical detection system. The model was also adopted to analyze the influence of flight height, mean and variance, and background clutter on the space-based optical detection availability.
For space-based optical detection systems, there is usually a difference between actual on-orbit operational performance and designed requirements based on fixed scenarios. To assess the availability of space-based optical detection systems in different backgrounds, the radiation characteristics of aerial targets have been simulated using body radiation and atmospheric transmission models. The background radiation characteristics were also statistically analyzed. Then, for the parameters of the fixed space-based optical detection system, the signal-to-clutter and availability were evaluated under different conditions. A linear relationship between the radiation intensity and the flight height of the target was obtained. For a space-based optical detection system, the analytical availability model was constructed. Finally, multiple groups of data under different simulation conditions were used to validate the universality and reliability of the model. This availability model could significantly reduce the time required to predict the availability of the space-based optical detection system. The model was also adopted to analyze the influence of flight height, mean and variance, and background clutter on the space-based optical detection availability.
2023, 19(10):605-613.
DOI: https://doi.org/10.1007/s11801-023-3037-6
Abstract:
Transmission of intense femtosecond laser pulses in air is accompanied by energy depletion. By measuring the transmitted spectra of the focused femtosecond pulses in air, we study the influence of air pressure and initial pulse energy on the spectra around the central wavelength (800 nm) after the interaction of the focused femtosecond laser with air. On this basis, the energy transmittance of the central wavelength of the femtosecond pulses is obtained. It is found that as the air pressure is lower than 1 kPa, the transmitted spectra of focused femtosecond pulses change with the pressure, but there is almost no energy depletion, while as the air pressure is higher than 1 kPa, femtosecond filamentation occurs and the energy transmittance of the central wavelength of the femtosecond pulses decreases with the increase of air pressure and pulse energy. According to the different regimes (i.e., nonfilamentation, and filamentation regimes), we discuss the effect of energy conversion and transfer on the energy transmittance. This work can help to understand the energy depletion during the transmission of ultrashort intense laser pulses in air and provide a guidance for the practical applications of femtosecond filamentation.
Transmission of intense femtosecond laser pulses in air is accompanied by energy depletion. By measuring the transmitted spectra of the focused femtosecond pulses in air, we study the influence of air pressure and initial pulse energy on the spectra around the central wavelength (800 nm) after the interaction of the focused femtosecond laser with air. On this basis, the energy transmittance of the central wavelength of the femtosecond pulses is obtained. It is found that as the air pressure is lower than 1 kPa, the transmitted spectra of focused femtosecond pulses change with the pressure, but there is almost no energy depletion, while as the air pressure is higher than 1 kPa, femtosecond filamentation occurs and the energy transmittance of the central wavelength of the femtosecond pulses decreases with the increase of air pressure and pulse energy. According to the different regimes (i.e., nonfilamentation, and filamentation regimes), we discuss the effect of energy conversion and transfer on the energy transmittance. This work can help to understand the energy depletion during the transmission of ultrashort intense laser pulses in air and provide a guidance for the practical applications of femtosecond filamentation.
2023, 19(10):614-622.
DOI: https://doi.org/10.1007/s11801-023-3041-x
Abstract:
Aiming at the trouble of low detection accuracy and the problem of large model size, this paper proposes a lightweight flame-and-smoke detection model depending on global awareness of images. The proposed method replaces the Conv+BatchNorm+SiLU (CBS) module of original you only look once version 5 (YOLOv5) in the backbone with DSConv+BatchNorm+SiLU (DBS), and the C3 module with GC3, and thus constructs a lightweight backbone network. Besides, involution (InvC3) module is proposed to enhance the global modeling ability and compress the model size, and a module using adaptive receptive fields, named FConv, is proposed to enhance the model’s perception capacity for foreground complex flame-and-smoke information in feature maps. Experimental results show that the proposed model increases the mean average precision of all categories at 0.5 IOU (mAP@0.5) to 70.8%, the mAP@0.5:0.95 to 39.7%, reduces the number of parameters to 3.57M and the amount of calculation to 7.4 giga floating-point operations per second (GFLOPs) under the premise of ensuring the detection speed. It has been verified that the model can achieve high-precision real-time detection of flame and smoke.
Aiming at the trouble of low detection accuracy and the problem of large model size, this paper proposes a lightweight flame-and-smoke detection model depending on global awareness of images. The proposed method replaces the Conv+BatchNorm+SiLU (CBS) module of original you only look once version 5 (YOLOv5) in the backbone with DSConv+BatchNorm+SiLU (DBS), and the C3 module with GC3, and thus constructs a lightweight backbone network. Besides, involution (InvC3) module is proposed to enhance the global modeling ability and compress the model size, and a module using adaptive receptive fields, named FConv, is proposed to enhance the model’s perception capacity for foreground complex flame-and-smoke information in feature maps. Experimental results show that the proposed model increases the mean average precision of all categories at 0.5 IOU (mAP@0.5) to 70.8%, the mAP@0.5:0.95 to 39.7%, reduces the number of parameters to 3.57M and the amount of calculation to 7.4 giga floating-point operations per second (GFLOPs) under the premise of ensuring the detection speed. It has been verified that the model can achieve high-precision real-time detection of flame and smoke.
