Enhancement of Radar Imagery by Maximum Entropy Processing

1977 ◽  
Vol 21 (3) ◽  
pp. 241-243 ◽  
Author(s):  
Clanton E. Mancill
Keyword(s):  
Maximum Entropy ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Doppler Frequency ◽  
Frequency Measurement ◽  
Entropy Method ◽  
Frequency Resolution ◽  
Sampled Data ◽  
Maximum Entropy Spectrum ◽  
Using Data

The maximum entropy spectrum (MES), a sampled data power spectrum estimator, is applied to the enhancement of imagery obtained by synthetic array radar (SAR) imaging systems. MES offers better frequency resolution than conventional Fourier transform methods for certain signal classes. Since azimuth ground resolution in SAR systems is obtained by doppler frequency measurement of the radar return, the method is capable of enhancing the resolution of SAR maps. The principal signal requirement is adequate signal-to-noise ratio. The maximum entropy method has been tested using data obtained by the Hughes FLAMR radar system. The super-resolution capabilities of the method are demonstrated using FLAMR images of corner reflector arrays.

Frequency resolution of Fourier and maximum entropy spectral estimates

Geophysics ◽  
1982 ◽  
Vol 47 (9) ◽  
pp. 1303-1307 ◽  
Author(s):  
S. L. Marple
Keyword(s):  
Maximum Entropy ◽  
Signal To Noise Ratio ◽  
Frequency Resolution ◽  
Sampled Data ◽  
Signal To Noise ◽  
Analytic Determination ◽  
Spectral Estimates ◽  
The Mean ◽  
Better Than

An analytic determination of the frequency resolution for maximum entropy and conventional Blackman‐Tukey spectral estimates is made for the case of known autocorrelation. As the signal‐to‐noise ratio decreases, the maximum entropy resolution is no better than that achievable by the Blackman‐Tukey spectral estimate. The mean resolution of an ensemble of spectra constructed from sampled data sequences agrees with the analytic result.

Nonlinearity of the maximum entropy method in resolution enhancement

1992 ◽  
Vol 70 (12) ◽  
pp. 2887-2894 ◽  
Author(s):  
J. K. Kauppinen ◽  
D. J. Moffatt ◽  
H. H. Mantsch
Keyword(s):  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Linear Prediction ◽  
Signal To Noise Ratio ◽  
Resolution Enhancement ◽  
Nonlinear Behavior ◽  
Entropy Method ◽  
Spectral Lines ◽  
High Signal ◽  
Intensity Pattern

The nonlinear behavior of the filter-type Maximum Entropy Method (MEM) was investigated from a theoretical and a practical point of view. The integrated intensity of the output spectral lines of MEM was probed as a function of the input intensity pattern, the filter length, and the S/N ratio of the input spectrum. The nonlinear behavior of MEM has been explained and the results compared with those derived by another method, LOMEP (Lineshape Optimized Maximum Entropy linear Prediction). The study was carried out with the aim of resolution enhancement of spectra that have high signal-to-noise ratio.

A new method for NMR data inversion based on double-parameter regularization

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. JM39-JM49 ◽  
Author(s):  
Jiangfeng Guo ◽  
Ranhong Xie ◽  
Youlong Zou ◽  
Guowen Jin ◽  
Lun Gao ◽  
...  
Keyword(s):  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Regularization Parameter ◽  
Signal To Noise Ratio ◽  
Simulated Data ◽  
Entropy Method ◽  
Petroleum Exploration ◽  
Tight Sandstone ◽  
Nmr Data ◽  
Parameter Regularization

Nuclear magnetic resonance (NMR) technology plays a significant role in petroleum exploration. NMR data can be processed using inversion methods to reflect the relaxation information of all the components. We have developed a new double-parameter regularization (DPR) method for the inversion of NMR data, whose regularization terms consist of Tikhonov regularization and maximum entropy regularization. The objective function for the DPR method was solved using the Levenberg-Marquardt method, the proportional coefficient of the regularization parameter was obtained using an iteration procedure, and the optimum regularization parameter of the DPR method was selected using an S-curve. The relationship between the optimum regularization parameter and the signal-to-noise ratio (S/N) of the data was evaluated. Moreover, we compared the results of the NMR inversion obtained from the norm smooth method, the maximum entropy method, and the DPR method for simulated data. We evaluated how the proportional coefficient of the regularization parameter affected the inverted [Formula: see text] distributions and processed field NMR log data for a tight sandstone reservoir using the DPR method. The results indicated that the optimum regularization parameter for the DPR method gradually decreases with increasing data S/N. The accuracy is higher for the DPR method than for the norm smooth method and the maximum entropy method under low-S/N conditions. It is of great importance to select the proportional coefficient for the DPR method. The inverted [Formula: see text] distributions are similar for the DPR method and the norm smooth method when the proportional coefficient is small, and this is similar for the DPR method and the maximum entropy method when the proportional coefficient is large.

