Segmentation Fitting Research Articles

Repeatability and reproducibility comparisons of liver IVIM imaging with free-breathing or respiratory-triggered sequences.

For liver intravoxel incoherent motion (IVIM) data acquisition, respiratory-triggering (RT) MRI is commonly used, and there are strong motivations to shorten the scan duration. For the same scan duration, more b values or higher numbers of excitations can be allowed for free-breathing (FB) imaging than for RT. We studied whether FB can be used to replace RT when careful IVIM image acquisition and image processing are conducted. MRI data of 22 healthy participants were acquired using a 3.0 T scanner. Diffusion imaging was based on a single-shot spin-echo-type echo-planar sequence and 16 b values of 0, 2, 4, 7, 10, 15, 20, 30, 46, 60, 72, 100, 150, 200, 400, and 600 s/mm2 . Each subject attended two scan sessions with an interval of 10-20 days. For each scan session, a subject was scanned twice, first with RT and then with FB. The mean image acquisition time was 5.4 min for FB and 10.8 min for RT. IVIM parameters were calculated with bi-exponential model segmented fitting with a threshold b value of 60 s/mm2 , and fitting started from b= 2 s/mm2 . There was no statistically significant difference between IVIM parameters measured with FB imaging or RT imaging. Perfusion fraction ICC (intraclass correlation coefficient) for FB imaging and RT imaging in the same scan session was 0.824. For perfusion fraction, wSD (within-subject standard deviation), BA (Bland-Altman) difference, BA 95% limit, and ICC were 0.022, 0.0001, -0.0635~0.0637, and 0.687 for FB and 0.031, 0.0122, -0.0723~0.0967, and 0.611 for RT. For Dslow (×10-3 s/mm2 ), wSD, BA difference, BA 95% limit, and ICC were 0.057, 0.0268, -0.1258~0.1793, and 0.471 for FB and 0.073, -0.0078, -0.2170-0.2014, and <0.4 for RT. The Dfast coefficient of variation was 0.20 for FB imaging and 0.28 for RT imaging. All reproducibility indicators slightly favored FB imaging.

Modeling and Simulation of the Stepping Motor Operation Curve

To improve the open-loop control performance of stepper motors, research was conducted on their operating curves. The acceleration-velocity characteristic equations of three common operating curves were established and compared with the torque frequency characteristic curves of stepper motors. Based on this, an improved segmented correction fitting S-shaped (SCFS) curve design method based on a high torque utilization sine curve was proposed to solve the problem of rotor blockage and overshoot caused by uneven acceleration during the operation of stepper motors. In the simulation of the stepper motor load motion system, the SCFS curve was compared with common curves. The simulation results showed that the position tracking error was reduced by approximately 82% compared to the trapezoidal curve and approximately 70% compared to the exponential curve. Research has found that this method is suitable for the design of high-precision motor operating curves and has a high application value in the field of precision motion control.

Research on the cable-driven endoscopic manipulator for fusion reactors

In this paper, a cable-driven endoscopic manipulator (CEM) is designed for the Chinese latest compact fusion reactor. The whole CEM arm is more than 3000 mm long and includes end vision tools, an endoscopic manipulator/control system, a feeding system, a drag chain system, support systems, a neutron shield door, etc. It can cover a range of ±45° of the vacuum chamber by working in a wrap-around mode, etc., to meet the need for observation at any position and angle. By placing all drive motors in the end drive box via a cable drive, cooling, and radiation protection of the entire robot can be facilitated. To address the CEM motion control problem, a discrete trajectory tracking method is proposed. By restricting each joint of the CEM to the target curve through segmental fitting, the trajectory tracking control is completed. To avoid the joint rotation angle overrun, a joint limit rotation angle optimization method is proposed based on the equivalent rod length principle. Finally, the CEM simulation system is established. The rationality of the structure design and the effectiveness of the motion control algorithm are verified by the simulation.

