Application of three-dimensional speckle tracking technique in measuring left ventricular myocardial function in patients with diabetes

BACKGROUND Diabetic cardiomyopathy is considered as a chronic complication of diabetes mellitus(DM).Therefore,early detection of left ventricular systolic function(LVSF)damage in DM is essential.AIM To explore the use of the three-dimensional speckle tracking technique(3D-STI)for measuring LVSF in DM patients via meta-analysis.METHODS The electronic databases were retrieved from the initial accessible time to 29 April 2023.The current study involved 9 studies,including 970 subjects.We carried out this meta-analysis to estimate myocardial function in DM compared with controls according to myocardial strain attained by 3D-STI.RESULTS Night articles including 970 subjects were included.No significant difference was detected in the left ventricular ejection fraction between the control and the diabetic group(P>0.05),while differences in global longitudinal strain,global circumferential strain,global radial strain,and global area strain were markedly different between the controls and DM patients(all P<0.05).CONCLUSION The 3D-STI could be applied to accurately measure early LVSF damage in patients with DM.

WSN Mobile Target Tracking Based on Improved Snake-Extended Kalman Filtering Algorithm

A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking.First,the steps of extended Kalman filtering(EKF)are introduced.Second,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target.Finally,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed algorithm.The comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively.

Constrained auxiliary particle filtering for bearings-only maneuvering target tracking

To track the nonlinear,non-Gaussian bearings-only maneuvering target accurately online,the constrained auxiliary particle filtering(CAPF)algorithm is presented.To restrict the samples into the feasible area,the soft measurement constraints are implemented into the update routine via the1 regularization.Meanwhile,to enhance the sampling diversity and efficiency,the target kinetic features and the latest observations are involved into the evolution.To take advantage of the past and the current measurement information simultaneously,the sub-optimal importance distribution is constructed as a Gaussian mixture consisting of the original and modified priors with the fuzzy weighted factors.As a result,the corresponding weights are more evenly distributed,and the posterior distribution of interest is approximated well with a heavier tailor.Simulation results demonstrate the validity and superiority of the CAPF algorithm in terms of efficiency and robustness.

Maneuvering target tracking of UAV based on MN-DDPG and transfer learning

Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles(UAVs).In this paper,aiming to address the control problem of maneuvering target tracking and obstacle avoidance,an online path planning approach for UAV is developed based on deep reinforcement learning.Through end-to-end learning powered by neural networks,the proposed approach can achieve the perception of the environment and continuous motion output control.This proposed approach includes:(1)A deep deterministic policy gradient(DDPG)-based control framework to provide learning and autonomous decision-making capability for UAVs;(2)An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning;and(3)An algorithm of taskdecomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV’s control model built based on MN-DDPG.The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV’s flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments.

Three-dimensional mesoscale eddy identification and tracking algorithm based on pressure anomalies Liming

The Kuroshio Extension(KE)is one of the most eddy-energetic regions in the global ocean.However,most mesoscale eddy studies in the region are focused on surface eddies and the structure and characteristics of three-dimensional(3-D)eddies require additional research.In this study,we proposed a 3-D eddy identification and tracking algorithm based on pressure anomalies,similar to sea level anomalies(SLAs)for surface eddy identification.We applied this scheme to a 5-year(2008-2012)high-resolution numerical product to develop a 3-D eddy dataset in the KE.The reliability of the numerical product was verified by the 5-year temperature/salinity hydrological characteristics and surface eddy distribution.According to the 3-D eddy tracking dataset,the number of eddies decreased dramatically as the eddy existence-time increased and more anticyclonic eddies(AEs)had an existence-time longer than 1 week than cyclonic eddies(CEs).We presented daily variations in the 3-D structure of two 3-D eddy-tracking trajectories that exhibit a certain jump in depth and a shift toward the west and equator.In addition to the bowl,lens,and cone eddies that have been discovered by previous researchers,we found that there is a cylindrical eddy,and its eddy radii are almost consistent across all layers.CEs cause significant negative temperature anomalies,“negative-positive”salinity anomalies,and sinking current fields in the KE region,while AEs cause positive temperature anomalies,“positive-negative”salinity anomalies,and upward current fields.The four types of eddies have different effects on the temperature/salinity anomalies and current field distribution which are related to their structure.

