A High-Resolution Measurement Method for Inner and Outer 3D Surface Profiles of Laser Fusion Targets Using a Laser Differential Confocal–Atomic Force Probe Technique

The high-resolution and nondestructive co-reference measurement of the inner and outer threedimensional(3D)surface profiles of laser fusion targets is difficult to achieve.In this study,we propose a laser differential confocal(LDC)–atomic force probe(AFP)method to measure the inner and outer 3D surface profiles of laser fusion targets at a high resolution.This method utilizes the LDC method to detect the deflection of the AFP and exploits the high spatial resolution of the AFP to enhance the spatial resolution of the outer profile measurement.Nondestructive and co-reference measurements of the inner profile of a target were achieved using the tomographic characteristics of the LDC method.Furthermore,by combining multiple repositionings of the target using a horizontal slewing shaft,the inner and outer 3D surface profiles of the target were obtained,along with a power spectrum assessment of the entire surface.The experimental results revealed that the respective axial and lateral resolutions of the outer profile measurement were 0.5 and 1.3 nm,while the respective axial and lateral resolutions of the inner profile measurement were 2.0 nm and approximately 400.0 nm.The repeatabilities of the rootmean-square deviation measurements for the outer and inner profiles of the target were 2.6 and 2.4 nm,respectively.We believe our study provides a promising method for the high-resolution and nondestructive co-reference measurement of the inner and outer 3D profiles of laser fusion targets.

Target Detection on Water Surfaces Using Fusion of Camera and LiDAR Based Information

To address the challenges of missed detections in water surface target detection using solely visual algorithms in unmanned surface vehicle(USV)perception,this paper proposes a method based on the fusion of visual and LiDAR point-cloud projection for water surface target detection.Firstly,the visual recognition component employs an improved YOLOv7 algorithmbased on a self-built dataset for the detection of water surface targets.This algorithm modifies the original YOLOv7 architecture to a Slim-Neck structure,addressing the problemof excessive redundant information during feature extraction in the original YOLOv7 network model.Simultaneously,this modification simplifies the computational burden of the detector,reduces inference time,and maintains accuracy.Secondly,to tackle the issue of sample imbalance in the self-built dataset,slide loss function is introduced.Finally,this paper replaces the original Complete Intersection over Union(CIoU)loss function with the Minimum Point Distance Intersection over Union(MPDIoU)loss function in the YOLOv7 algorithm,which accelerates model learning and enhances robustness.To mitigate the problem of missed recognitions caused by complex water surface conditions in purely visual algorithms,this paper further adopts the fusion of LiDAR and camera data,projecting the threedimensional point-cloud data from LiDAR onto a two-dimensional pixel plane.This significantly reduces the rate of missed detections for water surface targets.

New Algorithm for Image Target Recognition Based on Fractal Feature Fusion

By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Comparison and simulation are performed on the new algorithm, the old algorithm based on single feature and the algorithm based on neural network. Results of the comparison and simulation illustrate that the new algorithm is feasible and valid.

An infrared target intrusion detection method based on feature fusion and enhancement

Infrared target intrusion detection has significant applications in the fields of military defence and intelligent warning.In view of the characteristics of intrusion targets as well as inspection difficulties,an infrared target intrusion detection algorithm based on feature fusion and enhancement was proposed.This algorithm combines static target mode analysis and dynamic multi-frame correlation detection to extract infrared target features at different levels.Among them,LBP texture analysis can be used to effectively identify the posterior feature patterns which have been contained in the target library,while motion frame difference method can detect the moving regions of the image,improve the integrity of target regions such as camouflage,sheltering and deformation.In order to integrate the advantages of the two methods,the enhanced convolutional neural network was designed and the feature images obtained by the two methods were fused and enhanced.The enhancement module of the network strengthened and screened the targets,and realized the background suppression of infrared images.Based on the experiments,the effect of the proposed method and the comparison method on the background suppression and detection performance was evaluated,and the results showed that the SCRG and BSF values of the method in this paper had a better performance in multiple data sets,and it’s detection performance was far better than the comparison algorithm.The experiment results indicated that,compared with traditional infrared target detection methods,the proposed method could detect the infrared invasion target more accurately,and suppress the background noise more effectively.

Highly maneuvering target tracking using multi-parameter fusion Singer model

An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer (MF-Singer) model is derived based on the Singer model and the fuzzy reasoning method by using radial acceleration and velocity of the target, and applied to the problem of maneuvering target tracking in strong maneuvering environment and operating environment. The tracking performance of the MF-Singer model is evaluated and compared with other manuevering tracking models. It is shown that the MF-Singer model outperforms these algorithms in several examples.

