The ultra-long electromagnetic wave remote sensing technique developed by Peking University is one of new future techniques, which can detect the submarine geological information from the depth of 20 to 10000 m below ...The ultra-long electromagnetic wave remote sensing technique developed by Peking University is one of new future techniques, which can detect the submarine geological information from the depth of 20 to 10000 m below the surface by receiving natural ultra-long electromagnetic waves (n Hz to n 100 Hz). The new remote sensor is composed of three parts: a main instrument with a portable computer, an antenna with an amplifier and an external power.展开更多
Straw incineration monitor is a key part of international environmental governance.In the paper,the combination of MODIS,MUX and TLC remote sensors is used to monitor straw burning fire points accurately.MODIS remote ...Straw incineration monitor is a key part of international environmental governance.In the paper,the combination of MODIS,MUX and TLC remote sensors is used to monitor straw burning fire points accurately.MODIS remote sensor has the characteristics of high temporal resolution and thermal infrared band,which can be used to judge the regional thermal abnormal variation and preliminary extract the suspicious thermal abnormal points.Combining with GIS information,the preliminary position of MODIS thermal abnormal points can be acquired.The MUX and TLC sensors of ZY-3 satellite in the preliminary position area can be pretreated,which includes radiometric calibration,atmospheric correction,geometric precision correction,ortho-rectification,etc.Through analyzing the physical properties and spectral information in the straw incineration area,the interpretation features of the straw incineration area will be determined.Then the high geographical resolution fusion image with two meters resolution can be interpreted,and the information of fire-point in high geographical resolution remote sensor can be extracted.Combining with the Google earth map to compare interpretation images in different time range of this area,and using ArcGIS platform to accurately position the confirmed fire point,the final position of the fire can be determined.Correspondingly,the combination of remote sensing sensors with high,medium and low resolution can be used to monitor the straw incineration point in county area.In experimental area,there are twenty-three straw burning fire points are found.The experimental results show that,this method can realize precise monitoring of straw incineration point in county area.However,straw incineration point monitoring in real time still need to be further investigated.展开更多
BACKGROUND Diagnostic errors in critical care settings are a significant challenge,often leading to adverse patient outcomes and increased healthcare costs.Millimeter-wave(mmWave)technology,with its ability to provide...BACKGROUND Diagnostic errors in critical care settings are a significant challenge,often leading to adverse patient outcomes and increased healthcare costs.Millimeter-wave(mmWave)technology,with its ability to provide high-resolution,real-time data,offers a transformative solution to enhance diagnostic accuracy and patient safety.This paper explores the integration of mmWave technology in intensive care units(ICUs)to enable non-invasive monitoring,minimize diagnostic errors,and improve clinical decision-making.By addressing key challenges,including data latency,signal interference,and implementation feasibility,this approach has the potential to revolutionize patient monitoring systems and set a new standard for critical care delivery.The paper discusses the high prevalence of diagnostic errors in medical care,particularly in primary care and ICUs,and emphasizes the need for improvement in diagnostic accuracy.Diagnostic errors are responsible for a significant number of deaths,disabilities,prolonged hospitalizations and delays in diagnosis worldwide.AIM To address this issue,the paper proposes the use of ultrafast wireless medical big data transmission in primary care,specifically in remote smart sensors monitoring devices.It suggests that wireless transmission with a speed up to 100 Gb/s(12.5 Gbytes/s)within a short distance(1-10 meters)is necessary to reduce diagnostic errors.METHODS The method used in the study,includes system design and testing a channel sounder operating at 63.4-64.4 GHz frequency range.The system demonstrated dynamic range of 70 dB,noise level of-110 dBm,and a time resolution of 1 ns.The experiment measured the impulse response of the channel in 36 locations within the primary care/ICU scenario.RESULTS The system was tested in a simulated ICU environment to evaluate the Latency:Assessing the time delay in data transmission and processing.The results of the study showed that the system met the requirements of ICUs,providing excellent latency values.The delay spread and excess delay values were within acceptable limits,indicating successful resolution of ICU requirements.The paper suggests timely deployment of such a system.Impact on data transmission:A 100 MB magnetic resonance imaging scan can be transmitted in approximately 0.008 seconds;A 1 GB scan would take approximately 0.08 seconds;This capability could revolutionize healthcare,enabling real-time remote diagnostics and comparisons with artificial Intelligence models,even in large-scale systems.CONCLUSION The experiment demonstrated the feasibility of using high-speed wireless transmission for improved diagnostics in ICUs,offering potential benefits in terms of reduced errors and improved patient outcomes.The findings are deemed valuable to the medical community and public healthcare systems,and it is suggested further research in this area.展开更多
