An enhancement of mid-wavelength infrared absorbance is achieved via a cost-effectively chemical method to bend the flakes by grafting two types of alkane octane(C_(8)H_(18))and dodecane(C_(12)H_(26))onto the surface ...An enhancement of mid-wavelength infrared absorbance is achieved via a cost-effectively chemical method to bend the flakes by grafting two types of alkane octane(C_(8)H_(18))and dodecane(C_(12)H_(26))onto the surface terminals respectively.The chain-length of alkane exceeds the bond-length of surface functionalities T(x=O,-OH,-F)so as to introduce intra-flake and inter-flake strains into Ti_(3)C_(2)T_(x)MXene.The electronic microscopy(TEM/AFM)shows obvious edge-fold and tensile/compressive deformation of flake.The alkane termination increases the intrinsic absorbance of Ti_(3)C_(2)T_(x)MXene from no more than 50%up to more than 99%in the mid-wavelength in⁃frared region from 2.5μm to 4.5μm.Such an absorption enhancement attributes to the reduce of infrared reflec⁃tance of Ti_(3)C_(2)T_(x)MXene.The C-H bond skeleton vibration covers the aforementioned region and partially reduces the surface reflectance.Meanwhile,the flake deformation owing to edge-fold and tensile/compression increases the specific surface area so as to increase the absorption as well.These results have applicable value in the area of mid-infrared camouflage.展开更多
Although compressive measurements save data storage and bandwidth usage, they are difficult to be used directly for target tracking and classification without pixel reconstruction. This is because the Gaussian random ...Although compressive measurements save data storage and bandwidth usage, they are difficult to be used directly for target tracking and classification without pixel reconstruction. This is because the Gaussian random matrix destroys the target location information in the original video frames. This paper summarizes our research effort on target tracking and classification directly in the compressive measurement domain. We focus on one particular type of compressive measurement using pixel subsampling. That is, original pixels in video frames are randomly subsampled. Even in such a special compressive sensing setting, conventional trackers do not work in a satisfactory manner. We propose a deep learning approach that integrates YOLO (You Only Look Once) and ResNet (residual network) for multiple target tracking and classification. YOLO is used for multiple target tracking and ResNet is for target classification. Extensive experiments using short wave infrared (SWIR), mid-wave infrared (MWIR), and long-wave infrared (LWIR) videos demonstrated the efficacy of the proposed approach even though the training data are very scarce.展开更多
Pixel-wise Code Exposure (PCE) camera is one type of compressive sensing camera that has low power consumption and high compression ratio. Moreover, a PCE camera can control individual pixel exposure time that can ena...Pixel-wise Code Exposure (PCE) camera is one type of compressive sensing camera that has low power consumption and high compression ratio. Moreover, a PCE camera can control individual pixel exposure time that can enable high dynamic range. Conventional approaches of using PCE camera involve a time consuming and lossy process to reconstruct the original frames and then use those frames for target tracking and classification. In this paper, we present a deep learning approach that directly performs target tracking and classification in the compressive measurement domain without any frame reconstruction. Our approach has two parts: tracking and classification. The tracking has been done using YOLO (You Only Look Once) and the classification is achieved using Residual Network (ResNet). Extensive experiments using mid-wave infrared (MWIR) and long-wave infrared (LWIR) videos demonstrated the efficacy of our proposed approach.展开更多
