scholarly journals Spectral distortion level resulting in a just-noticeable difference between an a priori signal-to-noise ratio estimate and its instantaneous case

2020 ◽  
Vol 148 (4) ◽  
pp. 1879-1889 ◽  
Author(s):  
Aaron Nicolson ◽  
Kuldip K. Paliwal
Keyword(s):  
Signal To Noise Ratio ◽  
A Priori ◽  
Just Noticeable Difference ◽  
Spectral Distortion ◽  
Signal To Noise ◽  
Ratio Estimate ◽  
Distortion Level ◽  
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.

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.

scholarly journals Algorithm for preliminary processing of charge coupled devices array data based on the adaptive Wiener filter

Informatics ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. 72-83
Author(s):  
Hl. S. Litvinovich ◽  
I. I. Bruchkouski
Keyword(s):  
Vegetation Index ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Wiener Filter ◽  
Spectral Brightness ◽  
Signal To Noise ◽  
Statistical Parameters ◽  
Charge Coupled Devices ◽  
Noise Ratio ◽  
Priori Information

The researcher should choose the modes of recording spectra which allow to achieve the highest accuracy of spectral measurements in remote sensing systems. When registering a signal from aircraft which provide maximum coverage of the studied area, it is important to obtain a signal with the maximum signal-to- noise ratio in a minimum time, since the accumulation of spectra samples for averaging is impossible. The paper presents the experimental results of determining the noise components (readout noise, photon, electronic shot, pattern noise) for a monochrome uncooled CCD-line detector Toshiba TCD1304DG (CCD – charge-coupled devices) with various conditions of spectrum registration: detector temperature, exposition. Obtained dependences of the noise components make it possible to estimate the noise level for well-known conditions of spectra registration. The algorithm for processing CCD data based on an adaptive Wiener filter is proposed to increase the signal-to-noise ratio by using a priori information about the statistical parameters of the noise components. Such approach has allowed to increase the signal-to-noise ratio of sky spectral brightness by 4–9 dB for exposure times. The practical application of the algorithm has reduced the uncertainty in the vegetation index NDVI by 1.5 times when recording the reflection spectra of vegetation from the aircraft in the nadir measurement geometry.

Asymptotically linear velocity analysis with high resolution in time domain

Geophysics ◽  
1991 ◽  
Vol 56 (11) ◽  
pp. 1840-1848 ◽  
Author(s):  
Simon Katz
Keyword(s):  
Penalty Function ◽  
Time Domain ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Linear Velocity ◽  
Velocity Analysis ◽  
Signal To Noise ◽  
Ratio Estimate ◽  
Analysis Technique ◽  
Noise Ratio

A new method of velocity analysis based on the joint use of a linear velocity analysis technique and a nonlinear penalty function is described. The penalty function is defined as a monotonic continuous function of the signal‐to‐noise ratio estimate, tending to a constant or to zero with this estimate converging respectively to infinity or to zero. The signal‐to‐noise ratio estimate is obtained as the ratio of the estimates of the energy of the signal with given velocity and the energy of the residual field. The resulting velocity analysis technique is characterized by higher noise suppression and higher resolution in the velocity and the time domain compared to the semblance based velocity analysis or the velocity analysis based on the slant stacking of input data.

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

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