Data fusion of target characteristic in multistatic passive radar

Radar cross section(RCS)is an important attribute of radar targets and has been widely used in automatic target recognition(ATR).In a passive radar,only the RCS multiplied by a coefficient is available due to the unknown transmitting parameters.For different transmitter-receiver(bistatic)pairs,the coefficients are different.Thus,the recovered RCS in different transmitter-receiver(bistatic)pairs cannot be fused for further use.In this paper,we propose a quantity named quasi-echo-power(QEP)as well as a method for eliminating differences of this quantity among different transmitter-receiver(bistatic)pairs.The QEP is defined as the target echo power after being compensated for distance and pattern propagation factor.The proposed method estimates the station difference coefficients(SDCs)of transmitter-receiver(bistatic)pairs relative to the reference transmitter-receiver(bistatic)pair first.Then,it compensates the QEP and gets the compensated QEP.The compensated QEP possesses a linear relationship with the target RCS.Statistical analyses on the simulated and real-life QEP data show that the proposed method can effectively estimate the SDC between different stations,and the compensated QEP from different receiving stations has the same distribution characteristics for the same target.

Research on PCA and KPCA Self-Fusion Based MSTAR SAR Automatic Target Recognition Algorithm

This paper proposes a PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm. This algorithm combines the linear feature extracted from principal component analysis （PCA） and nonlinear feature extracted from kernel principal component analysis （KPCA） respectively, and then utilizes the adaptive feature fusion algorithm which is based on the weighted maximum margin criterion （WMMC） to fuse the features in order to achieve better performance. The linear regression classifier is used in the experiments. The experimental results indicate that the proposed self-fusion algorithm achieves higher recognition rate compared with the traditional PCA and KPCA feature fusion algorithms.

Fast Z-Pinch,A New Approach for Promising Fusion Energy

The basic concept of fast Z-pinch,and late progress in fast Z-pinch plasma research as HEDP and ICF research,especially as an approach for high yield low-cost fusion energy research,are summarized in this paper.The possible technical challenges of fast Z-pinch-driven ICF as fusion energy and it application prospect are discussed.

Three dimensional passive underwater target motion analysis using correlated data fusion

In this paper a new method of passive underwater TMA (target motion analysis) using data fusion is presented. The findings of this research are based on an understanding that there is a powerful sonar system that consists of many types of sonar but with one own-ship, and that different target parameter measurements can be obtained simultaneously. For the analysis 3 data measurements, passive bearing, elevation and multipath time-delay, are used, which are divided into two groups: a group with estimates of two preliminary target parameter obtained by dealing with each group measurement independently, and a group where correlated estimates are sent to a fusion center where the correlation between two data groups are considered so that the passive underwater TMA is realized. Simulation results show that curves of parameter estimation errors obtained by using the data fusion have fast convergence and the estimation accuracy is noticeably improved. The TMA algorithm presented is verified and is of practical significance because it is easy to be realized in one ship.

ATMS Based Information Fusion Target Recognition Method

The non-monotonic problem exited in information fusion systems is solved. Through the introducing of non-monotonic reasoning method, which was realized with ATMS, into the information fusion system, it gains the ability to process insufficient information with flexibility and non-monotonic behavior. In the simulation test of our system, our system manifests its ability of dealing the insufficient and contradictory information, which partly solves the decision dilemma brought out by the insufficient information in battle situations. The information fusion target recognition system can process the information in battle situation fast and with flexibility.

SMALL TARGET TRACKING TECHNIQUE WITH DATA FUSION OF DISTRIBUTED SENSOR NET

ＳＭＡＬＬＴＡＲＧＥＴＴＲＡＣＫＩＮＧＴＥＣＨＮＩＱＵＥＷＩＴＨＤＡＴＡＦＵＳＩＯＮＯＦＤＩＳＴＲＩＢＵＴＥＤＳＥＮＳＯＲＮＥＴＣＨＥＮＧＨｏｎｇｗｅｉ（程洪玮），ＺＨＯＵＹｉｙｕ（周一宇），ＳＵＮＺｈｏｎｇｋａｎｇ（孙仲康）（Ｆａｃｕｌｔｙ４０６，...

