Optimised Spatial Sampling Scheme for Soil Electriclal Conductivity Based on Variance Quad-Tree (VQT) Method

The acquisition of precise soil data representative of the entire survey area, is a critical issue for many treatments such as irrigation or fertilization in precision agriculture. The aim of this study was to investigate the spatial variability of soil bulk electrical conductivity （ECb） in a coastal saline field and design an optimized spatial sampling scheme of ECb based on a sampling design algorithm, the variance quad-tree （VQT） method. Soil ECb data were collected from the field at 20 m interval in a regular grid scheme. The smooth contour map of the whole field was obtained by ordinary kriging interpolation, VQT algorithm was then used to split the smooth contour map into strata of different number desired, the sampling locations can be selected within each stratum in subsequent sampling. The result indicated that the probability of choosing representative sampling sites was increased significantly by using VQT method with the sampling number being greatly reduced compared to grid sampling design while retaining the same prediction accuracy. The advantage of the VQT method is that this scheme samples sparsely in fields where the spatial variability is relatively uniform and more intensive where the variability is large. Thus the sampling efficiency can be improved, hence facilitate an assessment methodology that can be applied in a rapid, practical and cost-effective manner.

一种支持多维空间数据管理的Quad-tree索引结构

本文讨论一种支持多维空间数据管理的Quad-tree索引结构,主要解决多维空间数据的快速查找问题。Quad-tree索引组织把索引空间划分为若干规则子空间,使得这些空间之间能建立起包含偏序关系,从而实现对空间数据的索引直接查找。文中还讨论了怎样用B^+树实现这种Quad-tree索引结构。

P2P环境下面向不确定数据的Top-k查询

分布式环境中的top-k查询已经有了广泛的研究.由于仪器不精确和网络延时等原因,大多数分布式数据都存在不确定性.文中基于水平分布在P2P网络中的不确定数据提出了一个有效的top-k查询处理方法.首先利用Quad-tree构建一个分布式的不确定数据的索引,并基于索引提出了一个空间剪枝算法.然后,根据局部top-k概率与全局top-k概率之间的关系提出不确定数据成为top-k概率的上界,根据top-k概率与skyline概率之间的关系提出不确定数据成为top-k概率的下界,通过两种概率剪枝算法来减少top-k查询在网络中的传输和计算代价,并且进一步减少候选集大小.最后文中采用采样的方法来计算候选集的top-k概率以确定最终的top-k查询结果.大量的实验验证了算法的有效性.

Large-Scale Terrain Simplification and Visualization Based on TIN

In the preprocessing phase, the global terrain model is partitioned into blocks with their feature points being picked out to generate TIN model for each terrain block, then the multi-resolution models of terrain organized in the form of quad-tree is created bottom-up. Cracks between terrain blocks are avoided by inserting vertices to form common boundaries. At run-time, a view-dependent LOD algorithm is used to control the loading and unloading of the proper blocks by an additional synchronous thread. To eliminate the artifacts created by LOD transitions, geomorphing is used in real-time. These rendering strategies increase the throughput of GPU and avoid imbalance of load among CPU, GPU and Disk I/O. Experimental results show that the system can perform visually smooth rendering of large-scale terrain scenes with fine quality at an average rate of 80 fps.

Spatio-temporal model for soil characteristic of reclamation land

The development of spatio-temporal data model is introduced. According to the soil characteristic of reclamation land, we adopt the base state with amendments model of multi-layer raster to organize the spatio-temporal data, using the combined data structure on linear quadtree and linear octree to code. The advantage of this model is that it can easily obtain the information of certain layer and integratedly analyze the data with other methods. Then, the methods of obtain and analyses are introduced. The method can provide a tool for the research of the soil characteristic change and spatial distribution in reclamation land.

High-Efficiency Video Coder in Pruned Environment Using Adaptive Quantization Parameter Selection

The high-efficiency video coder(HEVC)is one of the most advanced techniques used in growing real-time multimedia applications today.However,they require large bandwidth for transmission through bandwidth,and bandwidth varies with different video sequences/formats.This paper proposes an adaptive information-based variable quantization matrix(AIVQM)developed for different video formats having variable energy levels.The quantization method is adapted based on video sequence using statistical analysis,improving bit budget,quality and complexity reduction.Further,to have precise control over bit rate and quality,a multi-constraint prune algorithm is proposed in the second stage of the AI-VQM technique for pre-calculating K numbers of paths.The same should be handy to selfadapt and choose one of the K-path automatically in dynamically changing bandwidth availability as per requirement after extensive testing of the proposed algorithm in the multi-constraint environment for multiple paths and evaluating the performance based on peak signal to noise ratio(PSNR),bit-budget and time complexity for different videos a noticeable improvement in rate-distortion(RD)performance is achieved.Using the proposed AIVQM technique,more feasible and efficient video sequences are achieved with less loss in PSNR than the variable quantization method(VQM)algorithm with approximately a rise of 10%–20%based on different video sequences/formats.