The Chinese GF-4 satellite remote sensor is the highest spatial resolution among the civil satellite on the geosynchronous orbit, which carries on a camera with spatial resolution of 50 meter in the bands of visible a...The Chinese GF-4 satellite remote sensor is the highest spatial resolution among the civil satellite on the geosynchronous orbit, which carries on a camera with spatial resolution of 50 meter in the bands of visible and near infrared red and 400 meter in middle infrared red band. The thermal design of the spacecraft was challenging because the high resolution and the sensitivity requirement to achieve the desired scientific objectives. This paper presents the thermal analysis and test of the GF-4 in GEO orbit. The major findings of the analyses are the following. The GF-4 experiences complex, alternating external heat flux and faces direct sunlight in most of the orbital period. By applying a finite element model, the predicted temperature variation of the components remains in the desired temperature regime even in the extreme conditions. Comparing the thermal analysis results, the difference between the predicted and measured temperatures was less than 3°C for most of the components. The thermal control system functioned properly and the thermal model simulated the actual thermal design of GF-4. This thermal design method realizes the high efficiency and precision thermal control of the first high resolution geostationary orbit camera in China, which can provide reference for the high precision and stability thermal control of large aperture optical camera.展开更多
Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and stru...Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and struggle with diverse data acquisition techniques.This research presents a novel approach for vehicle classification and recognition in aerial image sequences,integrating multiple advanced techniques to enhance detection accuracy.The proposed model begins with preprocessing using Multiscale Retinex(MSR)to enhance image quality,followed by Expectation-Maximization(EM)Segmentation for precise foreground object identification.Vehicle detection is performed using the state-of-the-art YOLOv10 framework,while feature extraction incorporates Maximally Stable Extremal Regions(MSER),Dense Scale-Invariant Feature Transform(Dense SIFT),and Zernike Moments Features to capture distinct object characteristics.Feature optimization is further refined through a Hybrid Swarm-based Optimization algorithm,ensuring optimal feature selection for improved classification performance.The final classification is conducted using a Vision Transformer,leveraging its robust learning capabilities for enhanced accuracy.Experimental evaluations on benchmark datasets,including UAVDT and the Unmanned Aerial Vehicle Intruder Dataset(UAVID),demonstrate the superiority of the proposed approach,achieving an accuracy of 94.40%on UAVDT and 93.57%on UAVID.The results highlight the efficacy of the model in significantly enhancing vehicle detection and classification in aerial imagery,outperforming existing methodologies and offering a statistically validated improvement for intelligent traffic monitoring systems compared to existing approaches.展开更多
Aims Mapping vegetation through remotely sensed images involves various considerations,processes and techniques.Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technolo...Aims Mapping vegetation through remotely sensed images involves various considerations,processes and techniques.Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources.Various sources of imagery are known for their differences in spectral,spatial,radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping.Generally,it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level.Then,correlations of the vegetation types(communities or species)within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified.These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process,which is also called image processing.This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover.Methods Specifically,this paper focuses on the comparisons of popular remote sensing sensors,commonly adopted image processing methods and prevailing classification accuracy assessments.Important findings The basic concepts,available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced,analyzed and compared.The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures,which can be utilized to study vegetation cover from remote sensed images.展开更多
Flexible integrated circuits(FlexICs)have drawn increasing attention,particularly in remote sensors and wearables operating in a limited power budget.Here,we present an ultra-low power timer designed to wake-up an ext...Flexible integrated circuits(FlexICs)have drawn increasing attention,particularly in remote sensors and wearables operating in a limited power budget.Here,we present an ultra-low power timer designed to wake-up an external circuit periodically,from a deep-sleep state into an active state,thereby largely reducing the system power consumption.We achieved this with a circuit topology that exploits the transistor’s leakage current to generate a low frequency wake-up signal.This topology is compatible with IC technologies where only n-type transistors are available.The design was implemented with the sustainable FlexIC process of PragmatIC,that is based on Indium Gallium Zinc Oxide(IGZO)thin-film transistors.Our timer generates mean wake-up frequency of 0.24±0.15 Hz,with a mean power consumption of 26.7±14.1 nW.In this paper,we provide details of the Wake-Up timer’s design and performance at different supply voltages,under temperature variations and different light conditions.展开更多
This study proposes an automatic procedure for individual fruit tree identification using GeoEye-1 sensor data.Depending on site-specific pruning practices,the morphologic characteristics of tree crowns may generate o...This study proposes an automatic procedure for individual fruit tree identification using GeoEye-1 sensor data.Depending on site-specific pruning practices,the morphologic characteristics of tree crowns may generate one or more brightness peaks(tree top)on the imagery.To optimize tree counting and to minimize typical background noises from orchards(i.e.bare soil,weeds,and man-made objects),a four-step algorithm was implemented with spatial transforms and functions suitable for spaced stands(asymmetrical smoothing filter,local minimum filter,mask layer,and spatial aggregation operator).System perfor-mance was evaluated through objective criteria,showing consistent results in fast capturing tree position for precision agriculture tasks.展开更多
文摘The ultra-long electromagnetic wave remote sensing technique developed by Peking University is one of new future techniques, which can detect the submarine geological information from the depth of 20 to 10000 m below the surface by receiving natural ultra-long electromagnetic waves (n Hz to n 100 Hz). The new remote sensor is composed of three parts: a main instrument with a portable computer, an antenna with an amplifier and an external power.
