Measuring method for chrominance signal-to-random noise ratio for video tape recorders

2015 ◽  
Keyword(s):  
Random Noise ◽  
Measuring Method ◽  
Video Tape ◽  
Noise Ratio

HYDROPHONE STREAMER NOISE

Geophysics ◽  
1974 ◽  
Vol 39 (6) ◽  
pp. 781-793 ◽  
Author(s):  
M. Schoenberger ◽  
J. F. Mifsud
Keyword(s):  
Noise Reduction ◽  
Near Field ◽  
Random Noise ◽  
Original System ◽  
Coherent Noise ◽  
Signal Ratio ◽  
Motion Sensitivity ◽  
Noise Characteristics ◽  
Noise Ratio ◽  
Boat Speed

Experiments were performed to determine the noise characteristics of a hydrophone streamer that had incorporated a number of noise reduction features. In the original system, the channels to which the depth‐controller birds were attached were 3 to 4 times noisier than nonbird channels. Fortunately, the bird noise is near‐field and is eliminated simply by increasing bird/hydrophone separation to 9 ft. On this cable, no other discrete noise sources are evident. The boat, propulsion system, lead‐in cable, tail buoy, and ambient sea conditions (moderate seas) do not generate significant noise at towing speeds above 5 knots. The noise on individual hydrophones not near birds is mainly random with only a small coherent component traveling horizontally through the water from the direction of the boat. However, since the 145-ft hydrophone arrays of 20 detectors are much more effective in reducing random noise than coherent noise, the array output consists of approximately equal portions of each. A twofold decrease in the total noise‐to‐signal ratio would result from doubling the array length (to 290 ft) while maintaining the same hydrophone density. This would result in a four to fivefold decrease in the coherent noise‐to‐signal ratio and a 30 percent decrease in the random noise‐to‐signal ratio. Additional noise reduction would result from increasing the hydrophone density and decreasing the motion sensitivity of the hydrophones. (The streamer hydrophones are not the motion canceling type.) At a towing speed of 5.3 knots, the noise level recorded on an array (not near a bird) is equivalent to pressures of 1 μbar. In normal operations with an 8-gun sleeve exploder source, a stacked section signal‐to‐towing noise ratio of 3 was obtained at 3.0 sec. However, the towing noise increases as the cube of the boat speed, and the S/N ratio would decrease by a factor of 11 if the boat speed were doubled. Conversely, decreasing the boat speed by 18 percent would double the signal‐to‐towing noise ratio.

Random denoising and signal nonlinearity approach by time-frequency peak filtering using weighted frequency reassignment

Geophysics ◽  
2013 ◽  
Vol 78 (6) ◽  
pp. V229-V237 ◽  
Author(s):  
Hongbo Lin ◽  
Yue Li ◽  
Baojun Yang ◽  
Haitao Ma
Keyword(s):  
Weight Function ◽  
Instantaneous Frequency ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Signal To Noise ◽  
Transform Method ◽  
Time Frequency ◽  
Frequency Peak ◽  
Noise Ratio

Time-frequency peak filtering (TFPF) may efficiently suppress random noise and hence improve the signal-to-noise ratio. However, the errors are not always satisfactory when applying the TFPF to fast-varying seismic signals. We begin with an error analysis for the TFPF by using the spread factor of the phase and cumulants of noise. This analysis shows that the nonlinear signal component and non-Gaussian random noise lead to the deviation of the pseudo-Wigner-Ville distribution (PWVD) peaks from the instantaneous frequency. The deviation introduces the signal distortion and random oscillations in the result of the TFPF. We propose a weighted reassigned smoothed PWVD with less deviation than PWVD. The proposed method adopts a frequency window to smooth away the residual oscillations in the PWVD, and incorporates a weight function in the reassignment which sharpens the time-frequency distribution for reducing the deviation. Because the weight function is determined by the lateral coherence of seismic data, the smoothed PWVD is assigned to the accurate instantaneous frequency for desired signal components by weighted frequency reassignment. As a result, the TFPF based on the weighted reassigned PWVD (TFPF_WR) can be more effective in suppressing random noise and preserving signal as compared with the TFPF using the PWVD. We test the proposed method on synthetic and field seismic data, and compare it with a wavelet-transform method and [Formula: see text] prediction filter. The results show that the proposed method provides better performance over the other methods in signal preserving under low signal-to-noise ratio.

