High Accuracy Passive Magnetic Field-Based Localization for Feedback Control Using Principal Component Analysis

Foong, Shaohui; Sun, Zhenglong

doi:10.3390/s16081280

Cited by 5 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sensors of magnetic field based on the Hall effect are the most used magnetic sensors and allow us to know the linear position, angular position, speed, rotation, current [56][57][58][59] and the three components of the field magnetic [60][61][62][63][64].…”

Section: Magnetic Sensormentioning

confidence: 99%

Smartphones Magnetic Sensors within Physics Lab

Escobar¹,

Ramirez-Vazquez²,

González-Rubio³

et al. 2018

Preprint

View full text Add to dashboard Cite

Current smartphones incorporate different types of sensors that allow us to know our spatial position, they give us information about pressure, speed, acceleration, time, acoustic level, and other different physical magnitudes. These smartphones measure each component of the magnetic field, bearing in mind that any current perpendicular to a magnetic field produces a small potential difference, transversal to the said current, being this voltage easily measurable by Hall sensors. With the implementation of three Hall sensors, and an appropriate app, we can measure the three components of the magnetic field vector, and with this we can obtain information and deduce properties of the physical systems considered. In this paper we are exploring the use of smartphones in a physics laboratory for freshman students. To do this, we have measured, using Hall sensors, the magnetic field created by a linear quadrature, and we have obtained, first of all, its dependence on the distance between the quadrupole and the magnetic sensor. The second purpose of this work is to show that the laboratory is a powerful tool that increases the significant learning of freshman students through advanced technological tools.

show abstract

Section: Magnetic Sensormentioning

confidence: 99%

Smartphones Magnetic Sensors within Physics Lab

Escobar¹,

Ramirez-Vazquez²,

González-Rubio³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Until now, many source localization algorithms [ 12 , 13 , 14 ] have been proposed for seeking real brain activity locations, wherein beamforming techniques [ 15 ] comprise a well-known method family and plays a key role in signal processing and neuroimaging [ 16 ]. The characteristic of those methodologies is spatial filtering that the MEG sensor signals are filtered by different beams based on lead-field vectors corresponding to specific source-grid points [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Source Localization with Magnetoencephalography Based on Sensor Array Decomposition and Beamforming

Lin

Yang

et al. 2017

Sensors

View full text Add to dashboard Cite

In recent years, the source localization technique of magnetoencephalography (MEG) has played a prominent role in cognitive neuroscience and in the diagnosis and treatment of neurological and psychological disorders. However, locating deep brain activities such as in the mesial temporal structures, especially in preoperative evaluation of epilepsy patients, may be more challenging. In this work we have proposed a modified beamforming approach for finding deep sources. First, an iterative spatiotemporal signal decomposition was employed for reconstructing the sensor arrays, which could characterize the intrinsic discriminant features for interpreting sensor signals. Next, a sensor covariance matrix was estimated under the new reconstructed space. Then, a well-known vector beamforming approach, which was a linearly constraint minimum variance (LCMV) approach, was applied to compute the solution for the inverse problem. It can be shown that the proposed source localization approach can give better localization accuracy than two other commonly-used beamforming methods (LCMV, MUSIC) in simulated MEG measurements generated with deep sources. Further, we applied the proposed approach to real MEG data recorded from ten patients with medically-refractory mesial temporal lobe epilepsy (mTLE) for finding epileptogenic zone(s), and there was a good agreement between those findings by the proposed approach and the clinical comprehensive results.

show abstract