In this paper, it is described that the time-frequency resolution of geophysical signals is affected by the time window function attenuation coefficient and sampling interval and how such effects are eliminated effect...In this paper, it is described that the time-frequency resolution of geophysical signals is affected by the time window function attenuation coefficient and sampling interval and how such effects are eliminated effectively. Improving the signal resolution is the key to signal time-frequency analysis processing and has wide use in geophysical data processing and extraction of attribute parameters. In this paper, authors research the effects of the attenuation coefficient choice of the Gabor transform window function and sampling interval on signal resolution. Unsuitable parameters not only decrease the signal resolution on the frequency spectrum but also miss the signals. It is essential to first give the optimum window and range of parameters through time-frequency analysis simulation using the Gabor transform. In the paper, the suggestions about the range and choice of the optimum sampling interval and processing methods of general seismic signals are given.展开更多
Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(...Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].展开更多
The resolution of seismic data is critical to seismic data processing and the subsequent interpretation of fine structures. In conventional resolution improvement methods, the seismic data is assumed stationary and th...The resolution of seismic data is critical to seismic data processing and the subsequent interpretation of fine structures. In conventional resolution improvement methods, the seismic data is assumed stationary and the noise level not changes with space, whereas the actual situation does not satisfy this assumption, so that results after resolution improvement processing is not up to the expected effect. To solve these problems, we propose a seismic resolution improvement method based on the secondary time-frequency spectrum. First, we propose the secondary time-frequency spectrum based on S transform （ST） and discuss the reflection coefficient sequence and time-dependent wavelet in the secondary time frequency spectrum. Second, using the secondary time frequency spectrum, we design a two- dimensional filter to extract the amplitude spectrum of the time-dependent wavelet. Then, we discuss the improvement of the resolution operator in noisy environments and propose a novel approach for determining the broad frequency range of the resolution operator in the time- fi＇equency-space domain. Finally, we apply the proposed method to synthetic and real data and compare the results of the traditional spectrum-modeling deconvolution and Q compensation method. The results suggest that the proposed method does not need to estimate the Q value and the resolution is not limited by the bandwidth of the source. Thus, the resolution of the seismic data is improved sufficiently based on the signal-to-noise ratio （SNR）.展开更多
Seismic inversion can be divided into time-domain inversion and frequency-domain inversion based on different transform domains.Time-domain inversion has stronger stability and noise resistance compared to frequencydo...Seismic inversion can be divided into time-domain inversion and frequency-domain inversion based on different transform domains.Time-domain inversion has stronger stability and noise resistance compared to frequencydomain inversion.Frequency domain inversion has stronger ability to identify small-scale bodies and higher inversion resolution.Therefore,the research on the joint inversion method in the time-frequency domain is of great significance for improving the inversion resolution,stability,and noise resistance.The introduction of prior information constraints can effectively reduce ambiguity in the inversion process.However,the existing modeldriven time-frequency joint inversion assumes a specific prior distribution of the reservoir.These methods do not consider the original features of the data and are difficult to describe the relationship between time-domain features and frequency-domain features.Therefore,this paper proposes a high-resolution seismic inversion method based on joint data-driven in the time-frequency domain.The method is based on the impedance and reflectivity samples from logging,using joint dictionary learning to obtain adaptive feature information of the reservoir,and using sparse coefficients to capture the intrinsic relationship between impedance and reflectivity.The optimization result of the inversion is achieved through the regularization term of the joint dictionary sparse representation.We have finally achieved an inversion method that combines constraints on time-domain features and frequency features.By testing the model data and field data,the method has higher resolution in the inversion results and good noise resistance.展开更多
Due to the limitations of spatial bandwidth product and data transmission bandwidth,the field of view,resolution,and imaging speed constrain each other in an optical imaging system.Here,a fast-zoom and high-resolution...Due to the limitations of spatial bandwidth product and data transmission bandwidth,the field of view,resolution,and imaging speed constrain each other in an optical imaging system.Here,a fast-zoom and high-resolution sparse compound-eye camera(CEC)based on dual-end collaborative optimization is proposed,which provides a cost-effective way to break through the trade-off among the field of view,resolution,and imaging speed.In the optical end,a sparse CEC based on liquid lenses is designed,which can realize large-field-of-view imaging in real time,and fast zooming within 5 ms.In the computational end,a disturbed degradation model driven super-resolution network(DDMDSR-Net)is proposed to deal with complex image degradation issues in actual imaging situations,achieving high-robustness and high-fidelity resolution enhancement.Based on the proposed dual-end collaborative optimization framework,the angular resolution of the CEC can be enhanced from 71.6"to 26.0",which provides a solution to realize high-resolution imaging for array camera dispensing with high optical hardware complexity and data transmission bandwidth.Experiments verify the advantages of the CEC based on dual-end collaborative optimization in high-fidelity reconstruction of real scene images,kilometer-level long-distance detection,and dynamic imaging and precise recognition of targets of interest.展开更多
