scholarly journals Impact of Micro-, Mini- and Multi-Electrode Mapping on Ventricular Substrate Characterisation

2020 ◽  
Vol 9 (3) ◽  
pp. 128-135
Author(s):  
Benjamin Berte ◽  
Katja Zeppenfeld ◽  
Roderick Tung
Keyword(s):  
Ventricular Tachycardia ◽  
Cardiac Arrhythmias ◽  
Signal To Noise Ratio ◽  
Near Field ◽  
Field Of View ◽  
Ablation Catheter ◽  
Signal To Noise ◽  
Voltage Mapping ◽  
Practice Variations ◽  
Catheter Technology

Accurate substrate characterisation may improve the evolving understanding and treatment of cardiac arrhythmias. During substrate-based ablation techniques, wide practice variations exist with mapping via dedicated multi-electrode catheter or conventional ablation catheters. Recently, newer ablation catheter technology with embedded mapping electrodes have been introduced. This article focuses on the general misconceptions of voltage mapping and more specific differences in unipolar and bipolar signal morphology, field of view, signal-to-noise ratio, mapping capabilities (density and resolution), catheter-specific voltage thresholds and impact of micro-, mini- and multi-electrodes for substrate mapping. Efficiency and cost-effectiveness of different catheter types are discussed. Increasing sampling density with smaller electrodes allows for higher resolution with a greater likelihood to record near-field electrical information. These advances may help to further improve our mechanistic understanding of the correlation between substrate and ventricular tachycardia, as well as macro-reentry arrhythmia in humans.

scholarly journals Distance scaling method for accurate prediction of slowly varying magnetic fields in satellite missions

2016 ◽  
Vol 5 (2) ◽  
pp. 281-288 ◽  
Author(s):  
Panagiotis P. Zacharias ◽  
Elpida G. Chatzineofytou ◽  
Sotirios T. Spantideas ◽  
Christos N. Capsalis
Keyword(s):  
Signal To Noise Ratio ◽  
Near Field ◽  
Magnetic Behavior ◽  
Field Measurements ◽  
High Signal ◽  
Signal To Noise ◽  
Scaling Method ◽  
Magnetic Signature ◽  
The Magnetic Field

Abstract. In the present work, the determination of the magnetic behavior of localized magnetic sources from near-field measurements is examined. The distance power law of the magnetic field fall-off is used in various cases to accurately predict the magnetic signature of an equipment under test (EUT) consisting of multiple alternating current (AC) magnetic sources. Therefore, parameters concerning the location of the observation points (magnetometers) are studied towards this scope. The results clearly show that these parameters are independent of the EUT's size and layout. Additionally, the techniques developed in the present study enable the placing of the magnetometers close to the EUT, thus achieving high signal-to-noise ratio (SNR). Finally, the proposed method is verified by real measurements, using a mobile phone as an EUT.

scholarly journals Near-field sound source localization using principal component analysis–multi-output support vector regression

2020 ◽  
Vol 16 (4) ◽  
pp. 155014772091640
Author(s):  
Lanmei Wang ◽  
Yao Wang ◽  
Guibao Wang ◽  
Jianke Jia
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Regression ◽  
Covariance Matrix ◽  
Signal To Noise Ratio ◽  
Near Field ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Signal To Noise ◽  
Noise Ratio

In this article, principal component analysis method, which is applied to image compression and feature extraction, is introduced into the dimension reduction of input characteristic variable of support vector regression, and a method of joint estimation of near-field angle and range based on principal component analysis dimension reduction is proposed. Signal-to-noise ratio and calculation amount are the decisive factors affecting the performance of the algorithm. Principal component analysis is used to fuse the main characteristics of training data and discard redundant information, the signal-to-noise ratio is improved, and the calculation amount is reduced accordingly. Similarly, support vector regression is used to model the signal, and the upper triangular elements of the signal covariance matrix are usually used as input features. Since the covariance matrix has more upper triangular elements, training it as a feature input will affect the training speed to some extent. Principal component analysis is used to reduce the dimensionality of the upper triangular element of the covariance matrix of the known signal, and it is used as the input feature of the multi-output support vector regression machine to construct the near-field parameter estimation model, and the parameter estimation of unknown signal is herein obtained. Simulation results show that this method has high estimation accuracy and training speed, and has strong adaptability at low signal-to-noise ratio, and the performance is better than that of the back-propagation neural network algorithm and the two-step multiple signal classification algorithm.

