Country-wide data of ecosystem structure from the third Dutch airborne laser scanning survey.

The third Dutch national airborne laser scanning flight campaign (AHN3, Actueel Hoogtebestand Nederland) conducted between 2014 and 2019 during the leaf-off season (October-April) across the whole Netherlands provides a free and open-access, country-wide dataset with ∼700 billion points and a point density of ∼10(-20) points/m2. The AHN3 point cloud was obtained with Light Detection And Ranging (LiDAR) technology and contains for each point the x, y, z coordinates and additional characteristics (e.g. return number, intensity value, scan angle rank and GPS time). Moreover, the point cloud has been pre-processed by 'Rijkswaterstraat' (the executive agency of the Dutch Ministry of Infrastructure and Water Management), comes with a Digital Terrain Model (DTM) and a Digital Surface Model (DSM), and is delivered with a pre-classification of each point into one of six classes (0: Never Classified, 1: Unclassified, 2: Ground, 6: Building, 9: Water, 26: Reserved [bridges etc.]). However, no detailed information on vegetation structure is available from the AHN3 point cloud. We processed the AHN3 point cloud (∼16 TB uncompressed data volume) into 10 m resolution raster layers of ecosystem structure at a national extent, using a novel high-throughput workflow called 'Laserfarm' and a cluster of virtual machines with fast central processing units, high memory nodes and associated big data storage for managing the large amount of files. The raster layers (available as GeoTIFF files) capture 25 LiDAR metrics of vegetation structure, including ecosystem height (e.g. 95th percentiles of normalized z), ecosystem cover (e.g. pulse penetration ratio, canopy cover, and density of vegetation points within defined height layers), and ecosystem structural complexity (e.g. skewness and variability of vertical vegetation point distribution). The raster layers make use of the Dutch projected coordinate system (EPSG:28992 Amersfoort / RD New), are each ∼1 GB in size, and can be readily used by ecologists in a geographic information system (GIS) or analytical open-source software such as R and Python. Even though the class '1: Unclassified' mainly includes vegetation points, other objects such as cars, fences, and boats can also be present in this class, introducing potential biases in the derived data products. We therefore validated the raster layers of ecosystem structure using >180,000 hand-labelled LiDAR points in 100 randomly selected sample plots (10 m × 10 m each) across the Netherlands. Besides vegetation, objects such as boats, fences, and cars were identified in the sampled plots. However, the misclassification rate of vegetation points (i.e. non-vegetation points that were assumed to be vegetation) was low (∼0.05) and the accuracy of the 25 LiDAR metrics derived from the AHN3 point cloud was high (∼90%). To minimize existing inaccuracies in this country-wide data product (e.g. ships on water bodies, chimneys on roofs, or cars on roads that might be incorrectly used as vegetation points), we provide an additional mask that captures water bodies, buildings and roads generated from the Dutch cadaster dataset. This newly generated country-wide ecosystem structure data product provides new opportunities for ecology and biodiversity science, e.g. for mapping the 3D vegetation structure of a variety of ecosystems or for modelling biodiversity, species distributions, abundance and ecological niches of animals and their habitats.

Modified Smoothing Algorithm for Tracking Multiple Maneuvering Targets in Clutter.

This research work extends the fixed interval smoothing based on the joint integrated track splitting (FIsJITS) filter in the multi-maneuvering-targets (MMT) tracking environment. We contribute to tackling unknown dynamics of the multi-maneuvering-targets (MMT) using the standard kinematic model. This work is referred to as smoothing MMT using the JITS (MMT-sJITS). The existing FIsJITS algorithm is computationally more complex to solve for the MMT situation because it enumerates a substantial number of measurement-to-track assignments and calculates their posteriori probabilities globally. The MMT-sJITS updates a current target track by assuming the joint (common) measurements detected by neighbor tracks are modified clutters (or pretended spurious measurements). Thus, target measurement concealed by a joint measurement is optimally estimated based on measurement density of the modified clutter. This reduces computational complexity and provides improved tracking performance. The MMT-sJITS generates forward tracks and backward tracks using the measurements collected by a sensor such as a radar. The forward and backward multi-tracks state predictions are fused to obtain priori smoothing multi-track state prediction, as well as their component existence probabilities. This calculates the smoothing estimate required to compute the forward JITS state estimate, which reinforces the MMT tracking efficiently. Monte Carlo simulation is used to verify best false-track discrimination (FTD) analysis in comparison with existing multi-targets tracking algorithms.

Multi-Sonar Distributed Fusion for Target Detection and Tracking in Marine Environment.

