Finding optimal region for bichromatic reverse nearest neighbor in two- and three-dimensional spaces

2015 ◽  
Vol 20 (3) ◽  
pp. 351-384 ◽  
Author(s):  
Huaizhong Lin ◽  
Fangshu Chen ◽  
Yunjun Gao ◽  
Dongming Lu
Keyword(s):  
Nearest Neighbor ◽  
Three Dimensional ◽  
Reverse Nearest Neighbor ◽  
Optimal Region

Maximizing bichromatic reverse nearest neighbor for L p -norm in two- and three-dimensional spaces

2011 ◽  
Vol 20 (6) ◽  
pp. 893-919 ◽  
Author(s):  
Raymond Chi-Wing Wong ◽  
M. Tamer Özsu ◽  
Ada Wai-Chee Fu ◽  
Philip S. Yu ◽  
Lian Liu ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Three Dimensional ◽  
Reverse Nearest Neighbor

Energies and Electric Dipole Moments of the Bound Vibrational States of HN2+ and DN2+

2008 ◽  
Vol 73 (6-7) ◽  
pp. 873-897 ◽  
Author(s):  
Vladimír Špirko ◽  
Ota Bludský ◽  
Wolfgang P. Kraemer
Keyword(s):  
Dipole Moment ◽  
Nearest Neighbor ◽  
Energy Surface ◽  
Dipole Moments ◽  
Three Dimensional ◽  
Vibrational States ◽  
Spacing Distribution ◽  
Triatomic Molecules ◽  
Vibrational Levels ◽  
Different Levels

The adiabatic three-dimensional potential energy surface and the corresponding dipole moment surface describing the ground electronic state of HN2+ (Χ1Σ+) are calculated at different levels of ab initio theory. The calculations cover the entire bound part of the potential up to its lowest dissociation channel including the isomerization barrier. Energies of all bound vibrational and low-lying ro-vibrational levels are determined in a fully variational procedure using the Suttcliffe-Tennyson Hamiltonian for triatomic molecules. They are in close agreement with the available experimental numbers. From the dipole moment function effective dipoles and transition moments are obtained for all the calculated vibrational and ro-vibrational states. Statistical tools such as the density of states or the nearest-neighbor level spacing distribution (NNSD) are applied to describe and analyse general patterns and characteristics of the energy and dipole results calculated for the massively large number of states of the strongly bound HN2+ ion and its deuterated isotopomer.

scholarly journals Trajectory-Based Air-Writing Recognition Using Deep Neural Network and Depth Sensor

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 376 ◽  
Author(s):  
Md. Shahinur Alam ◽  
Ki-Chul Kwon ◽  
Md. Ashraful Alam ◽  
Mohammed Y. Abbass ◽  
Shariar Md Imtiaz ◽  
...  
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Short Term Memory ◽  
Three Dimensional ◽  
Recognition System ◽  
Writing Systems ◽  
Depth Sensor ◽  
Handheld Device ◽  
Proposed Model ◽  
Long Short Term Memory

Trajectory-based writing system refers to writing a linguistic character or word in free space by moving a finger, marker, or handheld device. It is widely applicable where traditional pen-up and pen-down writing systems are troublesome. Due to the simple writing style, it has a great advantage over the gesture-based system. However, it is a challenging task because of the non-uniform characters and different writing styles. In this research, we developed an air-writing recognition system using three-dimensional (3D) trajectories collected by a depth camera that tracks the fingertip. For better feature selection, the nearest neighbor and root point translation was used to normalize the trajectory. We employed the long short-term memory (LSTM) and a convolutional neural network (CNN) as a recognizer. The model was tested and verified by the self-collected dataset. To evaluate the robustness of our model, we also employed the 6D motion gesture (6DMG) alphanumeric character dataset and achieved 99.32% accuracy which is the highest to date. Hence, it verifies that the proposed model is invariant for digits and characters. Moreover, we publish a dataset containing 21,000 digits; which solves the lack of dataset in the current research.

Probabilistic Reverse Nearest Neighbor Queries on Uncertain Data

2010 ◽  
Vol 22 (4) ◽  
pp. 550-564 ◽  
Author(s):  
Muhammad Aamir Cheema ◽  
Xuemin Lin ◽  
Wei Wang ◽  
Wenjie Zhang ◽  
Jian Pei
Keyword(s):  
Nearest Neighbor ◽  
Uncertain Data ◽  
Reverse Nearest Neighbor ◽  
Nearest Neighbor Queries

Estimating Three-Dimensional Cloud Structure via Statistically Blended Satellite Observations

2014 ◽  
Vol 53 (2) ◽  
pp. 437-455 ◽  
Author(s):  
Steven D. Miller ◽  
John M. Forsythe ◽  
Philip T. Partain ◽  
John M. Haynes ◽  
Richard L. Bankert ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Dynamic Range ◽  
3D Structure ◽  
Three Dimensional ◽  
Near Neighbor ◽  
Model Verification ◽  
Cloud Base ◽  
Cloud Structure ◽  
Ground Track ◽  
Microphysical Properties

AbstractThe launch of the NASA CloudSat in April 2006 enabled the first satellite-based global observation of vertically resolved cloud information. However, CloudSat’s nonscanning W-band (94 GHz) Cloud Profiling Radar (CPR) provides only a nadir cross section, or “curtain,” of the atmosphere along the satellite ground track, precluding a full three-dimensional (3D) characterization and thus limiting its utility for certain model verification and cloud-process studies. This paper details an algorithm for extending a limited set of vertically resolved cloud observations to form regional 3D cloud structure. Predicated on the assumption that clouds of the same type (e.g., cirrus, cumulus, and stratocumulus) often share geometric and microphysical properties as well, the algorithm identifies cloud-type-dependent correlations and uses them to estimate cloud-base height and liquid/ice water content vertical structure. These estimates, when combined with conventional retrievals of cloud-top height, result in a 3D structure for the topmost cloud layer. The technique was developed on multiyear CloudSat data and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) swath data from the NASA Aqua satellite. Data-exclusion experiments along the CloudSat ground track show improved predictive skill over both climatology and type-independent nearest-neighbor estimates. More important, the statistical methods, which employ a dynamic range-dependent weighting scheme, were also found to outperform type-dependent near-neighbor estimates. Application to the 3D cloud rendering of a tropical cyclone is demonstrated.

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