scholarly journals Estimation of Indoor Location Through Magnetic Field Data: An Approach Based On Convolutional Neural Networks

2020 ◽  
Vol 9 (4) ◽  
pp. 226
Author(s):  
Carlos E. Galván-Tejada ◽  
Laura A. Zanella-Calzada ◽  
Antonio García-Domínguez ◽  
Rafael Magallanes-Quintanar ◽  
Huizilopoztli Luna-García ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Area Under The Curve ◽  
Training Data ◽  
Spectral Evolution ◽  
Data Set ◽  
Magnetic Field Data ◽  
Indoor Location ◽  
Location Data ◽  
The Magnetic Field

Estimation of indoor location represents an interesting research topic since it is a main contextual variable for location bases services (LBS), eHealth applications and commercial systems, among others. For instance, hospitals require location data of their employees, as well as the location of their patients to offer services based on these locations at the correct moments of their needs. Several approaches have been proposed to tackle this problem using different types of artificial or natural signals (i.e., wifi, bluetooth, rfid, sound, movement, etc.). In this work, it is proposed the development of an indoor location estimator system, relying in the data provided by the magnetic field of the rooms, which has been demonstrated that is unique and quasi-stationary. For this purpose, it is analyzed the spectral evolution of the magnetic field data viewed as a bidimensional heatmap, avoiding temporal dependencies. A Fourier transform is applied to the bidimensional heatmap of the magnetic field data to feed a convolutional neural network (CNN) to generate a model to estimate the user’s location in a building. The evaluation of the CNN model to deploy an indoor location system (ILS) is done through measuring the Receiver Operating Characteristic (ROC) curve to observe the behavior in terms of sensitivity and specificity. Our experiments achieve a 0.99 Area Under the Curve (AUC) in the training data-set and a 0.74 in a total blind data set.

Mitigating remanent magnetization effects in magnetic data using the normalized source strength

Geophysics ◽  
2013 ◽  
Vol 78 (3) ◽  
pp. J25-J32 ◽  
Author(s):  
Mark Pilkington ◽  
Majid Beiki
Keyword(s):  
Magnetic Field ◽  
Remanent Magnetization ◽  
Field Data ◽  
Magnetic Data ◽  
Magnetization Distribution ◽  
Source Strength ◽  
Inversion Algorithm ◽  
Data Set ◽  
Magnetic Field Data ◽  
The Magnetic Field

We have developed an approach for the interpretation of magnetic field data that can be used when measured anomalies are affected by significant remanent magnetization components. The method deals with remanent effects by using the normalized source strength (NSS), a quantity calculated from the eigenvectors of the magnetic gradient tensor. The NSS is minimally affected by the direction of remanent magnetization present and compares well with other transformations of the magnetic field that are used for the same purpose. It therefore offers a way of inverting magnetic data containing the effects of remanent magnetizations, particularly when these are unknown and are possibly varying within a given data set. We use a standard 3D inversion algorithm to invert NSS data from an area where varying remanence directions are apparent, resulting in a more reliable image of the subsurface magnetization distribution than possible using the observed magnetic field data directly.

scholarly journals Characteristics of the Taylor microscale in the solar wind/foreshock: magnetic field and electron velocity measurements

2013 ◽  
Vol 31 (11) ◽  
pp. 2063-2075 ◽  
Author(s):  
C. Gurgiolo ◽  
M. L. Goldstein ◽  
W. H. Matthaeus ◽  
A. Viñas ◽  
A. N. Fazakerley
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Fluid Velocity ◽  
Electron Velocity ◽  
Data Sets ◽  
Velocity Data ◽  
Data Set ◽  
Magnetic Field Data ◽  
Spacecraft Data ◽  
The Magnetic Field

Abstract. The Taylor microscale is one of the fundamental turbulence scales. Not easily estimated in the interplanetary medium employing single spacecraft data, it has generally been studied through two point correlations. In this paper we present an alternative, albeit mathematically equivalent, method for estimating the Taylor microscale (λT). We make two independent determinations employing multi-spacecraft data sets from the Cluster mission, one using magnetic field data and a second using electron velocity data. Our results using the magnetic field data set yields a scale length of 1538 ± 550 km, slightly less than, but within the same range as, values found in previous magnetic-field-based studies. During time periods where both magnetic field and electron velocity data can be used, the two values can be compared. Relative comparisons show λT computed from the velocity is often significantly smaller than that from the magnetic field data. Due to a lack of events where both measurements are available, the absolute λT based on the electron fluid velocity is not able to be determined.

