Accurate crop distribution mapping is required for crop yield prediction and field management. Due to rapid progress in remote sensing technology, fine spatial resolution(FSR) remotely sensed imagery now offers great ...Accurate crop distribution mapping is required for crop yield prediction and field management. Due to rapid progress in remote sensing technology, fine spatial resolution(FSR) remotely sensed imagery now offers great opportunities for mapping crop types in great detail. However, within-class variance can hamper attempts to discriminate crop classes at fine resolutions. Multi-temporal FSR remotely sensed imagery provides a means of increasing crop classification from FSR imagery, although current methods do not exploit the available information fully. In this research, a novel Temporal Sequence Object-based Convolutional Neural Network(TS-OCNN) was proposed to classify agricultural crop type from FSR image time-series. An object-based CNN(OCNN) model was adopted in the TS-OCNN to classify images at the object level(i.e., segmented objects or crop parcels), thus, maintaining the precise boundary information of crop parcels. The combination of image time-series was first utilized as the input to the OCNN model to produce an ‘original’ or baseline classification. Then the single-date images were fed automatically into the deep learning model scene-by-scene in order of image acquisition date to increase successively the crop classification accuracy. By doing so, the joint information in the FSR multi-temporal observations and the unique individual information from the single-date images were exploited comprehensively for crop classification. The effectiveness of the proposed approach was investigated using multitemporal SAR and optical imagery, respectively, over two heterogeneous agricultural areas. The experimental results demonstrated that the newly proposed TS-OCNN approach consistently increased crop classification accuracy, and achieved the greatest accuracies(82.68% and 87.40%) in comparison with state-of-the-art benchmark methods, including the object-based CNN(OCNN)(81.63% and85.88%), object-based image analysis(OBIA)(78.21% and 84.83%), and standard pixel-wise CNN(79.18%and 82.90%). The proposed approach is the first known attempt to explore simultaneously the joint information from image time-series with the unique information from single-date images for crop classification using a deep learning framework. The TS-OCNN, therefore, represents a new approach for agricultural landscape classification from multi-temporal FSR imagery. Besides, it is readily generalizable to other landscapes(e.g., forest landscapes), with a wide application prospect.展开更多
For the high resolution required in a digital interface circuit of an accelerometer used in feeble gravity measurement, a switched-capacitor （SC） sigma-delta modulator （SDM） is proposed. Based on the principle and...For the high resolution required in a digital interface circuit of an accelerometer used in feeble gravity measurement, a switched-capacitor （SC） sigma-delta modulator （SDM） is proposed. Based on the principle and the topology structure of the SDMs, the influence of oversampling ratio, bits of an internal quantizer and the cascaded structure on weak signal detecting precision is analyzed, and an ideal low-distortion SDM with a second-order 1-bit structure satisfying the high- resolution interface circuit of an accelerometer is designed. With the research on non-idealities of each SDM block in the SC circuit implementation and their impacts on power consumption, the realized parameters of low-power SDMs based on different bandwidths are devised and the power consumption of each SDM is estimated. Time-domain behavioral simulation is explored based on Simulink. The results demonstrate that a 21- bit resolution of the designed SDMs can be achieved on the premise of low power, and the parameters for the circuit implementation can be directed to the transistor-level circuit design.展开更多
This paper presents an analysis method, based on MacCormack＇s technique, for the evaluation of the time domain sensitivity of distributed parameter elements in high-speed circuit networks. Sensitivities can be calcul...This paper presents an analysis method, based on MacCormack＇s technique, for the evaluation of the time domain sensitivity of distributed parameter elements in high-speed circuit networks. Sensitivities can be calculated from electrical and physical parameters of the distributed parameter elements. The proposed method is a direct numerical method of time-space discretization and does not require complicated mathematical deductive process. Therefore, it is very convenient to program this method. It can be applied to sensitivity analysis of general transmission lines in linear or nonlinear circuit networks. The proposed method is second-order-accurate. Numerical experiment is presented to demonstrate its accuracy and efficiency.展开更多
