Object identification and three-dimensional reconstruction techniques are always attractive research interests in machine vision,virtual reality,augmented reality,and biomedical engineering.Optical computing metasurfa...Object identification and three-dimensional reconstruction techniques are always attractive research interests in machine vision,virtual reality,augmented reality,and biomedical engineering.Optical computing metasurface,as a two-dimensional artificial design component,has displayed the supernormal character of controlling phase,amplitude,polarization,and frequency distributions of the light beam,capable of performing mathematical operations on the input light field.Here,we propose and demonstrate an all-optical object identification technique based on optical computing metasurface,and apply it to 3D reconstruction.Unlike traditional mechanisms,this scheme reduces memory consumption in the processing of the contour surface extraction.The identification and reconstruction of experimental results from high-contrast and low-contrast objects agree well with the real objects.The exploration of the all-optical object identification and 3D reconstruction techniques provides potential applications of high efficiencies,low consumption,and compact systems.展开更多
At present,the identification of tropical cyclone remote precipitation(TRP)requires subjective participation,leading to inconsistent results among different researchers despite adopting the same identification standar...At present,the identification of tropical cyclone remote precipitation(TRP)requires subjective participation,leading to inconsistent results among different researchers despite adopting the same identification standard.Thus,establishing an objective identification method is greatly important.In this study,an objective synoptic analysis technique for TRP(OSAT_TRP)is proposed to identify TRP using daily precipitation datasets,historical tropical cyclone(TC)track data,and the ERA5 reanalysis data.This method includes three steps:first,independent rain belts are separated,and those that might relate to TCs'remote effects are distinguished according to their distance from the TCs.Second,the strong water vapor transport belt from the TC is identified using integrated horizontal water vapor transport(IVT).Third,TRP is distinguished by connecting the first two steps.The TRP obtained through this method can satisfy three criteria,as follows:1)the precipitation occurs outside the circulation of TCs,2)the precipitation is affected by TCs,and 3)a gap exists between the TRP and TC rain belt.Case diagnosis analysis,compared with subjective TRP results and backward trajectory analyses using HYSPLIT,indicates that OSAT_TRP can distinguish TRP even when multiple TCs in the Northwest Pacific are involved.Then,we applied the OSAT_TRP to select typical TRPs and obtained the synoptic-scale environments of the TRP through composite analysis.展开更多
Photons that are entangled or correlated in orbital angular momentum have been extensively used for remote sensing,object identification and imaging.It has recently been demonstrated that intensity fluctuations give r...Photons that are entangled or correlated in orbital angular momentum have been extensively used for remote sensing,object identification and imaging.It has recently been demonstrated that intensity fluctuations give rise to the formation of correlations in the orbital angular momentum components and angular positions of random light.Here we demonstrate that the spatial signatures and phase information of an object with rotational symmetries can be identified using classical orbital angular momentum correlations in random light.The Fourier components imprinted in the digital spiral spectrum of the object,as measured through intensity correlations,unveil its spatial and phase information.Sharing similarities with conventional compressive sensing protocols that exploit sparsity to reduce the number of measurements required to reconstruct a signal,our technique allows sensing of an object with fewer measurements than other schemes that use pixel-by-pixel imaging.One remarkable advantage of our technique is that it does not require the preparation of fragile quantum states of light and operates at both low-and high-light levels.In addition,our technique is robust against environmental noise,a fundamental feature of any realistic scheme for remote sensing.展开更多
Ghost imaging could be used to make a quick identification of orthogonal objects by means of photocurrent correlation measurements. In this paper, we extend the method to identify nonorthogonal objects. In the method,...Ghost imaging could be used to make a quick identification of orthogonal objects by means of photocurrent correlation measurements. In this paper, we extend the method to identify nonorthogonal objects. In the method, an object is illuminated by one photon from an entangled pair, and the other one is diffracted into a particular direction by a pre-established multiple-exposure hologram in the idler arm. By the correlation measurements, the nonorthogonal object in the signal arm could be discriminated within a very short time. The constraints for the identification of nonorthogonal objects are presented, which show that the nonorthogonal objects can be discriminated when the overlapping portion between any two objects is less than half of all the objects in the set. The numerical simulations further verify the result.展开更多
The northeastern China cold vortex(NCCV)plays an important role in regional rainstorms over East Asia.Using the National Centers for Environmental Prediction Final reanalysis dataset and the Global Precipitation Measu...The northeastern China cold vortex(NCCV)plays an important role in regional rainstorms over East Asia.Using the National Centers for Environmental Prediction Final reanalysis dataset and the Global Precipitation Measurement product,an objective algorithm for identifying heavy-precipitation NCCV(HPCV)events was designed,and the climatological features of 164 HPCV events from 2001 to 2019 were investigated.The number of HPCV events showed an upward linear trend,with the highest frequency of occurrence in summer.The most active region of HPCV samples was the Northeast China Plain between 40°–55°N.Most HPCV events lasted 3–5 days and had radii ranging from 250 to 1000 km.The duration of HPCV events with larger sizes was longer.About half of the HPCV events moved into(moved out of)the definition region(35°–60°N,115°–145°E),and half initiated(dissipated)within the region.The initial position was close to the western boundary of the definition region,and the final position was mainly near the eastern boundary.The locations associated with the precipitation were mostly concentrated within 2000 km southeast of the HPCV systems,and they were farther from the center in the cold season than in the warm season.展开更多
