Map fragmentation in two- and three-dimensional environments

2013 ◽  
Vol 36 (5) ◽  
pp. 569-570 ◽  
Author(s):  
Homare Yamahachi ◽  
May-Britt Moser ◽  
Edvard I. Moser
Keyword(s):  
Small Area ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Brain Circuits ◽  
Three Dimensional Space

AbstractThe suggestion that three-dimensional space is represented by a mosaic of neural map fragments, each covering a small area of space in the plane of locomotion, receives support from studies in complex two-dimensional environments. How map fragments are linked, which brain circuits are involved, and whether metric is preserved across fragments are questions that remain to be determined.

scholarly journals FOUR-DIMENSIONAL BALL IN A GRAPHIC REPRESENTATION

2021 ◽  
pp. 37-46
Author(s):  
Helena Bidnichenko
Keyword(s):  
Dimensional Space ◽  
Three Dimensional ◽  
Geometric Modelling ◽  
Vector Model ◽  
Original Position ◽  
Two Dimensional ◽  
Axis Of Rotation ◽  
Dimensional Ball ◽  
Three Dimensional Space ◽  
Horizontal Vector

The paper presents a method for geometric modelling of a four-dimensional ball. For this, the regularities of the change in the shape of the projections of simple geometric images of two-dimensional and three-dimensional spaces during rotation are considered. Rotations of a segment and a circle around an axis are considered; it is shown that during rotation the shape of their projections changes from the maximum value to the degenerate projection. It was found that the set of points of the degenerate projection belongs to the axis of rotation, and each n-dimensional geometric image during rotation forms a body of a higher dimension, that is, one that belongs to (n + 1) -dimensional space. Identified regularities are extended to the four-dimensional space in which the ball is placed. It is shown that the axis of rotation of the ball will be a degenerate projection in the form of a circle, and the ball, when rotating, changes its size from a volumetric object to a flat circle, then increases again, but in the other direction (that is, it turns out), and then in reverse order to its original position. This rotation is more like a deformation, and such a ball of four-dimensional space is a hypersphere. For geometric modelling of the hypersphere and the possibility of its projection image, the article uses the vector model proposed by P.V. Filippov. The coordinate system 0xyzt is defined. The algebraic equation of the hypersphere is given by analogy with the three-dimensional space along certain coordinates of the center a, b, c, d. A variant of hypersection at t = 0 is considered, which confirms by equations obtaining a two-dimensional ball of three-dimensional space, a point (a ball of zero radius), which coincides with the center of the ball, or an imaginary ball. For the variant t = d, the equation of a two-dimensional ball is obtained, in which the radius is equal to R and the coordinates of all points along the 0t axis are equal to d. The variant of hypersection t = k turned out to be interesting, in which the equation of a two-dimensional sphere was obtained, in which the coordinates of all points along the 0t axis are equal to k, and the radius is . Horizontal vector projections of hypersection are constructed for different values of k. It is concluded that the set of horizontal vector projections of hypersections at t = k defines an ellipse.  

scholarly journals Capacity of Inhomogeneous Hybrid Wireless Networks in Three-Dimensional Space

2015 ◽  
Vol 11 (9) ◽  
pp. 47
Author(s):  
Feng Wu ◽  
Jiang Zhu ◽  
Yilong Tian ◽  
Zhipeng Xi
Keyword(s):  
Sensor Node ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Network Capacity ◽  
Throughput Capacity ◽  
Two Dimensional ◽  
Hybrid Wireless Networks ◽  
Node Distribution ◽  
Analytical Expressions ◽  
Three Dimensional Space

Network capacity has been widely studied in recent years. However, most of the literatures focus on the networks where nodes are distributed in a two-dimensional space. In this paper, we propose a 3D hybrid sensor network model. By setting different sensor node distribution probabilities for cells, we divide all the cells in the network into dense cells and sparse cells. Analytical expressions of the aggregate throughput capacity are obtained. We also find that suitable inhomogeneity can increase the network throughput capacity.

scholarly journals The Algorithm for Determining the Coordinates of a Point in Three-Dimensional Space by Using the Auxiliary Point

2013 ◽  
Vol 48 (4) ◽  
pp. 141-145 ◽  
Author(s):  
Bartlomiej Oszczak ◽  
Eliza Sitnik
Keyword(s):  
Fixed Point ◽  
Precise Measurement ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Satellite Navigation ◽  
Reference Points ◽  
Two Dimensional ◽  
Approximate Location ◽  
The Many ◽  
Three Dimensional Space

ABSTRACT During the process of satellite navigation, and also in the many tasks of classical positioning, we need to calculate the corrections to the initial (or approximate) location of the point using precise measurement of distances to the permanent points of reference (reference points). In this paper the authors have provided a way of developing Hausbrandt's equations, on the basis of which the exact coordinates of the point in two-dimensional space can be determined by using the computed correction to the coordinates of the auxiliary point. The authors developed generalised equations for threedimensional space introducing additional fixed point and have presented proof of derived formulas.

