Clustering and merging for real-time forest rendering

Rendering of large-scale forest scenes is a challenging task, whose highly geometric complexity will put heavy burden on current graphics hardware. When navigating the scene, the overall visual result is generally considered as the core concern. A new method is proposed in this paper for large-scale forest rendering using clustering and merging strategies. Our method improves the rendering effect by clustering polygons according to the point information with relation to neighbours. A fast forest rendering system is developed accordingly. The relative techniques in the system can improve the visual quality on demand of different applications.

Improved Region Merging Algorithm for Remote Sensing Images

To segment high-resolution remote sensing images(RSIs)accurately on an object level and meet the precise boundary dividing requirement,an improved superpixel segmentation and region merging algorithm is proposed.Simple linear iterative clustering(SLIC)is widely used because of its advantages in performance and effect;however,it causes over-segmentation,which is very disadvantageous to information extraction.In this proposed method,SLIC is firstly adopted for initial superpixel partition.The second stage follows the iterative merging procedure,which uses a hierarchical clustering algorithm and introduces a local binary pattern(LBP)texture feature operator during the process of merging.The experimental results indicate that the proposed method achieved a good segmentation and region merging performance,and worked effectively on cloud detection preprocessing in high-resolution RSIs with cloud and snow overlap situations.

A Strategy for Merging Objective Estimates of Global Daily Precipitation from Gauge Observations, Satellite Estimates, and Numerical Predictions

This paper describes a strategy for merging daily precipitation information from gauge observations, satellite estimates （SEs）, and numerical predictions at the global scale. The strategy is designed to remove systemic bias and random error from each individual daily precipitation source to produce a better gridded global daily precipitation product through three steps. First, a cumulative distribution function matching procedure is performed to remove systemic bias over gauge-located land areas. Then, the overall biases in SEs and model predictions （MPs） over ocean areas are corrected using a rescaled strategy based on monthly precipitation. Third, an optimal interpolation （OI）-based merging scheme （referred as the HL-OI scheme） is used to combine unbiased gahge observations, SEs, and MPs to reduce random error from each source and to produce a gauge--satellite-model merged daily precipitation analysis, called BMEP-d （Beijing Climate Center Merged Estimation of Precipitation with daily resolution）, with complete global coverage. The BMEP-d data from a four-year period （2011- 14） demonstrate the ability of the merging strategy to provide global daily precipitation of substantially improved quality. Benefiting from the advantages of the HL-OI scheme for quantitative error estimates, the better source data can obtain more weights during the merging processes. The BMEP-d data exhibit higher consistency with satellite and gauge source data at middle and low latitudes, and with model source data at high latitudes. Overall, independent validations against GPCP-1DD （GPCP one-degree daily） show that the consistencies between B MEP-d and GPCP-1DD are higher than those of each source dataset in terms of spatial pattern, temporal variability, probability distribution, and statistical precipitation events.

Analyzing floor-stair merging flow based on experiments and simulation

In most situations,staircase is the only egress to evacuate from high-rise buildings.The merging flow on the stair landing has a great influence on the evacuation efficiency.In this paper,we develop an improved cellular automaton model to describe the merging behavior,and the model is validated by a series of real experiments.It is found that the flow rate of simulation results is similar to the drills,which means that the improved model is reasonable and can be used to describe the merging behavior on stairs.Furthermore,some scenarios with different door locations and building floor numbers are simulated by the model.The results show that(i)the best door location is next to the upward staircase;(ii)the total evacuation time and the building floor number are linearly related to each other;(iii)the pedestrians on upper floors have a negative influence on the evacuation flow rate.

An investigation of merging and diverging cars on a multi-lane road using a cellular automation model

In this paper, we have investigated two observed situations in a multi-lane road. The first one concerns a fast merging vehicle. The second situation is related to the case of a fast vehicle leaving the fastest lane back into the slowest lane and targeting a specific way out. We are interested in the relaxation time T, i.e., which is the time that the merging （diverging） vehicle spends before reaching the desired lane. Using analytical treatment and numerical simulations for the NaSch model, we have found two states, namely, the free state in which the merging （diverging） vehicle reaches the desired lane, and the trapped state in which T diverges. We have established phase diagrams for several values of the braking probability. In the second situation, we have shown that diverging from the fast lane targeting a specific way out is not a simple task. Even if the diverging vehicle is in the free phase, two different states can be distinguished. One is the critical state, in which the diverging car can probably reach the desired way out. The other is the safe state, in which the diverging car can surely reach the desired way out. In order to be in the safe state, we have found that the driver of the diverging car must know the critical distance （below which the way out will be out of his reach） in each lane. Furthermore, this critical distance depends on the density of cars, and it follows an exponential law.