The present study identifies wintertime cold fronts in Eurasia from gridded datasets using a new objective two-step identification scheme.The simple and classic conception of a front is adopted,where a cold front is i...The present study identifies wintertime cold fronts in Eurasia from gridded datasets using a new objective two-step identification scheme.The simple and classic conception of a front is adopted,where a cold front is identified as the warm boundary of the frontal zone with a suitable horizontal temperature gradient and cold advection.We combine the traditional thermal front parameter with temperature advection to first identify the cold frontal zone,and then its eastern and southern boundaries are objectively plotted as a cold front in Eurasia.By comparing different cold front identification methods,the results from this two-step cold front identification method and subjective analysis are more consistent,and the positions of the cold front identified with our method are more reasonable.This objective technique is also applied to a nationwide cold wave event over China.Results show that the horizontal extent and movement of the cold front are in good agreement with the related circulation and the associated cold weather.The proposed method and results in this study may shed light on the rapid identification of cold fronts in operational weather analysis and facilitate further research on the long-term activity characteristics of continental cold fronts.展开更多
Considering the differences between the Northeast China Cold Vortex （CV） and the Mid-Summer （MS） rainy period and their corresponding atmospheric circulations are comprehensively analyzed, and the objective identi...Considering the differences between the Northeast China Cold Vortex （CV） and the Mid-Summer （MS） rainy period and their corresponding atmospheric circulations are comprehensively analyzed, and the objective identification methods of defining the annual beginning and ending dates of Northeast China CV and MS rainy periods are developed respectively. The annual beginning date of the CV （MS） rainy period is as follows. In a period from April to August, if daily regional mean precipitation ryi is larger than yearly regional mean precipitation R （or 2R） on a certain day, the station precipitation rs is larger than the station yearly mean precipitation （r/ （or 2（r）） in at least 50% of stations in Northeast China, and this condition is satisfied in the following 2 （7） days, then this date is defined as the beginning date of the CV （MS） rainy period. While the definition of the ending date of the MS rainy period shows the opposite process to its beginning date. With this objective identification method, the multi-year average （1981-2010） beginning date of the CV rainy period is May 3, the beginning date of the MS rainy period is June 27, the ending day of the CV rainy period is defined as the day before the beginning date of the MS rainy period, and the ending date of the MS rainy period is August 29. Meanwhile, corresponding anomaly analysis at a 500-hPa geopotential height, 850-hPa wind, Omega and relative humidity fields all show that the definitions of the average beginning and ending dates of the CV and MS rainy periods have a certain circulation meaning. Furthermore, the daily evolution of the CV index, meridional and zonal wind index, etc. all show that these objectively defined beginning and ending dates of the CV and MS rainy periods have climate significance.展开更多
Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has b...Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has been constrained by high computational demands.Here,we developed GBiDC-PEST,a mobile application that incorporates an improved,lightweight detection algorithm based on the You Only Look Once(YOLO)series singlestage architecture,for real-time detection of four tiny pests(wheat mites,sugarcane aphids,wheat aphids,and rice planthoppers).GBiDC-PEST incorporates several innovative modules,including GhostNet for lightweight feature extraction and architecture optimization by reconstructing the backbone,the bi-directional feature pyramid network(BiFPN)for enhanced multiscale feature fusion,depthwise convolution(DWConv)layers to reduce computational load,and the convolutional block attention module(CBAM)to enable precise feature focus.The newly developed GBiDC-PEST was trained and validated using a multitarget agricultural tiny pest dataset(Tpest-3960)that covered various field environments.GBiDC-PEST(2.8 MB)significantly reduced the model size to only 20%of the original model size,offering a smaller size than the YOLO series(v5-v10),higher detection accuracy than YOLOv10n and v10s,and faster detection speed than v8s,v9c,v10m and v10b.In Android deployment experiments,GBiDCPEST demonstrated enhanced performance in detecting pests against complex backgrounds,and the accuracy for wheat mites and rice planthoppers was improved by 4.5-7.5%compared with the original model.The GBiDC-PEST optimization algorithm and its mobile deployment proposed in this study offer a robust technical framework for the rapid,onsite identification and localization of tiny pests.This advancement provides valuable insights for effective pest monitoring,counting,and control in various agricultural settings.展开更多
Referring expression comprehension(REC)aims to locate a specific region in an image described by a natural language.Existing two-stage methods generate multiple candidate proposals in the first stage,followed by selec...Referring expression comprehension(REC)aims to locate a specific region in an image described by a natural language.Existing two-stage methods generate multiple candidate proposals in the first stage,followed by selecting one of these proposals as the grounding result in the second stage.Nevertheless,the number of candidate proposals generated in the first stage significantly exceeds ground truth and the recall of critical objects is inadequate,thereby enormously limiting the overall network performance.To address the above issues,the authors propose an innovative method termed Separate Non-Maximum Suppression(Sep-NMS)for two-stage REC.Particularly,Sep-NMS models information from the two stages independently and collaboratively,ultimately achieving an overall improvement in comprehension and identification of the target objects.Specifically,the authors propose a Ref-Relatedness module for filtering referent proposals rigorously,decreasing the redundancy of referent proposals.A CLIP†Relatedness module based on robust multimodal pre-trained encoders is built to precisely assess the relevance between language and proposals to improve the recall of critical objects.It is worth mentioning that the authors are the pioneers in utilising a multimodal pre-training model for proposal filtering in the first stage.Moreover,an Information Fusion module is designed to effectively amalgamate the multimodal information across two stages,ensuring maximum uti-lisation of the available information.Extensive experiments demonstrate that the approach achieves competitive performance with previous state-of-the-art methods.The datasets used are publicly available:RefCOCO,RefCOCO+:https://doi.org/10.1007/978-3-319-46475-6_5 and RefCOCOg:https://doi.org/10.1109/CVPR.2016.9.展开更多
