基于遗传算法的AUV三维海底路径规划

研究AUV在三维海底地形环境中的路径规划问题。针对三维地形中路径的不同特点，将路径分为3种类型。设计了优化路径的遗传算法实现方案，所提出的惩罚函数及启发性知识的使用，使算法能灵活的得到具有不同特点的最优路径。仿真实验表明了该算法的可行性与可靠性，对AUV的安全航行具有重要的理论意义及应用参考价值。     

一个输出恒定的三维地形多分辨率简化算法

提出了一种输出恒定的三维地形多分辨率简化算法。该算法根据飞行漫游交互仿真的视点参数对三维地形数据进行裁剪简化,并根据人眼视觉心理和显示设备特性进行多分辨率简化。其简化结果数据的数据量与三维地形总数据量和视点位置无关,从而实现恒定数据量输出,并能够通过参数来调节和评价简化的效果。由于实现了输出质量控制和数据量恒定,这一算法很适合应用于基于网络的大规模地形实时漫游系统。     

基于帧速率的地形渲染数据量自适应控制算法

针对大规模地形三维可视化系统在不同性能计算机平台上运行速度的差异,提出了一种基于帧速率的地形渲染数据量负反馈自适应控制算法,该算法以数据量与帧速率关系模型为基础,通过获取精确的系统渲染周期耗时,计算符合帧速率要求的地形数据量,并进行动态数据调整.基于FLTK(和OpenGL的系统实验表明,该算法可以根据特定计算机平台的性能,快速调整渲染数据量,使地形可视化系统稳定运行干预先设定的帧速率区间.该算法有效增强了三维地形可视化系统的平台适应能力,同时算法思想对于类似系统负载与耗时关系的研究也有借鉴意义.     

基于VRML和Java的三维地形生成方法研究

三维地形是地理信息系统的重要组成部分。文中基于Internet，利用VRML，Java实现了三维地形自动生成系统，并介绍了该系统的工作流程，若干关键技术及三维山脉地形生成实例。     

MapGis煤矿地质模型应用研究

本文主要研究了煤矿地质模型的建立以及应用，相比二维和三维之间的建模效果，三维地质模型直观，以及有利用后面的分析应用，二维相对来说，就显得没有那么好的效果，但是在分析的时候，二维地质模型就比较灵活了，对于地表面点的查询、地形的分析、剖面分析的剖面线高程的显示就有三维体现不了的效果，或者说三维有的就不能实现。文章将展开对于二维、三维地质模型的建立与应用优势的论述。     

基于鸡群算法的近场声源三维定位MUSIC算法

针对三维多重信号分类（Multiple Signal Classification，MUSIC）算法估计声源位置时计算速度慢，计算量大等缺点，提出了一种基于鸡群优化（Chicken Swarm Optimization，CSO）算法的近场声源三维定位算法。首先建立近场声源信号接收的数学模型，并选取三维MUSIC算法中的空间谱函数为文章算法中的适应度函数。通过不断迭代和局部搜索，以适应度值为指标对鸡群个体进行排序，最终得到最优鸡群个体的位置，即近场待测声源的坐标。仿真和实验结果表明：文中算法具有定位精度高、计算效率高、实时性好等优点，文中算法的平均用时仿真时为三维MUSIC算法平均用时的1.9%，实验时为三维MUSIC算法用时的3.2%。     

三维构造矢量模型的栅格表示方法及应用

根据三维空间数据模型的研究现状,提出了一种三维矢量模型的栅格表示方法,该方法采用在二维栅格上记录关键点对的方法来表示三维空间结构,能够表达空间中存在复杂断层的情况。作为这种表示方法的应用,给出了生成属性模型的算法。在栅格模型的基础上构建等时面模型,给出了等时面构建算法的计算公式和算法描述。根据等时面模型和测井数据,给出了生成精确三维属性栅格模型的算法。最后用实例验证了算法的有效性。     

