Comparison of a priori signal-to-noise ratio assessment methods used in noise reduction algorithms

2016 ◽  
Author(s):  
Arkadiy Prodeus
Keyword(s):  
Noise Reduction ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Assessment Methods ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals Noise reduction and signal-to-noise ratio improvement of atomic magnetometers with optical gradiometer configurations

2015 ◽  
Vol 23 (5) ◽  
pp. 6976 ◽  
Author(s):  
Keigo Kamada ◽  
Yosuke Ito ◽  
Sunao Ichihara ◽  
Natsuhiko Mizutani ◽  
Tetsuo Kobayashi
Keyword(s):  
Noise Reduction ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals An SNR Estimation Technique Based on Deep Learning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1139 ◽  
Author(s):  
Kai Yang ◽  
Zhitao Huang ◽  
Xiang Wang ◽  
Fenghua Wang
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Intermediate Frequency ◽  
Estimation Technique ◽  
Signal To Noise ◽  
Snr Estimation ◽  
Application Range ◽  
Noise Ratio ◽  
Priori Information

Signal-to-noise ratio (SNR) is a priori information necessary for many signal processing algorithms or techniques. However, there are many problems exsisting in conventional SNR estimation techniques, such as limited application range of modulation types, narrow effective estimation range of signal-to-noise ratio, and poor ability to accommodate non-zero timing offsets and frequency offsets. In this paper, an SNR estimation technique based on deep learning (DL) is proposed, which is a non-data-aid (NDA) technique. Second and forth moment (M2M4) estimator is used as a benchmark, and experimental results show that the performance and robustness of the proposed method are better, and the applied ranges of modulation types is wider. At the same time, the proposed method is not only applicable to the baseband signal and the incoherent signal, but can also estimate the SNR of the intermediate frequency signal.

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.

scholarly journals Noise reduction with reflection supervirtual interferometry

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. V249-V256
Author(s):  
Kai Lu ◽  
Zhaolun Liu ◽  
Sherif Hanafy ◽  
Gerard Schuster
Keyword(s):  
Noise Reduction ◽  
Field Data ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Data Set ◽  
Reflection Data ◽  
The Poor ◽  
The Earth ◽  
The Common ◽  
Noise Ratio

To image deeper portions of the earth, geophysicists must record reflection data with much greater source-receiver offsets. The problem with these data is that the signal-to-noise ratio (S/N) significantly diminishes with greater offset. In many cases, the poor S/N makes the far-offset reflections imperceptible on the shot records. To mitigate this problem, we have developed supervirtual reflection interferometry (SVI), which can be applied to far-offset reflections to significantly increase their S/N. The key idea is to select the common pair gathers where the phases of the correlated reflection arrivals differ from one another by no more than a quarter of a period so that the traces can be coherently stacked. The traces are correlated and summed together to create traces with virtual reflections, which in turn are convolved with one another and stacked to give the reflection traces with much stronger S/Ns. This is similar to refraction SVI except far-offset reflections are used instead of refractions. The theory is validated with synthetic tests where SVI is applied to far-offset reflection arrivals to significantly improve their S/N. Reflection SVI is also applied to a field data set where the reflections are too noisy to be clearly visible in the traces. After the implementation of reflection SVI, the normal moveout velocity can be accurately picked from the SVI-improved data, leading to a successful poststack migration for this data set.

scholarly journals PEMILIHAN METODE PENGURANGAN NOISE PADA CITRA ULTRASONOGRAFI OVARIUM

2020 ◽  
Vol 15 (1) ◽  
pp. 13
Author(s):  
Eliyani Eliyani ◽  
Ahmad Riyadi Maulana
Keyword(s):  
Noise Reduction ◽  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Median Filtering ◽  
Mean Square ◽  
Signal To Noise ◽  
Noise Ratio