Downscaling of Oceanic Chlorophyll-a with a Spatiotemporal Fusion Model: A Case Study on the North Coast of the Yellow Sea

Chlorophyll-a concentration (Chl-a) is an important indicator of coastal eutrophication. Remote sensing technology provides a global view of it. However, different types of sensors are subject to design constraints and cannot meet the requirements of high temporal and spatial resolution on nearshore engineering simultaneously. To obtain high-spatiotemporal-resolution images, this study examines the performance of the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) on GOCI and Landsat Chl-a data fusion. Considering the rapidly changing rate and consistency of oceanic Chl-a, the ESTARFM was modified via segmented fitting and numerical conversion. The results show that both fusion models can fuse multiple data advantages to obtain high-spatiotemporal-resolution Chl-a images. Compared with the ESTARFM, the modified solution has a better performance in terms of the root mean square error and correlation coefficient, and its results have better spatial consistency for coastal Chl-a. In addition, the new solution expands the data utilization range of data fusion by reducing the influence of the time interval of original data and realizes better monitoring of nearshore Chl-a changes.

Measurements and Protocols Used for Weight-Bearing Cone-Beam CT Scans for Evaluating the Alignment of the Foot and Ankle: A Systematic Literature Review

Category: Hindfoot; Midfoot/Forefoot Introduction/Purpose: Weight bearing CT has been widely used recently to provide detailed 3D information of the foot and ankle. Orthopedic surgeons have been exposed to this new technology, but do not have the equivalent tools to use to explore it. A lot of metrics were directly borrowed from plain radiographic imaging, while some new parameters were developed using traditional axes and angular concepts but on only one specific WBCT view out of hundreds of scans. Some metrics used similar measurement concepts, but on 3D reconstructed bones, while a few new parameters were introduced based on semi 3D and 3D thinking. Small sample sizes, obscure inclusion/exclusion criteria, and skewed validation methods are noted, and a thorough review of WBCT-based measurements used in foot and ankle is warranted. Methods: A literature search was conducted in two databases: PubMed (MEDLINE) and EMBASE (Elsevier). The main search terms and logics were: ((foot) OR (ankle) OR (hindfoot) OR (midfoot) OR (forefoot)) AND ((WBCT) OR (Weightbearing CT) OR (Cone beam CT scan) OR (PedCAT)). A longer string of key words was utilized to further narrow down the results to WBCT imaging associated with foot and ankle pathologies. The terms were expanded using the MeSH and Emtree expansion features of PubMed and EMBASE, respectively. All articles were reviewed by two independent senior foot and ankle surgeons based on pre-designed exclusion criteria. Off-topic articles were removed, and remaining articles were categorized based on measurement techniques. All articles were managed using Zotero. (Figure 1) Results: A total of 216 articles were selected for retrieval and further organized by measurement techniques. Seven groups were set by the type of the methodology used. Among them, 123 studies utilized traditional 2D measurement parameters (eg, hindfoot moment arm, Meary’s angle, talonavicular uncoverage, hallux valgus angle, etc.), 8 introduced new parameters but based on 2D axial/angular concepts (eg, middle facet subluxation percentage and angle), 22 used semi-automated 3D measurement techniques (3D segmentation + 2D axial and angular methods), 19 using semi-3D measurement techniques (Foot and Ankle Offset), 29 measured real 3D parameters (volume evaluation in the syndesmosis and Lisfranc joints, distance mapping), 3 morphological studies used 3D segmentation and sphere fitting. The advantages and disadvantages of each type of measurement were carefully examined and explored. Conclusion: 3D imaging measurements with WBCT appear to be highly variegated. Axial and angular measurements do not apply well to irregular bones and there is a low reproducibility in 3D scenarios. Although inter- and intraobserver reproducibility scores have been reported in multiple studies, they were only part of the validation system that should be done. A high correlation between parameters such as 2D and 3D hindfoot angles has been reported, but debate remains as to whether one method of measurement is more accurate than the other. Furthermore, variations in analytical methods warrant a more uniformly defined coordinate or anatomical system.

Fast quasi-coaxial vertical 3D measurement based on galvanometric scanner

Vertical measurement is widely studied for its ability to measure objects with drastic changes in surface shape such as deep holes and steps. This paper proposes a fast quasi-coaxial modulations-based 3D measurement system by using a galvanometric scanner, whose rotation is triggered by a field-programmable gate array (FPGA) and is synchronous with the imaging acquisition. In addition, to reduce the influence of background light on reconstruction, a segmented fitting algorithm is proposed, which can remove the background light intensity more appropriately. Experiments demonstrate the validity of the system and background light removal method proposed in this paper.