Influence of multiplexing conditions on artefact signal and the signal-to-noise ratio in the decoded data in Hadamard transform ion mobility spectrometry

2020 ◽  
Vol 26 (3) ◽  
pp. 204-212
Author(s):  
Anastasia Sarycheva ◽  
Alexey Adamov ◽  
Sergey S Poteshin ◽  
Sergey S Lagunov ◽  
Alexey A Sysoev
Keyword(s):  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Utilization Efficiency ◽  
Hadamard Transform ◽  
Signal Averaging ◽  
Signal To Noise ◽  
Pseudorandom Sequences ◽  
Noise Ratio

In Hadamard transform ion mobility spectrometry (HT IMS), the signal-to-noise ratio is always lower for non-modified pseudorandom sequences than for modified sequences. Since the use of non-modified modulating pseudorandom sequences is strategically preferable from a duty cycle standpoint, we investigated the change in the interference signal when transitioning from non-modified modulating sequences to sequences modified by the addition of 1,3,5 and 7 zeros. The interfering signal in HT IMS with modified pseudorandom sequences was shown to be mainly random noise for all the cases except for modifying by incorporation of 1 zero. For standard samples of tetraalkylammonium halides, modulation by non-modified pseudorandom sequences is beneficial in the case of small numbers of averaged spectra (below ∼40 averaged spectra compared to any modified pseudorandom sequences except for 1 zero modified and below ∼200 averaged spectra compared to signal averaging ion mobility spectrometry) and worsens the signal-to-noise ratio in the case of large numbers of averaged spectra. Contrarily, modulation by modified pseudorandom sequences is beneficial for any number of averaged spectra, except for very small ones (below 15 averaged spectra compared to modulation by non-modified sequences). Pseudorandom sequence modified with 1 zero incorporation is beneficial in the case of below ∼400 averaged spectra compared to any modified and non-modified pseudorandom sequences. The signal-to-noise ratio in conventional signal averaging mode ion mobility spectrometry is affected by random noise, whereas the HT IMS with non-modified pseudorandom sequences was demonstrated to be primarily affected by a systematic noise-like artefact signal. Because noise-like artefact signals were found to be reproducible, predicting models for interference signals could be generated to improve signal-to-noise ratio. This is significant because non-modified modulating sequences are limited by their poor signal-to-noise ratio. This improvement would increase the viability of non-modified modulating sequences which are preferred because of their higher sample utilization efficiency.

OCT Signal Processing Method Based on Sequential Kalman Filtering

2015 ◽  
Vol 1092-1093 ◽  
pp. 300-303 ◽  
Author(s):  
Yu Heng Yan ◽  
Yan Song Li
Keyword(s):  
Kalman Filter ◽  
Output Signal ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Current Transformer ◽  
Current Signal ◽  
Signal To Noise ◽  
Measured Current ◽  
Noise Characteristics ◽  
Noise Ratio

Optical current transformer (OCT) measured current signal which is mixed with strong random noise. The measured readings can’t accurately reflect the value of the measured current. Since the optical current transformer noise inside the band is basically where the measured current signal overlap,we can not use the traditional method to filter it out. This paper describes the measurement principle based on the Faraday effect of optical current transformer and signal to noise characteristics. Considering optical current transformer for low SNR characteristics, and embedded systems do not have the characteristics of a matrix library, we proposed using sequential Kalman filter to improve the real-time output signal to noise ratio. In the measured current for DC and AC conditions,we established an appropriate state space model Kalman filter.,and conduct simulation on matlab. Practice shows that the sequential Kalman filter algorithm can effectively improve the output signal to noise ratio and accuracy.

scholarly journals Performance Analysis of IIR & FIR Windowing Techniques in Electroencephalography Signal Processing

2019 ◽  
Vol 8 (10) ◽  
pp. 3568-3578
Keyword(s):  
Performance Analysis ◽  
Impulse Response ◽  
Finite Impulse Response ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Data Retrieval ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Signal To Noise ◽  
Noise Ratio

A very small amplitude (μV) of the electroencephalography (EEG) signal is infected by diverse artifacts. These artifacts have an effect on the distinctiveness of the signal because of which medical psychoanalysis and data retrieval is difficult. Therefore, EEG signals are initially preprocessed to eliminate the artifacts to produce signals that can serve as a base for further processing and analysis. Different filters are implemented to eliminate the artifacts present in the EEG signal. Recent research shows that window technique Finite Impulse Response (FIR) filter is usually used. In this paper, digital Infinite Impulse Response (IIR) filter and different Finite Impulse Response (FIR) window filters (Hanning, Hamming, Kaiser, Blackman) of various orders are implemented to eradicate the random noise added to EEG signals. Their performance analysis has been done in Matlab (R2016a) by calculating the mean square error, mean absolute error, signal to noise ratio, peak signal to noise ratio and cross-correlation. The results show that Kaiser Window based finite impulse response filter outperforms in removing the noise from the electroencephalogram signal. This research focuses on eradicating random noise in electroencephalogram signals but this approach will be extended to a different source of electroencephalogram contamination.

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