基金supported by NCIAE special key fund project ( No. ZD-2013-04 )NCIAE doctoral scientific fund project ( No. 2008-02-B)
文摘Straw incineration monitor is a key part of international environmental governance.In the paper,the combination of MODIS,MUX and TLC remote sensors is used to monitor straw burning fire points accurately.MODIS remote sensor has the characteristics of high temporal resolution and thermal infrared band,which can be used to judge the regional thermal abnormal variation and preliminary extract the suspicious thermal abnormal points.Combining with GIS information,the preliminary position of MODIS thermal abnormal points can be acquired.The MUX and TLC sensors of ZY-3 satellite in the preliminary position area can be pretreated,which includes radiometric calibration,atmospheric correction,geometric precision correction,ortho-rectification,etc.Through analyzing the physical properties and spectral information in the straw incineration area,the interpretation features of the straw incineration area will be determined.Then the high geographical resolution fusion image with two meters resolution can be interpreted,and the information of fire-point in high geographical resolution remote sensor can be extracted.Combining with the Google earth map to compare interpretation images in different time range of this area,and using ArcGIS platform to accurately position the confirmed fire point,the final position of the fire can be determined.Correspondingly,the combination of remote sensing sensors with high,medium and low resolution can be used to monitor the straw incineration point in county area.In experimental area,there are twenty-three straw burning fire points are found.The experimental results show that,this method can realize precise monitoring of straw incineration point in county area.However,straw incineration point monitoring in real time still need to be further investigated.
文摘BACKGROUND Diagnostic errors in critical care settings are a significant challenge,often leading to adverse patient outcomes and increased healthcare costs.Millimeter-wave(mmWave)technology,with its ability to provide high-resolution,real-time data,offers a transformative solution to enhance diagnostic accuracy and patient safety.This paper explores the integration of mmWave technology in intensive care units(ICUs)to enable non-invasive monitoring,minimize diagnostic errors,and improve clinical decision-making.By addressing key challenges,including data latency,signal interference,and implementation feasibility,this approach has the potential to revolutionize patient monitoring systems and set a new standard for critical care delivery.The paper discusses the high prevalence of diagnostic errors in medical care,particularly in primary care and ICUs,and emphasizes the need for improvement in diagnostic accuracy.Diagnostic errors are responsible for a significant number of deaths,disabilities,prolonged hospitalizations and delays in diagnosis worldwide.AIM To address this issue,the paper proposes the use of ultrafast wireless medical big data transmission in primary care,specifically in remote smart sensors monitoring devices.It suggests that wireless transmission with a speed up to 100 Gb/s(12.5 Gbytes/s)within a short distance(1-10 meters)is necessary to reduce diagnostic errors.METHODS The method used in the study,includes system design and testing a channel sounder operating at 63.4-64.4 GHz frequency range.The system demonstrated dynamic range of 70 dB,noise level of-110 dBm,and a time resolution of 1 ns.The experiment measured the impulse response of the channel in 36 locations within the primary care/ICU scenario.RESULTS The system was tested in a simulated ICU environment to evaluate the Latency:Assessing the time delay in data transmission and processing.The results of the study showed that the system met the requirements of ICUs,providing excellent latency values.The delay spread and excess delay values were within acceptable limits,indicating successful resolution of ICU requirements.The paper suggests timely deployment of such a system.Impact on data transmission:A 100 MB magnetic resonance imaging scan can be transmitted in approximately 0.008 seconds;A 1 GB scan would take approximately 0.08 seconds;This capability could revolutionize healthcare,enabling real-time remote diagnostics and comparisons with artificial Intelligence models,even in large-scale systems.CONCLUSION The experiment demonstrated the feasibility of using high-speed wireless transmission for improved diagnostics in ICUs,offering potential benefits in terms of reduced errors and improved patient outcomes.The findings are deemed valuable to the medical community and public healthcare systems,and it is suggested further research in this area.