The characteristic of interface depending on the atomic structure exerts an inportant,and sometime controlling,influence on performance of the interacial materials. The present paper reviews the main studies on fine s...The characteristic of interface depending on the atomic structure exerts an inportant,and sometime controlling,influence on performance of the interacial materials. The present paper reviews the main studies on fine structure of both the materials inter- faces and interfacial reaction products in semiconductor uperlattice,metal multilayer ceram- ics and composite materials by mean of selected area electron doffraction patterns and high resolution electron microscopy. The following features of interfaces are reviewed:the orientation relationships;the char- acteristic of steps,facets and ronghness of interfaces;atomic bonding across the interface;the degree of coherency,the structure of misfit dislocations and elastic relaxations at the inter- faces:the presence of defects at the onterfaces:the structure of the interfacial reaction prod- ucts as well as the reaction kinetics and reaetion mechanism.展开更多
Superconducting nanowire single-photon detectors(SNSPDs) are typical switching devices capable of detecting single photons with almost 100% detection efficiency. However, they cannot determine the exact number of inci...Superconducting nanowire single-photon detectors(SNSPDs) are typical switching devices capable of detecting single photons with almost 100% detection efficiency. However, they cannot determine the exact number of incident photons during a detection event. Multi-pixel SNSPDs employing multiple read-out channels can provide photon number resolvability(PNR), but they require increased cooling power and costly multi-channel electronic systems. In this work, a single-flux quantum(SFQ) circuit is employed, and PNR based on multi-pixel SNSPDs is successfully demonstrated. A multi-input magnetically coupled DC/SFQ converter(MMD2 Q) circuit with a mutual inductance M is used to combine and record signals from a multi-pixel SNSPD device. The designed circuit is capable of discriminating the amplitude of the combined signals in accuracy of Φ_(0)/M with Φ_(0) being a single magnetic flux quantum. By employing the MMD2 Q circuit,the discrimination of up to 40 photons can be simulated. A 4-parallel-input MMD2 Q circuit is fabricated, and a PNR of3 is successfully demonstrated for an SNSPD array with one channel reserved for the functional verification. The results confirm that an MMD2 Q circuit is an effective tool for implementing PNR with multi-pixel SNSPDs.展开更多
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA28070503)the National Key Research and Development Program of China(2021YFD1500100)+2 种基金the Open Fund of State Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University (20R04)Land Observation Satellite Supporting Platform of National Civil Space Infrastructure Project(CASPLOS-CCSI)a PhD studentship ‘‘Deep Learning in massive area,multi-scale resolution remotely sensed imagery”(EAA7369),sponsored by Lancaster University and Ordnance Survey (the national mapping agency of Great Britain)。
文摘Accurate crop distribution mapping is required for crop yield prediction and field management. Due to rapid progress in remote sensing technology, fine spatial resolution(FSR) remotely sensed imagery now offers great opportunities for mapping crop types in great detail. However, within-class variance can hamper attempts to discriminate crop classes at fine resolutions. Multi-temporal FSR remotely sensed imagery provides a means of increasing crop classification from FSR imagery, although current methods do not exploit the available information fully. In this research, a novel Temporal Sequence Object-based Convolutional Neural Network(TS-OCNN) was proposed to classify agricultural crop type from FSR image time-series. An object-based CNN(OCNN) model was adopted in the TS-OCNN to classify images at the object level(i.e., segmented objects or crop parcels), thus, maintaining the precise boundary information of crop parcels. The combination of image time-series was first utilized as the input to the OCNN model to produce an ‘original’ or baseline classification. Then the single-date images were fed automatically into the deep learning model scene-by-scene in order of image acquisition date to increase successively the crop classification accuracy. By doing so, the joint information in the FSR multi-temporal observations and the unique individual information from the single-date images were exploited comprehensively for crop classification. The effectiveness of the proposed approach was investigated using multitemporal SAR and optical imagery, respectively, over two heterogeneous agricultural areas. The experimental results demonstrated that the newly proposed TS-OCNN approach consistently increased crop classification accuracy, and achieved the greatest accuracies(82.68% and 87.40%) in comparison with state-of-the-art benchmark methods, including the object-based CNN(OCNN)(81.63% and85.88%), object-based image analysis(OBIA)(78.21% and 84.83%), and standard pixel-wise CNN(79.18%and 82.90%). The proposed approach is the first known attempt to explore simultaneously the joint information from image time-series with the unique information from single-date images for crop classification using a deep learning framework. The TS-OCNN, therefore, represents a new approach for agricultural landscape classification from multi-temporal FSR imagery. Besides, it is readily generalizable to other landscapes(e.g., forest landscapes), with a wide application prospect.