An Algorithm for Building Exterior Facade Corner Point Extraction Based on UAV Images and Point Clouds

The high-precision building exterior facade corner point (BEFCP) is an essential element in topographic and cadastral surveys. However, current extraction methods rely on the interactions of humans with the 3D real-scene models produced by unmanned aerial vehicle (UAV) oblique photogrammetry, which have a high workload, low efficiency, poor precision, and cannot satisfy the requirements of automation. The dense point cloud contains discrete 3D building structure information. Still, it is challenging to accurately filter out the partial point cloud characterizing the building structure from it in order to achieve BEFCP extraction. The BEFCPs are always located on the plumb line of the building’s exterior wall. Thus, this paper back-calculated the plumb line from the image and designed a photographic ray corresponding to the image point and point cloud intersection point calculation algorithm to recover its approximate spatial position in order to successfully extract the accurate point cloud in the building structure neighborhood. It then utilized the high signal-to-noise ratio property of the point cloud as a base to eliminate the noise points and, finally, accurately located the building exterior façade corner points by recovering the building structure through segmental linear fitting of the point cloud. The proposed algorithm conducted automated building exterior facade corner point extraction via both of planar-to-stereo and rough-to-precise strategies, reached a 92.06% correctness rate and ±4.5 cm point mean square location error in the experiment, and was able to extract and distinguish the building exterior facade corner points under eaves obstruction and extreme proximity. It is suitable for all high-precision surveying and mapping tasks in building areas based on oblique photogrammetry, which can effectively improve the automation of mapping production.

Automatic segment-wise restoration for wide irregular stripe noise in SDGSAT-1 multispectral data using side-slither data

The raw orbital images captured by the new launched SDGSAT-1 Multispectral Image for Inshore (MII) are plagued by wide-irregular stripe noise, due to inconsistent unit response. This paper proposed a new method for destriping wide-irregular stripe noise in MII using the characteristics of side-slither data. Firstly, the raw side-slither data was standardized using line detection to guarantee that each row observed the same ground object. Then, the whole orbital side-slither image was segmented into blocks of equal length, and it was found that the response of wide-irregular stripe noise is consistent within a certain length. The Inverse Distance Weight was used to interpolate the DN values of striped pixels as referenced values, and the segmented length was determined by calculating Pearson correlation coefficient between the original and referenced DN values. Thirdly, the Random Sample Consensus (RANSAC) algorithm was used to find the inliers and calculate the correction parameters, after it was discovered that the original and referenced DN values had a linear correlation. The proposed method, SIR (consists of image segmentation, pixel interpolation and RANSAC fitting), can directly destripe the raw orbital image. One orbital side-slither data and six ordinary orbital data were selected for verification. Twelve state-of-the-art methods were chosen for comparison with SIR. The accuracy scores of SIR on three assessment indexes were higher than those of twelve other methods. The destriping outcomes for the images of city, cloud, forest, and river demonstrated the effectiveness of SIR in correcting wide-irregular stripe noise in MII images.

An Improved Doppler-Aided Smoothing Code Algorithm for BeiDou-2/BeiDou-3 un-Geostationary Earth Orbit Satellites in Consideration of Satellite Code Bias

The extensive use of carrier-aided smoothing code (CSC) filters has led to a reduction in the noise level of raw code measurements in GNSS positioning and navigation applications. However, the existing CSC technique is sensitive to the changes in the integer ambiguity, and then the smoothing procedure needs to be restarted in the presence of cycle slips. As the Doppler shift is instantaneously observed and immune to cycle slips, the Doppler-aided smoothing code (DSC) algorithm would be more promising in a challenged environment. Based on the Hatch filter, an optimal DSC approach is proposed with the principle of minimum variance. Meanwhile, to inhibit the effect of the integral cumulative error of the Doppler, a balance factor is adopted to adjust the contributions of raw code and DSC. The noise level of code observable is not only affected by thermal noise, but also limited by systematic bias. Satellite code bias (SCB) was identified in the raw code observable on each frequency for each BDS-2 satellite. By minimizing the sum of the absolute value of residuals, the polynomial segment fitting algorithm as a function of elevation angles is applied to establish the SCB correction model based on epoch-differenced multipath (MP) deviations. Finally, different types of experiments demonstrate the validity and efficiency of the refined DSC filter with SCB corrections on each available frequency for BDS un-GEO satellites.