星载SAR影像辐射误差校正算法研究

从SAR影像辐射误差校正原理出发，发展了一种利用DEM和SAR地理编码模型计算地面散射面积和当地入射角，进而校正地形引起的辐射误差的算法，并对计算公式的推导过程进行了论述。通过研究实例对该校正算法的处理效果进行了分析，结果表明算法可以在一定程度上去除地形引起的辐射误差，而且该算法仅要求用户具备产生地理编码影像的能力，而不需要自己建立地理编码模型，因此具有一定的实际应用和推广价值。     

水利水电工程三维数字地形建模与分析

以锦屏一级水电工程为例，分别利用Delaunay算法和多细节层次技术实现了坝区地形TIN模型和施工总布置区域巡航所需的多分辨率地形模型。并根据三维地质建模的特殊需要，引入了NURBS技术对TIN地形模型进行了简化，所获得的NURBS地形模型不仅存储量小，精度损失低，而且易于图形操作运算，便于可视化分析，满足多方面的要求。该模型的实现与成功应用，在很大程度上推动了水利水电工程向着数字化、可视化和智能化的方向发展。     

椭球体三维数据场可视化模型及实现

分析了三维数据场可视化的典型应用及两类基本构造方法；提出一种椭球体三维数据场可视化模型，详细论述该模型的物理意义及实现算法，并给出利用该模型及其算法实现三维数据场可视化的实例。通过对效果图的分析表明，该方法所建立的三维图像具有良好的真实感，运行速度快且交互性能强。     

3D scanning data processing and registration for the construction of urban scenes via image methods

The difficulty of the 3D registration mainly lies in the huge computation for a precise alignment, especially for large data of urban scenes. In this paper, we proposed an algorithm that converts this 3D problem into 2D case. The main idea is to map the point cloud onto the image. Then, SIFT algorithm is adopted to detect the key points of the images, and key points from two images corresponding to the two point clouds ready to be registered are matched to form several pairs. Next, the key point pairs construct an intrinsic link between the images. From the one-to-one correspondence between pixel and point, this relation can be converted back to 3D space, according to which a transform matrix can be consequently established. The resultant matrix aims at guiding the spatial transform to achieve an ideal 3D registration. Later experiments illustrate that it will obtain a preferable result in much less time.     

Research on 3D integer SDCT transform and quantisation

Abstract

Nowadays, the 3D discrete cosine transform (DCT) is applied widely in video coding. But the transform matrix of DCT is expressed with floating-point numbers, so the computational complexity is high, and more system resources are occupied. In addition, the 3D DCT is accomplished by operating 1D DCT to the rows, columns and pages of 3D data successively, which cannot embody the overall space performance of 3D transform well. To overcome these drawbacks, 3D integer submatrix discrete cosine transform (SDCT) method was proposed in the paper. First, several matrix operation methods were defined. Then, the basic principle and calculation method of 3D integer SDCT was deduced in detail. The main idea was to take the 3D data as a whole, and adopt the integer transform matrix instead of the floating-point transform matrix. Finally, the performances of 3D integer DCT operation were analysed, and the experimental results show that the transform effects based on 3D integer SDCT and 3D DCT are very similar.     

Three-dimensional range data compression using computer graphics rendering pipeline

This paper presents the idea of naturally encoding three-dimensional (3D) range data into regular two-dimensional (2D) images utilizing computer graphics rendering pipeline. The computer graphics pipeline provides a means to sample 3D geometry data into regular 2D images, and also to retrieve the depth information for each sampled pixel. The depth information for each pixel is further encoded into red, green, and blue color channels of regular 2D images. The 2D images can further be compressed with existing 2D image compression techniques. By this novel means, 3D geometry data obtained by 3D range scanners can be instantaneously compressed into 2D images, providing a novel way of storing 3D range data into its 2D counterparts. We will present experimental results to verify the performance of this proposed technique.     