2023, 19(10):623-628.
DOI: https://doi.org/10.1007/s11801-023-3078-x
Abstract:
Aiming at the problem of low detection accuracy of vehicle and pedestrian detection models, this paper proposes an improved you only look once v4 (YOLOv4)-tiny vehicle and pedestrian target detection algorithm. Convolutional block attention module (CBAM) is introduced into cross stage partial Darknet-53 (CSPDarknet53)-tiny module to enhance feature extraction capabilities. In addition, the cross stage partial dense block layer (CSP-DBL) module is used to replace the original simple convolutional module superposition, which compensates for the high-resolution characteristic information and further improves the detection accuracy of the network. Finally, the test results on the BDD100K traffic dataset show that the mean average precision (mAP) value of the final network of the proposed method is 88.74%, and the detection speed reaches 63 frames per second (FPS), which improves the detection accuracy of the network and meets the real-time detection speed.
Aiming at the problem of low detection accuracy of vehicle and pedestrian detection models, this paper proposes an improved you only look once v4 (YOLOv4)-tiny vehicle and pedestrian target detection algorithm. Convolutional block attention module (CBAM) is introduced into cross stage partial Darknet-53 (CSPDarknet53)-tiny module to enhance feature extraction capabilities. In addition, the cross stage partial dense block layer (CSP-DBL) module is used to replace the original simple convolutional module superposition, which compensates for the high-resolution characteristic information and further improves the detection accuracy of the network. Finally, the test results on the BDD100K traffic dataset show that the mean average precision (mAP) value of the final network of the proposed method is 88.74%, and the detection speed reaches 63 frames per second (FPS), which improves the detection accuracy of the network and meets the real-time detection speed.
2023, 19(10):629-634.
DOI: https://doi.org/10.1007/s11801-023-3028-7
Abstract:
Existing action recognition methods based on event cameras have not fully exploited the advantages of event cameras, such as compressing event streams into frames for subsequent calculation, which greatly sacrifices the time information of event streams. Meanwhile, the conventional PointCloud-based methods suffer from large computational complexity while processing event data, which make it difficult to handle long-term actions. To tackle the above problems, we propose a dynamic graph memory-boosting recurrent neural network (DG-MBRNN). The proposed DG-MBRNN splits the event stream into sequential graph data for preserving structural information, then uses the recurrent neural network (RNN) with boosting spatiotemporal memory to handle long-term sequences of actions. In addition, the proposed method introduces a dynamic reorganization mechanism for the graph based on the distances of features, which can effectively increase the ability to extract local features. In order to cope with the situation that the existing datasets have too simple actions and too limited categories, we propose a new event-based dataset containing 36 complex actions. This dataset will greatly promote the development of event-based action recognition research. Experimental results show the effectiveness of the proposed method in completing the event-based action recognition task.
Existing action recognition methods based on event cameras have not fully exploited the advantages of event cameras, such as compressing event streams into frames for subsequent calculation, which greatly sacrifices the time information of event streams. Meanwhile, the conventional PointCloud-based methods suffer from large computational complexity while processing event data, which make it difficult to handle long-term actions. To tackle the above problems, we propose a dynamic graph memory-boosting recurrent neural network (DG-MBRNN). The proposed DG-MBRNN splits the event stream into sequential graph data for preserving structural information, then uses the recurrent neural network (RNN) with boosting spatiotemporal memory to handle long-term sequences of actions. In addition, the proposed method introduces a dynamic reorganization mechanism for the graph based on the distances of features, which can effectively increase the ability to extract local features. In order to cope with the situation that the existing datasets have too simple actions and too limited categories, we propose a new event-based dataset containing 36 complex actions. This dataset will greatly promote the development of event-based action recognition research. Experimental results show the effectiveness of the proposed method in completing the event-based action recognition task.
2023, 19(10):635-640.
DOI: https://doi.org/10.1007/s11801-023-3042-9
Abstract:
Compared with the traditional feature-based image stitching algorithm, the free-view image stitching algorithm based on deep learning has the advantages of fast stitching speed and good effect. However, these algorithms still cannot achieve real-time splicing speed. For the image reconstruction stage, we redesign a new fast image reconstruction network. This network is designed based on ShuffleNet, and the new network structure and loss function will reduce the time required for image reconstruction. In addition, this network can also reduce the performance loss after the network is lightweight. It is proved by experiments that the fast image reconstruction network can realize real-time high-resolution free-view image reconstruction.
Compared with the traditional feature-based image stitching algorithm, the free-view image stitching algorithm based on deep learning has the advantages of fast stitching speed and good effect. However, these algorithms still cannot achieve real-time splicing speed. For the image reconstruction stage, we redesign a new fast image reconstruction network. This network is designed based on ShuffleNet, and the new network structure and loss function will reduce the time required for image reconstruction. In addition, this network can also reduce the performance loss after the network is lightweight. It is proved by experiments that the fast image reconstruction network can realize real-time high-resolution free-view image reconstruction.
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