Perception of Movement and Correlation in Stroboscopically Presented Noise Patterns

Perception ◽  
1985 ◽  
Vol 14 (2) ◽  
pp. 209-224 ◽  
Author(s):  
Andrea J van Doorn ◽  
Jan J Koenderink ◽  
Wim A van de Grind
Keyword(s):  
Visual Field ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Temporal Correlation ◽  
Motion Aftereffect ◽  
Luminance Contrast ◽  
Signal To Noise ◽  
Field Evidence ◽  
Spatiotemporal Integration ◽  
Moving Pattern

The detection of spatiotemporal correlation in visual displays has been studied with stroboscopically presented random-noise patterns and with a signal-to-noise ratio paradigm in which the moving pattern was masked with spatiotemporal white noise. These methods reveal the ability of the visual system to detect correlation of spatiotemporal structures, rather than luminance contrast. The effects of stroboscopic rate, exposure duration, target size, and the extent of discrete spatial shifts were studied in both the central and the peripheral visual field. Evidence for orientation-selective and speed-selective mechanisms was found, as well as for extensive spatiotemporal integration. Bounds on parameters of spatial and temporal correlation and integration were obtained. The results are similar to those reported earlier, and also extend them. Their relation to results obtained through other paradigms (eg the motion aftereffect) is explored.

ECG Signal De-Noising with Asynchronous Averaging and Filtering Algorithm

2012 ◽  
pp. 199-205
Author(s):  
Alka Gautam ◽  
Hoon-Jae Lee ◽  
Wan-Young Chung
Keyword(s):  
Signal To Noise Ratio ◽  
Random Noise ◽  
Noise Cancellation ◽  
Peak Detection ◽  
Lms Algorithm ◽  
Ecg Signal ◽  
Maximum Point ◽  
Signal To Noise ◽  
Filtering Algorithm ◽  
Overall Performance

In this study, a new algorithm is proposed—Asynchronous Averaging and Filtering (AAF) for ECG signal de-noising. R-peaks are detected with another proposed algorithm—Minimum Slot and Maximum Point selecting method (MSMP). AAF algorithm reduces random noise (major component of EMG noise) from ECG signal and provides comparatively good results for baseline wander noise cancellation. Signal to noise ratio (SNR) improves in filtered ECG signal, while signal shape remains undistorted. The authors conclude that R-peak detection with MSMP method gives comparable results from existing algorithm like Pan-Tomkins algorithm. AAF algorithm is advantageous over adaptation algorithms like Wiener and LMS algorithm. Overall performance of proposed algorithms is comparatively good.

scholarly journals A Novel Scheme for Merging Active and Passive Satellite Soil Moisture Retrievals Based on Maximizing the Signal to Noise Ratio

2020 ◽  
Vol 12 (22) ◽  
pp. 3804
Author(s):  
B. G. Mousa ◽  
Hong Shu ◽  
Mohamed Freeshah ◽  
Aqil Tariq
Keyword(s):  
Soil Moisture ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Correlation Coefficients ◽  
Temporal Correlation ◽  
Global Scale ◽  
Signal To Noise ◽  
Significance Levels ◽  
Land Data Assimilation ◽  
Land Data Assimilation System

In this research, we developed and evaluated a new scheme for merging soil moisture (SM) retrievals from both passive and active microwave satellite estimates, based on maximized signal-to-noise ratios, in order to produce improved SM products using least-squares theory. The fractional mean-squared-error (fMSE) derived from the triple collocation method (TCM) was used for this purpose. The proposed scheme was applied by using a threshold between signal and noise at fMSE equal to 0.5 to maintain the high-quality SM observations. In the regions where TCM is unreliable, we propose four scenarios based on the determinations of correlations between all three SM products of TCM at significance levels (i.e., p-values). The proposed scheme was applied to combine SM retrievals from Soil Moisture Active Passive (SMAP), Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) to produce SMAP+ASCAT and AMSR2+ASCAT SM datasets at a global scale for the period from June 2015 to December 2017. The merged SM dataset performance was assessed against SM data from ground measurements of international soil moisture network (ISMN), Global Land Data Assimilation System-Noah (GLDAS-Noah) and ERA5. The results show that the two merged SM datasets showed significant improvement over their parent products in the high average temporal correlation coefficients (R) and the lowest root mean squared difference (RMSE), compared with in-situ measurements over different networks of ISMN. Moreover, these datasets outperformed their parent products over different land cover types in most regions of the world, with a high overall average temporal R and the lowest overall average RMSE value with GLDAS and ERA5. In addition, the suggested scenarios improved SM performance in the regions with unreliable TCMs.