The study of the charge conjugation and parity(CP)violation of hyperon is the precision frontier for probing possible new CP violation sources beyond the standard model(SM).With the large number of quantum entangled h...The study of the charge conjugation and parity(CP)violation of hyperon is the precision frontier for probing possible new CP violation sources beyond the standard model(SM).With the large number of quantum entangled hyperonantihyperon pairs to be produced at Super Tau-Charm Facility(STCF),the CP asymmetry of hyperon is expected to be tested with a statistical sensitivity of 10^(−4) or even better.To cope with the statistical precision,the systematic effects from various aspects are critical and need to be studied in detail.In this paper,the sensitivity effects on the CP violation parameters associated with the detector resolution,including those of the position and momentum,are studied and discussed in detail.The results provide valuable guidance for the design of STCF detector.展开更多
The CUG_CLMFM3D series comprises high-resolution three-dimensional lithospheric magnetic field models for China and its surroundings.The first version,CUG_CLMFM3Dv1,is a spherical cap harmonic model integrating the WD...The CUG_CLMFM3D series comprises high-resolution three-dimensional lithospheric magnetic field models for China and its surroundings.The first version,CUG_CLMFM3Dv1,is a spherical cap harmonic model integrating the WDMAMv2(World Digital Magnetic Anomaly Map version 2)global magnetic anomaly grid and nearly a decade of CHAMP(Challenging Minisatellite Payload for Geophysical Research and Application)satellite vector data.It achieves a~5.7 km resolution but has limitations:the WDMAMv2 grid lacks high-resolution data in the southern Xinjiang and Tibet regions,which leads to missing small-to medium-scale anomalies,and unfiltered CHAMP data introduce low-frequency conflicts with global spherical harmonic models.Above the altitude of 150 km,correlations with global models drop below 0.9.The second version,CUG_CLMFM3Dv2,addresses these issues by incorporating 5-km-resolution aeromagnetic data and rigorously processed satellite data from CHAMP,Swarm,CSES-1(China Seismo-Electromagnetic Satellite 1),and MSS-1(Macao Science Satellite 1).The comparison analysis shows that the CUG_CLMFM3Dv2 captures finer high-frequency details and more stable long-wavelength signals,offering improved magnetic anomaly maps for further geological and geophysical studies.展开更多
This article proposes a three-dimensional light field reconstruction method based on neural radiation field(NeRF)called Infrared NeRF for low resolution thermal infrared scenes.Based on the characteristics of the low ...This article proposes a three-dimensional light field reconstruction method based on neural radiation field(NeRF)called Infrared NeRF for low resolution thermal infrared scenes.Based on the characteristics of the low resolution thermal infrared imaging,various optimizations have been carried out to improve the speed and accuracy of thermal infrared 3D reconstruction.Firstly,inspired by Boltzmann's law of thermal radiation,distance is incorporated into the NeRF model for the first time,resulting in a nonlinear propagation of a single ray and a more accurate description of the physical property that infrared radiation intensity decreases with increasing distance.Secondly,in terms of improving inference speed,based on the phenomenon of high and low frequency distribution of foreground and background in infrared images,a multi ray non-uniform light synthesis strategy is proposed to make the model pay more attention to foreground objects in the scene,reduce the distribution of light in the background,and significantly reduce training time without reducing accuracy.In addition,compared to visible light scenes,infrared images only have a single channel,so fewer network parameters are required.Experiments using the same training data and data filtering method showed that,compared to the original NeRF,the improved network achieved an average improvement of 13.8%and 4.62%in PSNR and SSIM,respectively,while an average decreases of 46%in LPIPS.And thanks to the optimization of network layers and data filtering methods,training only takes about 25%of the original method's time to achieve convergence.Finally,for scenes with weak backgrounds,this article improves the inference speed of the model by 4-6 times compared to the original NeRF by limiting the query interval of the model.展开更多
Microsphere and microcylinder-assisted microscopy(MAM)has grown steadily over the last decade and is still an intensively studied optical far-field imaging technique that promises to overcome the fundamental lateral r...Microsphere and microcylinder-assisted microscopy(MAM)has grown steadily over the last decade and is still an intensively studied optical far-field imaging technique that promises to overcome the fundamental lateral resolution limit of microscopy.However,the physical effects leading to resolution enhancement are still frequently debated.In addition,various configurations of MAM operating in transmission mode as well as reflection mode are examined,and the results are sometimes generalized.We present a rigorous simulation model of MAM and introduce a way to quantify the resolution enhancement.The lateral resolution is compared for microscope arrangements in reflection and transmission modes.Furthermore,we discuss different physical effects with respect to their contribution to resolution enhancement.The results indicate that the effects impacting the resolution in MAM strongly depend on the arrangement of the microscope and the measurement object.As a highlight,we outline that evanescent waves in combination with whispering gallery modes also improve the imaging capabilities,enabling super-resolution under certain circumstances.This result is contrary to the conclusions drawn from previous studies,where phase objects have been analyzed,and thus further emphasizes the complexity of the physical mechanisms underlying MAM.展开更多
In February 2025,a startup satellite manufacturer,Albedo(Broomfield,CO,USA)is expected to launch its first satellite,Clarity-1,into orbit aboard SpaceX’s Transporter-13,a Falcon 9 rideshare mission[1].Like many imagi...In February 2025,a startup satellite manufacturer,Albedo(Broomfield,CO,USA)is expected to launch its first satellite,Clarity-1,into orbit aboard SpaceX’s Transporter-13,a Falcon 9 rideshare mission[1].Like many imaging satellites,Clarity-1’s mis-sion will be to take high-resolution aerial photos for clients in var-ious economic sectors including agriculture,insurance,energy,mapping,utilities,and defense.What makes this satellite unique is both its industry-leading 10 cm spatial resolution and its extre-mely low orbit of 200 km,far closer to Earth than the 450 km or higher orbits of most of its peers with similar missions.展开更多