A Joint Algorithm for Resource Allocation in D2D 5G Wireless Networks

With the rapid development of Internet technology, users have an increasing demand for data. The continuous popularization of traffic-intensive applications such as high-definition video, 3D visualization, and cloud computing has promoted the rapid evolution of the communications industry. In order to cope with the huge traffic demand of today’s users, 5G networks must be fast, flexible, reliable and sustainable. Based on these research backgrounds, the academic community has proposed D2D communication. The main feature of D2D communication is that it enables direct communication between devices, thereby effectively improve resource utilization and reduce the dependence on base stations, so it can effectively improve the throughput of multimedia data. One of the most considerable factor which affects the performance of D2D communication is the co-channel interference which results due to the multiplexing of multiple D2D user using the same channel resource of the cellular user. To solve this problem, this paper proposes a joint algorithm time scheduling and power control. The main idea is to effectively maximize the number of allocated resources in each scheduling period with satisfied quality of service requirements. The constraint problem is decomposed into time scheduling and power control subproblems. The power control subproblem has the characteristics of mixed-integer linear programming of NP-hard. Therefore, we proposed a gradual power control method. The time scheduling subproblem belongs to the NP-hard problem having convex-cordinality, therefore, we proposed a heuristic scheme to optimize resource allocation. Simulation results show that the proposed algorithm effectively improved the resource allocation and overcome the co-channel interference as compared with existing algorithms.     

基于集成学习的强鲁棒性三维点云数据分类研究

目的通过三维扫描仪得到的点云数据往往存在很多异常值,例如噪点、遗失点和外部点等。在这些异常值存在的情况下,为了提高三维点云数据的分类精度,提出一种基于集成学习的强鲁棒性三维点云数据分类方法。方法提出一种基于最大投票法的集成学习思想,将2个深度神经网络的分类结果进行集成,从而提高网络的泛化性和准确性;采用全局特征增强和中心损失函数来优化神经网络结构,提高分类精度并增强鲁棒性。结果文中方法缩短模型训练时间至30个迭代次数,且在有噪点、丢失点和外部点的情况下分类精度均得到有效提升。结论提出的EL-3D算法在含有噪点、丢失点和外部点的情况下,鲁棒性效果要优于目前的点云分类方法。     

Exploring three-dimensional matrix-assisted laser desorption/ionization imaging mass spectrometry data: three-dimensional spatial segmentation of mouse kidney

Three-dimensional (3D) imaging has a significant impact on many challenges of life sciences. Three-dimensional matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) is an emerging label-free bioanalytical technique capturing the spatial distribution of hundreds of molecular compounds in 3D by providing a MALDI mass spectrum for each spatial point of a 3D sample. Currently, 3D MALDI-IMS cannot tap its full potential due to the lack efficient computational methods for constructing, processing, and visualizing large and complex 3D MALDI-IMS data. We present a new pipeline of efficient computational methods, which enables analysis and interpretation of a 3D MALDI-IMS data set. Construction of a MALDI-IMS data set was done according to the state-of-the-art protocols and involved sample preparation, spectra acquisition, spectra preprocessing, and registration of serial sections. For analysis and interpretation of 3D MALDI-IMS data, we applied the spatial segmentation approach which is well-accepted in analysis of two-dimensional (2D) MALDI-IMS data. In line with 2D data analysis, we used edge-preserving 3D image denoising prior to segmentation to reduce strong and chaotic spectrum-to-spectrum variation. For segmentation, we used an efficient clustering method, called bisecting k-means, which is optimized for hierarchical clustering of a large 3D MALDI-IMS data set. Using the proposed pipeline, we analyzed a central part of a mouse kidney using 33 serial sections of 3.5 μm thickness after the PAXgene tissue fixation and paraffin embedding. For each serial section, a 2D MALDI-IMS data set was acquired following the standard protocols with the high spatial resolution of 50 μm. Altogether, 512?495 mass spectra were acquired that corresponds to approximately 50 gigabytes of data. After registration of serial sections into a 3D data set, our computational pipeline allowed us to reveal the 3D kidney anatomical structure based on mass spectrometry data only. Finally, automated analysis discovered molecular masses colocalized with major anatomical regions. In the same way, the proposed pipeline can be used for analysis and interpretation of any 3D MALDI-IMS data set in particular of pathological cases.     