The advanced data mining technologies and the large quantities of remotely sensed Imagery provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data s...The advanced data mining technologies and the large quantities of remotely sensed Imagery provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data sets composed of images and associated ground data can be of importance in object identification, community planning, resource discovery and other areas. In this paper, a data field is presented to express the observed spatial objects and conduct behavior mining on them. First, most of the important aspects are discussed on behavior mining and its implications for the future of data mining. Furthermore, an ideal framework of the behavior mining system is proposed in the network environment. Second, the model of behavior mining is given on the observed spatial objects, including the objects described by the first feature data field and the main feature data field by means of the potential function. Finally, a case study about object identification in public is given and analyzed. The experimental results show that the new model is feasible in behavior mining.展开更多
Panoramic images are widely used in many scenes,especially in virtual reality and street view capture.However,they are new for street furniture identification which is usually based on mobile laser scanning point clou...Panoramic images are widely used in many scenes,especially in virtual reality and street view capture.However,they are new for street furniture identification which is usually based on mobile laser scanning point cloud data or conventional 2D images.This study proposes to perform semantic segmentation on panoramic images and transformed images to separate light poles and traffic signs from background implemented by pre-trained Fully Convolutional Networks(FCN).FCN is the most important model for deep learning applied on semantic segmentation for its end to end training process and pixel-wise prediction.In this study,we use FCN-8s model that pre-trained on cityscape dataset and finetune it by our own data.Then replace cross entropy loss function with focal loss function in the FCN model and train it again to produce the predictions.The results show that in all results from pre-trained model,fine-tuning,and FCN model with focal loss,the light poles and traffic signs are detected well and the transformed images have better performance than panoramic images in the prediction according to the Recall and IoU evaluation.展开更多
According to the frequency property of Phasedarray ground penetrating radar(PGPR),this paper gives a frequency point slice method based on Wigner time-frequency analysis.This method solves the problem of analysis for ...According to the frequency property of Phasedarray ground penetrating radar(PGPR),this paper gives a frequency point slice method based on Wigner time-frequency analysis.This method solves the problem of analysis for the PGPR's superposition data and makes detecting outcome simpler and detecting target more recognizable.At last,the analytical results of road test data of the Three Gorges prove the analytical method efficient.展开更多
The increasing negligence of"blind lanes"on streets in metropolises such as Shanghai is an inconvenience to the blind population,therefore,alternatives should be explored.When walking on these lanes,there sh...The increasing negligence of"blind lanes"on streets in metropolises such as Shanghai is an inconvenience to the blind population,therefore,alternatives should be explored.When walking on these lanes,there should not be any obstacles for the users,yet bikes can be seen parked there in addition to littered objects.This makes it potentially more dangerous to walk on them in comparison to walking on non-tactile paved lanes.An alternative to these lanes has been discovered which is a camera that is attached to a blind staff.This camera provides auditory feedbacks in regard to the user's surroundings.Using YOLOv3(You Only Look Once,Version 3),the software is trained using 140 images to identify three different classes which are blind lanes,waist-high obstacles,and dog feces,as well as the right direction of these objects.If the camera captures any of these three categories,it will provide and voice feedback,hence,warning the user.With this system,the blind can essentially have functional vision that would better guarantee their safety when walking on streets.展开更多
CONSPECTUS:The ability to gather information about materials and products,such as their origin,physicochemical properties or history of experienced environmental stimuli,is valuable for quality control,predictive main...CONSPECTUS:The ability to gather information about materials and products,such as their origin,physicochemical properties or history of experienced environmental stimuli,is valuable for quality control,predictive maintenance,delivery tracking,recycling,and more.Integrating additives capable of recording and storing information into materials offers a flexible approach to create“materials intelligence”.Common strategies utilize luminescent markers or DNA sequences that enable object identification and environmental impact monitoring.展开更多
Reengineering legacy systems written in conventional procedural languages to equivalent OO systems makes software more maintainable and reliable. This paper proposes a method for extracting objects from legacy Ada83 s...Reengineering legacy systems written in conventional procedural languages to equivalent OO systems makes software more maintainable and reliable. This paper proposes a method for extracting objects from legacy Ada83 systems using module features. First, metrics are developed to measure module cohesion. Then, effects on cohesion from changing module components are analyzed and rules about how to extract inheritance relations among objects are given. At the end of this paper, an object-extracting algorithm using module features is proposed.展开更多
In this study,an objective algorithm to identify meso-γ-scale vortexes(MVs)using radial velocity observations from an S-band radar is developed.Then,for the 237 Extreme Hourly Precipitation(EXHP;>75 mm)records at ...In this study,an objective algorithm to identify meso-γ-scale vortexes(MVs)using radial velocity observations from an S-band radar is developed.Then,for the 237 Extreme Hourly Precipitation(EXHP;>75 mm)records at the surface weather stations in the Pearl River Delta(PRD)during five warm seasons,the properties and environmental conditions of the EXHP-associated MVs are analyzed.Further,the spatiotemporal distributions of the MV,instantaneous rain rate,and EXHP are illustrated for three events with the most abundant EXHP records.The major findings are as follows.About 42%EXHP records are accompanied by 57 MVs,including 84%of weak shear intensity,12%of weak mesocyclone intensity,and 4%of moderate mesocyclone intensity,with the rotational speeds between 5 and12 m s^(-1),12 and 16 m s^(-1),and 16 and 21 m s^(-1),respectively.The duration and core depth of the MVs are highly correlated(coefficient