MERGING AND SPLITTING SECOND-ORDER SELF-SIMILAR PROCESSES (TRAFFICS)

Recent traffic measurements in corporate LANs, Variable Bit Rate (VBR) video sources, ISDN control channels, and other communication systems, have indicated traffic behavior of self similar nature, which has implications for design, control and analysis of high speed networks. Merging and splitting are two basic networking operations. This paper gave the necessary and sufficient conditions for that merging of second order self similar traffic streams also results in a second order self similar stream. It shows that splitting traffic streams of the second order self similar stream are still self similar streams by the independent splitting operation.

Approximate merging of B-spline curves and surfaces

Applying the distance function between two B-spline curves with respect to the L2 norm as the approximate error, we investigate the problem of approximate merging of two adjacent B-spline curves into one B-spline curve. Then this method can be easily extended to the approximate merging problem of multiple B-spline curves and of two adjacent surfaces. After minimizing the approximate error between curves or surfaces, the approximate merging problem can be transformed into equations solving. We express both the new control points and the precise error of approximation explicitly in matrix form. Based on homogeneous coordinates and quadratic programming, we also introduce a new framework for approximate merging of two adjacent NURBS curves. Finally, several numerical examples demonstrate the effectiveness and validity of the algorithm.

A Ka-band Solid-state Transmitter Cloud Radar and Data Merging Algorithm for Its Measurements

This study concerns a Ka-band solid-state transmitter cloud radar, made in China, which can operate in three different work modes, with different pulse widths, and coherent and incoherent integration numbers, to meet the requirements for cloud remote sensing over the Tibetan Plateau. Specifically, the design of the three operational modes of the radar（i.e., boundary mode M1, cirrus mode M2, and precipitation mode M3） is introduced. Also, a cloud radar data merging algorithm for the three modes is proposed. Using one month＇s continuous measurements during summertime at Naqu on the Tibetan Plateau,we analyzed the consistency between the cloud radar measurements of the three modes. The number of occurrences of radar detections of hydrometeors and the percentage contributions of the different modes＇ data to the merged data were estimated.The performance of the merging algorithm was evaluated. The results indicated that the minimum detectable reflectivity for each mode was consistent with theoretical results. Merged data provided measurements with a minimum reflectivity of -35 dBZ at the height of 5 km, and obtained information above the height of 0.2 km. Measurements of radial velocity by the three operational modes agreed very well, and systematic errors in measurements of reflectivity were less than 2 dB. However,large discrepancies existed in the measurements of the linear depolarization ratio taken from the different operational modes.The percentage of radar detections of hydrometeors in mid- and high-level clouds increased by 60% through application of pulse compression techniques. In conclusion, the merged data are appropriate for cloud and precipitation studies over the Tibetan Plateau.

Effect of Merging of the Convective Cloud Clusters on Occurrence of Heavy Rainfall

Sun et al., (1983) have given some favourable environmental conditions and have shown that there are four common features in convective rainstorms. In this paper, an important process of evolution of cloud systems was revealed when heavy rainfall occurred based on the diagnostic analysis of heavy rainfall cases. When the different cloud systems merge into a large one, the mesoscale heavy rainfall occurs and enhances. In other words, the process of evolution of cloud systems emphasized in this paper is the process of interaction between two cloud systems when the heavy rainfall occurs. The favourable environmental condition is also investigated.

Visualizing diffusion tensor fields on streamsurfaces with merging ellipsoids

Streamsurfaces in diffusion tensor fields are used to represent structures with pri- marily planar diffusion. So far, however, no effort has been made on the visualization of the anisotropy of diffusion on them, although this information is very important to identify the problematic regions of these structures. We propose two methods to display this anisotropy information. The first one employs a set of merging ellipsoids, which simultaneously character- ize the local tensor details - anisotropy - on them and portray the shape of the streamsurfaces. The weight between the streamsurfaces continuity and the discrete local tensors can be inter- actively adjusted by changing some given parameters. The second one generates a dense LIC （line integral convolution） texture of the two tangent eigenvector fields along the streamsurfaces firstly, and then blends in some color mapping indicating the anisotropy information. For high speed and high quality of texture images, we confine both the generation and the advection of the LIC texture in the image space. Merging ellipsoids method reveals the entire anisotropy information at discrete points by exploiting the geometric attribute of ellipsoids, and thus suits for local and detailed examination of the anisotropy; the texture-based method gives a global representation of the anisotropy on the whole streamsurfaces with texture and color attributes. To reveal the anisotropy information more efficiently, we integrate the two methods and use them at two different levels of details.