scholarly journals Filling of three-dimensional space by two-dimensional sheet growth

2015 ◽  
Vol 92 (4) ◽  
Author(s):  
Merlin A. Etzold ◽  
Peter J. McDonald ◽  
David A. Faux ◽  
Alexander F. Routh
Keyword(s):  
Dimensional Space ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Three Dimensional Space

scholarly journals Chaotic Attractor Generation via Space Function Controls

2011 ◽  
Vol 2011 ◽  
pp. 1-11
Author(s):  
Hong Shi ◽  
Guangming Xie ◽  
Desheng Liu
Keyword(s):  
Linear Systems ◽  
Chaotic Attractor ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Underlying Mechanism ◽  
Two Dimensional ◽  
One Dimensional ◽  
Suitable Space ◽  
Space Function ◽  
Three Dimensional Space

The analysis of chaotic attractor generation is given, and the generation of novel chaotic attractor is introduced in this paper. The underlying mechanism involves two simple linear systems with one-dimensional, two-dimensional, or three-dimensional space functions. Moreover, it is demonstrated by simulation that various attractor patterns are generated conveniently by adjusting suitable space functions' parameters and the statistic behavior is also discussed.

A COMPARISON BETWEEN THE TWO-DIMENSIONAL AND THREE-DIMENSIONAL LATTICES

2004 ◽  
Vol 18 (25) ◽  
pp. 1301-1309 ◽  
Author(s):  
ANDREI DOLOCAN ◽  
VOICU OCTAVIAN DOLOCAN ◽  
VOICU DOLOCAN
Keyword(s):  
Lattice Constant ◽  
Cohesive Energy ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Coupling Constants ◽  
Two Dimensional ◽  
Coupling Constant ◽  
Dimensional Lattice ◽  
Analytical Formulae ◽  
Three Dimensional Space

By using a new Hamiltonian of interaction we have calculated the interaction energy for two-dimensional and three-dimensional lattices. We present also, approximate analytical formulae and the analytical formulae for the constant of the elastic force. The obtained results show that in the three-dimensional space, the two-dimensional lattice has the lattice constant and the cohesive energy which are smaller than that of the three-dimensional lattice. For appropriate values of the coupling constants, the two-dimensional lattice in a two-dimensional space has both the lattice constant and the cohesive energy, larger than that of the two-dimensional lattice in a three-dimensional space; this means that if there is a two-dimensional space in the Universe, this should be thinner than the three-dimensional space, while the interaction forces should be stronger. On the other hand, if the coupling constant in the two-dimensional lattice in the two-dimensional space is close to zero, the cohesive energy should be comparable with the cohesive energy from three-dimensional space but this two-dimensional space does not emit but absorbs radiation.

Sensor Localization in Three-Dimensional Space

2017 ◽  
pp. 120-144
Author(s):  
Habib M. Ammari ◽  
Angela J. Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Wireless Sensor ◽  
Two Dimensional ◽  
Localization Problem ◽  
Sensor Localization ◽  
Battery Power ◽  
Three Dimensional Space

Thanks to key advances in wireless communication and electronics, sensors have emerged as an appealing technology for several interesting applications, such as civilian (health and environment monitoring), natural (disaster detection), military (battlefield surveillance), and agricultural (precision agriculture) applications, to name a few. When grouped together, these sensors form a network to measure and gather data of the surrounding environment with respect to a specific phenomenon. The sensors are battery-powered, tiny devices that possess all the characteristics of a traditional computer, including storage, processing, and communication capabilities. In addition, these sensors are capable of sensing the environment and collecting data regarding several parameters, such as temperature, light, sound, vibration, etc. Unfortunately, all the sensors' capabilities are limited due to their physical size. In particular, the sensors have limited battery power as usually they are equipped with AA/AAA batteries whose lifetime is short. Therefore, the main challenge in the design of this type of network is the sensors' battery power (or energy), which is a critical component for the operation of the whole network. Moreover, these sensors communicate (possibly) wirelessly with each other to collect sensed data and accomplish the goals of their missions. To this end, the sensors are required to know their locations and those of their neighbors. Therefore, sensor localization is a crucial aspect for the design and development of wireless sensor networks. Various algorithms and protocols have been developed for sensor localization in both two-dimensional and three- dimensional wireless sensor networks. However, the problem of sensor localization in a three-dimensional space has not been investigated in the literature as extensively as its counterpart in a two-dimensional space. In this book chapter, we propose to study the sensor localization problem in three-dimensional wireless sensor networks. More precisely, this book chapter's sole focus will be on three-dimensional sensor deployment, and it aims to provide an overview of the existing solutions to the localization problem in a three-dimensional space. Basically, it proposes a classification of localization algorithms, and discusses different three-dimensional sensor localization approaches along with their motivation and evaluation.

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