of 0.67)with averages of 39 min and 699 m,respectively.The hourly rainfall accumulation of an EXHP tends to increase with the influencing duration of MVs on the EXHP,while a majority of MVs might result from the EXHP-associated forcing such as condensational latent heating.Relative to the EXHP events with MVs in the United States,those in the PRD feature smaller environmental 0-3-km storm relative helicity(SRH)and 0-1-km vertical wind shear(VWS).However,compared to the non-rotational EXHP in the PRD,the rotational EXHP events are associated with significantly higher 0-1-km VWS,0-3-km SRH,humidity,and larger convective available potential energy.In the three selected events,rainstorms exhibit an irregular shape,a quasi-circular shape,and a quasi-banded shape of strong echo,respectively.The MVs are often located inside the strong radar reflectivity region,and some are next to its bow-shaped portion.Those longer-lived MVs with stronger rotation are collocated with the extreme 6-min rainfall accumulation(≥10 mm)in space and time,suggesting presence of positive feedback between low-level rotation and short-term rain rate.In the event influenced by a tropical storm,four MVs appear at almost the same location in succession and move along roughly the same path,forming an MV back-building process.展开更多
Vortices play a fundamental role in fluid dynamics,but mathematically defining them remains elusive.While many vortex identification methods are scalar-valued,vortices are inherently rotational,vector-based phenomena....Vortices play a fundamental role in fluid dynamics,but mathematically defining them remains elusive.While many vortex identification methods are scalar-valued,vortices are inherently rotational,vector-based phenomena.Liutex,as a vector quantity,addresses these limitations by accurately capturing the local rotational characteristics of fluid elements while remaining independent of shear influences.This unique property makes Liutex particularly well-suited for vortex identification and the quantitative analysis of turbulent flows.This paper explores the statistical analysis of Liutex in various turbulence regimes and proposes an objective Liutex-based vortex identification method.The objective method is rooted in the statistical properties of Liutex.Furthermore,the paper investigates the performance of Liutex-based subgrid models in large eddy simulation(LES).The effectiveness of these models is evaluated by comparing their performance in different flow conditions,such as decaying homogeneous isotropic turbulence and turbulent channel flows,against conventional models.Results demonstrate that the inclusion of Liutex significantly enhances the ability of subgrid models to accurately capture flow structures.Importantly,the new model maintains the same form regardless of whether strong or weak shear is present,ensuring robustness and consistency in both vortex identification and turbulence modeling.These findings highlight the significant potential of Liutex to improve turbulence modeling in both theoretical and practical contexts,with ongoing research aimed at further refining its theoretical foundations and expanding its application in more complex flow scenarios.展开更多
Object identification is one of the major challenges in integrating data from multiple information sources. Since being short of global identifiers, it is hard to find all records referring to the same object in an in...Object identification is one of the major challenges in integrating data from multiple information sources. Since being short of global identifiers, it is hard to find all records referring to the same object in an integrated database. Traditional object identification techniques tend to use character-based or vector space model-based similarity computing in judging, but they cannot work well in merchandise databases. This paper brings forward a new approach to object identification. First, we use merchandise images to judge whether two records belong to the same object; then, we use Naive Bayesian Model to judge whether two merchandise names have similar meaning. We do experiments on data downloaded from shopping websites, and the results show good performance.展开更多
Rampant cloned vehicle offenses have caused great damage to transportation management as well as public safety and even the world economy.It necessitates an efficient detection mechanism to identify the vehicles with ...Rampant cloned vehicle offenses have caused great damage to transportation management as well as public safety and even the world economy.It necessitates an efficient detection mechanism to identify the vehicles with fake license plates accurately,and further explore the motives through discerning the behaviors of cloned vehicles.The ubiquitous inspection spots that deployed in the city have been collecting moving information of passing vehicles,which opens up a new opportunity for cloned vehicle detection.Existing detection methods cannot detect the cloned vehicle effectively due to that they use the fixed speed threshold.In this paper,we propose a two-phase framework,called CVDF,to detect cloned vehicles and discriminate behavior patterns of vehicles that use the same plate number.In the detection phase,cloned vehicles are identified based on speed thresholds extracted from historical trajectory and behavior abnormality analysis within the local neighborhood.In the behavior analysis phase,consider the traces of vehicles that uses the same license plate will be mixed together,we aim to differentiate the trajectories through matching degree-based clustering and then extract frequent temporal behavior patterns.The experimental results on the real-world data show that CVDF framework has high detection precision and could reveal cloned vehicles’behavior effectively.Our proposal provides a scientific basis for traffic management authority to solve the crime of cloned vehicle.展开更多
To receive habitus accurately and to obtain the reorganization and decision information of the object fully,spatial and temporal fusion must be taken into account.In this paper,a spatial–temporal two-layer architectu...To receive habitus accurately and to obtain the reorganization and decision information of the object fully,spatial and temporal fusion must be taken into account.In this paper,a spatial–temporal two-layer architecture model is presented.In this architecture,the data monitored by different sensors at the same time are fused first in space,and then in time order.Based on this model,we give an algorithm based on the theory of D–S evidence and fuzzy integral for sensor fusion.Simulation results indicate that this algorithm can improve the efficiency of object identification and the reliability of system.The simulation of the algorithm in multisensor object identification is also presented.展开更多
基金support from the National Natural Science Foundation of China(Grant Nos.12174097 and 12304321)the Natural Science Foundation of Hunan Province(Grant Nos.2021JJ10008 and 2023JJ40202)the Research Foundation of Education Bureau of Hunan Province(Grant No.22B0871).