The multi-sonar distributed fusion system has been pervasively deployed to jointly detect and track marine targets. In the realistic scenario, the origin of locally transmitted tracks is uncertain due to clutter disturbance and the presence of multi-target. Moreover, attributed to the different sonar internal processing times and diverse communication delays between sonar and the fusion center, tracks unavoidably arrive in the fusion center with temporal out-of-sequence (OOS), both problems pose significant challenges to the fusion system. Under the distributed fusion framework with memory, this paper proposes a novel multiple forward prediction-integrated equivalent measurement fusion (MFP-IEMF) method, it fuses the multi-lag OOST with track origin uncertainty in an optimal manner and is capable to be implemented in both the synchronous and asynchronous multi-sonar tracks fusion system. Furthermore, a random central track initialization technique is also proposed to detect the randomly born marine target in time via quickly initiating and confirming true tracks. The numerical results show that the proposed algorithm achieves the same optimality as the existing OOS reprocessing method, and delivers substantially improved detection and tracking performance in terms of both ANCTT and estimation accuracy compared to the existing OOST discarding fusion method and the ANF-IFPFD method.

A patient with Turner syndrome received the percutaneous vertebroplasty seven times: a case report and literature review.

BACKGROUND: Turner syndrome (TS) is characterized as the complete or partial absence of one X chromosome and is an extremely rare disease affecting approximately 1:2500 live female births. Though the prevalence of osteoporosis among women with TS is estimated to be around 55-64% and they suffer more frequently from fractures than normal, few reports concerning TS patients with osteoporosis are able to be seen due to tiny number of patients. CASE PRESENTATION: Here, we report a rare case of TS with osteoporosis, who has undergone percutaneous vertebroplasty (PVP) seven times because of several vertebral compression fractures (VCFs). G-banded karyotype analysis was performed and the result was 45,X[43]/47,XXX[17], indicating that the patient was a mosaicism of TS karyotype and Trisomy X syndrome karyotype. TS is the underlying cause of low level of estrogen for this patient. The interaction of aging, estrogen deficiency and intestinal dysbacteriosis leads to her severe osteoporosis and multi-segmental VCFs. The aim of this report is to provide recommendations regarding the management of TS patients with osteoporosis by reviewing the clinical presentation of TS, the influence of estrogen deficiency in osteoporosis, etc. CONCLUSIONS: Early diagnosis and hormone replacement treatment are essential for TS patients to prevent osteoporosis and reduce the risk of fractures. This is a rare case report describing TS patient with severe osteoporosis and VCFs.

A Hybrid Newton-Raphson and Particle Swarm Optimization Method for Target Motion Analysis by Batch Processing.

Bearing-only target motion analysis (BO-TMA) by batch processing remains a challenge due to the lack of information on underwater target maneuvering and the nonlinearity of sensor measurements. Traditional batch estimation for BO-TMA is mainly performed based on deterministic algorithms, and studies performed with heuristic algorithms have recently been reported. However, since the two algorithms have their own advantages and disadvantages, interest in a hybrid method that complements the disadvantages and combines the advantages of the two algorithms is increasing. In this study, we proposed Newton-Raphson particle swarm optimization (NRPSO): a hybrid method that combines the Newton-Raphson method and the particle swarm optimization method, which are representative methods that utilize deterministic and heuristic algorithms, respectively. The BO-TMA performance obtained using the proposed NRPSO was tested by varying the measurement noise and number of measurements for three targets with different maneuvers. The results showed that the advantages of both methods were well combined, which improved the performance.

A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements.

Target detection and tracking is important in military as well as in civilian applications. In order to detect and track high-speed incoming threats, modern surveillance systems are equipped with multiple sensors to overcome the limitations of single-sensor based tracking systems. This research proposes the use of information from RADAR and Infrared sensors (IR) for tracking and estimating target state dynamics. A new technique is developed for information fusion of the two sensors in a way that enhances performance of the data association algorithm. The measurement acquisition and processing time of these sensors is not the same; consequently the fusion center measurements arrive out of sequence. To ensure the practicality of system, proposed algorithm compensates the Out of Sequence Measurements (OOSMs) in cluttered environment. This is achieved by a novel algorithm which incorporates a retrodiction based approach to compensate the effects of OOSMs in a modified Bayesian technique. The proposed modification includes a new gating strategy to fuse and select measurements from two sensors which originate from the same target. The state estimation performance is evaluated in terms of Root Mean Squared Error (RMSE) for both position and velocity, whereas, track retention statistics are evaluated to gauge the performance of the proposed tracking algorithm. The results clearly show that the proposed technique improves track retention and and false track discrimination (FTD).

Distributed Target Tracking in Challenging Environments Using Multiple Asynchronous Bearing-Only Sensors.