With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communicat...With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communication system.In particular,the abnormal signals may emulate the normal signals,which makes it very challenging for abnormal signal recognition.In this paper,we propose a new abnormal signal recognition scheme,which combines time-frequency analysis with deep learning to effectively identify synthetic abnormal communication signals.Firstly,we emulate synthetic abnormal communication signals including seven jamming patterns.Then,we model an abnormal communication signals recognition system based on the communication protocol between the transmitter and the receiver.To improve the performance,we convert the original signal into the time-frequency spectrogram to develop an image classification algorithm.Simulation results demonstrate that the proposed method can effectively recognize the abnormal signals under various parameter configurations,even under low signal-to-noise ratio(SNR)and low jamming-to-signal ratio(JSR)conditions.展开更多
Seismic time-frequency(TF)transforms are essential tools in reservoir interpretation and signal processing,particularly for characterizing frequency variations in non-stationary seismic data.Recently,sparse TF trans-f...Seismic time-frequency(TF)transforms are essential tools in reservoir interpretation and signal processing,particularly for characterizing frequency variations in non-stationary seismic data.Recently,sparse TF trans-forms,which leverage sparse coding(SC),have gained significant attention in the geosciences due to their ability to achieve high TF resolution.However,the iterative approaches typically employed in sparse TF transforms are computationally intensive,making them impractical for real seismic data analysis.To address this issue,we propose an interpretable convolutional sparse coding(CSC)network to achieve high TF resolution.The proposed model is generated based on the traditional short-time Fourier transform(STFT)transform and a modified UNet,named ULISTANet.In this design,we replace the conventional convolutional layers of the UNet with learnable iterative shrinkage thresholding algorithm(LISTA)blocks,a specialized form of CSC.The LISTA block,which evolves from the traditional iterative shrinkage thresholding algorithm(ISTA),is optimized for extracting sparse features more effectively.Furthermore,we create a synthetic dataset featuring complex frequency-modulated signals to train ULISTANet.Finally,the proposed method’s performance is subsequently validated using both synthetic and field data,demonstrating its potential for enhanced seismic data analysis.展开更多
In clinical diagnosis,magnetic resonance imaging(MRI)allows different contrast images to be obtained.High-resolution(HR)MRI presents fine anatomical structures,which is important for improving the efficiency of expert...In clinical diagnosis,magnetic resonance imaging(MRI)allows different contrast images to be obtained.High-resolution(HR)MRI presents fine anatomical structures,which is important for improving the efficiency of expert diagnosis and realising smart healthcare.However,due to the cost of scanning equipment and the time required for scanning,obtaining an HR brain MRI is quite challenging.Therefore,to improve the quality of images,reference-based super-resolution technology has come into existence.Nevertheless,the existing methods still have some drawbacks:(1)The advantages of different contrast images are not fully utilised.(2)The slice-by-slice scanning nature of magnetic resonance imaging is not considered.(3)The ability to capture contextual information and to match and fuse multi-scale,multi-contrast features is lacking.In this paper,we propose the multi-slice aware matching and fusion(MSAMF)network,which makes full use of multi-slice reference images information by introducing a multi-slice aware module and multi-scale matching strategy to capture corresponding contextual information in reference features at other scales.To further integrate matching features,a multi-scale fusion mechanism is also designed to progressively fuse multi-scale matching features,thereby generating more detailed super-resolution images.The experimental results support the benefits of our network in enhancing the quality of brain MRI reconstruction.展开更多
Osteoporosis is a major cause of bone fracture and can be characterised by both mass loss and microstructure deterioration of the bone.The modern way of osteoporosis assessment is through the measurement of bone miner...Osteoporosis is a major cause of bone fracture and can be characterised by both mass loss and microstructure deterioration of the bone.The modern way of osteoporosis assessment is through the measurement of bone mineral density,which is not able to unveil the pathological condition from the mesoscale aspect.To obtain mesoscale information from computed tomography(CT),the super-resolution(SR)approach for volumetric imaging data is required.A deep learning model AESR3D is proposed to recover high-resolution(HR)Micro-CT from low-resolution Micro-CT and implement an unsupervised segmentation for better trabecular observation and measurement.A new regularisation overcomplete autoencoder framework for the SR task is proposed and theoretically analysed.The best performance is achieved on structural similarity measure of trabecular CT SR task compared with the state-of-the-art models in both natural and medical image SR tasks.The HR and SR images show a high correlation(r=0.996,intraclass correlation coefficients=0.917)on trabecular bone morphological indicators.The results also prove the effectiveness of our regularisation framework when training a large capacity model.展开更多
