scholarly journals Modeling and the signal-to-noise ratio research of magnetoelectric sensors at low frequency

2007 ◽  
Vol 91 (14) ◽  
pp. 142905 ◽  
Author(s):  
Zengping Xing ◽  
Jiefang Li ◽  
Dwight Viehland
Keyword(s):  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Signal To Noise ◽  
Magnetoelectric Sensors ◽  
Noise Ratio

Influence of the quality factor on the signal to noise ratio of magnetoelectric sensors based on the delta-E effect

2019 ◽  
Vol 114 (18) ◽  
pp. 183504 ◽  
Author(s):  
Benjamin Spetzler ◽  
Christine Kirchhof ◽  
Jens Reermann ◽  
Phillip Durdaut ◽  
Michael Höft ◽  
...  
Keyword(s):  
Quality Factor ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Magnetoelectric Sensors ◽  
Noise Ratio

scholarly journals Signal-to-noise ratio in the membrane potential of the owl's auditory coincidence detectors

2012 ◽  
Vol 108 (10) ◽  
pp. 2837-2845 ◽  
Author(s):  
Go Ashida ◽  
Kazuo Funabiki ◽  
Paula T. Kuokkanen ◽  
Richard Kempter ◽  
Catherine E. Carr
Keyword(s):  
Neural Circuit ◽  
Signal To Noise Ratio ◽  
Phase Locking ◽  
Low Frequency ◽  
Membrane Potentials ◽  
Signal To Noise ◽  
Theoretical Predictions ◽  
Noise Ratio ◽  
Band Limited

Owls use interaural time differences (ITDs) to locate a sound source. They compute ITD in a specialized neural circuit that consists of axonal delay lines from the cochlear nucleus magnocellularis (NM) and coincidence detectors in the nucleus laminaris (NL). Recent physiological recordings have shown that tonal stimuli induce oscillatory membrane potentials in NL neurons (Funabiki K, Ashida G, Konishi M. J Neurosci 31: 15245–15256, 2011). The amplitude of these oscillations varies with ITD and is strongly correlated to the firing rate. The oscillation, termed the sound analog potential, has the same frequency as the stimulus tone and is presumed to originate from phase-locked synaptic inputs from NM fibers. To investigate how these oscillatory membrane potentials are generated, we applied recently developed signal-to-noise ratio (SNR) analysis techniques (Kuokkanen PT, Wagner H, Ashida G, Carr CE, Kempter R. J Neurophysiol 104: 2274–2290, 2010) to the intracellular waveforms obtained in vivo. Our theoretical prediction of the band-limited SNRs agreed with experimental data for mid- to high-frequency (>2 kHz) NL neurons. For low-frequency (≤2 kHz) NL neurons, however, measured SNRs were lower than theoretical predictions. These results suggest that the number of independent NM fibers converging onto each NL neuron and/or the population-averaged degree of phase-locking of the NM fibers could be significantly smaller in the low-frequency NL region than estimated for higher best-frequency NL.

scholarly journals Detection of Small Magnetic Fields Using Serial Magnetic Tunnel Junctions with Various Geometrical Characteristics

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5704
Author(s):  
Zhenhu Jin ◽  
Yupeng Wang ◽  
Kosuke Fujiwara ◽  
Mikihiko Oogane ◽  
Yasuo Ando
Keyword(s):  
Magnetic Fields ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
High Signal ◽  
Free Layer ◽  
Signal To Noise ◽  
Noise Spectral Density ◽  
Junction Area ◽  
Geometrical Characteristics ◽  
Noise Ratio

Thanks to their high magnetoresistance and integration capability, magnetic tunnel junction-based magnetoresistive sensors are widely utilized to detect weak, low-frequency magnetic fields in a variety of applications. The low detectivity of MTJs is necessary to obtain a high signal-to-noise ratio when detecting small variations in magnetic fields. We fabricated serial MTJ-based sensors with various junction area and free-layer electrode aspect ratios. Our investigation showed that their sensitivity and noise power are affected by the MTJ geometry due to the variation in the magnetic shape anisotropy. Their MR curves demonstrated a decrease in sensitivity with an increase in the aspect ratio of the free-layer electrode, and their noise properties showed that MTJs with larger junction areas exhibit lower noise spectral density in the low-frequency region. All of the sensors were able detect a small AC magnetic field (Hrms = 0.3 Oe at 23 Hz). Among the MTJ sensors we examined, the sensor with a square-free layer and large junction area exhibited a high signal-to-noise ratio (4792 ± 646). These results suggest that MTJ geometrical characteristics play a critical role in enhancing the detectivity of MTJ-based sensors.