Design of an active detection system for ice and snow pollutants and freezing temperature on runway

This paper studied the problem of ice and snow pollutants identification and freezing temperature detection under winter runway operation, and designed an active detection system for runway ice and snow pollutants and freezing temperature. The temperature change trend during the liquid freezing process was researched, and the freezing temperature detection model based on sequence segmented linear fitting and inflection point identification was proposed in combination with active cooling technology. The differences in dielectric properties of runway snow and ice cover are studied, and a multi-frequency detection-based forked-finger planar capacitive sensor was identified for snow and ice pollutants detection considering practical application scenarios. A finite element simulation model of the forked-finger planar capacitive sensor was established, to optimize the structural parameters of the sensor and verify the feasibility of the capacitor sensor. Finally, the detection device and control system were designed and fabricated, and built an experimental platform for system test. The field experimental results showed that the system has good reliability and stability, and the error of freezing temperature detection model is less than 0.3 °C. At the same time, it can identify three types of pollutants, including water, ice and ice–water mixture, with an accuracy of 89%.

Evaluation of a multiphasic parasite clearance profile after treatment of experimental human infection with the investigational anti-malarial M5717 using segmented mixed effect models

BackgroundEvaluation of parasite clearance patterns in experimental human infection trials helps increase understanding of drug action. In a previously reported phase Ib trial of a new investigational anti-malarial drug M5717, parasite clearance showed a biphasic linear pattern: slow removal phase with a near flat clearance rate followed by a fast clearance phase with a steep slope. In this study three statistical approaches were implemented and compared to estimate the parasite clearance rate for each phase and the time point corresponding to the change of clearance rates (changepoint between the two phases).MethodsData using three M5717 doses 150 mg (n = 6), 400 mg (n = 8), 800 mg (n = 8) were used to estimate biphasic clearance rates. Three models were investigated: firstly, segmented mixed models with estimated changepoint—models with/without random effects in various parameters were compared. Secondly, a segmented mixed model using grid search—this method is similar to the first except that changepoints were not estimated, instead they were selected based on model fit from given candidate values. Thirdly, a two-stage approach whereby a segmented regression model fit to each participant followed by a meta-analysis method. Hourly rate of parasite clearance (HRPC) interpreted as the percentage of parasites removed each hour was calculated.ResultsThe three models generated similar results. Using segmented mixed models, the estimated changepoints after treatment in hours (95% CI) were: 150 mg: 33.9 (28.7, 39.1); 400 mg: 57.4 (52.5, 62.4); and 800 mg: 52.8 (47.4, 58.1). For all three treatment groups, there was nearly no clearance before the changepoints, but rapid clearance in the second phase (HRPC [95% CI]): 150 mg: 16.8% (14.3, 19.1%); 400 mg: 18.6% (16.0, 21.1%); and 800 mg: 11.7% (9.3, 14.1%).ConclusionsAll three statistical approaches are effective tools to characterize the bi-phasic clearance of M5717 in the phase 1b experimental Plasmodium falciparum malaria human infection study. The statistical approaches produced similar results to estimate the two-phase clearance rates and the changepoint for each treatment dose of M5717. However, the segmented mixed model with random changepoints has several advantages: it is computationally efficient, provides precision for changepoint estimates and is robust concerning outlying datapoints or individuals.

High-Precision State of Charge Estimation of Urban-Road-Condition Lithium-Ion Batteries Based on Optimized High-Order Term Compensation-Adaptive Extended Kalman Filtering

It is significant to improve the accuracy of estimating the state of charge (SOC) of lithium-ion batteries under different working conditions on urban roads. In this study, an improved second-order polarized equivalent circuit (SO-PEC) modeling method is proposed. Accuracy test using segmented parallel exponential fitting parameter identification method. Online parameter identification using recursive least squares with variable forgetting factors(VFFRLS). An optimized higher-order term compensation-adaptive extended Kalman filter (HTC-AEKF) is proposed in the process of estimating SOC. The algorithm incorporates a noise-adaptive algorithm that introduces noise covariance into the recursive process in real-time to reduce the impact of process noise and observation noise on the accuracy of SOC estimation. Multiple iterations are performed for some of the processes in the extended Kalman filter(EKF) to compensate for the accuracy impact of missing higher-order terms in the linearization process. Model validation results show over 98% accuracy. The results after comparing with the EKF algorithm show a 4.1% improvement in SOC estimation accuracy under Hybrid Pulse Power Characterization(HPPC) working conditions. 2.7% improvement in accuracy in Dynamic Stress Test(DST) working conditions. 2.1% improvement in Beijing Bus Dynamic Stress Test(BBDST) working conditions. The superiority of the algorithm is demonstrated.