Energy resolution calibration is crucial for gamma-ray spectral analysis,as measured using a scintillation detector.A locally constrained regularization method was proposed to determine the resolution calibration para...Energy resolution calibration is crucial for gamma-ray spectral analysis,as measured using a scintillation detector.A locally constrained regularization method was proposed to determine the resolution calibration parameters.First,a Monte Carlo simulation model consistent with an actual measurement system was constructed to obtain the energy deposition distribution in the scintillation crystal.Subsequently,the regularization objective function is established based on weighted least squares and additional constraints.Additional constraints were designed using a special weighting scheme based on the incident gamma-ray energies.Subsequently,an intelligent algorithm was introduced to search for the optimal resolution calibration parameters by minimizing the objective function.The most appropriate regularization parameter was determined through mathematical experiments.When the regularization parameter was 30,the calibrated results exhibited the minimum RMSE.Simulations and test pit experiments were conducted to verify the performance of the proposed method.The simulation results demonstrate that the proposed algorithm can determine resolution calibration parameters more accurately than the traditional weighted least squares,and the test pit experimental results show that the R-squares between the calibrated and measured spectra are larger than 0.99.The accurate resolution calibration parameters determined by the proposed method lay the foundation for gamma-ray spectral processing and simulation benchmarking.展开更多
Dispute resolution mechanisms play a critical role in sustaining collaborative efforts in space exploration,particularly in partnerships involving diverse stakeholders with varying interests.This study examines the le...Dispute resolution mechanisms play a critical role in sustaining collaborative efforts in space exploration,particularly in partnerships involving diverse stakeholders with varying interests.This study examines the legal framework governing dispute resolution within the Sino-Africa space cooperation,analyzing foundational principles,legal theories,international treaties,national legislation,and tailored conflict-resolution mechanisms.By assessing key legal instruments such as the Outer Space Treaty(1967)and the bilateral agreements,the research explores how arbitration,mediation and adjudication processes can address disputes arising from joint space endeavors.The study highlights the importance of structured legal and procedural frameworks in mitigating conflicts,ensuring compliance,and fostering longer-term cooperation between China and African nations in space exploration.Through this analysis,the study contributes to broader discussions on enhancing the efficacy of dispute resolution mechanisms in international space collaborations.展开更多
Herein,we report the dynamic kinetic resolution asymmetric acylation ofγ-hydroxy-γ-perfluoroalkyl butenolides/phthalides catalyzed by amino acid-derived bifunctional organocatalysts,and a series of ketals were obtai...Herein,we report the dynamic kinetic resolution asymmetric acylation ofγ-hydroxy-γ-perfluoroalkyl butenolides/phthalides catalyzed by amino acid-derived bifunctional organocatalysts,and a series of ketals were obtained in high yields(up to 95%)and excellent enantioselectivities(up to 99%).In terms of synthetic utility,the reaction can be performed on a gram scale,and the product can be converted into potential biological nucleoside analog.展开更多
Since 1960,there have been more than thirty UN peacekeeping missions across Africa,the most of any region in the context of the conflicts that have plagued the region for decades.It has become increasingly evident tha...Since 1960,there have been more than thirty UN peacekeeping missions across Africa,the most of any region in the context of the conflicts that have plagued the region for decades.It has become increasingly evident that official diplomacy is not enough to resolve these crises.Experience shows that given the people’s reliance on religion,religion has continued to act as a force of conflict prevention and resolution in the region.The role played by faith-based diplomats has gained the trust of the conflict parties such that it would be unwise for national and international actors to neglect their role in policy making and conflict prevention and resolution.展开更多
A method is proposed for high-resolution neutron spectrum regulation across the entire energy domain.It was applied to in-reactor transuranic isotope production.This method comprises four modules:a neutron spectrum pe...A method is proposed for high-resolution neutron spectrum regulation across the entire energy domain.It was applied to in-reactor transuranic isotope production.This method comprises four modules:a neutron spectrum perturbation module,a neutron spectrum calculation module,a neutron spectrum valuation module,and an intelligent optimization module.It makes it possible to determine the optimal neutron spectrum for transuranic isotope production and a regulation scheme to establish this neutron spectrum within the reactor.The state-of-the-art production schemes for^(252)Cf and^(238)Pu in the High Flux Isotope Reactor were optimized,improving the yield of^(252)Cf by 12.16%and that of^(238)Pu by 7.53-25.84%.Moreover,the proposed optimization schemes only disperse certain nuclides into the targets without modifying the reactor design parameters,making them simple and feasible.The new method achieves efficient and precise neutron spectrum optimization,maximizing the production of transuranic isotopes.展开更多
This study addresses a challenge of parametrizing a resolution function of a neutron beam from the neutron time of flight facility n_TOF at CERN.A difficulty stems from a fact that a resolution function exhibits rathe...This study addresses a challenge of parametrizing a resolution function of a neutron beam from the neutron time of flight facility n_TOF at CERN.A difficulty stems from a fact that a resolution function exhibits rather strong variations in shape,over approximately ten orders of magnitude in neutron energy.To avoid a need for a manual identification of the appropri-ate analytical forms-hindering past attempts at its parametrization-we take advantage of the versatile machine learning techniques.Specifically,we parametrized it by training a multilayer feedforward neural network,relying on a key idea that such network acts as a universal approximator.The proof-of-concept is presented for a resolution function for the first experimental area of the n_TOF facility from the third phase of its operation.We propose an optimal network structure for a resolution function in question,which is also expected to be optimal or near-optimal for other experimental areas and for different phases of n_TOF operation.To reconstruct several resolution function forms in common use from a single para-metrized form,we provide a practical tool in the form of a specialized C++class encapsulating the computationally efficient procedures suited to the task.展开更多
基金This work was funded by National Natural Science Foundation of China-（No. 40474044）.