Pengurangan noise merupakan upaya untuk memperbaiki kualitas citra yang akan memudahkan tahapan selanjutnya dalam pengolahan citra. Noise Reduction atau mengurangi noise untuk menghasilkan citra lebih baik sehingga informasi data citra tidak hilang dan citra dapat diintepretasikan oleh mata manusia. Penelitian ini menggunakan data gambar ultrasonografi ovarium untuk membantu menganalisa kondisi kesehatan ovarium perempuan. Gambar ultrasonografi ovarium biasanya terdapat noise, metode pengurangan noise yang akan digunakan pada penelitian ini adalah Median Filtering dan Adaptive Median Filtering. Hasil filtering dari 2 metode tersebut akan dibandingkan menggunakan Mean Square Error(MSE) dan Peak Signal To Noise Ratio(PNSR). Ukuran kernel untuk Median Filtering dan Adaptive Median Filtering dipilih sebagai 3x3, 5x5, dan 7x7. Penelitian ini menghasilkan metode filtering dengan kinerja terbaik yaitu Adaptive Median Filtering dengan ukuran window 5x5 yang ditunjukan dari nilai Mean Square Error dan Peak Signal To Noise Ratio .

scholarly journals A PHD-SLAM Method for Mixed Birth Map Information Based on Amplitude Information

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fei Zhang ◽  
Zijing Zhang ◽  
Luxi Yang ◽  
Xinyu Zhang
Keyword(s):  
Prior Information ◽  
Likelihood Function ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Estimation Accuracy ◽  
Indoor Environments ◽  
Signal To Noise ◽  
Probability Hypothesis Density ◽  
Localization And Mapping ◽  
Noise Ratio

The Simultaneous Localization and Mapping (SLAM) method of mobile robots has the problem of low accuracy in complex environments with dense clutter and various map features, such as complex indoor environments and underwater environments. This problem is mainly embodied in estimating the location and number of feature points on the map and the position of the robot itself. In order to solve this problem, a new method based on the probability hypothesis density (PHD) SLAM is proposed in this paper, a PHD-SLAM Method for Mixed Birth Map Information Based on Amplitude Information (AI-MBMI-PHD-SLAM). Firstly, this paper proposes a PHD-SLAM method based on amplitude information (AI-PHD-SLAM). The method uses the amplitude information of map features to obtain more precise map features. Then, the clutter likelihood function is used to improve the estimation accuracy of the feature map in the SLAM process. Meanwhile, this paper studies the performance of the PHD-SLAM method with the amplitude information under the condition of the known signal-to-noise ratio or the unknown signal-to-noise ratio. Secondly, aiming at the problem that PHD-SLAM lacks a priori information in the prediction stage, an AI-PHD-SLAM-based mixed birth map information method is added. In this method, map information that has been detected before the previous moment is added to the observation information in the map prediction phase as a new map information set in the prediction phase. This can increase the prior information and improve the problem of insufficient prior information in the prediction stage. The results of the experiments show that the proposed method and the improved method outperform the RB-PHD-SLAM method in estimating the number and location accuracy of map features and have higher computational efficiency.

AN AUDITORY BRAIN-COMPUTER INTERFACE WITH ACCURACY PREDICTION

2012 ◽  
Vol 22 (03) ◽  
pp. 1250009 ◽  
Author(s):  
M. A. LOPEZ-GORDO ◽  
F. PELAYO ◽  
A. PRIETO ◽  
E. FERNANDEZ
Keyword(s):  
Signal To Noise Ratio ◽  
A Priori ◽  
Signal To Noise ◽  
Computer Interfaces ◽  
Dichotic Listening Task ◽  
Shift Keying ◽  
Trial Basis ◽  
Noise Ratio ◽  
Listening Task ◽  
Binary Phase Shift Keying

Fully auditory Brain-computer interfaces based on the dichotic listening task (DL-BCIs) are suited for users unable to do any muscular movement, which includes gazing, exploration or coordination of their eyes looking for inputs in form of feedback, stimulation or visual support. However, one of their disadvantages, in contrast with the visual BCIs, is their lower performance that makes them not adequate in applications that require a high accuracy. To overcome this disadvantage, we employed a Bayesian approach in which the DL-BCI was modeled as a Binary phase shift keying receiver for which the accuracy can be estimated a priori as a function of the signal-to-noise ratio. The results showed the measured accuracy to match the predefined target accuracy, thus validating this model that made possible to estimate in advance the classification accuracy on a trial-by-trial basis. This constitutes a novel methodology in the design of fully auditory DL-BCIs that let us first, define the target accuracy for a specific application and second, classify when the signal-to-noise ratio guarantees that target accuracy.

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