Formation Process of Magnetized Fusion Target on the YingGuang 1 Device

Magnetized target fusion is an alternative method to fulfill the goal of controlled fusion, which combines advan- tages of both magnetic confinement fusion and inertial confinement fusion since its parameter space lies between the two traditional ways. Field reversed configuration （FFtC） is a good candidate of magnetized targets due to its translatable, compressible, high /3 and high energy density properties. Dynamic formation process of high density FFtC is observed on the YingGuang 1 device for the first time in China. The evolution of a magnetic field is detected with magnetic probes, and the compression process can be clearly seen from images taken with a high-speed multi-frame CCD camera. The process is also studied with two-dimensional magneto hydrodynamic code MPF-2D theoretically, and the results agree well with the experiment. Combining the experimental data and the theoretical analysis, the length of the formed FRC is about 39 cm, the diameter is about 2-2. 7cm, the average density is 1.3× 1016 cm-3, and the average temperature is 137eV.

Profiling of gene fusion involving targetable genes in Chinese gastric cancer

BACKGROUND Approximately half of all new cases of gastric cancer(GC)and related deaths occur in China.More than 80%of patients with GC are diagnosed at an advanced stage,which results in poor prognosis.Although HER2-directed therapy and immune checkpoint inhibitors have been somewhat successful,new drugs are still needed for the treatment of GC.Notably,several gene fusion-targeted drugs have been approved by the United States Food and Drug Administration for solid tumors,including GC,such as larotrectinib for NTRK fusion-positive cancers and zenocutuzumab for NRG1 fusion-positive cancers.However,gene fusions involving targetable genes have not been well characterized in Chinese patients with GC.AIM To identify the profile of fusions involving targetable genes in Chinese patients with GC using clinical specimens and determine the distribution of patients with gene fusion variants among the molecular subtypes of GC.METHODS We retrospectively analyzed gene fusion events in tumor tissue samples from 954 Chinese patients with GC.Clinicopathological characteristics were obtained from their medical records.Genetic alterations,such as single nucleotide variants,indels,amplifications,and gene fusions,were identified using a targeted sequencing panel containing 825 genes.Fusions were validated by fluorescence in situ hybridization(FISH)using break-apart probes.The microsatellite instability(MSI)status was evaluated using MSIsensor from the targeted sequencing panel data.Tumor mutational burden(TMB)was calculated using the total number of nonsynonymous mutations divided by the total genomic targeted region.Chi-square analysis was used to determine the enrichment of gene fusions associated with the molecular subtypes of GC.RESULTS We found that 1.68%(16/954)of patients harbored 20 fusion events involving targetable genes.RARA fusions(n=5)were the most common,followed by FGFR2,BRAF,MET,FGFR3,RET,ALK,EGFR,NTRK2,and NRG1 fusions.Two of the RARA fusions,EML4-ALK(E6:E20)and EGFRSEPTIN14(E7:E10),have been identified in other tumors but not in GC.Surprisingly,18 gene fusion events were previously not reported in any cancer types.Twelve of the eighteen novel gene fusions included complete exons encoding functional domains of targetable genes,such as the tyrosine kinase domain of receptor tyrosine kinases and the DNA-and ligand-binding domains of RARA.Consistent with the results of detection using the targeted sequencing fusion panel,the results of FISH(fluorescence in situ hybridization)confirmed the rearrangement of FGFR2 and BRAF in tumors from patients 04 and 09,respectively.Genetic analysis indicated that the fusion genes were significantly enriched in patients with ERBB2 amplification(P=0.02);however,there were no significant differences between fusion-positive and fusion-negative patients in age,sex,MSI status,and TMB.CONCLUSION We characterized the landscape of fusions involving targetable genes in a Chinese GC cohort and found that 1.68%of patients with GC harbor potential targetable gene fusions,which were enriched in patients with ERBB2 amplification.Gene fusion detection may provide a potential treatment strategy for patients with GC with disease progression following standard therapy.

Fusion Reaction Rate Coefficient for Different Beam and Target Scenarios

Fusion power output is proportional not only to the fuel particle number densities participating in reaction but also to the fusion reaction rate coefficient （or reactivity）, which is dependent on reactant velocity distribution functions. They are usuMly assumed to be dual Maxwellian distribution functions with the same temperature for thermal nuclear fusion circumstances. However, if high power neutral beam injection and minority ion species ICRF plasma heating, or multi-pinched plasma beam head-on collision, in a converging region are required and investigated in future large scale fusion reactors, then the fractions of the injected energetic fast ion tail resulting from ionization or charge exchange will be large enough and their contribution to the non-Maxwellian distribution functions is not negligible, hence to the fusion reaction rate coefficient or calculation of fusion power. In such cases, beam-target, and beam-beam reaction enhancement effect contributions should play very important roles. In this paper, several useful formulae to calculate the fusion reaction rate coefticient for different beam and target combination scenarios are derived in detail

Algorithm for Multi-laser-target Tracking Based on Clustering Fusion

Multi-laser-target tracking is an important subject in the field of signal processing of laser warners. A clustering method is applied to the measurement of laser warner, and the space-time fusion for measurements in the same cluster is accomplished. Real-time tracking of multi-laser-target and real-time picking of multi-laser-signal are introduced using data fusion of the measurements. A prototype device of the algorithm is built up. The results of experiments show that the algorithm is very effective.