Three-dimensional angle measurement based on propagation vector analysis of digital holography

We propose what we believe to be a novel method for highly accurate three-dimensional (3D) angle measurement based on propagation vector analysis of digital holography. Three-dimensional rotations in space can be achieved by use of a CCD camera and a multifacet object, which reflects an incident wave into different directions. The propagation vectors of the reflected waves from the object can be extracted by analyzing the object spectrum of the recorded hologram. Any small rotation of the object will induce a change in the propagation vectors in space, which can then be used for 3D angle measurement. Experimental results are presented to verify the idea.     

3D multiwavelet based block coding algorithm for compression of volumetric medical images

Three dimensional (3D) medical images possess some specific characteristics that should be utilized by an efficient compression scheme. In this article, one such compression scheme for volumetric 3D medical image data is presented. Two processes involved in this scheme are decorrelation and encoding. Decorrelation of the 3D data is realized through 3D multiwavelet transform with apt prefiltering so as to give good representation of the image which could be exploited by the encoder. Encoding is done through proposed Block Coding Algorithm, which is embedded, block based, and wavelet transform coding algorithm without maintaining any list structures. The idea behind this algorithm is to sort the 3D transform coefficients in to a 1D array with respect to declining thresholds and to use state table to keep track of the blocks and coefficients that has been coded. In the experiment conducted on various 3D magnetic resonance and computed tomography images of human brain with multiwavelets such as Geronimo–Hardin–Massopust, Chui‐Lian, and orthogonal symmetric/antisymmetric (SA4), efficiency of the proposed scheme was weighed against the state of art encoders such as 3D Set Partitioning in Hierarchical Trees, 2D Set Partitioned Embedded BloCK Coder, and No List SPIHT. Attributes used for performance measurements are peak signal to noise ratio, bit rate, and structural similarity index of reconstructed image with respect to original image. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 182–192, 2014     

Observer for a thick layer of solid deuterium-tritium using backlit optical shadowgraphy and interferometry

Our work is in the context of the French "laser mégajoule" project, about fusion by inertial confinement. The project leads to the problem of characterizing the inner surface, of the approximately spherical target, by optical shadowgraphy techniques. Our work is entirely based on the basic idea that optical shadowgraphy produces "caustics" of systems of optical rays, which contain a great deal of 3D information about the surface to be characterized. We develop a method of 3D reconstruction based upon this idea plus a "small perturbations" technique. Although computations are made in the special "spherical" case, the method is in fact general and may be extended to several other situations.     

Augmenting the bootstrap to analyze high dimensional genomic data

The data produced by high-throughput genomic techniques are often high dimensional and undersampled. In these settings, statistical analyses that require the inversion of covariance matrices, such as those pursuing supervised dimension reduction or the assessment of interdependence structures, are problematic. In this article we show how the idea of adding noise to the bootstrap, pioneered by Efron, and Silverman and Young, in the late seventies and eighties, can be used to overcome undersampling and effectively estimate the inverse covariance matrix for data sets in which the number of observations is small relative to the number of variables. We demonstrate the performance of this approach, which we call augmented bootstrap, on simulated data and on data derived from genomic DNA sequences and microarray experiments. This invited paper is discussed in the comments available at: , , , , , , , . This work was partially supported by NIH grant HG02238 to W. Miller, NIH grant R01-GM072264 to K. Makova, and NSF grant DMS-0704621 to R.D. Cook, B. Li and F. Chiaromonte.