Optimizing b-values schemes for diffusion MRI of the brain with segmented Intravoxel Incoherent Motion (IVIM) model.

To define an optimal set of b-values for accurate derivation of diffusion MRI parameters in the brain with segmented Intravoxel Incoherent Motion (IVIM) model. Simulations of diffusion signals were performed to define an optimal set of b-values targeting different perfusion regimes, by relying on an optimization procedure which minimizes the total relative error on estimated IVIM parameters computed with a segmented fitting procedure. Then, the optimal b-values set was acquired in vivo on healthy subjects and skull base chordoma patients to compare the optimized protocol with a clinical one. The total relative error on simulations decreased of about 40% when adopting the optimal set of 13 b-values (0 10 20 40 50 60 200 300 400 1200 1300 1400 1500 s/mm2 ), showing significant differences and increased precision on D and f estimates with respect to simulations with a non-optimized b-values set. Similarly, in vivo acquisitions demonstrated a dependency of IVIM parameters on the b-values array, with differences between the optimal set of b-values and a clinical non-optimized acquisition. IVIM parameters were compatible to literature values, with D (0.679/0.701 [0.022/0.008] ·10-3 mm2 /s), f (5.49/5.80 [0.70/1.14] %), and D* (8.25/7.67 [0.92/0.83] ·10-3 mm2 /s) median [interquartile range] estimates for white matter/gray matter in volunteers and D (0.709/0.715/1.06 [0.035/0.023/0.271] ·10-3 mm2 /s), f (7.08/7.84/21.54 [1.20/1.06/6.05] %), and D* (10.85/11.84/2.32 [1.38/2.32/4.94] ·10-3 mm2 /s) for white matter/gray matter/Gross Tumor Volume in patients with skull-base chordoma tumor. The definition of an optimal b-values set can improve the estimation of quantitative IVIM parameters. This allows setting up an optimized approach that can be adopted for IVIM studies in the brain.

Histopathological graded liver lesions: what role does the IVIM analysis method have?

This study aims to investigate three different image processing methods on quantitative parameters of IVIM sequence, as well as apparent diffusion coefficients and simple perfusion fractions, for benign and malignant liver tumors. IVIM images with 8 b-values (0-1000s/mm2) and 1.5T MRI scanner in 16 patients and 3 healthy people were obtained. Next, the regions of interest were selected for malignant, benign, and healthy liver regions (50, 56, and 12, respectively). Then, the bi-exponential equation of the IVIM technique was fitted with two segmented fitting methods as well as one full fitting method (three methods in total). Using the segmented fitting method, diffusion coefficient (D) is fixed with a mono-exponential equation with b-values that are greater than 200s/mm2. The perfusion fraction (f) can then be calculated by extrapolating, as the first method, or fitting simultaneously with the pseudo-diffusion coefficient (D*) as the second method. In the full fitting method, as the third method, all IVIM parameters were obtained simultaneously. The mean values of parameters from different methods were compared in different grades of lesions. Our results indicate that the image processing method can change statistical comparisons between different groups for each parameter. The D value is the only quantity in this technique that does not depend on the fitting process and can be used as an indicator of comparison between studies (P < 0.05). The most effective method to distinguish liver lesions is the extrapolated f method (first method). This method created a significant difference (P < 0.05) between the perfusion parameters between benign and malignant lesions. Using extrapolated f is the most effective method of distinguishing liver lesions using IVIM parameters. The comparison between groups does not depend on the fitting method only for parameter D.

Tree Species Classification Using Airborne LiDAR Data Based on Individual Tree Segmentation and Shape Fitting

Individual tree species classification is of strategic importance for forest monitoring, analysis, and management, which are critical for sustainable forestry development. In this regard, the paper proposes a method based on the profile of segmented individual tree laser scanning points to identify tree species. The proposed methodology mainly takes advantage of three-dimensional geometric features of a tree crown captured by a laser point cloud to identify tree species. Firstly, the Digital Terrain Model (DTM) and Digital Surface Model (DSM) are used for Crown Height Model (CHM) generation. Then, local maximum algorithms and improved rotating profile-based delineations are used to segment individual trees from the profile CHM point data. In the next step, parallel-line shape fitting is used to fit the tree crown shape. In particular, three basic geometric shapes, namely, triangle, rectangle, and arc are used to fit the tree crown shapes of different tree species. If the crown belongs to the same crown shape or shape combination, parameter classification is used, such as the ratio of crown width and crown height or the apex angle range of the triangles. The proposed method was tested by two real datasets which were acquired from two different sites located at Tiger and Leopard National Park in Northeast China. The experimental results indicate that the average tree classification accuracy is 90.9% and the optimal classification accuracy reached 95.9%, which meets the accuracy requirements for rapid forestry surveying.

