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Realism and Performance
Published in Aditi Majumder, M. Gopi, Introduction to Visual Computing, 2018
Object space subdivision using bounding volumes as seen in the previous section can adapt to unique shapes of the objects and are effective in applications such as collision detection and view frustum culling. However, in applications that requires computation of relative positioning of objects, for example, from a view point in a particular direction, spatial subdivision of the scene becomes more useful than object level subdivision. A few spatial subdivision techniques in 3D $ 3 {\text{D}} $ include octree, k-d tree, and binary space partitioning. We will discuss octree subdivision in this section. For an in-depth treatise on other kinds of spatial partitioning techniques, refer to [Jimenez et al. 01].
Streaming Data Classification in Clustered Wireless Sensor Networks
Published in Ibrahiem M. M. El Emary, Anna Brzozowska, Shaping the Future of ICT, 2017
Manal Abdullah, Yassmeen Alghamdi
D-Stream is a density grid-based algorithm in which the data points are mapped to the corresponding grids and the grids are clustered based on the density (Amini, Saboohi, and Wah 2013). MR-Stream is an algorithm that can cluster data streams at multiple resolutions. The algorithm partitions the data space into cells and a tree-like data structure keeps the space partitioning. The MR-Stream increases the performance of clustering by determining the exact time to generate the clusters (Amini, Saboohi, and Wah 2013). FlockStream is a density-based clustering algorithm based on a bioinspired model. It uses the flocking model where agents are microclusters and they work independently. FlockStream merges online and offline phases. It can get the clustering results without performing offline clustering (Amini, Saboohi, and Wah 2013). DenStream, MR-Stream, D-Stream, and FlockStream are based on density-based clustering. They can effectively detect arbitrary shape clusters and handle noise, but their quality decreases when they are used for clusters with varying densities (Amini, Saboohi, and Wah 2013).
Delay Tolerant Monitoring of Mobility-Assisted WSN
Published in Athanasios Vasilakos, Yan Zhang, Thrasyvoulos V. Spyropoulos, Delay Tolerant Networks: Protocols and Applications, 2016
Abdelmajid Khelil, Faisal Karim Shaikh, Azad Ali, Neeraj Suri, Christian Reinl
For space partitioning, Voxel grid, triangulation (e.g., Voronoi or Delaunay), octree, k-d tree and BSP tree [37] can be used. All these schemes except the Voxel grid are dependent on the input data. For this reason we select the simple Voxel grid for space partitioning. The primitive parameter to divide the sensor field is the size of smallest fragment of area, i.e., grid-cell size or the partitioning resolution (r). The energy density in the cell is the basis to form a region. Selecting r is a crucial decision for creating the eMap. Depending on this resolution a cell may contain more than one SN. We refer to the residual energy value of one cell by the sum of the energy values of all the nodes in that cell.
Grid k-d tree approach for point location in polyhedral data sets – application to explicit MPC
Published in International Journal of Control, 2020
K-d trees are space-partitioning data structures for organising points in a k-dimensional space. They are a special case of binary space partitioning trees, and the online computation complexity is similar to that of BST. The K-d tree calculates the axis-aligned HP as the splitting HP, and as moving down the tree, the method cycles through the axis used to select the splitting HPs. The root node of the tree would have a 1st-axis-aligned splitting HP, the root’s children would have a 2nd-axis-aligned splitting HP, the root’s grandchildren would all have a 3rd-axis-aligned splitting HP, and so on. The next circle will be the same.