A stacked metamaterial MEMS(meta-MEMS)chip is proposed,which can perfectly absorb electromagnetic waves,convert them into mechanical energy,drive movement of the optical micro-reflectors array,and detect millimeter wa...A stacked metamaterial MEMS(meta-MEMS)chip is proposed,which can perfectly absorb electromagnetic waves,convert them into mechanical energy,drive movement of the optical micro-reflectors array,and detect millimeter waves.It is equivalent to using visible light to image a millimeter wave.The meta-MEMS adopts the design of upper and lower chip separation and then stacking to achieve the"dielectric-resonant-air-ground"structure,reduce the thickness of the metamaterial and MEMS structures,and improve the performance of millimeter wave imaging.For verification,we designed and prepared a 94 GHz meta-MEMS focal plane array chip,in which the sum of the thickness of the metamaterial and MEMS structures is only 1/2500 wavelength,the pixel size is less than 1/3 wavelength,but the absorption rate is as high as 99.8%.Moreover,a light readout module was constructed to test the millimeter wave imaging performance.The results show that the response speed can reach 144 Hz and the lens-less imaging resolution is 1.5mm.展开更多
Terahertz(THz)scattering-type scanning near-field optical microscopy(s-SNOM)is an important means of studying and revealing material properties at the nanoscale.The nanotip is one of the core components of THz s-SNOM,...Terahertz(THz)scattering-type scanning near-field optical microscopy(s-SNOM)is an important means of studying and revealing material properties at the nanoscale.The nanotip is one of the core components of THz s-SNOM,which has a decisive impact on the resolution of the system.In this paper,we focus on the theory and design of the nanotip and conduct comprehensive research on it through simulation.The theoretical model is based on full-wave numerical simulation and dipole moment analysis,which can describe the overall nanotip electromagnetic response under the incident field.A comprehensive design model of nanotip geometry,sample materials,and incident field is established to significantly improve the near-field coupling efficiency and spatial resolution to achieve optimal performance.展开更多
With the vigorous development of intelligence agriculture,the progress of automated largescale and intensive pig farming has accelerated significantly.As a biological feature,the pig face has important research signif...With the vigorous development of intelligence agriculture,the progress of automated largescale and intensive pig farming has accelerated significantly.As a biological feature,the pig face has important research significance for precise breeding of pigs and traceability of health.In the management of live pigs,many managers adopt traditional methods,including color marking and RFID identification,but there will be problems such as off-label,mixed-label and waste of manpower.This work proposes a non-invasive way to study the identification of multiple individuals in pigs.The model was to first replace the original backbone network of YOLOv4 with MobileNet-v3,a popular lightweight network.Then depth-wise separable convolution was adopted in YOLOv40s feature extraction network SPP and PANet to further reduce network parameters.Moreover,CBAM attention mechanism formed by the concatenation of CAM and SAM was added to PANet to ensure the network accuracy while reducing the model weight.The introduction of multi-attention mechanism selectively strengthened key areas of pig face and filtered out weak correlation features,so as to improve the overall model effect.Finally,an improved MobileNetv3-YOLOv4-PACNet(M-YOLOv4-C)network model was proposed to identify individual sows.The mAP were 98.15%,the detection speed FPS were 106.3frames/s,and the model parameter size was only 44.74 MB,which can be well implanted into the small-volume pig house management sensors and applied to the pig management system in a lightweight,fast and accurate manner.This model will provide model support for subsequent pig behavior recognition and posture analysis.展开更多
文摘A stacked metamaterial MEMS(meta-MEMS)chip is proposed,which can perfectly absorb electromagnetic waves,convert them into mechanical energy,drive movement of the optical micro-reflectors array,and detect millimeter waves.It is equivalent to using visible light to image a millimeter wave.The meta-MEMS adopts the design of upper and lower chip separation and then stacking to achieve the"dielectric-resonant-air-ground"structure,reduce the thickness of the metamaterial and MEMS structures,and improve the performance of millimeter wave imaging.For verification,we designed and prepared a 94 GHz meta-MEMS focal plane array chip,in which the sum of the thickness of the metamaterial and MEMS structures is only 1/2500 wavelength,the pixel size is less than 1/3 wavelength,but the absorption rate is as high as 99.8%.Moreover,a light readout module was constructed to test the millimeter wave imaging performance.The results show that the response speed can reach 144 Hz and the lens-less imaging resolution is 1.5mm.
基金supported by the National Natural Science Foundation of China(Nos.12061131010 and 12074198).
文摘Terahertz(THz)scattering-type scanning near-field optical microscopy(s-SNOM)is an important means of studying and revealing material properties at the nanoscale.The nanotip is one of the core components of THz s-SNOM,which has a decisive impact on the resolution of the system.In this paper,we focus on the theory and design of the nanotip and conduct comprehensive research on it through simulation.The theoretical model is based on full-wave numerical simulation and dipole moment analysis,which can describe the overall nanotip electromagnetic response under the incident field.A comprehensive design model of nanotip geometry,sample materials,and incident field is established to significantly improve the near-field coupling efficiency and spatial resolution to achieve optimal performance.
基金supported by General Project of National Natural Science Foundation of China,Research on Model Construction and Early Warning Method of Abnormal State and Acoustic Sensing Mechanism of Pigs(32172784).
文摘With the vigorous development of intelligence agriculture,the progress of automated largescale and intensive pig farming has accelerated significantly.As a biological feature,the pig face has important research significance for precise breeding of pigs and traceability of health.In the management of live pigs,many managers adopt traditional methods,including color marking and RFID identification,but there will be problems such as off-label,mixed-label and waste of manpower.This work proposes a non-invasive way to study the identification of multiple individuals in pigs.The model was to first replace the original backbone network of YOLOv4 with MobileNet-v3,a popular lightweight network.Then depth-wise separable convolution was adopted in YOLOv40s feature extraction network SPP and PANet to further reduce network parameters.Moreover,CBAM attention mechanism formed by the concatenation of CAM and SAM was added to PANet to ensure the network accuracy while reducing the model weight.The introduction of multi-attention mechanism selectively strengthened key areas of pig face and filtered out weak correlation features,so as to improve the overall model effect.Finally,an improved MobileNetv3-YOLOv4-PACNet(M-YOLOv4-C)network model was proposed to identify individual sows.The mAP were 98.15%,the detection speed FPS were 106.3frames/s,and the model parameter size was only 44.74 MB,which can be well implanted into the small-volume pig house management sensors and applied to the pig management system in a lightweight,fast and accurate manner.This model will provide model support for subsequent pig behavior recognition and posture analysis.