Size, shape and orientation matter: fast and semi-automatic measurement of grain geometries from 3D point clouds

Abstract. The grain-scale morphology and size distribution of sediments are important factors controlling the erosion efficiency, sediment transport and the aquatic ecosystem quality. In turn, characterizing the spatial evolution of grain size and shape can help understand the dynamics of erosion and sediment transport in coastal, hillslope and fluvial environments. However, the size distribution of sediments is generally assessed using insufficiently representative field measurements, and determining the grain-scale shape of sediments remains a real challenge in geomorphology. Here we determine the size distribution and grain-scale shape of sediments located in coastal and river environments with a new methodology based on the segmentation and geometric fitting of 3D point clouds. Point cloud segmentation of individual grains is performed using a watershed algorithm applied here to 3D point clouds. Once the grains are segmented into several sub-clouds, each grain-scale morphology is determined by fitting a 3D geometrical model applied to each sub-cloud. If different geometrical models can be tested, this study focuses mostly on ellipsoids to describe the geometry of grains. G3Point is a semi-automatic approach that requires a trial-and-error approach to determine the best combination of parameter values. Validation of the results is performed either by comparing the obtained size distribution to independent measurements (e.g., hand measurements) or by visually inspecting the quality of the segmented grains. The main benefits of this semi-automatic and non-destructive method are that it provides access to (1) an un-biased estimate of surface grain-size distribution on a large range of scales, from centimeters to meters; (2) a very large number of data, mostly limited by the number of grains in the point cloud data set; (3) the 3D morphology of grains, in turn allowing the development of new metrics that characterize the size and shape of grains; and (4) the in situ orientation and organization of grains. The main limit of this method is that it is only able to detect grains with a characteristic size significantly greater than the resolution of the point cloud.

Image downsampling expedited adaptive least-squares (IDEAL) fitting improves intravoxel incoherent motion (IVIM) analysis in the human kidney.

To improve the reliability of intravoxel incoherent motion (IVIM) model parameter estimation for the DWI in the kidney using a novel image downsampling expedited adaptive least-squares (IDEAL) approach. The robustness of IDEAL was investigated using simulated DW-MRI data corrupted with different levels of Rician noise. Subsequently, the performance of the proposed method was tested by fitting bi- and triexponential IVIM model to in vivo renal DWI data acquired on a clinical 3 Tesla MRI scanner and compared to conventional approaches (fixed D* and segmented fitting). The numerical simulations demonstrated that the IDEAL algorithm provides robust estimates of the IVIM parameters in the presence of noise (SNR of 20) as indicated by relatively low absolute percentage bias (maximal sMdPB <20%) and normalized RMSE (maximal RMSE <28%). The analysis of the in vivo data showed that the IDEAL-based IVIM parameter maps were less noisy and more visually appealing than those obtained using the fixed D* and segmented methods. Further, coefficients of variation for nearly all IVIM parameters were significantly reduced in cortex and medulla for IDEAL-based biexponential (coefficients of variation: 4%-50%) and triexponential (coefficients of variation: 7.5%-75%) IVIM modelling compared to the segmented (coefficients of variation: 4%-120%) and fixed D* (coefficients of variation: 17%-174%) methods, reflecting greater accuracy of this method. The proposed fitting algorithm yields more robust IVIM parameter estimates and is less susceptible to poor SNR than the conventional fitting approaches. Thus, the IDEAL approach has the potential to improve the reliability of renal DW-MRI analysis for clinical applications.