文摘In this paper, it is described that the time-frequency resolution of geophysical signals is affected by the time window function attenuation coefficient and sampling interval and how such effects are eliminated effectively. Improving the signal resolution is the key to signal time-frequency analysis processing and has wide use in geophysical data processing and extraction of attribute parameters. In this paper, authors research the effects of the attenuation coefficient choice of the Gabor transform window function and sampling interval on signal resolution. Unsuitable parameters not only decrease the signal resolution on the frequency spectrum but also miss the signals. It is essential to first give the optimum window and range of parameters through time-frequency analysis simulation using the Gabor transform. In the paper, the suggestions about the range and choice of the optimum sampling interval and processing methods of general seismic signals are given.
文摘Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].
基金financially supported by the National 973 Project(No.2014CB239006)the National Natural Science Foundation of China(No.41104069 and 41274124)the Fundamental Research Funds for Central Universities(No.R1401005A)
文摘The resolution of seismic data is critical to seismic data processing and the subsequent interpretation of fine structures. In conventional resolution improvement methods, the seismic data is assumed stationary and the noise level not changes with space, whereas the actual situation does not satisfy this assumption, so that results after resolution improvement processing is not up to the expected effect. To solve these problems, we propose a seismic resolution improvement method based on the secondary time-frequency spectrum. First, we propose the secondary time-frequency spectrum based on S transform （ST） and discuss the reflection coefficient sequence and time-dependent wavelet in the secondary time frequency spectrum. Second, using the secondary time frequency spectrum, we design a two- dimensional filter to extract the amplitude spectrum of the time-dependent wavelet. Then, we discuss the improvement of the resolution operator in noisy environments and propose a novel approach for determining the broad frequency range of the resolution operator in the time- fi＇equency-space domain. Finally, we apply the proposed method to synthetic and real data and compare the results of the traditional spectrum-modeling deconvolution and Q compensation method. The results suggest that the proposed method does not need to estimate the Q value and the resolution is not limited by the bandwidth of the source. Thus, the resolution of the seismic data is improved sufficiently based on the signal-to-noise ratio （SNR）.
文摘Seismic inversion can be divided into time-domain inversion and frequency-domain inversion based on different transform domains.Time-domain inversion has stronger stability and noise resistance compared to frequencydomain inversion.Frequency domain inversion has stronger ability to identify small-scale bodies and higher inversion resolution.Therefore,the research on the joint inversion method in the time-frequency domain is of great significance for improving the inversion resolution,stability,and noise resistance.The introduction of prior information constraints can effectively reduce ambiguity in the inversion process.However,the existing modeldriven time-frequency joint inversion assumes a specific prior distribution of the reservoir.These methods do not consider the original features of the data and are difficult to describe the relationship between time-domain features and frequency-domain features.Therefore,this paper proposes a high-resolution seismic inversion method based on joint data-driven in the time-frequency domain.The method is based on the impedance and reflectivity samples from logging,using joint dictionary learning to obtain adaptive feature information of the reservoir,and using sparse coefficients to capture the intrinsic relationship between impedance and reflectivity.The optimization result of the inversion is achieved through the regularization term of the joint dictionary sparse representation.We have finally achieved an inversion method that combines constraints on time-domain features and frequency features.By testing the model data and field data,the method has higher resolution in the inversion results and good noise resistance.
基金financial supports from National Natural Science Foundation of China(Grant Nos.U23A20368 and 62175006)Academic Excellence Foundation of BUAA for PhD Students.
文摘Due to the limitations of spatial bandwidth product and data transmission bandwidth,the field of view,resolution,and imaging speed constrain each other in an optical imaging system.Here,a fast-zoom and high-resolution sparse compound-eye camera(CEC)based on dual-end collaborative optimization is proposed,which provides a cost-effective way to break through the trade-off among the field of view,resolution,and imaging speed.In the optical end,a sparse CEC based on liquid lenses is designed,which can realize large-field-of-view imaging in real time,and fast zooming within 5 ms.In the computational end,a disturbed degradation model driven super-resolution network(DDMDSR-Net)is proposed to deal with complex image degradation issues in actual imaging situations,achieving high-robustness and high-fidelity resolution enhancement.Based on the proposed dual-end collaborative optimization framework,the angular resolution of the CEC can be enhanced from 71.6"to 26.0",which provides a solution to realize high-resolution imaging for array camera dispensing with high optical hardware complexity and data transmission bandwidth.Experiments verify the advantages of the CEC based on dual-end collaborative optimization in high-fidelity reconstruction of real scene images,kilometer-level long-distance detection,and dynamic imaging and precise recognition of targets of interest.
基金supported by the National Key R&D Program of China(2022YFA1602200)the International Partnership Program of the Chinese Academy of Sciences(211134KYSB20200057).
文摘The study of the charge conjugation and parity(CP)violation of hyperon is the precision frontier for probing possible new CP violation sources beyond the standard model(SM).With the large number of quantum entangled hyperonantihyperon pairs to be produced at Super Tau-Charm Facility(STCF),the CP asymmetry of hyperon is expected to be tested with a statistical sensitivity of 10^(−4) or even better.To cope with the statistical precision,the systematic effects from various aspects are critical and need to be studied in detail.In this paper,the sensitivity effects on the CP violation parameters associated with the detector resolution,including those of the position and momentum,are studied and discussed in detail.The results provide valuable guidance for the design of STCF detector.