文摘Object identification and three-dimensional reconstruction techniques are always attractive research interests in machine vision,virtual reality,augmented reality,and biomedical engineering.Optical computing metasurface,as a two-dimensional artificial design component,has displayed the supernormal character of controlling phase,amplitude,polarization,and frequency distributions of the light beam,capable of performing mathematical operations on the input light field.Here,we propose and demonstrate an all-optical object identification technique based on optical computing metasurface,and apply it to 3D reconstruction.Unlike traditional mechanisms,this scheme reduces memory consumption in the processing of the contour surface extraction.The identification and reconstruction of experimental results from high-contrast and low-contrast objects agree well with the real objects.The exploration of the all-optical object identification and 3D reconstruction techniques provides potential applications of high efficiencies,low consumption,and compact systems.
基金supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX22_1136)the National Natural Scientific Foundation of China(No.42275037)+2 种基金the Basic Research Fund of CAMS(No.2023Z016)the Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province(No.SCSF202202)supported by the Jiangsu Collaborative Innovation Center for Climate Change。
文摘At present,the identification of tropical cyclone remote precipitation(TRP)requires subjective participation,leading to inconsistent results among different researchers despite adopting the same identification standard.Thus,establishing an objective identification method is greatly important.In this study,an objective synoptic analysis technique for TRP(OSAT_TRP)is proposed to identify TRP using daily precipitation datasets,historical tropical cyclone(TC)track data,and the ERA5 reanalysis data.This method includes three steps:first,independent rain belts are separated,and those that might relate to TCs'remote effects are distinguished according to their distance from the TCs.Second,the strong water vapor transport belt from the TC is identified using integrated horizontal water vapor transport(IVT).Third,TRP is distinguished by connecting the first two steps.The TRP obtained through this method can satisfy three criteria,as follows:1)the precipitation occurs outside the circulation of TCs,2)the precipitation is affected by TCs,and 3)a gap exists between the TRP and TC rain belt.Case diagnosis analysis,compared with subjective TRP results and backward trajectory analyses using HYSPLIT,indicates that OSAT_TRP can distinguish TRP even when multiple TCs in the Northwest Pacific are involved.Then,we applied the OSAT_TRP to select typical TRPs and obtained the synoptic-scale environments of the TRP through composite analysis.
基金support from the program of the China Scholarship Council(no.201506210145)the support from the National Natural Science Foundation of China,no.11504337+1 种基金the partial support from the Natural Science Foundation of China under Grant nos 11175094 and 91221205the National Basic Research Program of China under Grant no.2015CB921002。
文摘Photons that are entangled or correlated in orbital angular momentum have been extensively used for remote sensing,object identification and imaging.It has recently been demonstrated that intensity fluctuations give rise to the formation of correlations in the orbital angular momentum components and angular positions of random light.Here we demonstrate that the spatial signatures and phase information of an object with rotational symmetries can be identified using classical orbital angular momentum correlations in random light.The Fourier components imprinted in the digital spiral spectrum of the object,as measured through intensity correlations,unveil its spatial and phase information.Sharing similarities with conventional compressive sensing protocols that exploit sparsity to reduce the number of measurements required to reconstruct a signal,our technique allows sensing of an object with fewer measurements than other schemes that use pixel-by-pixel imaging.One remarkable advantage of our technique is that it does not require the preparation of fragile quantum states of light and operates at both low-and high-light levels.In addition,our technique is robust against environmental noise,a fundamental feature of any realistic scheme for remote sensing.
基金supported in part by the National Natural Science Foundation of China (Grant Nos. 61271238, 61475075)the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20123223110003)+1 种基金the University Natural Science Research Foundation of Jiang Su Province (11KJA510002)the open research fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education (NYKL2015011)
文摘Ghost imaging could be used to make a quick identification of orthogonal objects by means of photocurrent correlation measurements. In this paper, we extend the method to identify nonorthogonal objects. In the method, an object is illuminated by one photon from an entangled pair, and the other one is diffracted into a particular direction by a pre-established multiple-exposure hologram in the idler arm. By the correlation measurements, the nonorthogonal object in the signal arm could be discriminated within a very short time. The constraints for the identification of nonorthogonal objects are presented, which show that the nonorthogonal objects can be discriminated when the overlapping portion between any two objects is less than half of all the objects in the set. The numerical simulations further verify the result.
基金supported by the National Key R&D Program of China under Grant No.2018YFC1507302the National Natural Science Foundation of China under Grant No.42175006+1 种基金Jiangsu Youth Talent Promotion Project(2021-084)the Basic Research Fund of CAMS under Grant No.2020R002.
文摘The northeastern China cold vortex(NCCV)plays an important role in regional rainstorms over East Asia.Using the National Centers for Environmental Prediction Final reanalysis dataset and the Global Precipitation Measurement product,an objective algorithm for identifying heavy-precipitation NCCV(HPCV)events was designed,and the climatological features of 164 HPCV events from 2001 to 2019 were investigated.The number of HPCV events showed an upward linear trend,with the highest frequency of occurrence in summer.The most active region of HPCV samples was the Northeast China Plain between 40°–55°N.Most HPCV events lasted 3–5 days and had radii ranging from 250 to 1000 km.The duration of HPCV events with larger sizes was longer.About half of the HPCV events moved into(moved out of)the definition region(35°–60°N,115°–145°E),and half initiated(dissipated)within the region.The initial position was close to the western boundary of the definition region,and the final position was mainly near the eastern boundary.The locations associated with the precipitation were mostly concentrated within 2000 km southeast of the HPCV systems,and they were farther from the center in the cold season than in the warm season.