Using an induction coil sensor to indirectly measure the B-field response in the bandwidth of the transient electromagnetic method

Geophysics ◽  
2000 ◽  
Vol 65 (5) ◽  
pp. 1489-1494 ◽  
Author(s):  
Richard S. Smith ◽  
A. Peter Annan
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Indirect Measurement ◽  
Rate Of Change ◽  
Induction Coil ◽  
Voltage Response ◽  
Magnetic Field Data ◽  
Transient Electromagnetic ◽  
Different Characteristics ◽  
The Magnetic Field

The traditional sensor used in transient electromagnetic (EM) systems is an induction coil. This sensor measures a voltage response proportional to the time rate of change of the magnetic field in the EM bandwidth. By simply integrating the digitized output voltage from the induction coil, it is possible to obtain an indirect measurement of the magnetic field in the same bandwidth. The simple integration methodology is validated by showing that there is good agreement between synthetic voltage data integrated to a magnetic field and synthetic magnetic‐field data calculated directly. Further experimental work compares induction‐coil magnetic‐field data collected along a profile with data measured using a SQUID magnetometer. These two electromagnetic profiles look similar, and a comparison of the decay curves at a critical point on the profile shows that the two types of measurements agree within the bounds of experimental error. Comparison of measured voltage and magnetic‐field data show that the two sets of profiles have quite different characteristics. The magnetic‐field data is better for identifying, discriminating, and interpreting good conductors, while suppressing the less conductive targets. An induction coil is therefore a suitable sensor for the indirect collection of EM magnetic‐field data.

scholarly journals Magnetic Fields in OH Maser Clouds

1974 ◽  
Vol 60 ◽  
pp. 275-292 ◽  
Author(s):  
R. D. Davies
Keyword(s):  
Magnetic Field ◽  
Angular Momentum ◽  
Magnetic Fields ◽  
Magnetic Flux ◽  
Field Data ◽  
Zeeman Splitting ◽  
Galactic Rotation ◽  
Magnetic Field Data ◽  
The Magnetic Field ◽  
Maser Sources

Observations of Class I OH maser sources show a range of features which are predicted on the basis of Zeeman splitting in a source magnetic field. Magnetic field strengths of 2 to 7 mG are derived for eight OH maser sources. The fields in all the clouds are directed in the sense of galactic rotation. A model of W3 OH is proposed which incorporates the magnetic field data. It is shown that no large amount of magnetic flux or angular momentum has been lost since the condensation from the interstellar medium began.

scholarly journals Enhancing Performance of Magnetic Field Based Indoor Localization Using Magnetic Patterns from Multiple Smartphones

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2704 ◽  
Author(s):  
Imran Ashraf ◽  
Soojung Hur ◽  
Yongwan Park
Keyword(s):  
Magnetic Field ◽  
Geomagnetic Field ◽  
Field Data ◽  
Performance Comparison ◽  
Magnetic Sensors ◽  
Location Based Services ◽  
Field Pattern ◽  
Magnetic Field Data ◽  
Localization Performance ◽  
The Magnetic Field

Wide expansion of smartphones triggered a rapid demand for precise localization that can meet the requirements of location-based services. Although the global positioning system is widely used for outdoor positioning, it cannot provide the same accuracy for the indoor. As a result, many alternative indoor positioning technologies like Wi-Fi, Bluetooth Low Energy (BLE), and geomagnetic field localization have been investigated during the last few years. Today smartphones possess a rich variety of embedded sensors like accelerometer, gyroscope, and magnetometer that can facilitate estimating the current location of the user. Traditional geomagnetic field-based fingerprint localization, although it shows promising results, it is limited by the fact that various smartphones have embedded magnetic sensors from different manufacturers and the magnetic field strength that is measured from these smartphones vary significantly. Consequently, the localization performance from various smartphones is different even when the same localization approach is used. So devising an approach that can provide similar performance with various smartphones is a big challenge. Contrary to previous works that build the fingerprint database from the geomagnetic field data of a single smartphone, this study proposes using the geomagnetic field data collected from multiple smartphones to make the geomagnetic field pattern (MP) database. Many experiments are carried out to analyze the performance of the proposed approach with various smartphones. Additionally, a lightweight threshold technique is proposed that can detect user motion using the acceleration data. Results demonstrate that the localization performance for four different smartphones is almost identical when tested with the database made using the magnetic field data from multiple smartphones than that of which considers the magnetic field data from only one smartphone. Moreover, the performance comparison with previous research indicates that the overall performance of smartphones is improved.

Solar flare forecasting model using 3D magnetic field data

2020 ◽  
Author(s):  
Xin Huang
Keyword(s):  
Magnetic Field ◽  
Deep Learning ◽  
Solar Flare ◽  
Solar Flares ◽  
Field Data ◽  
Active Regions ◽  
Forecasting Model ◽  
Magnetic Field Data ◽  
The Magnetic Field

<p>Solar flares originate from the release of the energy stored in the magnetic field of solar active regions. Generally, the photospheric magnetograms of active regions are used as the input of the solar flare forecasting model. However, solar flares are considered to occur in the low corona. Therefore, the role of 3D magnetic field of active regions in the solar flare forecast should be explored. We extrapolate the 3D magnetic field using the potential model for all the active regions during 2010 to 2017, and then the deep learning method is applied to extract the precursors of solar flares in the 3D magnetic field data. We find that the 3D magnetic field of active regions is helpful to build a deep learning based forecasting model.</p>

Automatic boundary extraction from magnetic field data using triangular meshes

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. J47-J60 ◽  
Author(s):  
Nathan Leon Foks ◽  
Yaoguo Li
Keyword(s):  
Magnetic Field ◽  
Synthetic Data ◽  
Magnetic Data ◽  
Lake Area ◽  
Boundary Extraction ◽  
Data Set ◽  
Magnetic Field Data ◽  
Line Segments ◽  
Extraction Algorithm ◽  
The Magnetic Field