In the multiple asynchronous bearing-only (BO) sensors tracking system, there usually exist two main challenges: (1) the presence of clutter measurements and the target misdetection due to imperfect sensing; (2) the out-of-sequence (OOS) arrival of locally transmitted information due to diverse sensor sampling interval or internal processing time or uncertain communication delay. This paper simultaneously addresses the two problems by proposing a novel distributed tracking architecture consisting of the local tracking and central fusion. To get rid of the kinematic state unobservability problem in local tracking for a single BO sensor scenario, we propose a novel local integrated probabilistic data association (LIPDA) method for target measurement state tracking. The proposed approach enables eliminating most of the clutter measurement disturbance with increased target measurement accuracy. In the central tracking, the fusion center uses the proposed distributed IPDA-forward prediction fusion and decorrelation (DIPDA-FPFD) approach to sequentially fuse the OOS information transmitted by each BO sensor. The track management is carried out at local sensor level and also at the fusion center by using the recursively calculated probability of target existence as a track quality measure. The efficiency of the proposed methodology was validated by intensive numerical experiments.

X-ray Pulsar-Based Navigation Considering Spacecraft Orbital Motion and Systematic Biases.

The accuracy of X-ray pulsar-based navigation is greatly affected by the Doppler effect caused by the spacecraft orbital motion and the systematic biases introduced by the pulsar directional error, spacecraft-borne clock error, etc. In this paper, an innovative navigation method simultaneously employing the pulse phase (PP), the difference of two neighbor PPs (DPP) and the Doppler frequency (DF) of X-ray pulsars as measurements is proposed to solve this problem. With the aid of the spacecraft orbital dynamics, a single pair of PP and DF relative to the spacecraft's state estimation error can be estimated by using the joint probability density function of the arrival photon timestamps as the likelihood function. The systematic biases involved to the PP is proved to be nearly invariant over two adjacent navigation periods and the major part of it is eliminated in the DPP; therefore, the DPP is also exploited as additional navigation measurement to weaken the impact of systematic biases on navigation accuracy. Results of photon-level simulations show that the navigation accuracy of the proposed method is remarkably better than that of the method only using PP, the method using both PP and DF and the method using both PP and DPP for Earth orbit.

An Efficient Multi-Path Multitarget Tracking Algorithm for Over-The-Horizon Radar.

In target tracking environments using over-the-horizon radar (OTHR), one target may generate multiple detections through different signal propagation paths. Trackers need to jointly handle the uncertainties stemming from both measurement origin and measurement path. Traditional multitarget tracking algorithms suffer from high computational loads in such environments since they need to enumerate all possible joint measurement-to-track assignments considering the measurements paths unless they employ some approximations regarding the measurements and their corresponding paths. In this paper, we propose a novel algorithm, named multi-path linear multitarget integrated probabilistic data association (MP-LM-IPDA), to efficiently track multitarget in multiple detection environments. Instead of generating all possible joint assignments, MP-LM-IPDA calculates the modulated clutter measurement density for each measurement cell of each track. The modulated clutter measurement density considers the possibility that the measurement cells originate from the clutter as well as from other potential targets. By incorporating the modulated clutter measurement density, the single target tracking structure can be applied for multitarget tracking, which significantly reduces the computational load. The simulation results demonstrate the effectiveness and efficiency of the proposed algorithm.

Multisensor-Based Target-Tracking Algorithm with Out-of-Sequence-Measurements in Cluttered Environments.

A localization and tracking algorithm for an early-warning tracking system based on the information fusion of Infrared (IR) sensor and Laser Detection and Ranging (LADAR) is proposed. The proposed Kalman filter scheme incorporates Out-of-Sequence Measurements (OOSMs) to address long-range, high-speed incoming targets to be tracked by networked Remote Observation Sites (ROS) in cluttered environments. The Rauch⁻Tung⁻Striebel (RTS) fixed lag smoothing algorithm is employed in the proposed technique to further improve tracking accuracy, which, in turn, is used for target profiling and efficient filter initialization at the targeted platform. This efficient initialization increases the probability of target engagement by increasing the distance at which it can be effectively engaged. The increased target engagement range also reduces risk of any damage from debris of the engaged target. Performance of the proposed target localization algorithm with OOSM and RTS smoothing is evaluated in terms of root mean square error (RMSE) for both position and velocity, which accurately depicts the improved performance of the proposed algorithm in comparison with existing retrodiction-based OOSM filtering algorithms. The effects of assisted target state initialization at the targeted platform are also evaluated in terms of Time to Impact (TTI) and true track retention, which also depict the advantage of the proposed strategy.

Improved Multitarget Tracking in Clutter Using Bearings-Only Measurements.