基金This work is supported by the National Key Research and Development Pro-gram of China under contract(Grant No.2019YFC1510201 and Grant No.2018YFC1505602).
文摘The present study identifies wintertime cold fronts in Eurasia from gridded datasets using a new objective two-step identification scheme.The simple and classic conception of a front is adopted,where a cold front is identified as the warm boundary of the frontal zone with a suitable horizontal temperature gradient and cold advection.We combine the traditional thermal front parameter with temperature advection to first identify the cold frontal zone,and then its eastern and southern boundaries are objectively plotted as a cold front in Eurasia.By comparing different cold front identification methods,the results from this two-step cold front identification method and subjective analysis are more consistent,and the positions of the cold front identified with our method are more reasonable.This objective technique is also applied to a nationwide cold wave event over China.Results show that the horizontal extent and movement of the cold front are in good agreement with the related circulation and the associated cold weather.The proposed method and results in this study may shed light on the rapid identification of cold fronts in operational weather analysis and facilitate further research on the long-term activity characteristics of continental cold fronts.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.41205040 and 41375078)the State Key Development Program for Basic Research,China(Grant No.2012CB955203)the Special Scientific Research Project for Public Interest(Grant No.GYHY201306021)
文摘Considering the differences between the Northeast China Cold Vortex （CV） and the Mid-Summer （MS） rainy period and their corresponding atmospheric circulations are comprehensively analyzed, and the objective identification methods of defining the annual beginning and ending dates of Northeast China CV and MS rainy periods are developed respectively. The annual beginning date of the CV （MS） rainy period is as follows. In a period from April to August, if daily regional mean precipitation ryi is larger than yearly regional mean precipitation R （or 2R） on a certain day, the station precipitation rs is larger than the station yearly mean precipitation （r/ （or 2（r）） in at least 50% of stations in Northeast China, and this condition is satisfied in the following 2 （7） days, then this date is defined as the beginning date of the CV （MS） rainy period. While the definition of the ending date of the MS rainy period shows the opposite process to its beginning date. With this objective identification method, the multi-year average （1981-2010） beginning date of the CV rainy period is May 3, the beginning date of the MS rainy period is June 27, the ending day of the CV rainy period is defined as the day before the beginning date of the MS rainy period, and the ending date of the MS rainy period is August 29. Meanwhile, corresponding anomaly analysis at a 500-hPa geopotential height, 850-hPa wind, Omega and relative humidity fields all show that the definitions of the average beginning and ending dates of the CV and MS rainy periods have a certain circulation meaning. Furthermore, the daily evolution of the CV index, meridional and zonal wind index, etc. all show that these objectively defined beginning and ending dates of the CV and MS rainy periods have climate significance.
基金support of the Natural Science Foundation of Jiangsu Province,China(BK20240977)the China Scholarship Council(201606850024)+1 种基金the National High Technology Research and Development Program of China(2016YFD0701003)the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(SJCX23_1488)。
文摘Deep learning-based intelligent recognition algorithms are increasingly recognized for their potential to address the labor-intensive challenge of manual pest detection.However,their deployment on mobile devices has been constrained by high computational demands.Here,we developed GBiDC-PEST,a mobile application that incorporates an improved,lightweight detection algorithm based on the You Only Look Once(YOLO)series singlestage architecture,for real-time detection of four tiny pests(wheat mites,sugarcane aphids,wheat aphids,and rice planthoppers).GBiDC-PEST incorporates several innovative modules,including GhostNet for lightweight feature extraction and architecture optimization by reconstructing the backbone,the bi-directional feature pyramid network(BiFPN)for enhanced multiscale feature fusion,depthwise convolution(DWConv)layers to reduce computational load,and the convolutional block attention module(CBAM)to enable precise feature focus.The newly developed GBiDC-PEST was trained and validated using a multitarget agricultural tiny pest dataset(Tpest-3960)that covered various field environments.GBiDC-PEST(2.8 MB)significantly reduced the model size to only 20%of the original model size,offering a smaller size than the YOLO series(v5-v10),higher detection accuracy than YOLOv10n and v10s,and faster detection speed than v8s,v9c,v10m and v10b.In Android deployment experiments,GBiDCPEST demonstrated enhanced performance in detecting pests against complex backgrounds,and the accuracy for wheat mites and rice planthoppers was improved by 4.5-7.5%compared with the original model.The GBiDC-PEST optimization algorithm and its mobile deployment proposed in this study offer a robust technical framework for the rapid,onsite identification and localization of tiny pests.This advancement provides valuable insights for effective pest monitoring,counting,and control in various agricultural settings.
基金funded by the National Natural Science Foundation of China(No.62076032).
