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Approaches and Techniques for Maintenance and Operation of Multisink Wireless Sensor Networks
Published in Christopher Siu, Krzysztof Iniewski, IoT and Low-Power Wireless, 2018
Miriam Carlos-Mancilla, Ernesto López-Mellado, Mario Siller
In Buratti et al. (2007), a mathematical formulation used to optimize the average number of children per parent and the number of levels in one tree (tree height) through maximization of the network association probability is proposed. The topology formation is based on the IEEE 802.15.4 protocol. The authors run the algorithm M times; each time an independent topology is created, having fixed N nodes and S sinks on the network (multisink environment). A completely random distribution in space, i.e., Complete Spatial Randomness (CSR), is assumed to derive an average number of children per node and maximize the number of levels. The energy consumption and reconfiguration are not considered.
Study of silver electrodeposition in deep eutectic solvents using atomic force microscopy
Published in Transactions of the IMF, 2018
A. P. Abbott, M. Azam, K. S. Ryder, S. Saleem
Analysis of the nearest neighbour distance (NND) is a useful tool to characterise the spatial distribution.36 The spatial correlation between islands is examined using point pattern analysis which looks at the 2D distribution of nuclei as they grow.37Figure 5 shows the probability distribution of islands for NNDs at different times up to 100 sec; the data were obtained from the five AFM images (20 µm × 20 µm) and a total of 140 nuclei are analysed in each case. At short times, between 10 and 20 s, the spatial distribution is narrow but it broadens at longer times due to the coalescence of nuclei resulting in adatom depletion zones around the islands which suppresses nucleation in these regions. This phenomenon is consistent with surface diffusion-mediated lateral island growth. The solid lines in Figure 5 correspond to the fitted Poisson distribution for complete spatial randomness. The experimental data fit well to the Poisson distribution i.e. the probability of finding ‘n’ particles in any arbitrary region of area, A, follows a Poisson distribution,38Where ρ is the number of particles per unit area and corresponds to the island density if islands are considered as zero-volume particles.
Inferring 3D ellipsoids based on cross-sectional images with applications to porosity control of additive manufacturing
Published in IISE Transactions, 2018
Jianguo Wu, Yuan Yuan, Haijun Gong, Tzu-Liang (Bill) Tseng
The above problem is intractable in general and thus certain assumptions need to be made. In this article, we adopt one widely used assumption in spatial point pattern analysis and add another two assumptions specific to our problem as follows: The ellipsoidal particles are uniformly distributed in the 3D space of specimens with Complete Spatial Randomness (CSR), where all particles follow a homogeneous Poisson distribution and are independent of each other. CSR has been widely used to model spatial uniformity in point pattern analysis (Diggle, 2013; Zhou et al., 2014). It is the most fundamental assumption in stereological analysis (Russ, 2015), without which the statistical inference of 3D particles based on 2D sections is almost impossible.All of the particles are ellipsoidal in shape with semi-principal axes of length r1, r2, and r3 where r1, r2, and r3 are continuous random variables and r1 ⩽ r2 ⩽ r3. This assumption is more general than spherical shapes, where the latter is just a special case of the former with equal semi-principal axes. Again, this assumption is more realistic in practice, as in nanocomposites and additive manufactured products it is rare to see perfectly spherical particles or pores. The circularity and elongation of the intersected contours (Gong, 2013) indicate that an ellipsoid is more accurate in approximating the shape of particles.The orientations of the three principal axes of ellipsoidal particles are uniformly distributed in 3D space. This assumption indicates that if a particle is intersected, it could be cut by the image plane at any location and orientation with equal possibility.