Boundary extraction is a collective term that we use for the process of extracting the locations of faults, lineaments, and lateral boundaries between geologic units using geophysical observations, such as measurements of the magnetic field. The process typically begins with a preprocessing stage, where the data are transformed to enhance the visual clarity of pertinent features and hence improve the interpretability of the data. The majority of the existing methods are based on raster grid enhancement techniques, and the boundaries are extracted as a series of points or line segments. In contrast, we set out a methodology for boundary extraction from magnetic data, in which we represent the transformed data as a surface in 3D using a mesh of triangular facets. After initializing the mesh, we modify the node locations, such that the mesh smoothly represents the transformed data and that facet edges are aligned with features in the data that approximate the horizontal locations of subsurface boundaries. To illustrate our boundary extraction algorithm, we first apply it to a synthetic data set. We then apply it to identify boundaries in a magnetic data set from the McFaulds Lake area in Ontario, Canada. The extracted boundaries are in agreement with known boundaries and several of the regions that are completely enclosed by extracted boundaries coincide with regions of known mineralization.

Identification of Complex Shear Modulus of MR Layer Placed in Three-Layer Beam – Part 2: Algorithm

2015 ◽  
Vol 759 ◽  
pp. 15-25
Author(s):  
Mateusz Romaszko ◽  
Jacek Snamina ◽  
Sebastian Pakuła
Keyword(s):  
Magnetic Field ◽  
Shear Modulus ◽  
Field Data ◽  
Free Vibrations ◽  
Complex Shear Modulus ◽  
Mr Fluid ◽  
Magnetic Field Data ◽  
Complex Modes ◽  
Complex Shear ◽  
The Magnetic Field

The paper presents the procedure of identification of a complex shear modulus which describes properties of MR fluid in the pre-yield regime as a function of magnetic field. Data necessary for identification were collected basing on measurements of free vibrations of a three-layered cantilever beam at a special laboratory stand. Magnetic field exerting on MR fluid placed in the beam was generated by electromagnet. In the next step, complex modes of beam vibrations for various places of applying the magnetic field and its strength were calculated.

scholarly journals Interinstrument calibration using magnetic field data from the flux-gate magnetometer (FGM) and electron drift instrument (EDI) onboard Cluster

2014 ◽  
Vol 3 (1) ◽  
pp. 1-11 ◽  
Author(s):  
R. Nakamura ◽  
F. Plaschke ◽  
R. Teubenbacher ◽  
L. Giner ◽  
W. Baumjohann ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Spin Axis ◽  
Electron Drift ◽  
Magnetic Field Data ◽  
Field Magnitude ◽  
Magnetic Field Magnitude ◽  
Flux Gate ◽  
Axis Offset ◽  
The Magnetic Field

Abstract. We compare the magnetic field data obtained from the flux-gate magnetometer (FGM) and the magnetic field data deduced from the gyration time of electrons measured by the electron drift instrument (EDI) onboard Cluster to determine the spin-axis offset of the FGM measurements. Data are used from orbits with their apogees in the magnetotail, when the magnetic field magnitude was between about 20 and 500 nT. Offset determination with the EDI–FGM comparison method is of particular interest for these orbits, because no data from solar wind are available in such orbits to apply the usual calibration methods using the Alfvén waves. In this paper, we examine the effects of the different measurement conditions, such as direction of the magnetic field relative to the spin plane and field magnitude in determining the FGM spin-axis offset, and also take into account the time-of-flight offset of the EDI measurements. It is shown that the method works best when the magnetic field magnitude is less than about 128 nT and when the magnetic field is aligned near the spin-axis direction. A remaining spin-axis offset of about 0.4 ∼ 0.6 nT was observed for Cluster 1 between July and October 2003. Using multipoint multi-instrument measurements by Cluster we further demonstrate the importance of the accurate determination of the spin-axis offset when estimating the magnetic field gradient.

3-D Modeling the Magnetic Field due to Ocean Tidal Flow O1

2013 ◽  
Vol 658 ◽  
pp. 471-474
Author(s):  
Yan Xu ◽  
Jun Xu ◽  
Wei Hua Zhu ◽  
Xia Feng ◽  
Hai Yan Xie
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Tidal Flow ◽  
Ocean Water ◽  
Magnetic Field Data ◽  
Tidal Motion ◽  
The Earth ◽  
Sea Bottom ◽  
Tidal Constituents ◽  
The Magnetic Field

The tidal motion of the ocean water through the ambient magnetic field, generates secondary electric and magnetic field. The magnetic fields generated by the diurnal (O1) ocean flow can be clearly detected. We simulate the magnetic signals for tidal constituents –diurnal (O1) tides. The idea of exploiting tidal signals for EM studies of the Earth is not new, but so far it was used only for interpretation of inland and transoceanic magnetic field data due to O1. Emphasis in this work is made on a discussion of sea bottom electric field of the same origin.

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