A Novel Adaptive Back Propagation Neural Network-Unscented Kalman Filtering Algorithm for Accurate Lithium-Ion Battery State of Charge Estimation

Accurate State of Charge (SOC) estimation for lithium-ion batteries has great significance with respect to the correct decision-making and safety control. In this research, an improved second-order-polarization equivalent circuit (SO-PEC) modelling method is proposed. In the process of estimating the SOC, a joint estimation algorithm, the Adaptive Back Propagation Neural Network and Unscented Kalman Filtering algorithm (ABP-UKF), is proposed. It combines the advantages of the robust learning rate in the Back Propagation (BP) neural network and the linearization error reduction in the Unscented Kalman Filtering (UKF) algorithm. In the BP neural network part, the self-adjustment of the learning factor accompanies the whole estimation process, and the improvement of the self-adjustment algorithm corrects the shortcomings of the UKF algorithm. In the verification part, the model is validated using a segmented double-exponential fit. Using the Ampere-hour integration method as the reference value, the estimation results of the UKF algorithm and the Back Propagation Neural Network and Unscented Kalman Filtering (BP-UKF) algorithm are compared, and the estimation accuracy of the proposed method is improved by 1.29% under the Hybrid Pulse Power Characterization (HPPC) working conditions, 1.28% under the Beijing Bus Dynamic Stress Test (BBDST) working conditions, and 2.24% under the Dynamic Stress Test (DST) working conditions. The proposed ABP-UKF algorithm has good results in estimating the SOC of lithium-ion batteries and will play an important role in the high-precision energy management process.

Precision of region of interest-based tri-exponential intravoxel incoherent motion quantification and the role of the Intervoxel spatial distribution of flow velocities.

The purpose of this work was to obtain precise tri-exponential intravoxel incoherent motion (IVIM) quantification in the liver using 2D (b-value and first-order motion moment [M1 ]) IVIM-DWI acquisitions and region of interest (ROI)-based fitting techniques. Diffusion MRI of the liver was performed in 10 healthy volunteers using three IVIM-DWI acquisitions: conventional monopolar, optimized monopolar, and optimized 2D (b-M1 ). For each acquisition, bi-exponential and tri-exponential full, segmented, and over-segmented ROI-based fitting and a newly proposed blood velocity SDdistribution (BVD) fitting technique were performed to obtain IVIM estimates in the right and left liver lobes. Fitting quality was evaluated using corrected Akaike information criterion. Precision metrics (test-retest repeatability, inter-reader reproducibility, and inter-lobar agreement) were evaluated using Bland-Altman analysis, repeatability/reproducibility coefficients (RPCs), and paired sample t-tests. Precision was compared across acquisitions and fitting methods. High repeatability and reproducibility was observed in the estimations of the diffusion coefficient (Dtri = [1.03 ± 0.11] × 10-3 mm2 /s; RPCs ≤ 1.34 × 10-4 mm2 /s), perfusion fractions (F1 = 3.19 ± 1.89% and F2 = 16.4± 2.07%; RPCs ≤ 2.51%), and blood velocity SDs (Vb,1 = 1.44 ± 0.14 mm/s and Vb,2 = 3.62 ± 0.13 mm/s; RPCs ≤ 0.41 mm/s) in the right liver lobe using the 2D (b-M1 ) acquisition in conjunction with BVD fitting. Using these methods, significantly larger (p< 0.01) estimates of Dtri and F1 were observed in the left lobe in comparison to the right lobe, while estimates of Vb,1 and Vb,2 demonstrated high interlobar agreement (RPCs ≤ 0.45 mm/s). The 2D (b-M1 ) IVIM-DWI data acquisition in conjunction with BVD fitting enables highly precise tri-exponential IVIM quantification in the right liver lobe.

A Robust Gaussian Process-Based LiDAR Ground Segmentation Algorithm for Autonomous Driving

Robust and precise vehicle detection is the prerequisite for decision-making and motion planning in autonomous driving. Vehicle detection algorithms follow three steps: ground segmentation, obstacle clustering and bounding box fitting. The ground segmentation result directly affects the input of the subsequent obstacle clustering algorithms. Aiming at the problems of over-segmentation and under-segmentation in traditional ground segmentation algorithms, a ground segmentation algorithm based on Gaussian process is proposed in this paper. To ensure accurate search of real ground candidate points as training data for Gaussian process, the proposed algorithm introduces the height and slope criteria, which is more reasonable than the use of fixed height threshold for searching. After that, a sparse covariance function is introduced as the kernel function for calculation in Gaussian process. This function is more suitable for ground segmentation situation the radial basis function (RBF). The proposed algorithm is tested on our autonomous driving experimental platform and the public autonomous driving dataset KITTI, compared with the most used RANSAC algorithm and ray ground filter algorithm. Experiment results show that the proposed algorithm can avoid obvious over-segmentation and under-segmentation. In addition, compared with the RBF, the introduction of the sparse covariance function also reduces the computation time by 37.26%.