文摘Referring expression comprehension(REC)aims to locate a specific region in an image described by a natural language.Existing two-stage methods generate multiple candidate proposals in the first stage,followed by selecting one of these proposals as the grounding result in the second stage.Nevertheless,the number of candidate proposals generated in the first stage significantly exceeds ground truth and the recall of critical objects is inadequate,thereby enormously limiting the overall network performance.To address the above issues,the authors propose an innovative method termed Separate Non-Maximum Suppression(Sep-NMS)for two-stage REC.Particularly,Sep-NMS models information from the two stages independently and collaboratively,ultimately achieving an overall improvement in comprehension and identification of the target objects.Specifically,the authors propose a Ref-Relatedness module for filtering referent proposals rigorously,decreasing the redundancy of referent proposals.A CLIP†Relatedness module based on robust multimodal pre-trained encoders is built to precisely assess the relevance between language and proposals to improve the recall of critical objects.It is worth mentioning that the authors are the pioneers in utilising a multimodal pre-training model for proposal filtering in the first stage.Moreover,an Information Fusion module is designed to effectively amalgamate the multimodal information across two stages,ensuring maximum uti-lisation of the available information.Extensive experiments demonstrate that the approach achieves competitive performance with previous state-of-the-art methods.The datasets used are publicly available:RefCOCO,RefCOCO+:https://doi.org/10.1007/978-3-319-46475-6_5 and RefCOCOg:https://doi.org/10.1109/CVPR.2016.9.
基金Supported by the National 973 Program of China(No.2006CB701305,No.2007CB310804)the National Natural Science Fundation of China(No.60743001)+1 种基金the Best National Thesis Fundation (No.2005047)the National New Century Excellent Talent Fundation (No.NCET-06-0618)
文摘The advanced data mining technologies and the large quantities of remotely sensed Imagery provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data sets composed of images and associated ground data can be of importance in object identification, community planning, resource discovery and other areas. In this paper, a data field is presented to express the observed spatial objects and conduct behavior mining on them. First, most of the important aspects are discussed on behavior mining and its implications for the future of data mining. Furthermore, an ideal framework of the behavior mining system is proposed in the network environment. Second, the model of behavior mining is given on the observed spatial objects, including the objects described by the first feature data field and the main feature data field by means of the potential function. Finally, a case study about object identification in public is given and analyzed. The experimental results show that the new model is feasible in behavior mining.
文摘Panoramic images are widely used in many scenes,especially in virtual reality and street view capture.However,they are new for street furniture identification which is usually based on mobile laser scanning point cloud data or conventional 2D images.This study proposes to perform semantic segmentation on panoramic images and transformed images to separate light poles and traffic signs from background implemented by pre-trained Fully Convolutional Networks(FCN).FCN is the most important model for deep learning applied on semantic segmentation for its end to end training process and pixel-wise prediction.In this study,we use FCN-8s model that pre-trained on cityscape dataset and finetune it by our own data.Then replace cross entropy loss function with focal loss function in the FCN model and train it again to produce the predictions.The results show that in all results from pre-trained model,fine-tuning,and FCN model with focal loss,the light poles and traffic signs are detected well and the transformed images have better performance than panoramic images in the prediction according to the Recall and IoU evaluation.
基金Foundation item:Supported by the National Nature Science Founda-tion of China(50099620)and 863 Program Foundation of China(2001AA132050-03)
文摘According to the frequency property of Phasedarray ground penetrating radar(PGPR),this paper gives a frequency point slice method based on Wigner time-frequency analysis.This method solves the problem of analysis for the PGPR's superposition data and makes detecting outcome simpler and detecting target more recognizable.At last,the analytical results of road test data of the Three Gorges prove the analytical method efficient.
文摘The increasing negligence of"blind lanes"on streets in metropolises such as Shanghai is an inconvenience to the blind population,therefore,alternatives should be explored.When walking on these lanes,there should not be any obstacles for the users,yet bikes can be seen parked there in addition to littered objects.This makes it potentially more dangerous to walk on them in comparison to walking on non-tactile paved lanes.An alternative to these lanes has been discovered which is a camera that is attached to a blind staff.This camera provides auditory feedbacks in regard to the user's surroundings.Using YOLOv3(You Only Look Once,Version 3),the software is trained using 140 images to identify three different classes which are blind lanes,waist-high obstacles,and dog feces,as well as the right direction of these objects.If the camera captures any of these three categories,it will provide and voice feedback,hence,warning the user.With this system,the blind can essentially have functional vision that would better guarantee their safety when walking on streets.
基金funded by the European Union(ERC,SmartRust,Grant No.101123921).
文摘CONSPECTUS:The ability to gather information about materials and products,such as their origin,physicochemical properties or history of experienced environmental stimuli,is valuable for quality control,predictive maintenance,delivery tracking,recycling,and more.Integrating additives capable of recording and storing information into materials offers a flexible approach to create“materials intelligence”.Common strategies utilize luminescent markers or DNA sequences that enable object identification and environmental impact monitoring.
基金This work was supported by the National Natural Science Found
文摘Reengineering legacy systems written in conventional procedural languages to equivalent OO systems makes software more maintainable and reliable. This paper proposes a method for extracting objects from legacy Ada83 systems using module features. First, metrics are developed to measure module cohesion. Then, effects on cohesion from changing module components are analyzed and rules about how to extract inheritance relations among objects are given. At the end of this paper, an object-extracting algorithm using module features is proposed.