A Fuzzy Adaptive Algorithm Based on“Current”Statistical Model for Maneuvering Target Tracking

The basic"current"statistical model and adaptive Kalman filter algorithm can not track a weakly maneuvering target precisely,though it has good estimate accuracy for strongly maneuvering target.In order to solve this problem,a novel nonlinear fuzzy membership function was presented to adjust the upper and lower limit of target acceleration adaptively,and then the validity of the new algorithm for feeblish maneuvering target was proved in theory.At last,the computer simulation experiments indicated that the new algorithm has a great advantage over the basic"current"statistical model and adaptive algorithm.

Maneuvering target tracking algorithm based on CDKF in observation bootstrapping strategy

The selection and optimization of model filters affect the precision of motion pattern identification and state estimation in maneuvering target tracking directly.Aiming at improving performance of model filters,a novel maneuvering target tracking algorithm based on central difference Kalman filter in observation bootstrapping strategy is proposed.The framework of interactive multiple model(IMM) is used to realize identification of motion pattern,and a central difference Kalman filter(CDKF) is selected as the model filter of IMM.Considering the advantage of multi-sensor fusion method in improving the stability and reliability of observation information,the hardware cost of the observation system for multiple sensors is adopted,meanwhile,according to the data assimilation technique in Ensemble Kalman filter(En KF),a bootstrapping observation set is constructed by integrating the latest observation and the prior information of observation noise.On that basis,these bootstrapping observations are reasonably used to optimize the filtering performance of CDKF by means of weight fusion way.The object of new algorithm is to improve the tracking precision of observed target by the multi-sensor fusion method without increasing the number of physical sensors.The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.

Modified unscented Kalman filter using modified filter gain and variance scale factor for highly maneuvering target tracking

To improve the low tracking precision caused by lagged filter gain or imprecise state noise when the target highly maneuvers, a modified unscented Kalman filter algorithm based on the improved filter gain and adaptive scale factor of state noise is presented. In every filter process, the estimated scale factor is used to update the state noise covariance Qk, and the improved filter gain is obtained in the filter process of unscented Kalman filter(UKF)via predicted variance Pk|k-1, which is similar to the standard Kalman filter. Simulation results show that the proposed algorithm provides better accuracy and ability to adapt to the highly maneuvering target compared with the standard UKF.

A novel multi-sensor multiple model particle filter with correlated noises for maneuvering target tracking

Aiming at the effective realization of particle filter for maneuvering target tracking in multi-sensor measurements,a novel multi-sensor multiple model particle filtering algorithm with correlated noises is proposed.Combined with the kinetic evolution equation of target state,a multi-sensor multiple model particle filter is firstly constructed,which is also used as the basic framework of a new algorithm.In the new algorithm,in order to weaken the adverse influence from random measurement noises in the measuring process of particle weight,a weight optimization strategy is introduced to improve the reliability and stability of particle weight.In addition,considering the correlated noise existing in the practical engineering,a decoupling method of correlated noise is given by the rearrangement and transformation of the state transition equation and measurement equation.Since the weight optimization strategy and noise decoupling method adopt respectively the center fusion structure and the off-hne way,it improves the adverse effect effectively on computational complexity for increasing state dimension and sensor number.Finally,the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.

Maneuvering target tracking algorithm based on cubature Kalman filter with observation iterated update

Reasonable selection and optimization of a filter used in model estimation for a multiple model structure is the key to improve tracking accuracy of maneuvering target.Combining with the cubature Kalman filter with iterated observation update and the interacting multiple model method,a novel interacting multiple model algorithm based on the cubature Kalman filter with observation iterated update is proposed.Firstly,aiming to the structural features of cubature Kalman filter,the cubature Kalman filter with observation iterated update is constructed by the mechanism of iterated observation update.Secondly,the improved cubature Kalman filter is used as the model filter of interacting multiple model,and the stability and reliability of model identification and state estimation are effectively promoted by the optimization of model filtering step.In the simulations,compared with classic improved interacting multiple model algorithms,the theoretical analysis and experimental results show the feasibility and validity of the proposed algorithm.