An Improvement of Data Cleaning Method for Grain Big Data Processing Using Task Merging

Data quality has exerted important influence over the application of grain big data, so data cleaning is a necessary and important work. In MapReduce frame, parallel technique is often used to execute data cleaning in high scalability mode, but due to the lack of effective design, there are amounts of computing redundancy in the process of data cleaning, which results in lower performance. In this research, we found that some tasks often are carried out multiple times on same input files, or require same operation results in the process of data cleaning. For this problem, we proposed a new optimization technique that is based on task merge. By merging simple or redundancy computations on same input files, the number of the loop computation in MapReduce can be reduced greatly. The experiment shows, by this means, the overall system runtime is significantly reduced, which proves that the process of data cleaning is optimized. In this paper, we optimized several modules of data cleaning such as entity identification, inconsistent data restoration, and missing value filling. Experimental results show that the proposed method in this paper can increase efficiency for grain big data cleaning.

RESEARCH ON OPTIMIZING THE MERGING RESULTS OF MULTIPLE INDEPENDENT RETRIEVAL SYSTEMS BY A DISCRETE PARTICLE SWARM OPTIMIZATION

The result merging for multiple Independent Resource Retrieval Systems (IRRSs), which is a key component in developing a meta-search engine, is a difficult problem that still not effectively solved. Most of the existing result merging methods, usually suffered a great influence from the usefulness weight of different IRRS results and overlap rate among them. In this paper, we proposed a scheme that being capable of coalescing and optimizing a group of existing multi-sources-retrieval merging results effectively by Discrete Particle Swarm Optimization (DPSO). The experimental results show that the DPSO, not only can overall outperform all the other result merging algorithms it employed, but also has better adaptability in application for unnecessarily taking into account different IRRS's usefulness weight and their overlap rate with respect to a concrete query. Compared to other result merging algorithms it employed, the DPSO's recognition precision can increase nearly 24.6%, while the precision standard deviation for different queries can decrease about 68.3%.

Splitting and Merging Based Multi-model Fitting for Point Cloud Segmentation

This paper deals with the massive point cloud segmentation processing technology on the basis of machine vision, which is the second essential factor for the intelligent data processing of three dimensional conformation in digital photogrammetry. In this paper, multi-model fitting method is used to segment the point cloud according to the spatial distribution and spatial geometric structure of point clouds by fitting the point cloud into different geometric primitives models. Because point cloud usually possesses large amount of 3D points, which are uneven distributed over various complex structures, this paper proposes a point cloud segmentation method based on multi-model fitting. Firstly, the pre-segmentation of point cloud is conducted by using the clustering method based on density distribution. And then the follow fitting and segmentation are carried out by using the multi-model fitting method based on split and merging. For the plane and the arc surface, this paper uses different fitting methods, and finally realizing the indoor dense point cloud segmentation. The experimental results show that this method can achieve the automatic segmentation of the point cloud without setting the number of models in advance. Compared with the existing point cloud segmentation methods, this method has obvious advantages in segmentation effect and time cost, and can achieve higher segmentation accuracy. After processed by method proposed in this paper, the point cloud even with large-scale and complex structures can often be segmented into 3D geometric elements with finer and accurate model parameters, which can give rise to an accurate 3D conformation.

Quantitative estimation of hourly precipitation in the Tianshan Mountains based on area-to-point kriging downscaling and satellite-gauge data merging

Precipitation, a basic component of the water cycle, is significantly important for meteorological, climatological and hydrological research. However, accurate estimation on the precipitation remains considerably challenging because of the sparsity of gauge networks and the large spatial variability of precipitation over mountainous regions. Moreover, meteorological stations in mountainous areas are often dispersed and have difficulty in accurately reflecting the intensity and evolution of precipitation events. In this study,we proposed a novel method to produce high-quality,high-resolution precipitation estimates in the Tianshan Mountains, China, based on area-to-point kriging(ATPK) downscaling and a two-step correction, i.e., probability density function matching-optimum interpolation(PDF-OI). We obtained 1-km hourly precipitation data in the Tianshan Mountains by merging estimates from the Integrated Multisatellite Measurement(IMERG) product with observations from 1065 meteorological stations in the warm season(May to September) during 2016–2018. The spatial resolution and accuracy of the merged precipitation data greatly increased compared to IMERG.According to a cross-validation with gauged observations, the correlation coefficient(CC),probability of detection(POD) and critical success index(CSI) increased from 0.30, 0.50 and 0.24 for IMERG to 0.63, 0.65 and 0.38, respectively, for the merged estimates, and the root mean squared error(RMSE), mean error(ME) and false alarm ratio(FAR)decreased from 0.46 to 0.38 mm/h, 0.06 to 0.05 mm/h and 0.69 to 0.52, respectively. The proposed method will be useful for developing high-resolution precipitation estimates in mountainous areas such as central Asia and the Belt and Road Initiative regions.