scholarly journals Analysis of Signal to Noise Ratio in Coronagraph Observations of Coronal Mass Ejections

2020 ◽  
Author(s):  
Johannes Hinrichs ◽  
Jackie A. Davies ◽  
Matthew J. West ◽  
Volker Bothmer ◽  
Bram Bourgoignie ◽  
...  
Keyword(s):  
Coronal Mass Ejections ◽  
Signal To Noise Ratio ◽  
European Space Agency ◽  
Leading Edge ◽  
Outer Edge ◽  
Field Of View ◽  
Signal To Noise ◽  
Space Agency ◽  
Noise Ratio ◽  
Improved Performance

<p>Aims. We analyse the Signal-to-Noise Ratio (SNR) requirements of the European Space Agency (ESA)-funded Solar Coronagraph for OPErations (SCOPE) instrument with respect to the manual and automatic detection of Coronal Mass Ejections (CMEs) in its field of view of 2.5 to 30 solar radii.<br />Methods. For our analysis, SNR values are estimated from observations made by the C3 coronagraph on the Solar and Heliospheric Observatory (SOHO) spacecraft for a number of di erent CMEs. Additionally, we generate a series of artificial coronagraph images, each consisting of a modelled coronal background and a CME, the latter simulated using the Graduated Cylindrical Shell (GCS) model together with the SCRaytrace code available in the Interactive Data Language (IDL) SolarSoft library. Images are created with CME SNR levels between 0.5 and 10 at the outer<br />field of view (FOV) edge, generated by adding Poisson noise, and velocities between 700 km s-1 and 2800 kms-1. The images are analysed for the detectability of the CME above the noise with the automatic CME detection tool CACTus.<br />Results. We find in the analysed C3 images that CMEs near the outer edge of the field of view are typically 2%of the total brightness and have an SNR between 1 and 4 at their leading edge. The automated detection of CMEs in our simulated images by CACTus succeeded well down to SNR = 1 and for CME velocities up to 1400 kms-1. At lower SNR and higher velocity of 2100 kms-1 the detection started to break down. For SCOPE, the results from the two approaches confirm that the initial design goal of SNR = 4 would, if achieved, deliver improved performance over established data used in operations today.</p>

The effect of activity outside the field-of-view on image signal-to-noise ratio for 3D PET with15O

2011 ◽  
Vol 56 (10) ◽  
pp. 3061-3072 ◽  
Author(s):  
Masanobu Ibaraki ◽  
Shigeki Sugawara ◽  
Kazuhiro Nakamura ◽  
Fumiko Kinoshita ◽  
Toshibumi Kinoshita
Keyword(s):  
Signal To Noise Ratio ◽  
Field Of View ◽  
Signal To Noise ◽  
Noise Ratio ◽  
3D Pet

A high signal-to-noise ratio passive near-field microscope equipped with a helium-free cryostat

2017 ◽  
Vol 88 (1) ◽  
pp. 013706 ◽  
Author(s):  
Kuan-Ting Lin ◽  
Susumu Komiyama ◽  
Sunmi Kim ◽  
Ken-ichi Kawamura ◽  
Yusuke Kajihara
Keyword(s):  
Signal To Noise Ratio ◽  
Near Field ◽  
High Signal ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals Lens-free Multi-Laser Spectral Light-Field Fusion Microscopy

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Farnoud Kazemzadeh ◽  
Alexander Wong
Keyword(s):  
Light Field ◽  
Signal To Noise Ratio ◽  
Field Of View ◽  
Large Field ◽  
Light Fields ◽  
Signal To Noise ◽  
Pulsed Mode ◽  
Noise Ratio ◽  
Spectral Complex

<p>We present a device and method for performing lens-free spectral<br />light-field fusion microscopy at sub-pixel resolutions while taking<br />advantage of the large field-of-view capability. A collection of<br />lasers at different wavelengths is used in pulsed mode and enables<br />the capture of interferometric light-field encodings of a specimen<br />placed near the detector. Numerically fusing the spectral complex<br />light-fields obtained from the encodings produces an image of the<br />specimen at higher resolution and signal-to-noise-ratio while suppressing<br />various aberrations and artifacts.</p>

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