基金supported by the National Natural Science Foundation of China(Grant Nos.42250103,42174090,42250101,42250102,and 41774091)the Macao Foundation+1 种基金the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB2023ZR02)the MOST Special Fund from the State Key Laboratory of Geological Processes and Mineral Resources(Grant No.MSFGPMR2022-4)。
文摘The CUG_CLMFM3D series comprises high-resolution three-dimensional lithospheric magnetic field models for China and its surroundings.The first version,CUG_CLMFM3Dv1,is a spherical cap harmonic model integrating the WDMAMv2(World Digital Magnetic Anomaly Map version 2)global magnetic anomaly grid and nearly a decade of CHAMP(Challenging Minisatellite Payload for Geophysical Research and Application)satellite vector data.It achieves a~5.7 km resolution but has limitations:the WDMAMv2 grid lacks high-resolution data in the southern Xinjiang and Tibet regions,which leads to missing small-to medium-scale anomalies,and unfiltered CHAMP data introduce low-frequency conflicts with global spherical harmonic models.Above the altitude of 150 km,correlations with global models drop below 0.9.The second version,CUG_CLMFM3Dv2,addresses these issues by incorporating 5-km-resolution aeromagnetic data and rigorously processed satellite data from CHAMP,Swarm,CSES-1(China Seismo-Electromagnetic Satellite 1),and MSS-1(Macao Science Satellite 1).The comparison analysis shows that the CUG_CLMFM3Dv2 captures finer high-frequency details and more stable long-wavelength signals,offering improved magnetic anomaly maps for further geological and geophysical studies.
基金Support by the Fundamental Research Funds for the Central Universities(2024300443)the National Natural Science Foundation of China(NSFC)Young Scientists Fund(62405131)。
文摘This article proposes a three-dimensional light field reconstruction method based on neural radiation field(NeRF)called Infrared NeRF for low resolution thermal infrared scenes.Based on the characteristics of the low resolution thermal infrared imaging,various optimizations have been carried out to improve the speed and accuracy of thermal infrared 3D reconstruction.Firstly,inspired by Boltzmann's law of thermal radiation,distance is incorporated into the NeRF model for the first time,resulting in a nonlinear propagation of a single ray and a more accurate description of the physical property that infrared radiation intensity decreases with increasing distance.Secondly,in terms of improving inference speed,based on the phenomenon of high and low frequency distribution of foreground and background in infrared images,a multi ray non-uniform light synthesis strategy is proposed to make the model pay more attention to foreground objects in the scene,reduce the distribution of light in the background,and significantly reduce training time without reducing accuracy.In addition,compared to visible light scenes,infrared images only have a single channel,so fewer network parameters are required.Experiments using the same training data and data filtering method showed that,compared to the original NeRF,the improved network achieved an average improvement of 13.8%and 4.62%in PSNR and SSIM,respectively,while an average decreases of 46%in LPIPS.And thanks to the optimization of network layers and data filtering methods,training only takes about 25%of the original method's time to achieve convergence.Finally,for scenes with weak backgrounds,this article improves the inference speed of the model by 4-6 times compared to the original NeRF by limiting the query interval of the model.
基金supported by the German Research Foundation(DFG)(Grant Nos.LE 992/14-3 and LE 992/15-3).
文摘Microsphere and microcylinder-assisted microscopy(MAM)has grown steadily over the last decade and is still an intensively studied optical far-field imaging technique that promises to overcome the fundamental lateral resolution limit of microscopy.However,the physical effects leading to resolution enhancement are still frequently debated.In addition,various configurations of MAM operating in transmission mode as well as reflection mode are examined,and the results are sometimes generalized.We present a rigorous simulation model of MAM and introduce a way to quantify the resolution enhancement.The lateral resolution is compared for microscope arrangements in reflection and transmission modes.Furthermore,we discuss different physical effects with respect to their contribution to resolution enhancement.The results indicate that the effects impacting the resolution in MAM strongly depend on the arrangement of the microscope and the measurement object.As a highlight,we outline that evanescent waves in combination with whispering gallery modes also improve the imaging capabilities,enabling super-resolution under certain circumstances.This result is contrary to the conclusions drawn from previous studies,where phase objects have been analyzed,and thus further emphasizes the complexity of the physical mechanisms underlying MAM.
文摘In February 2025,a startup satellite manufacturer,Albedo(Broomfield,CO,USA)is expected to launch its first satellite,Clarity-1,into orbit aboard SpaceX’s Transporter-13,a Falcon 9 rideshare mission[1].Like many imaging satellites,Clarity-1’s mis-sion will be to take high-resolution aerial photos for clients in var-ious economic sectors including agriculture,insurance,energy,mapping,utilities,and defense.What makes this satellite unique is both its industry-leading 10 cm spatial resolution and its extre-mely low orbit of 200 km,far closer to Earth than the 450 km or higher orbits of most of its peers with similar missions.