Comparison of Cognitive Registration Transrectal Ultrasound-Guided Targeted Biopsy of Prostate to Systematic 12-Core Biopsy: A Retrospective, Multicentre Study

Introduction: Prostate cancer (PCa) is the third most prevalent cancer among Malaysian males, often diagnosed at advanced stages, leading to suboptimal outcomes. While transrectal ultrasound-guided systematic biopsy (TRUS-SB) is the primary diagnostic method, prebiopsy multiparametric magnetic resonance imaging (mpMRI) is gaining popularity in identifying suspicious lesions. This study addresses the lack of comprehensive investigations into the efficacy of cognitive registration TRUS targeted biopsy (COG-TB) compared to conventional TRUS-SB, considering the resource limitations of the Malaysian healthcare system. Materials and Methods: A retrospective cohort study was conducted in two Malaysian healthcare facilities. 116 adult patients with a prostate-specific antigen (PSA) level of more than 4 ng/mL who underwent both COG-TB and TRUS-SB between October 2020 and March 2022 were included. Primary outcomes were cancer detection rate and histopathological outcomes, including Gleason score. Results: COG-TB showed a higher overall cancer detection rate (50%) compared to TRUS-SB (44%). Clinically significant cancer detection rates were similar between COG-TB and TRUS-SB (37.1%). Further analysis revealed that both COG-TB and TRUS-SB detected clinically significant cancer in 30.2% of patients, did not detect it in 56.0%, and had conflicting findings in 16 patients (p Conclusion: COG-TB and TRUS-SB have comparable detection rates for clinically significant prostate cancer, with COG-TB showing a higher tendency to detect insignificant prostate cancer. Further studies comparing these methods are warranted.

Modeling of the Multi-Target Locating and Tracking in the Field Artillery System

A method for the multi target locating and tracking with the multi sensor in a field artillery system is studied. A general modeling structure of the system is established. Based on concepts of cluster and closed ball, an algorithm is put forward for multi sensor multi target data fusion and an optimal solution for state estimation is presented. The simulation results prove the algorithm works well for the multi stationary target locating and the multi moving target tracking under the condition of the sparse target environment. Therefore, this method can be directly applied to the field artillery C 3I system.

Asynchronous Data Fusion of Two Different Sensors

An algorithm is presented for fusion of tracks created by radar and IR sensor which have different dimensional measurement data. It’s assumed that these sensors are asynchronous and the measurement data are transmitted to a central station at different rates. By means of the technique of time matching, two sets of asynchronous data are fused and then the filter is updated according to the fused information. The results show that the accuracy of the filter effect has been improved.

Target recognition based on modified combination rule

Evidence theory is widely used in the field of target recognition. The invalidation problem of this theory when dealing with highly conflict evidences is a research hotspot. Several alternatives of the combination rule are analyzed and compared. A new combination approach is proposed. Calculate the reliabilities of evidence sources using existing evidences. Construct reliabilities judge matrixes and get the weights of each evidence source. Weight average all inputted evidences. Combine processed evidences with D-S combination rule repeatedly to identify a target. The application in multi-sensor target reeognition as well as the comparison with typical alternatives all validated that this approach can dispose highly conflict evidences efficiently and get reasonable reeognition results rapidly.

Point association analysis of vessel target detection with SAR, HFSWR and AIS

A space-borne synthetic aperture radar （SAR）, a high frequency surface wave radar （HFSWR）, and a ship automatic identification system （AIS） are the main remote sensors for vessel monitoring in a wide range. These three sensors have their own advantages and weaknesses, and they can complement each other in some situations. So it would improve the capability of vessel target detection to use multiple sensors including SAR, HFSWR, and A/S to identify non-cooperative vessel targets from the fusion results. During the fusion process of multiple sensors＇ detection results, point association is one of the key steps, and it can affect the accuracy of the data fusion and the efficiency of a non-cooperative target＇s recognition. This study investigated the point association analyses of vessel target detection under different conditions： space- borne SAR paired with AIS, as well as HFSWR, paired with AIS, and the characteristics of the SAR and the HFSWR and their capability of vessel target detection. Then a point association method of multiple sensors was proposed. Finally, the thresholds selection of key parameters in the points association （including range threshold, radial velocity threshold, and azimuth threshold） were investigated, and their influences on final association results were analyzed.