All-optical object identification and threedimensional reconstruction based on optical computing metasurface

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.

Objective Identification and Climatic Characteristics of Heavy-Precipitation Northeastern China Cold Vortexes

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.

Digital spiral object identification using random light

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.

Nonorthogonal object identification based on ghost imaging

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.

An Objective Identification Method for Wintertime Cold Fronts in Eurasia

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.

Objective identification research on cold vortex and mid-summer rainy periods in Northeast China

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.

Fully Convolutional Networks for Street Furniture Identification in Panorama Images

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.

Wigner Frequency Point Slice Analysis of Superposition Data for Phased-Array Ground Penetrating Radar

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. Key words phased-array ground penetrating radar - wigner time-frequency analysis - superposition data - object identification CLC number TN 715.7 Foundation item: Supported by the National Nature Science Foundation of China (50099620) and 863 Program Foundation of China (2001AA132050-03)Biography: ZOU Lian (1975-), male, Ph. D candidate, research direction: signal processing.

A Study on Intelligent Guide Stick Using Y0L0v3 Algorithm-Improving Spatial Awareness with Self-made Data Set

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.

Extracting Objects from Ada83 Programs: A Case Study

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.

A Novel Approach to Clustering Merchandise Records

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.

A framework for cloned vehicle detection

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.

Research of Spatial–Temporal Architecture Model and the Algorithm for Multisensor Information Fusion

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.

Analysis of Tibetan Plateau Vortex Activities Using ERA-Interim Data for the Period 1979-2013

Based on the ECMWF Interim reanalysis （ERA-Interim） 500-hPa height data for the period 1979-2013, the Tibetan Plateau vortex （TPV） activities are investigated with an objective identification and tracking algorithm. The vortex tracks over the Tibetan Plateau （TP） and its vicinity are identified, and the cold/warm nature of the TPV is revealed from the 500-hPa temperature fields. A TPV activity dataset is thus derived. Examination of the TPV frequency, intensity, persistence, geographical location, and migration from the TP suggests an average of 53 TPVs annually during the study period, with 6.7 of them shifting out of the TP. For these vortexes, the longer the lifetime, the lower the frequency, and 81% of the TPVs are initially warm in nature. The particularly high-intensity vortexes occur dominantly in the rainy period （May September）, with their origin mainly in western Nagqu and northern Ali. They disappear largely in the Tanggula maintain area on the east side of the vortex high-frequency center and the Dangqu River valley in western Qinghai Province, possibly due to the topography. It is also found that the frequency of TPVs is decreasing, with 2 fewer TPV occurrences per 10 yr. Meanwhile, it is statistically significant that 1.4 fewer TPVs move out of the TP and the percentage of TPVs moving out of the TP versus the total number of TPVs has reduced by 2.3% every 10 yr.

Climate Characteristics of Abnormal Double-Blocking Activities over the Ural Mountains and Sea of Okhotsk

By using NCEP–NCAR daily reanalysis data for June–August of 1948–2009, the synoptic situation of the doubleblocking high pressure process over the Ural Mountains–Sea of Okhotsk region was identified objectively, and the climatic characteristics and dynamic mechanism during the double blockings were also investigated. The results suggest that the Urals–Okhotsk double-blocking high experienced obviously an abrupt change around 1977. After the change, the occurrence frequency and number of sustained days of the blocking high reduced significantly.Moreover, the zonal spacing between the Urals blocking and the Okhotsk blocking also decreased, while their meridional locations did not change much. The double blockings became strengthened in general, and the blocking high over the Sea of Okhotsk extended northward evidently. However, during the process without double-blocking activities, the intensity and location of the double-blocking high showed no noticeable variation before and after the abrupt change. In addition, dynamic diagnosis shows that after the abrupt change, the energy of the double-blocking high mainly propagated vertically from low to high levels, and transferred horizontally from low to high latitudes; whereas, previously, before the abrupt change, this occurred from high to low latitudes. Meanwhile, the development of a low pressure system was fueled between the double blockings, which meant that the double-blocking activity was beneficial to the maintenance and development of the Northeast China cold vortex. In short, diagnosis of the wave energy has provided us with a better explanation of the life cycle characteristics of this double-blocking phenomenon before and after 1977.