Low Frequency NQR using Double Contact Cross-relaxation

2000 ◽  
Vol 55 (1-2) ◽  
pp. 37-40
Author(s):  
David Stephenson ◽  
John A. S. Smith
Keyword(s):  
Ammonium Sulphate ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Cross Relaxation ◽  
Relaxation Technique ◽  
Signal To Noise ◽  
Low Frequencies ◽  
Line Shapes ◽  
Ammonium Dichromate ◽  
Noise Ratio

A cross-relaxation technique is described which involves two spin contacts per double reso-nance cycle. The result is an improvement in signal to noise ratio particularly at low frequencies. Experimental spectra and analyses are presented: 14N in ammonium sulphate showing that the tech-nique gives essentially the same information as previous studies; 14N in ammonium dichromate determining e2Qq/h as (76±3) kHz and η = 0.84±.04; 7Li in lithium acetylacetonate for which the spectrum (corrected for Zeeman distortion) yields e2Qq/h = (152 ±5) kHz and η=.5 ±.2. Calculated spectra are presented to demonstrate the η dependence of the line shapes for 7Li.

scholarly journals Improving the Signal to Noise Ratio of QTF Preamplifiers Dedicated for QEPAS Applications

2020 ◽  
Vol 10 (12) ◽  
pp. 4105
Author(s):  
Piotr Z. Wieczorek ◽  
Tomasz Starecki ◽  
Frank K. Tittel
Keyword(s):  
Operational Amplifier ◽  
Narrow Band ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Electrical Signal ◽  
Low Noise ◽  
Detection Sensitivity ◽  
Noise Amplitude ◽  
Signal To Noise ◽  
Noise Ratio

The signal-to-noise ratio (SNR) is a major factor that limits the detection sensitivity of quartz-enhanced photoacoustic spectroscopy (QEPAS) sensors. The higher the electrical signal level compared to the noise amplitude is the lower the concentration of gases that can be detected. For this reason the preamplifier circuits used in QEPAS should be optimized for low-frequency narrow-band applications. Moreover, special care should be taken when choosing a particular operational amplifier in either a transimpedance or voltage (differential) configuration. It turns out that depending on the preamp topology different operational amplifier parameters should be carefully considered when a high SNR of the whole QEPAS system is required. In this article we analyzed the influence of the crucial parameters of low-noise operational preamplifiers used in QEPAS applications and show the resulting limitations of transimpedance and voltage configurations.

Detection of Life Characteristic Signals Based on High Order Statistics

2012 ◽  
Vol 239-240 ◽  
pp. 807-810
Author(s):  
Jian Jun Li ◽  
Jian Feng Zhao
Keyword(s):  
Order Statistics ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Time Estimation ◽  
High Order ◽  
Signal To Noise ◽  
Time Frequency ◽  
Noise Ratio ◽  
Short Time ◽  
High Order Statistics

Life parameters signal has characteristics of extremely low frequency, low signal-to-noise ratio, and the easy submerged in strong clutter noises. The method for detecting life signal based on filter bank and high order statistics is presented, in which neither the Gaussian supposition of the observed signal, nor a prior information about the waveform and arrival time of the observed signal is necessary. The principle of method is to separate the spectrum of input signal into many narrow frequency bands, whose Sub-band signal is followed by a short-time estimation of higher-order statistics so as to suppress Gaussian noises. Simulated results show that the method not only can completely descript life signals in the time-frequency domain, but improve the signal-to-noise ratio and the ability of detecting algorithm. Moreover, the method is effective and practical.

Low-Frequency Scanning Capacitance Microscopy

1997 ◽  
Vol 500 ◽  
Author(s):  
Š. Lányi ◽  
M. Hruškovic
Keyword(s):  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Point Of View ◽  
Dielectric Losses ◽  
Frequency Range ◽  
Signal To Noise ◽  
Input Stage ◽  
Frequency Scanning ◽  
Noise Ratio ◽  
Parallel Conductance

ABSTRACTThe operation principle and main properties of a Scanning Capacitance Microscope (SCM) are described. It is called low-frequency, because in its design typical low-frequency techniques are utilised. The main attention is focused on its lateral resolution, signal-to-noise ratio and the possibility to detect dielectric losses.Mapping the electrostatic field of a shielded microscope probe was used to calculate the stray capacitance, flux density, sensitivity and contrast obtained on a flat conducting surface, as well as on a surface covered by a thin dielectric film. The effect of dielectric losses, represented by a parallel conductance, on the detected capacitance and the resulting phase shift has been derived.Using the results of mapping, the requirements on a SCM input stage and the possible solutions are discussed. From the point of view of frequency range and noise the best is an electrometric input stage, with input impedance represented by its capacitance.The achieved signal-to-noise ratio of the low frequency Scanning Capacitance Microscope renders the extension of the working frequency range to lower frequencies. The input stage can be optimised for a frequency range from about 1 kHz to a few MHz, with the possibility to extend it to about 10 MHz at the cost of reduced sensitivity at the lowest frequencies.

Sign in / Sign up

Export Citation Format

Share Document