影响制冷型中波红外焦平面探测器图像质量的因素主要包括响应的非均匀性、响应的漂移特性和盲元等。因此,在红外焦平面成像系统中要进行实时的非均匀性校正、漂移补偿和盲元替代等信号处理。首先,设计一套高帧频低噪声640×512像元...影响制冷型中波红外焦平面探测器图像质量的因素主要包括响应的非均匀性、响应的漂移特性和盲元等。因此,在红外焦平面成像系统中要进行实时的非均匀性校正、漂移补偿和盲元替代等信号处理。首先,设计一套高帧频低噪声640×512像元的制冷型中波红外成像系统,并进行动态范围标定,实现其在全动态范围内NETD小于30 m K。接着,针对焦平面像元的响应特性,研究了适用于红外成像系统的非均匀性和盲元的校正方法,提出了基于辐射定标和场景融合的非均匀校正、盲元检测及替代算法。最后,进行外场红外图像数据采集。经实验验证,其图像校正效果优良,易于实现,且具有较强的环境适应性。展开更多
文摘An enhancement of mid-wavelength infrared absorbance is achieved via a cost-effectively chemical method to bend the flakes by grafting two types of alkane octane(C_(8)H_(18))and dodecane(C_(12)H_(26))onto the surface terminals respectively.The chain-length of alkane exceeds the bond-length of surface functionalities T(x=O,-OH,-F)so as to introduce intra-flake and inter-flake strains into Ti_(3)C_(2)T_(x)MXene.The electronic microscopy(TEM/AFM)shows obvious edge-fold and tensile/compressive deformation of flake.The alkane termination increases the intrinsic absorbance of Ti_(3)C_(2)T_(x)MXene from no more than 50%up to more than 99%in the mid-wavelength in⁃frared region from 2.5μm to 4.5μm.Such an absorption enhancement attributes to the reduce of infrared reflec⁃tance of Ti_(3)C_(2)T_(x)MXene.The C-H bond skeleton vibration covers the aforementioned region and partially reduces the surface reflectance.Meanwhile,the flake deformation owing to edge-fold and tensile/compression increases the specific surface area so as to increase the absorption as well.These results have applicable value in the area of mid-infrared camouflage.
文摘Although compressive measurements save data storage and bandwidth usage, they are difficult to be used directly for target tracking and classification without pixel reconstruction. This is because the Gaussian random matrix destroys the target location information in the original video frames. This paper summarizes our research effort on target tracking and classification directly in the compressive measurement domain. We focus on one particular type of compressive measurement using pixel subsampling. That is, original pixels in video frames are randomly subsampled. Even in such a special compressive sensing setting, conventional trackers do not work in a satisfactory manner. We propose a deep learning approach that integrates YOLO (You Only Look Once) and ResNet (residual network) for multiple target tracking and classification. YOLO is used for multiple target tracking and ResNet is for target classification. Extensive experiments using short wave infrared (SWIR), mid-wave infrared (MWIR), and long-wave infrared (LWIR) videos demonstrated the efficacy of the proposed approach even though the training data are very scarce.
文摘Pixel-wise Code Exposure (PCE) camera is one type of compressive sensing camera that has low power consumption and high compression ratio. Moreover, a PCE camera can control individual pixel exposure time that can enable high dynamic range. Conventional approaches of using PCE camera involve a time consuming and lossy process to reconstruct the original frames and then use those frames for target tracking and classification. In this paper, we present a deep learning approach that directly performs target tracking and classification in the compressive measurement domain without any frame reconstruction. Our approach has two parts: tracking and classification. The tracking has been done using YOLO (You Only Look Once) and the classification is achieved using Residual Network (ResNet). Extensive experiments using mid-wave infrared (MWIR) and long-wave infrared (LWIR) videos demonstrated the efficacy of our proposed approach.
文摘影响制冷型中波红外焦平面探测器图像质量的因素主要包括响应的非均匀性、响应的漂移特性和盲元等。因此,在红外焦平面成像系统中要进行实时的非均匀性校正、漂移补偿和盲元替代等信号处理。首先,设计一套高帧频低噪声640×512像元的制冷型中波红外成像系统,并进行动态范围标定,实现其在全动态范围内NETD小于30 m K。接着,针对焦平面像元的响应特性,研究了适用于红外成像系统的非均匀性和盲元的校正方法,提出了基于辐射定标和场景融合的非均匀校正、盲元检测及替代算法。最后,进行外场红外图像数据采集。经实验验证,其图像校正效果优良,易于实现,且具有较强的环境适应性。