基金Supported by the National Natural Science Foundation of China(42030610)the Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)the Startup Foundation for Introducing Talent of NUIST(2023r121)。
文摘In this study,an objective algorithm to identify meso-γ-scale vortexes(MVs)using radial velocity observations from an S-band radar is developed.Then,for the 237 Extreme Hourly Precipitation(EXHP;>75 mm)records at the surface weather stations in the Pearl River Delta(PRD)during five warm seasons,the properties and environmental conditions of the EXHP-associated MVs are analyzed.Further,the spatiotemporal distributions of the MV,instantaneous rain rate,and EXHP are illustrated for three events with the most abundant EXHP records.The major findings are as follows.About 42%EXHP records are accompanied by 57 MVs,including 84%of weak shear intensity,12%of weak mesocyclone intensity,and 4%of moderate mesocyclone intensity,with the rotational speeds between 5 and12 m s^(-1),12 and 16 m s^(-1),and 16 and 21 m s^(-1),respectively.The duration and core depth of the MVs are highly correlated(coefficient of 0.67)with averages of 39 min and 699 m,respectively.The hourly rainfall accumulation of an EXHP tends to increase with the influencing duration of MVs on the EXHP,while a majority of MVs might result from the EXHP-associated forcing such as condensational latent heating.Relative to the EXHP events with MVs in the United States,those in the PRD feature smaller environmental 0-3-km storm relative helicity(SRH)and 0-1-km vertical wind shear(VWS).However,compared to the non-rotational EXHP in the PRD,the rotational EXHP events are associated with significantly higher 0-1-km VWS,0-3-km SRH,humidity,and larger convective available potential energy.In the three selected events,rainstorms exhibit an irregular shape,a quasi-circular shape,and a quasi-banded shape of strong echo,respectively.The MVs are often located inside the strong radar reflectivity region,and some are next to its bow-shaped portion.Those longer-lived MVs with stronger rotation are collocated with the extreme 6-min rainfall accumulation(≥10 mm)in space and time,suggesting presence of positive feedback between low-level rotation and short-term rain rate.In the event influenced by a tropical storm,four MVs appear at almost the same location in succession and move along roughly the same path,forming an MV back-building process.
基金supported by the Jiangsu Shuangchuang Project(Grant No.JSSCTD202209)the National Science Foundation of the Jiangsu Higher Education Institutions of China(Grant No.22KJB130011).
文摘Vortices play a fundamental role in fluid dynamics,but mathematically defining them remains elusive.While many vortex identification methods are scalar-valued,vortices are inherently rotational,vector-based phenomena.Liutex,as a vector quantity,addresses these limitations by accurately capturing the local rotational characteristics of fluid elements while remaining independent of shear influences.This unique property makes Liutex particularly well-suited for vortex identification and the quantitative analysis of turbulent flows.This paper explores the statistical analysis of Liutex in various turbulence regimes and proposes an objective Liutex-based vortex identification method.The objective method is rooted in the statistical properties of Liutex.Furthermore,the paper investigates the performance of Liutex-based subgrid models in large eddy simulation(LES).The effectiveness of these models is evaluated by comparing their performance in different flow conditions,such as decaying homogeneous isotropic turbulence and turbulent channel flows,against conventional models.Results demonstrate that the inclusion of Liutex significantly enhances the ability of subgrid models to accurately capture flow structures.Importantly,the new model maintains the same form regardless of whether strong or weak shear is present,ensuring robustness and consistency in both vortex identification and turbulence modeling.These findings highlight the significant potential of Liutex to improve turbulence modeling in both theoretical and practical contexts,with ongoing research aimed at further refining its theoretical foundations and expanding its application in more complex flow scenarios.
文摘Object identification is one of the major challenges in integrating data from multiple information sources. Since being short of global identifiers, it is hard to find all records referring to the same object in an integrated database. Traditional object identification techniques tend to use character-based or vector space model-based similarity computing in judging, but they cannot work well in merchandise databases. This paper brings forward a new approach to object identification. First, we use merchandise images to judge whether two records belong to the same object; then, we use Naive Bayesian Model to judge whether two merchandise names have similar meaning. We do experiments on data downloaded from shopping websites, and the results show good performance.
基金Our research was supported by NSFC(Grant Nos.U1501252,U1711262,61702423 and U1811264).
文摘Rampant cloned vehicle offenses have caused great damage to transportation management as well as public safety and even the world economy.It necessitates an efficient detection mechanism to identify the vehicles with fake license plates accurately,and further explore the motives through discerning the behaviors of cloned vehicles.The ubiquitous inspection spots that deployed in the city have been collecting moving information of passing vehicles,which opens up a new opportunity for cloned vehicle detection.Existing detection methods cannot detect the cloned vehicle effectively due to that they use the fixed speed threshold.In this paper,we propose a two-phase framework,called CVDF,to detect cloned vehicles and discriminate behavior patterns of vehicles that use the same plate number.In the detection phase,cloned vehicles are identified based on speed thresholds extracted from historical trajectory and behavior abnormality analysis within the local neighborhood.In the behavior analysis phase,consider the traces of vehicles that uses the same license plate will be mixed together,we aim to differentiate the trajectories through matching degree-based clustering and then extract frequent temporal behavior patterns.The experimental results on the real-world data show that CVDF framework has high detection precision and could reveal cloned vehicles’behavior effectively.Our proposal provides a scientific basis for traffic management authority to solve the crime of cloned vehicle.
文摘To receive habitus accurately and to obtain the reorganization and decision information of the object fully,spatial and temporal fusion must be taken into account.In this paper,a spatial–temporal two-layer architecture model is presented.In this architecture,the data monitored by different sensors at the same time are fused first in space,and then in time order.Based on this model,we give an algorithm based on the theory of D–S evidence and fuzzy integral for sensor fusion.Simulation results indicate that this algorithm can improve the efficiency of object identification and the reliability of system.The simulation of the algorithm in multisensor object identification is also presented.