文摘The Chinese GF-4 satellite remote sensor is the highest spatial resolution among the civil satellite on the geosynchronous orbit, which carries on a camera with spatial resolution of 50 meter in the bands of visible and near infrared red and 400 meter in middle infrared red band. The thermal design of the spacecraft was challenging because the high resolution and the sensitivity requirement to achieve the desired scientific objectives. This paper presents the thermal analysis and test of the GF-4 in GEO orbit. The major findings of the analyses are the following. The GF-4 experiences complex, alternating external heat flux and faces direct sunlight in most of the orbital period. By applying a finite element model, the predicted temperature variation of the components remains in the desired temperature regime even in the extreme conditions. Comparing the thermal analysis results, the difference between the predicted and measured temperatures was less than 3°C for most of the components. The thermal control system functioned properly and the thermal model simulated the actual thermal design of GF-4. This thermal design method realizes the high efficiency and precision thermal control of the first high resolution geostationary orbit camera in China, which can provide reference for the high precision and stability thermal control of large aperture optical camera.
文摘Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and struggle with diverse data acquisition techniques.This research presents a novel approach for vehicle classification and recognition in aerial image sequences,integrating multiple advanced techniques to enhance detection accuracy.The proposed model begins with preprocessing using Multiscale Retinex(MSR)to enhance image quality,followed by Expectation-Maximization(EM)Segmentation for precise foreground object identification.Vehicle detection is performed using the state-of-the-art YOLOv10 framework,while feature extraction incorporates Maximally Stable Extremal Regions(MSER),Dense Scale-Invariant Feature Transform(Dense SIFT),and Zernike Moments Features to capture distinct object characteristics.Feature optimization is further refined through a Hybrid Swarm-based Optimization algorithm,ensuring optimal feature selection for improved classification performance.The final classification is conducted using a Vision Transformer,leveraging its robust learning capabilities for enhanced accuracy.Experimental evaluations on benchmark datasets,including UAVDT and the Unmanned Aerial Vehicle Intruder Dataset(UAVID),demonstrate the superiority of the proposed approach,achieving an accuracy of 94.40%on UAVDT and 93.57%on UAVID.The results highlight the efficacy of the model in significantly enhancing vehicle detection and classification in aerial imagery,outperforming existing methodologies and offering a statistically validated improvement for intelligent traffic monitoring systems compared to existing approaches.
文摘Aims Mapping vegetation through remotely sensed images involves various considerations,processes and techniques.Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources.Various sources of imagery are known for their differences in spectral,spatial,radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping.Generally,it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level.Then,correlations of the vegetation types(communities or species)within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified.These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process,which is also called image processing.This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover.Methods Specifically,this paper focuses on the comparisons of popular remote sensing sensors,commonly adopted image processing methods and prevailing classification accuracy assessments.Important findings The basic concepts,available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced,analyzed and compared.The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures,which can be utilized to study vegetation cover from remote sensed images.
基金supported by the European Union’s Horizon 2020 Research and Innovation Programme,under grant agreement Nr.951774support through the FPU fellowship grant(FPU22/01008)+3 种基金the sponsorship of the Alexander von Humboldt Professorship of the Humboldt Foundation and the Federal Ministry for Education and Research(Germany)the European Community’s Horizon Europe program(ERC-POC FLETRAD,grant agreement no.101082283)from National Funds through the FCT-Fundação para a Ciência e a Tecnologia,I.P.,projects LA/P/0037/2020,UIDP/50025/2020 and UIDB/50025/2020supported by the Generalitat de Catalunya through the grant 2021 SGR 01108.
文摘Flexible integrated circuits(FlexICs)have drawn increasing attention,particularly in remote sensors and wearables operating in a limited power budget.Here,we present an ultra-low power timer designed to wake-up an external circuit periodically,from a deep-sleep state into an active state,thereby largely reducing the system power consumption.We achieved this with a circuit topology that exploits the transistor’s leakage current to generate a low frequency wake-up signal.This topology is compatible with IC technologies where only n-type transistors are available.The design was implemented with the sustainable FlexIC process of PragmatIC,that is based on Indium Gallium Zinc Oxide(IGZO)thin-film transistors.Our timer generates mean wake-up frequency of 0.24±0.15 Hz,with a mean power consumption of 26.7±14.1 nW.In this paper,we provide details of the Wake-Up timer’s design and performance at different supply voltages,under temperature variations and different light conditions.
文摘This study proposes an automatic procedure for individual fruit tree identification using GeoEye-1 sensor data.Depending on site-specific pruning practices,the morphologic characteristics of tree crowns may generate one or more brightness peaks(tree top)on the imagery.To optimize tree counting and to minimize typical background noises from orchards(i.e.bare soil,weeds,and man-made objects),a four-step algorithm was implemented with spatial transforms and functions suitable for spaced stands(asymmetrical smoothing filter,local minimum filter,mask layer,and spatial aggregation operator).System perfor-mance was evaluated through objective criteria,showing consistent results in fast capturing tree position for precision agriculture tasks.