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.

Stacking seismic data using local correlation

Geophysics ◽  
2009 ◽  
Vol 74 (3) ◽  
pp. V43-V48 ◽  
Author(s):  
Guochang Liu ◽  
Sergey Fomel ◽  
Long Jin ◽  
Xiaohong Chen
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Image Point ◽  
Local Correlation ◽  
Seismic Profiles ◽  
Signal To Noise ◽  
Prestack Migration

Stacking plays an important role in improving signal-to-noise ratio and imaging quality of seismic data. However, for low-fold-coverage seismic profiles, the result of conventional stacking is not always satisfactory. To address this problem, we have developed a method of stacking in which we use local correlation as a weight for stacking common-midpoint gathers after NMO processing or common-image-point gathers after prestack migration. Application of the method to synthetic and field data showed that stacking using local correlation can be more effective in suppressing random noise and artifacts than other stacking methods.

scholarly journals Random Noise Attenuation by using FDNAT Filter on Seismic Data in East Diwaniya, South Eastern – Iraq

2019 ◽  
pp. 1297-1303
Author(s):  
Kamal K. Ali ◽  
Reem K. Ibrahim ◽  
Hassan A. Thabit
Keyword(s):  
Quality Control ◽  
Quality Factor ◽  
Spectrum Analysis ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Noise Attenuation ◽  
Signal To Noise ◽  
South Eastern ◽  
Data Line

The frequency dependent noise attenuation (FDNAT) filter was applied on 2D seismic data line DE21 in east Diwaniya, south eastern Iraq to improve the signal to noise ratio. After applied FDNAT on the seismic data, it gives good results and caused to remove a lot of random noise. This processing is helpful in enhancement the picking of the signal of the reflectors and therefore the interpretation of data will be easy later. The quality control by using spectrum analysis is used as a quality factor in proving the effects of FDNAT filter to remove the random noise.

Exploring Digital Image Dithering Techniques on a Broken Foot Image

2020 ◽  
Vol 10 (9) ◽  
pp. 2247-2251
Author(s):  
Yuan Li ◽  
Zili Xu ◽  
Xiangyang Liu ◽  
G. Sasi ◽  
M. Sundar Prakash Balaji ◽  
...  
Keyword(s):  
Digital Image ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Mean Square ◽  
Original Image ◽  
Signal To Noise ◽  
Uniform Quantization ◽  
Simulation Results ◽  
Computer Monitors ◽  
Lowpass Filtering

The contouring effects appear when an image is quantized rudely irrespective of the uniform or non-uniform quantization. To mitigate the effects of contouring, a small amount of random noise is added (dithered) to the original image before quantization. Techniques such as dithering and half-toning are widely used strategies in obtaining images and texts in magazines, newspapers, books, printers, computer monitors, and LCDs. This study explores the dithering technique on a broken foot image with more elaborative methods and results. All the experiments involved in this study, such as quantization, dithering, no dithering, and dithering, quantized, and filtered techniques, are conducted using the Matlab R2016b tool. Overall information and details are retained with the aid of lowpass filtering and highpass filtering, respectively. Simulation results such as Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) are obtained in every stage of the dithering procedure to analyze and compare the performance or accuracy.

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.

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