Molecular Dynamics Simulation of Behaviours of Non-Polar Droplets Merging and Interactions with Hydrophobic Surfaces

This paper presents a molecular dynamics simulation of the behaviours of non-polar droplets merging and also the fluid molecules interacting with a hydrophobic surface. Such behaviours and transport phenomena are popular in general microchannel flow boiling and two-phase flow. The droplets are assumed to be composed of Lennards-Jones type molecules. Periodic boundary conditions are applied in three coordinate directions of a 3-D system, where there exist two liquid droplets and their vapour. The two droplets merge when they come within the prescribed small distance. The merging of two droplets apart from each other at different initial distances is tested and the possible larger （or critical） non-dimensional distance, in which droplets merging can occur, is discussed. The evolution of the merging process is simulated numerically by employing the Molecular Dynamics （MD） method. For interactions with hydrophobic solid wall, a system with fluid confined between two walls is used to study the wetting phenomena of fluid and solid wall. The results are compared with those of hydrophilic wall to show the unique characteristics of hydrophobic interactions by microscopic methods.

Identification of VC merging source during multipoint-to-point communication

The VC merging algorithm is used widely for multipoint to point ATM communication. For AAL5 lacking multiplex ID field, the downstream receiving point cannot distinguish the multiplexed source in a multipoint to point VC after VC merging. So the source based rate distribution algorithms cannot work properly. We designed a source detection algorithm to enable the switch to know the number of sources and the cell rate of each source in a multipoint to point VC. These information is very important for the source based rate distribution algorithm. We used the ERICA algorithm in our simulations and the simulations demonstrated the proper operation of the source detection algorithm.

Optimized Content-Aware Image Resizing with Merging and Improved Importance Diffusion

A simple and effective content-aware image resizing method is proposed based on the row / column merging and improved importance diffusion,which preserves the important regions in an image as well as the global visual effect. By repeatedly merging two rows / columns into one row / column or inserting a new row /column between two rows / columns, this method realizes image-resolution reduction and expansion. The importance of the merged row / column is promoted and diffused to four rows / columns around the merged one,which is to avoid the unwanted image distortions resulted from excessively merging of un-important regions. In addition,the proposed method introduces the direction of gradient vector in the low-pass filter to reduce the interference caused by complex texture background and protect important content better. Furthermore,according to human mechanics principles,different weights are given to the row and column direction components of gradient vectors which can obtain better global visual effect. Experimented results show that the proposed method satisfied in not only visual effect but also objective evaluation.

Simulation on motion of particles in vortex merging process

In a two-phase flow, the vortex merging influences both the flow evolution and the particle motion. With the blobs-splitting-and-merging scheme, the vortex merging is calculated by a corrected core spreading vortex method （CCSVM）. The particle motion in the vortex merging process is calculated according to the particle kinetic model. The results indicate that the particle traces are spiral lines with the same rotation direction as the spinning vortex. The center of the particle group is in agreement with that of the merged vortex. The merging time is determined by the circulation and the initial ratio of the vortex radius and the vortex center distance. Under a certain initial condition, a stretched particle trail is generated, which is determined by the viscosity, the relative position between the particles and the vortex, and the asymmetrical circulation of the two merging vortices.

Multi-sensor data merging of sea ice concentration and thickness

With the rapid change in the Arctic sea ice,a large number of sea ice observations have been collected in recent years,and it is expected that an even larger number of such observations will emerge in the coming years.To make the best use of these observations,in this paper we develop a multi-sensor optimal data merging(MODM)method to merge any number of different sea ice observations.Since such merged data are independent on model forecast,they are valid for model initialization and model validation.Based on the maximum likelihood estimation theory,we prove that any model assimilated with the merged data is equivalent to assimilating the original multi-sensor data.This greatly facilitates sea ice data assimilation,particularly for operational forecast with limited computational resources.We apply the MODM method to merge sea ice concentration(SIC)and sea ice thickness(SIT),respectively,in the Arctic.For SIC merging,the Special Sensor Microwave Imager/Sounder(SSMIS)and Advanced Microwave Scanning Radiometer 2(AMSR2)data are merged together with the Norwegian Ice Service ice chart.This substantially reduces the uncertainties at the ice edge and in the coastal areas.For SIT merging,the daily Soil Moisture and Ocean Salinity(SMOS)data is merged with the weekly-mean merged CryoSat-2 and SMOS(CS2SMOS)data.This generates a new daily CS2SMOS SIT data with better spatial coverage for the whole Arctic.

Dynamic splitting and merging of an atom cloud on an atom chip

Chip-based atom interferometers bring together the advantages of atom chips and Bose-Einstein condensates. Their central prerequisite is that a condensate can be coherently split into two halves with a determined relative phase. This paper demonstrates the dynamical splitting and merging of an atom cloud with two U-wires on an atom chip. Symmetrical and asymmetrical splittings are realized by applying a bias field with different directions and magnitudes. The trajectories of the splitting are consistent with theoretical calculations. The atom chip is a good candidate for constructing an atom interferometer.