Multitarget tracking in clutter using bearings-only measurements is a challenging problem. In this paper, a performance improved nonlinear filter is proposed on the basis of the Random Finite Set (RFS) theory and is named as Gaussian mixture measurements-based cardinality probability hypothesis density (GMMbCPHD) filter. The GMMbCPHD filter enables to address two main issues: measurement-origin-uncertainty and measurement nonlinearity, which constitutes the key problems in bearings-only multitarget tracking in clutter. For the measurement-origin-uncertainty issue, the proposed filter estimates the intensity of RFS of multiple targets as well as propagates the posterior cardinality distribution. For the measurement-origin-nonlinearity issue, the GMMbCPHD approximates the measurement likelihood function using a Gaussian mixture rather than a single Gaussian distribution as used in extended Kalman filter (EKF). The superiority of the proposed GMMbCPHD are validated by comparing with several state-of-the-art algorithms via intensive simulation studies.

[Efficacy and safety of the Jinling procedure in the treatment of adult Hirschsprung disease].

OBJECTIVE: To investigate the safety, efficacy and long-term outcomes of Jinling procedure in the treatment of adult Hirschsprung disease. METHODS: Clinical and follow-up data of 125 patients with adult Hirschsprung disease undergoing Jinling procedure at the Department of General Surgery between January 2000 and January 2013 were summarized. All the patients were diagnosed by CT, barium enema, anorectal pressure detection and pathology examination. Abdominal symptoms, gastrointestinal quality of life index(GIQLI, the lower score, the worse quality of life), Wexner constipation score (higher score indicated worse symptom), defecography (evaluation included rectocele, mucosal prolapse, intramucosal intussusception, perineal prolapse) and other operative complications were compared before and after operation. RESULTS: Among 125 patients, 69 were male and 56 were female with median age of (41.2±15.5) (18 to 75) years. The follow-up rates were 94.4%(118/125), 92.0%(115/125), 89.6%(112/125) and 88.0%(110/125) at postoperative months 1, 3, 6, and 12. Incidences of abdominal distension and abdominal pain were 100% and 82.4%(103/125) before operation, and were 7.3%(8/110) and 20.9%(23/110) at 12 months after surgery. Wexner score was significantly lower at postoperative months 1(8.7±2.9), 3 (7.2±2.8), 6(6.7±2.2) and 12(6.3±1.7) than that before operation (21.4±7.2) (P<0.01). GIQLI score was 51.6±11.9 before operation, though it decreased at 1 month (47.3±5.5)(P<0.05) after surgery, but increased significantly at postoperative months 3, 6, 12(68.9±8.0, 96.5±8.2, 103.2±8.6)(P<0.01). Abnormal rate of defecography was 70.4%(81/115), 48.2%(54/112) and 27.3%(30/110) at postoperative months 3, 6, 12, which was significantly lower than 91.2%(114/125) before operation (P<0.01). Morbidity of postoperative complication was 29.6%(37/125), including 5 cases of surgical site infection (4.0%), 2 of anastomotic bleeding (1.6%), 8 of anastomotic leakage (6.4%, one died of severe abdominal infection), 4 of urinary retention (3.2%), 3 of recurrent constipation (2.4%, without megacolon relapse), 11 of bowel obstruction (8.8%), 2 of anastomotic stricture(1.6%) and 2 of refractory staphylococcus aureus enteritis (1.6%, diagnosed by stool smear and culture, and both died finally). CONCLUSION: Jinling procedure is a safe and effective surgical procedure for adult Hirschsprung's disease.

Transient widespread cortical and splenial lesions in acute encephalitis/encephalopathy associated with primary Epstein-Barr virus infection.

Infection with Epstein-Barr virus (EBV) is very common and usually occurs in childhood or early adulthood. Encephalitis/encephalopathy is an uncommon but serious neurological complication of EBV. A case of EBV-associated encephalitis/encephalopathy with involvement of reversible widespread cortical and splenial lesions is presented herein. An 8-year-old Chinese girl who presented with fever and headache, followed by seizures and drowsiness, was admitted to the hospital. Magnetic resonance imaging revealed high signal intensities on diffusion-weighted imaging in widespread cortical and splenial lesions. The clinical and laboratory examination results together with the unusual radiology findings suggested acute encephalitis/encephalopathy due to primary EBV infection. After methylprednisolone pulse therapy together with ganciclovir, the patient made a full recovery without any brain lesions. The hallmark clinical-radiological features of this patient included severe encephalitis/encephalopathy at onset, the prompt and complete recovery, and rapidly reversible widespread involvement of the cortex and splenium. Patients with EBV encephalitis/encephalopathy who have multiple lesions, even with the widespread involvement of cortex and splenium of the corpus callosum, may have a favorable outcome with complete disappearance of all brain lesions.