基金supported by Natural Science Foundation of China(No.62371231)Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu under Grant BK20222001Jiangsu Provincial Key Research and Development Program(No.BE2023027).
文摘With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communication system.In particular,the abnormal signals may emulate the normal signals,which makes it very challenging for abnormal signal recognition.In this paper,we propose a new abnormal signal recognition scheme,which combines time-frequency analysis with deep learning to effectively identify synthetic abnormal communication signals.Firstly,we emulate synthetic abnormal communication signals including seven jamming patterns.Then,we model an abnormal communication signals recognition system based on the communication protocol between the transmitter and the receiver.To improve the performance,we convert the original signal into the time-frequency spectrogram to develop an image classification algorithm.Simulation results demonstrate that the proposed method can effectively recognize the abnormal signals under various parameter configurations,even under low signal-to-noise ratio(SNR)and low jamming-to-signal ratio(JSR)conditions.
基金supported by the National Natural Science Foundation of China under Grant 42474139the Key Research and Development Program of Shaanxi under Grant 2024GX-YBXM-067.
文摘Seismic time-frequency(TF)transforms are essential tools in reservoir interpretation and signal processing,particularly for characterizing frequency variations in non-stationary seismic data.Recently,sparse TF trans-forms,which leverage sparse coding(SC),have gained significant attention in the geosciences due to their ability to achieve high TF resolution.However,the iterative approaches typically employed in sparse TF transforms are computationally intensive,making them impractical for real seismic data analysis.To address this issue,we propose an interpretable convolutional sparse coding(CSC)network to achieve high TF resolution.The proposed model is generated based on the traditional short-time Fourier transform(STFT)transform and a modified UNet,named ULISTANet.In this design,we replace the conventional convolutional layers of the UNet with learnable iterative shrinkage thresholding algorithm(LISTA)blocks,a specialized form of CSC.The LISTA block,which evolves from the traditional iterative shrinkage thresholding algorithm(ISTA),is optimized for extracting sparse features more effectively.Furthermore,we create a synthetic dataset featuring complex frequency-modulated signals to train ULISTANet.Finally,the proposed method’s performance is subsequently validated using both synthetic and field data,demonstrating its potential for enhanced seismic data analysis.
基金supported by the National Natural Science Foundation of China(Grants 62376184,62206196,62403345,62303445)Shanxi Provincial Special Guidance Program for the Transformation of Scientific and Technological Achievements(Grants 202304021301035,202404021301032)Central Guided Local Science and Technology Development Project(Grant YDZJSX20231A017).
文摘In clinical diagnosis,magnetic resonance imaging(MRI)allows different contrast images to be obtained.High-resolution(HR)MRI presents fine anatomical structures,which is important for improving the efficiency of expert diagnosis and realising smart healthcare.However,due to the cost of scanning equipment and the time required for scanning,obtaining an HR brain MRI is quite challenging.Therefore,to improve the quality of images,reference-based super-resolution technology has come into existence.Nevertheless,the existing methods still have some drawbacks:(1)The advantages of different contrast images are not fully utilised.(2)The slice-by-slice scanning nature of magnetic resonance imaging is not considered.(3)The ability to capture contextual information and to match and fuse multi-scale,multi-contrast features is lacking.In this paper,we propose the multi-slice aware matching and fusion(MSAMF)network,which makes full use of multi-slice reference images information by introducing a multi-slice aware module and multi-scale matching strategy to capture corresponding contextual information in reference features at other scales.To further integrate matching features,a multi-scale fusion mechanism is also designed to progressively fuse multi-scale matching features,thereby generating more detailed super-resolution images.The experimental results support the benefits of our network in enhancing the quality of brain MRI reconstruction.
基金Beijing Natural Science Foundation-Haidian original Innovation Joint Foundation,Grant/Award Number:L192016Joint Funds of the National Natural Science Foundation of China,Grant/Award Number:U21A20489+3 种基金National Natural Science Foundation of China,Grant/Award Number:62003330Shenzhen Fundamental Research Funds,Grant/Award Numbers:JCYJ20220818101608019,JCYJ20190807170407391,JCYJ20180507182415428Natural Science Foundation of Guangdong Province,Grant/Award Number:2019A1515011699Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems,Shenzhen Institute of Advanced Technology。
文摘Osteoporosis is a major cause of bone fracture and can be characterised by both mass loss and microstructure deterioration of the bone.The modern way of osteoporosis assessment is through the measurement of bone mineral density,which is not able to unveil the pathological condition from the mesoscale aspect.To obtain mesoscale information from computed tomography(CT),the super-resolution(SR)approach for volumetric imaging data is required.A deep learning model AESR3D is proposed to recover high-resolution(HR)Micro-CT from low-resolution Micro-CT and implement an unsupervised segmentation for better trabecular observation and measurement.A new regularisation overcomplete autoencoder framework for the SR task is proposed and theoretically analysed.The best performance is achieved on structural similarity measure of trabecular CT SR task compared with the state-of-the-art models in both natural and medical image SR tasks.The HR and SR images show a high correlation(r=0.996,intraclass correlation coefficients=0.917)on trabecular bone morphological indicators.The results also prove the effectiveness of our regularisation framework when training a large capacity model.