Multi-Scale Object Perception with Embedding Textural Space

This paper mainly focuses on the issues about generic multi-scale object perception for detection or recognition. A novel computational model in visually-feature space is presented for scene & object representation to purse the underlying textural manifold statistically in nonparametric manner. The associative method approximately makes perceptual hierarchy in human-vision biologically coherency in specific quad-tree-pyramid structure, and the appropriate scale-value of different objects can automatically be selected by evaluating from well-defined scale function without any priori knowledge. The sufficient experiments truly demonstrate the effectiveness of scale determination in textural manifold with object localization rapidly.

Multimodal compression applied to biomedical data

In this paper, we introduce a novel approach to compress jointly a medical image and a multichannel bio-signals (e.g. ECG, EEG). This technique is based on the idea of Multimodal Compression (MC) which requires only one codec instead of multiple codecs. Objectively, biosignal samples are merged in the spatial domain of the image using a specific mixing function. Afterwards, the whole mixture is compressed using JPEG 2000. The spatial mixing function inserts samples in low-frequency regions, defined using a set of operations, including down-sampling, interpolation, and quad-tree decomposition. The decoding is achieved by inverting the process using a separation function. Results show that this technique allows better performances in terms of Compression Ratio (CR) compared to approaches which encode separately modalities. The reconstruction quality is evaluated on a set of test data using the PSNR (Peak Signal Noise Ratio) and the PRD (Percent Root Mean Square Difference), respectively for the image and biosignals.

NUMERICAL SIMULATION FOR SHALLOW FLOW AND POLLUTANT DISPERSION BASED ON QUAD-TREE MESHES

A 2D depth-averaged flow-pollutant coupled model based on quad-tree meshes was established to accurately simulate flows in water areas with irregular natural boundaries in this paper. The grids were generated by recursive subdivision about seeding points. A new neighbor-finding algorithm was presented. The governing equations were discrctized in collocated conservative variables by using the finite volume method, and the normal flux of mass, momentum and pollutants across the interlace between cells were computed by a Godunov-type Flux Difference Splitting （FDS） scheme. The model was applied to simulate flow fields around a groin. The computed values are in agreement with observed data. The results indicate that quad-tree meshes have fine local resolution, high efficiency and easy local refinement. It is clear that the quad-tree grid model can offer gains in efficiency when applied to complex flow domains or strong shear flows. Finally the model is applied to flow fields and concentration fields simulation in Jiangsu Haizhou Bay. The simulated polluted area is matched well with observations. Therefore, this model can be used to predict flow and concentration fields of actual water area with irregular natural land boundaries.

Self‑supervised zero‑shot dehazing network based on dark channel prior

Most learning-based methods previously used in image dehazing employ a supervised learning strategy,which is timeconsuming and requires a large-scale dataset.However,large-scale datasets are difcult to obtain.Here,we propose a selfsupervised zero-shot dehazing network(SZDNet)based on dark channel prior,which uses a hazy image generated from the output dehazed image as a pseudo-label to supervise the optimization process of the network.Additionally,we use a novel multichannel quad-tree algorithm to estimate atmospheric light values,which is more accurate than previous methods.Furthermore,the sum of the cosine distance and the mean squared error between the pseudo-label and the input image is applied as a loss function to enhance the quality of the dehazed image.The most signifcant advantage of the SZDNet is that it does not require a large dataset for training before performing the dehazing task.Extensive testing shows promising performances of the proposed method in both qualitative and quantitative evaluations when compared with state-of-the-art methods.

Single Image Dehazing with V-transform and Dark Channel Prior

Single image dehazing algorithm based on the dark channel prior may cause block effect and color distortion.To improve these limitations,this paper proposes a single image dehazing algorithm based on the V-transform and the dark channel prior,in which a hazy RGB image is converted into the HSI color space,and each component H,I and S is processed separately.The hue component H remains unchanged,the saturation component S is stretched after being denoised by a median filter.In the procession of intensity component,a quad-tree algorithm is presented to estimate the atmospheric light,the dark channel prior and the V-transform are used to estimate the transmission map.To reduce the computational complexity,the intensity component I is decomposed by the V-transformfirst,coarse transmission map is then estimated by applying the dark channel prior on the low frequency reconstruction image,and the guided filter is finally employed to refine the coarse transmission map.For images with sky regions,the haze removal effectiveness can be greatly improved by just increasing the minimum value of the transmission map.The proposed algorithm has low time complexity and performs well on a wide variety of images.The recovered images have more nature color and less color distortion compared with some state-of-the-art methods.