2D Triangulation of Signals Source by Pole-Polar Geometric Models

The 2D point location problem has applications in several areas, such as geographic information systems, navigation systems, motion planning, mapping, military strategy, location and tracking moves. We aim to present a new approach that expands upon current techniques and methods to locate the 2D position of a signal source sent by an emitter device. This new approach is based only on the geometric relationship between an emitter device and a system composed of m≥2 signal receiving devices. Current approaches applied to locate an emitter can be deterministic, statistical or machine-learning methods. We propose to perform this triangulation by geometric models that exploit elements of pole-polar geometry. For this purpose, we are presenting five geometric models to solve the point location problem: (1) based on centroid of points of pole-polar geometry, PPC; (2) based on convex hull region among pole-points, CHC; (3) based on centroid of points obtained by polar-lines intersections, PLI; (4) based on centroid of points obtained by tangent lines intersections, TLI; (5) based on centroid of points obtained by tangent lines intersections with minimal angles, MAI. The first one has computational cost On and whereas has the computational cost Onlognwhere n is the number of points of interest.

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About the Point Location Problem

Handbook of Research on Geoinformatics ◽

10.4018/978-1-59140-995-3.ch013 ◽

2010 ◽

pp. 100-105

Author(s):

José Poveda ◽

Michael Gould

Keyword(s):

Location Problem ◽

Computational Cost ◽

Point Location ◽

Location Algorithm

In this chapter we present some well-known algorithms for the solution of the point location problem and for the more particular problem of point-in-polygon determination. These previous approaches to the problem are presented in the first sections. In the remainder of the paper, we present a quick location algorithm based on a quaternary partition of the space, as well as its associated computational cost.

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A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods

Applied Soft Computing ◽

10.1016/j.asoc.2021.107323 ◽

2021 ◽

pp. 107323

Author(s):

Ahmet Saygili

Keyword(s):

Machine Learning ◽

Computer Aided Detection ◽

Learning Methods ◽

New Approach ◽

X Ray ◽

Machine Learning Methods ◽

Computer Aided

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A thermal resistances-based approach for thermal-elastohydrodynamic calculations in point contacts

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406217713231 ◽

2017 ◽

Vol 232 (11) ◽

pp. 2088-2102 ◽

Cited By ~ 3

Author(s):

Eduardo de la Guerra Ochoa ◽

Javier Echávarri Otero ◽

Enrique Chacón Tanarro ◽

Benito del Río López

Keyword(s):

Friction Coefficient ◽

Film Thickness ◽

Thermal Effects ◽

Good Accuracy ◽

Computational Cost ◽

Point Contact ◽

Elastohydrodynamic Lubrication ◽

Point Contacts ◽

New Approach ◽

Ball On Disc

This article presents a thermal resistances-based approach for solving the thermal-elastohydrodynamic lubrication problem in point contact, taking the lubricant rheology into account. The friction coefficient in the contact is estimated, along with the distribution of both film thickness and temperature. A commercial tribometer is used in order to measure the friction coefficient at a ball-on-disc point contact lubricated with a polyalphaolefin base. These data and other experimental results available in the bibliography are compared to those obtained by using the proposed methodology, and thermal effects are analysed. The new approach shows good accuracy for predicting the friction coefficient and requires less computational cost than full thermal-elastohydrodynamic simulations.

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Subsurface reflectivity estimation from imaging of primaries and multiples using amplitude-normalized separated wavefields

Geophysics ◽

10.1190/geo2015-0385.1 ◽

2016 ◽

Vol 81 (3) ◽

pp. S101-S117 ◽

Cited By ~ 2

Author(s):

Alba Ordoñez ◽

Walter Söllner ◽

Tilman Klüver ◽

Leiv J. Gelius

Keyword(s):

