Temporal weighting of interaural time differences in low frequency noise presented at low signal-to-noise ratio

2014 ◽  
Vol 135 (4) ◽  
pp. 2283-2283
Author(s):  
Anna C. Diedesch ◽  
G. Christopher Stecker
Keyword(s):  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Signal To Noise ◽  
Interaural Time Differences ◽  
Noise Ratio

THE ELUSIVE FIRST ARRIVAL

Geophysics ◽  
1964 ◽  
Vol 29 (5) ◽  
pp. 806-813 ◽  
Author(s):  
J. G. Hagedoorn
Keyword(s):  
Frequency Response ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Dominant Frequency ◽  
Recording System ◽  
Transmission Curve ◽  
Frequency Noise ◽  
Signal To Noise ◽  
First Arrival ◽  
Noise Ratio

In first‐arrival refraction work, the initial deflection of the first loop of the signal arriving from the shotpoint must be readily recognised against a background of seismic disturbances due to sources other than the explosion. The accuracy of timing a first arrival is determined by this signal‐to‐noise ratio. It depends primarily on the location of shot and receivers, the size of the charge, and the existing ground unrest at the time of registration. Experiments carried out with these variables kept constant, by recording at the same location from the same shot, show how much the signal‐to‐noise ratio also depends on the characteristics of the recording equipment used. The best signal‐to‐noise ratio is certainly not obtained when the transmission curve of the entire system, comprising geophone, amplifier, and galvanometer, peaks at the apparent dominant frequency of the refraction signal. Practical examples show that the signal‐to‐noise ratio can be improved considerably by using recording systems that transmit a band of frequencies extending many octaves below the observed dominant frequency. The inception of an oscillatory signal was found to be particularly sensitive to the characteristics of a recording system. A seismometer, for example, will transform a starting sine wave with a frequency equal to the natural frequency of the seismometer into a signal with a first loop that is about half as high and half as long as the succeeding loops, the latter moreover being advanced by one‐quarter period. This relative constriction of the initial part of a signal is called the “cramping” effect. Such an effect will weaken a refraction first arrival relative to simultaneously arriving later parts of noise signals. This explains why a cramping effect will impair the signal‐to‐noise ratio. A cramping effect can, of course, be avoided by using a recording system with a flat frequency response. The opposite effect, which can be expected to improve the signal‐to‐noise ratio, could obviously be achieved by using systems with relatively increased low‐frequency response. The practical limit to this improvement would be set by the low‐frequency noise that is enhanced by this procedure.

scholarly journals A Combination Method for Electrocardiogram Rejection from Surface Electromyogram

2014 ◽  
Vol 8 (1) ◽  
pp. 13-19 ◽  
Author(s):  
Sara Abbaspour ◽  
Ali Fallah
Keyword(s):  
Neural Network ◽  
Relative Error ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Combination Method ◽  
Frequency Noise ◽  
Signal To Noise ◽  
The Neural Network ◽  
Spectrum Density ◽  
Noise Ratio

The electrocardiogram signal which represents the electrical activity of the heart provides interference in the recording of the electromyogram signal, when the electromyogram signal is recorded from muscles close to the heart. Therefore, due to impurities, electromyogram signals recorded from this area cannot be used. In this paper, a new method was developed using a combination of artificial neural network and wavelet transform approaches, to eliminate the electrocardiogram artifact from electromyogram signals and improve results. For this purpose, contaminated signal is initially cleaned using the neural network. With this process, a large amount of noise can be removed. However, low-frequency noise components remain in the signal that can be removed using wavelet. Finally, the result of the proposed method is compared with other methods that were used in different papers to remove electrocardiogram from electromyogram. In this paper in order to compare methods, qualitative and quantitative criteria such as signal to noise ratio, relative error, power spectrum density and coherence have been investigated for evaluation and comparison. The results of signal to noise ratio and relative error are equal to 15.6015 and 0.0139, respectively.

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.

Threshold Noise Reduction Research of Improved EEMD Method

2012 ◽  
Vol 226-228 ◽  
pp. 237-240 ◽  
Author(s):  
Mei Jun Zhang ◽  
Hao Chen ◽  
Chuang Wang ◽  
Qing Cao
Keyword(s):  
Noise Reduction ◽  
Signal To Noise Ratio ◽  
Frequency Noise ◽  
Signal To Noise ◽  
Time Frequency ◽  
Noise Ratio ◽  
Reduction Effect ◽  
Eemd Method ◽  
Simulation Signal ◽  
Improved Eemd

In order to extract effectively detection signals in the noise background for non-stationary signal.On the basis of EEMD, improved EEMD is put forward, the improve EEMD threshold noise reduction is researched in this paper.The simulation signal compared the noise reduction effect of the wavelet,EMD,EEMD,and the improved EEMD. The improved EEMD threshold noise reduction have the best noise reduction result , the highest signal-to-noise ratio, the smallest standard deviation error.After the improved EEMD threshold noise reduction , the measurement signal time domain waveform smooth. More high frequency noise was obviously reduced in Hilbert time- frequency spectrum. Signal-to-noise ratio significantly improve, and signal characteristics are very clear.

Frequency Domain Design Method of Wavelet Basis Based on Pulsar Signal

2020 ◽  
Vol 73 (6) ◽  
pp. 1223-1236
Author(s):  
Sihai You ◽  
Hongli Wang ◽  
Yiyang He ◽  
Qiang Xu ◽  
Lei Feng
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Design Method ◽  
Signal To Noise Ratio ◽  
Frequency Domain Analysis ◽  
Time Of Arrival ◽  
Frequency Noise ◽  
Wavelet Basis ◽  
Signal To Noise ◽  
Noise Ratio

During pulsar navigation, the high-frequency noise carried by the pulsar profile signal reduces the accuracy of the pulse TOA (Time of Arrival) estimation. At present, the main method to remove signal noise by using wavelet transform is to redesign the function of the threshold and level of wavelet transform. However, the signal-to-noise ratio and other indicators of the filtered signal need to be further optimised, so a more appropriate wavelet basis needs to be designed. This paper proposes a wavelet basis design method based on frequency domain analysis to improve the denoising effect of pulsar signals. This method first analyses the pulsar contour signal in the frequency domain and then designs a Crab pulsar wavelet basis (CPn, where n represents the wavelet basis length) based on its frequency domain characteristics. In order to improve the real-time performance of the algorithm, a wavelet lifting scheme is implemented. Through simulation, this method analyses the pulsar contour signal data at home and abroad. Results show the signal-to-noise ratio can be increased by 4 dB, the mean square error is reduced by 61% and the peak error is reduced by 45%. Therefore, this method has better filtering effect.

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.

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