Tracking maneuvering target based on neural fuzzy network with incremental neural leaning

The scheme for tracking maneuvering target bas ed on neural fuzzy network with incremental neural learning is proposed. When trac ked target maneuver occurs, the scheme can detect maneuver immediately and estimate the maneuver value accurately , then the tracking filter can be compensated correctly and duly by the estimated maneuver value. When environment changes, neural fuzzy network with incremental neural learning (INL-SONFIN) can find its optimal structure and parameters automatically to adopt to changed environment. So, it always produce estimated output very close to the true maneuver value that leads to good tracking performance and avoids miss-tracking. Sim ulation results show that the performance is superior to the traditional schemes and the scheme can fit changed dynamic environment to track maneuvering target accurately and duly.

ADAPTIVE RECURRENT NEURAL NETWORKS TRACKING-FILTER FOR MANEUVERING TARGET

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ALGORITHMS FOR TRACKING MANEUVERING TARGET WITH PHASED ARRAY RADAR

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A NEW MODELING AND FILTERING APPROACH FOR TRACKING MANEUVERING TARGETS

A new modeling and filtering approach for tracking maneuvering targets is presented in thispaper.The approach,which makes optimal estimate for the model With the random variable possible,depends on random step modeling of target maneuvers.In the new model,the unknown targetacceleration is treated as a random variable and then estimated directly.A detector is designed tofind out the target maneuvers and the estimation algorithm will be restarted when the maneuvers oc-cur.Combination of three-dimention Kalman filter with a detector forms a tracker for maneuveringtargets.The new tracking scheme is easy to implement and its capability is illustrated in two trackingexamples in which the new approach is compared with Mooses’on the performance.

Tracking the maneuvering spacecraft propelled by swing propulsion of constant magnitude

This paper proposes partially norm-preserving filtering for a class of spacecraft in the presence of time-varying, but constant-magnitude maneuver. The augmented state Kalman filter(ASKF) is commonly used to track the maneuvering spacecraft with unknown constant propulsion;however, if the maneuver varies via time, the estimation performance will be degraded. To promote the tracking performance of the ASKF in case of time-invariant,constant-magnitude disturbance, the partially norm-preserving ASKF is developed by applying the norm constraint on the unknown maneuver. The proposed estimator, which is decomposed into two partial estimators and iteratively propagated in turns,projects the unconstrained maneuver estimation onto the Euclidian surface spanned by the norm constraint. The illustrative numerical example is provided to show the efficiency of the proposed method.

New computing method of weighted coefficients for tracking a maneuvering target using PDAF in the presence of clutter

To avoid or reduce the influence of unpredictable motion mode on data association,a new computing method of weighted coefficients of measurements for PDAF is presented in which it is assumed that the current turn rate of a maneuvering target changes within a limited range and its turn may be in arbitrary direction during data association.Thus,the predicted center for computing the weighted coefficients is a curved surface in 3-D space,which differs from the predicted center for setting up a validation gate,namely,a point in 3-D space.The distance between a measurement and the curved surface is used to compute its weighted coefficient.To reduce the computational complexity of weighted coefficients,the formulas for computing the maneuvering direction angle and turn rate corresponding to a measurement are presented.Simulation results show the proposed method reduces the percentage of lost tracks and improves the state estimation accuracy in tracking a maneuvering target using PDAF in the presence of clutter.

Value of myocardial function in children with Kawasaki disease by two-dimensional and three-dimensional ultrasound speckle tracking