Integral Equation ◽

Fredholm Integral Equation ◽

Computational Cost ◽

Image Point ◽

Point Location ◽

Multiple Reflections ◽

Frequency Space ◽

Propagation Effects ◽

The Matrix ◽

Spatial Domains

Several studies have shown the benefits of including multiple reflections together with primaries in the structural imaging of subsurface reflectors. However, to characterize the reflector properties, there is a need to compensate for propagation effects due to multiple scattering and to properly combine the information from primaries and all orders of multiples. From this perspective and based on the wave equation and Rayleigh’s reciprocity theorem, recent works have suggested computing the subsurface image from the Green’s function reflection response (or reflectivity) by inverting a Fredholm integral equation in the frequency-space domain. By following Claerbout’s imaging principle and assuming locally reacting media, the integral equation may be reduced to a trace-by-trace deconvolution imaging condition. For a complex overburden and considering that the structure of the subsurface is angle-dependent, this trace-by-trace deconvolution does not properly solve the Fredholm integral equation. We have inverted for the subsurface reflectivity by solving the matrix version of the Fredholm integral equation at every subsurface level, based on a multidimensional deconvolution of the receiver wavefields with the source wavefields. The total upgoing pressure and the total filtered downgoing vertical velocity were used as receiver and source wavefields, respectively. By selecting appropriate subsets of the inverted reflectivity matrix and by performing an inverse Fourier transform over the frequencies, the process allowed us to obtain wavefields corresponding to virtual sources and receivers located in the subsurface, at a given level. The method has been applied on two synthetic examples showing that the computed reflectivity wavefields are free of propagation effects from the overburden and thus are suited to extract information of the image point location in the angular and spatial domains. To get the computational cost down, our approach is target-oriented; i.e., the reflectivity may only be computed in the area of most interest.

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Relative time seislet transform

Geophysics ◽

10.1190/geo2019-0212.1 ◽

2020 ◽

Vol 85 (2) ◽

pp. V223-V232 ◽

Cited By ~ 1

Author(s):

Zhicheng Geng ◽

Xinming Wu ◽

Sergey Fomel ◽

Yangkang Chen

Keyword(s):

Seismic Data ◽

Computational Cost ◽

Lifting Scheme ◽

Real Data ◽

Data Sets ◽

Relative Time ◽

New Approach ◽

New Formulation ◽

Wavelet Lifting ◽

Seismic Images

The seislet transform uses the wavelet-lifting scheme and local slopes to analyze the seismic data. In its definition, the designing of prediction operators specifically for seismic images and data is an important issue. We have developed a new formulation of the seislet transform based on the relative time (RT) attribute. This method uses the RT volume to construct multiscale prediction operators. With the new prediction operators, the seislet transform gets accelerated because distant traces get predicted directly. We apply our method to synthetic and real data to demonstrate that the new approach reduces computational cost and obtains excellent sparse representation on test data sets.

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Random Walk-Based Solution to Triple Level Stochastic Point Location Problem

IEEE Transactions on Cybernetics ◽

10.1109/tcyb.2015.2446198 ◽

2016 ◽

Vol 46 (6) ◽

pp. 1438-1451 ◽

Cited By ~ 24

Author(s):

Wen Jiang ◽

De-Shuang Huang ◽

Shenghong Li

Keyword(s):

Random Walk ◽

Location Problem ◽

Point Location

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A new approach to map matching for in-vehicle navigation systems: the rotational variation metric

ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585) ◽

10.1109/itsc.2001.948625 ◽

2002 ◽

Cited By ~ 14

Author(s):

R.R. Joshi

Keyword(s):

Vehicle Navigation ◽

Navigation Systems ◽

Map Matching ◽

New Approach

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Elderly Fall Detection with an Accelerometer Using Lightweight Neural Networks

Electronics ◽

10.3390/electronics8111354 ◽

2019 ◽

Vol 8 (11) ◽

pp. 1354 ◽

Cited By ~ 5

Author(s):

Gaojing Wang ◽

Qingquan Li ◽

Lei Wang ◽

Yuanshi Zhang ◽

Zheng Liu

Keyword(s):

Neural Networks ◽

Computational Cost ◽

Fall Detection ◽

The Elderly ◽

Research Problem ◽

Wearable Devices ◽

The Past ◽

Machine Learning Methods ◽

Open Research ◽

Different Types

Falls have been one of the main threats to people’s health, especially for the elderly. Detecting falls in time can prevent the long lying time, which is extremely fatal. This paper intends to show the efficacy of detecting falls using a wearable accelerometer. In the past decade, the fall detection problem has been extensively studied. However, since the hardware resources of wearable devices are limited, designing highly accurate embeddable models with feasible computational cost remains an open research problem. In this paper, different types of shallow and lightweight neural networks, including supervised and unsupervised models are explored to improve the fall detection results. Experiment results on a large open dataset show that the lightweight neural networks proposed have obtained much better results than machine learning methods used in previous work. Moreover, the storage and computation requirements of these lightweight models are only a few hundredths of deep neural networks in literature. In tested lightweight neural networks, the best one is proved to be the supervised convolutional neural network (CNN) that can achieve an accuracy beyond 99.9% with only 441 parameters. Its storage and computation requirements are only 1.2 KB and 0.008 MFLOPs, which make it more suitable to be implemented in wearable devices with restricted memory size and computation power.

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