基金supported by the National Natural Science Foundation of China(No.41804141)。
文摘Energy resolution calibration is crucial for gamma-ray spectral analysis,as measured using a scintillation detector.A locally constrained regularization method was proposed to determine the resolution calibration parameters.First,a Monte Carlo simulation model consistent with an actual measurement system was constructed to obtain the energy deposition distribution in the scintillation crystal.Subsequently,the regularization objective function is established based on weighted least squares and additional constraints.Additional constraints were designed using a special weighting scheme based on the incident gamma-ray energies.Subsequently,an intelligent algorithm was introduced to search for the optimal resolution calibration parameters by minimizing the objective function.The most appropriate regularization parameter was determined through mathematical experiments.When the regularization parameter was 30,the calibrated results exhibited the minimum RMSE.Simulations and test pit experiments were conducted to verify the performance of the proposed method.The simulation results demonstrate that the proposed algorithm can determine resolution calibration parameters more accurately than the traditional weighted least squares,and the test pit experimental results show that the R-squares between the calibrated and measured spectra are larger than 0.99.The accurate resolution calibration parameters determined by the proposed method lay the foundation for gamma-ray spectral processing and simulation benchmarking.
文摘Dispute resolution mechanisms play a critical role in sustaining collaborative efforts in space exploration,particularly in partnerships involving diverse stakeholders with varying interests.This study examines the legal framework governing dispute resolution within the Sino-Africa space cooperation,analyzing foundational principles,legal theories,international treaties,national legislation,and tailored conflict-resolution mechanisms.By assessing key legal instruments such as the Outer Space Treaty(1967)and the bilateral agreements,the research explores how arbitration,mediation and adjudication processes can address disputes arising from joint space endeavors.The study highlights the importance of structured legal and procedural frameworks in mitigating conflicts,ensuring compliance,and fostering longer-term cooperation between China and African nations in space exploration.Through this analysis,the study contributes to broader discussions on enhancing the efficacy of dispute resolution mechanisms in international space collaborations.
基金supported by the National Natural Science Foundation of China(Nos.82130103,82151525 and 81903465)the Central Plains Scholars and Scientists Studio Fund(2018002)+1 种基金the Natural Science Foundation of Henan Province(No.212300410051)the Science and Technology Major Project of Henan Province(No.221100310300)。
文摘Herein,we report the dynamic kinetic resolution asymmetric acylation ofγ-hydroxy-γ-perfluoroalkyl butenolides/phthalides catalyzed by amino acid-derived bifunctional organocatalysts,and a series of ketals were obtained in high yields(up to 95%)and excellent enantioselectivities(up to 99%).In terms of synthetic utility,the reaction can be performed on a gram scale,and the product can be converted into potential biological nucleoside analog.
文摘Since 1960,there have been more than thirty UN peacekeeping missions across Africa,the most of any region in the context of the conflicts that have plagued the region for decades.It has become increasingly evident that official diplomacy is not enough to resolve these crises.Experience shows that given the people’s reliance on religion,religion has continued to act as a force of conflict prevention and resolution in the region.The role played by faith-based diplomats has gained the trust of the conflict parties such that it would be unwise for national and international actors to neglect their role in policy making and conflict prevention and resolution.
基金sponsored by the National Natural Science Foundation of China(No.12305190)the Lingchuang Research Project of the China National Nuclear Corporation(CNNC)。
文摘A method is proposed for high-resolution neutron spectrum regulation across the entire energy domain.It was applied to in-reactor transuranic isotope production.This method comprises four modules:a neutron spectrum perturbation module,a neutron spectrum calculation module,a neutron spectrum valuation module,and an intelligent optimization module.It makes it possible to determine the optimal neutron spectrum for transuranic isotope production and a regulation scheme to establish this neutron spectrum within the reactor.The state-of-the-art production schemes for^(252)Cf and^(238)Pu in the High Flux Isotope Reactor were optimized,improving the yield of^(252)Cf by 12.16%and that of^(238)Pu by 7.53-25.84%.Moreover,the proposed optimization schemes only disperse certain nuclides into the targets without modifying the reactor design parameters,making them simple and feasible.The new method achieves efficient and precise neutron spectrum optimization,maximizing the production of transuranic isotopes.
基金supported by the Croatian Science Foundation under the project number HRZZ-IP-2022-10-3878funding from the European Union’s Horizon Europe Research and Innovation programme under Grant Agreement No 101057511Funding Open access funding provided by CERN (European Organization for Nuclear Research).
文摘This study addresses a challenge of parametrizing a resolution function of a neutron beam from the neutron time of flight facility n_TOF at CERN.A difficulty stems from a fact that a resolution function exhibits rather strong variations in shape,over approximately ten orders of magnitude in neutron energy.To avoid a need for a manual identification of the appropri-ate analytical forms-hindering past attempts at its parametrization-we take advantage of the versatile machine learning techniques.Specifically,we parametrized it by training a multilayer feedforward neural network,relying on a key idea that such network acts as a universal approximator.The proof-of-concept is presented for a resolution function for the first experimental area of the n_TOF facility from the third phase of its operation.We propose an optimal network structure for a resolution function in question,which is also expected to be optimal or near-optimal for other experimental areas and for different phases of n_TOF operation.To reconstruct several resolution function forms in common use from a single para-metrized form,we provide a practical tool in the form of a specialized C++class encapsulating the computationally efficient procedures suited to the task.