Objective:To investigate the value of two-dimensional ultrasound speckle tracking(2D-STI)and three-dimensional ultrasound speckle tracking(3D-STI)in evaluating myocardial function in children with Kawasaki disease.Methods 92 children with Kawasaki disease admitted to our hospital from February 2017 to February 2019 were retrospectively analyzed.50 children who underwent 3D-STI examination were taken as observation group and 42 children who underwent 2D-STI examination were taken as control group.The left ventricular systolic function index,storage time and analysis time of the image,the diameter of coronary artery,the strain difference of left ventricular basal segment,middle segment,apical segment and whole segment were observed.Results The levels of left ventricular end-diastolic volume(LVEDV),left ventricular end-systolic volume(LVESV),left ventricular myocardial mass(LVMI)in the observation group were higher than those in the control group(P<0.05),but there was no statistical difference in left ventricular ejection fraction(LVEF)between the two groups(P>0.05).The storage time and analysis time of the image in the observation group were significantly lower than those in the control group(P<0.05).The left coronary artery(LCA)and right coronary artery(RCA)in the observation group were higher than those in the control group(P<0.05).There was no statistical difference between left anterior descending(LAD)in the two groups(P>0.05).The longitudinal peak systolic strain(LS),circumferential peak systolic strain(CS)and radial peak systolic strain(RS)in the observation group were higher than those in the control group(P<0.05).The global longitudinal peak strain(GLS),global circumferential peak strain(GCS)and global radial peak strain(GRS)in the observation group were higher than those in the control group(P<0.05).LS and CS in the middle segment of the observation group were higher than those in the control group(P<0.05).Conclusions Compared with 2D-STI,3D-STI can objectively and accurately reflect the myocardial function of children with Kawasaki disease.

Correlation between three-dimensional speckle tracking imaging parameter and the cardiotoxicity of chemotherapeutics in patients with lung cancer chemotherapy

Objective: To study the correlation between three-dimensional speckle tracking imaging (3D-STI) parameter global area strain (GAS) and the cardiotoxicity of chemotherapeutics in patients with lung cancer chemotherapy. Methods: Patients with lung cancer who underwent chemotherapy in the Second Affiliated Hospital of Xi'an Medical University between February 2016 and May 2017 were selected as the chemotherapy group, the healthy subjects who received physical examination during the same period were selected as the control group, the 3D-STI examination was performed and GAS was calculated;the serum was collected to determine the contents of cardiotoxicity markers as well as apoptosis and oxidative stress indexes, and the peripheral blood was collected to determine the expression of apoptosis and oxidative stress molecules. Results: GAS level, serum ALDH2 and CAT contents as well as peripheral blood Keap1 and Bcl-2 expression intensity of chemotherapy group were lower than those of control group whereas serum Copeptin, CK-MB, cTnI, cMyBP-c, sTWEAK, sFas, MDA and 8-isoPGF2α contents as well as peripheral blood Nrf-2, gp91phox, p22 phox and Caspase-3 expression intensity were significantly higher than those of control group;the GAS level in chemotherapy group was negatively correlated with serum Copeptin, CK-MB, cTnI, cMyBP-c, sTWEAK, sFas, MDA and 8-isoPGF2α contents as well as peripheral blood Nrf-2, gp91phox, p22 phox and Caspase-3 expression intensity, and positively correlated with serum ALDH2 and CAT contents as well as peripheral blood Keap1 and Bcl-2 expression intensity. Conclusion: The changes of 3D-STI parameter GAS in patients with lung cancer chemotherapy can reflect the degree of cardiotoxicity induced by oxidative stress and apoptosis.

Correlation between three-dimensional speckle tracking parameters and serum index changes during left ventricular remodeling in patients with coronary heart disease

Objective:To study the correlation between three-dimensional speckle tracking parameters and serum index changes during left ventricular remodeling in patients with coronary heart disease. Methods: Patients who were diagnosed with coronary heart disease and angina pectoris in our hospital between March 2015 and May 2017 were selected as the CHD group of the study, and the healthy subjects who received medical examination in our hospital during the same period were taken as the control group;the three-dimensional speckle tracking parameters, peripheral blood signal molecule expression as well as serum cytokine and collagen metabolism index levels of the two groups were measured.Results: GLS and AGS levels in CHD group were significantly lower than those in control group whereas GCS and GRS levels were not significantly different from those in control group, and peripheral blood Notch1, Hes1, NF-κB and PKC expression intensity as well as serum sTWEAK, FGF23, TGF-β1, GDF15, sSema4D, CaN, MMP14, PINP and ICTP contents were significantly higher than those of control group;GLS and AGS levels in CHD group were negatively correlated with peripheral blood Notch1, Hes1, NF-κB and PKC expression intensity as well as serum sTWEAK, FGF23, TGF-β1, GDF15, sSema4D, CaN, MMP14, PINP and ICTP contents.Conclusions:The changes of three-dimensional speckle tracking parameters GLS and AGS in patients with coronary heart disease are closely related to the changes in signal pathway function, cytokine secretion and collagen metabolism during left ventricular remodeling.