基金The National High Technology Research and Development Program of China (863 Program) ( No. 2006AA12Z302)
文摘For the high resolution required in a digital interface circuit of an accelerometer used in feeble gravity measurement, a switched-capacitor （SC） sigma-delta modulator （SDM） is proposed. Based on the principle and the topology structure of the SDMs, the influence of oversampling ratio, bits of an internal quantizer and the cascaded structure on weak signal detecting precision is analyzed, and an ideal low-distortion SDM with a second-order 1-bit structure satisfying the high- resolution interface circuit of an accelerometer is designed. With the research on non-idealities of each SDM block in the SC circuit implementation and their impacts on power consumption, the realized parameters of low-power SDMs based on different bandwidths are devised and the power consumption of each SDM is estimated. Time-domain behavioral simulation is explored based on Simulink. The results demonstrate that a 21- bit resolution of the designed SDMs can be achieved on the premise of low power, and the parameters for the circuit implementation can be directed to the transistor-level circuit design.
文摘This paper presents an analysis method, based on MacCormack＇s technique, for the evaluation of the time domain sensitivity of distributed parameter elements in high-speed circuit networks. Sensitivities can be calculated from electrical and physical parameters of the distributed parameter elements. The proposed method is a direct numerical method of time-space discretization and does not require complicated mathematical deductive process. Therefore, it is very convenient to program this method. It can be applied to sensitivity analysis of general transmission lines in linear or nonlinear circuit networks. The proposed method is second-order-accurate. Numerical experiment is presented to demonstrate its accuracy and efficiency.
文摘The characteristic of interface depending on the atomic structure exerts an inportant,and sometime controlling,influence on performance of the interacial materials. The present paper reviews the main studies on fine structure of both the materials inter- faces and interfacial reaction products in semiconductor uperlattice,metal multilayer ceram- ics and composite materials by mean of selected area electron doffraction patterns and high resolution electron microscopy. The following features of interfaces are reviewed:the orientation relationships;the char- acteristic of steps,facets and ronghness of interfaces;atomic bonding across the interface;the degree of coherency,the structure of misfit dislocations and elastic relaxations at the inter- faces:the presence of defects at the onterfaces:the structure of the interfacial reaction prod- ucts as well as the reaction kinetics and reaetion mechanism.
基金supported by the National Key R&D Program of China (Grant No. 2017YFA0304000)the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA18000000)the Science and Technology Commission of Shanghai Municipality, China (Grant No. 18511110200)。
文摘Superconducting nanowire single-photon detectors(SNSPDs) are typical switching devices capable of detecting single photons with almost 100% detection efficiency. However, they cannot determine the exact number of incident photons during a detection event. Multi-pixel SNSPDs employing multiple read-out channels can provide photon number resolvability(PNR), but they require increased cooling power and costly multi-channel electronic systems. In this work, a single-flux quantum(SFQ) circuit is employed, and PNR based on multi-pixel SNSPDs is successfully demonstrated. A multi-input magnetically coupled DC/SFQ converter(MMD2 Q) circuit with a mutual inductance M is used to combine and record signals from a multi-pixel SNSPD device. The designed circuit is capable of discriminating the amplitude of the combined signals in accuracy of Φ_(0)/M with Φ_(0) being a single magnetic flux quantum. By employing the MMD2 Q circuit,the discrimination of up to 40 photons can be simulated. A 4-parallel-input MMD2 Q circuit is fabricated, and a PNR of3 is successfully demonstrated for an SNSPD array with one channel reserved for the functional verification. The results confirm that an MMD2 Q circuit is an effective tool for implementing PNR with multi-pixel SNSPDs.