scholarly journals CALCULATION OF DIRECTIONAL WAVE SPECTRA BY THE MAXIMUM ENTROPY METHOD OF SPECTRAL ANALYSIS

1984 ◽  
Vol 1 (19) ◽  
pp. 33 ◽  
Author(s):  
MIchael J. Briggs
Keyword(s):  
Spectral Analysis ◽  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Adaptive Method ◽  
Entropy Method ◽  
Wave Spectra ◽  
Spectral Estimates ◽  
Directional Wave ◽  
Data Adaptive ◽  
Using Data

Two analysis techniques for calculating directional wave spectra from measured pressure and biaxial current components were intercompared using data from the 25 October 1980 Atlantic Remote Sensing Land Ocean Experiment (ARSLOE) storm. The two methods are the conventional Fast Fourier Transform (FFT) method and a Maximum Entropy Method (MEM). The MEM is a nonlinear data adaptive method of spectral analysis which is capable of generating higher resolution spectral estimates from shorter data records than conventional FFT methods. The MEM has shown good agreement with the frequency and directional wave spectra calculated using conventional methods.

Maximum entropy analysis of dispersed seismic signals

Geophysics ◽  
1986 ◽  
Vol 51 (12) ◽  
pp. 2225-2234 ◽  
Author(s):  
K. B. Cox ◽  
I. M. Mason
Keyword(s):  
Maximum Entropy ◽  
Fourier Transforms ◽  
Signal To Noise Ratio ◽  
Characteristic Parameter ◽  
Moving Window ◽  
Power Spectral ◽  
Maximum Entropy Spectrum ◽  
Waveguide System ◽  
Nyquist Rate ◽  
Spectral Estimator

Conventional moving‐window analyzers based on Fourier transforms sometimes lack the resolution required to separate each of the modes in a seismic waveguide. It is possible to enhance the resolution of a moving‐window analyzer by using a maximum entropy power spectral estimator to approximate the spectrum of each windowed segment of a trace. Barrodale and Erickson have developed a suitable maximum entropy algorithm which can also be applied to estimating the parameters required in the fast recompression of inseam seismic arrivals. The Barrodale‐Erickson maximum entropy algorithm appears to need a sample rate of approximately ten times the Nyquist rate in order to generate meaningful maximum entropy spectra. The required increase can be achieved in the laboratory by applying an accurate interpolator to field records. Noise captures the maximum entropy spectrum if the input signal‐to‐noise ratio falls much below 10 dB. Use of a maximum entropy spectral analyzer aids in both identifying modes in a waveguide system and estimating the group velocity‐frequency characteristic parameter.

scholarly journals 3D super-resolved imaging in live cells using sub-diffractive plasmonic localization of hybrid nanopillar arrays

Nanophotonics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 2847-2859
Author(s):  
Soojung Kim ◽  
Hyerin Song ◽  
Heesang Ahn ◽  
Seung Won Jun ◽  
Seungchul Kim ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Imaging Techniques ◽  
Optical Loss ◽  
Super Resolution ◽  
Living Cells ◽  
Live Cells ◽  
Complex Biological System ◽  
Observation Volume ◽  
Resolution Imaging ◽  
Nanopillar Arrays

AbstractAnalysing dynamics of a single biomolecule using high-resolution imaging techniques has been had significant attentions to understand complex biological system. Among the many approaches, vertical nanopillar arrays in contact with the inside of cells have been reported as a one of useful imaging applications since an observation volume can be confined down to few-tens nanometre theoretically. However, the nanopillars experimentally are not able to obtain super-resolution imaging because their evanescent waves generate a high optical loss and a low signal-to-noise ratio. Also, conventional nanopillars have a limitation to yield 3D information because they do not concern field localization in z-axis. Here, we developed novel hybrid nanopillar arrays (HNPs) that consist of SiO2 nanopillars terminated with gold nanodisks, allowing extreme light localization. The electromagnetic field profiles of HNPs are obtained through simulations and imaging resolution of cell membrane and biomolecules in living cells are tested using one-photon and 3D multiphoton fluorescence microscopy, respectively. Consequently, HNPs present approximately 25 times enhanced intensity compared to controls and obtained an axial and lateral resolution of 110 and 210 nm of the intensities of fluorophores conjugated with biomolecules transported in living cells. These structures can be a great platform to analyse complex intracellular environment.

Sensor-Based Estimation of Dim Light Melatonin Onset Using Features of Two Time Scales

2021 ◽  
Vol 2 (3) ◽  
pp. 1-15
Author(s):  
Cheng Wan ◽  
Andrew W. Mchill ◽  
Elizabeth B. Klerman ◽  
Akane Sano
Keyword(s):  
Time Scales ◽  
Time Scale ◽  
Light Exposure ◽  
Biological Activities ◽  
Second Step ◽  
Sampled Data ◽  
Dim Light Melatonin Onset ◽  
Dim Light ◽  
Using Data ◽  
Mean Square Errors

Circadian rhythms influence multiple essential biological activities, including sleep, performance, and mood. The dim light melatonin onset (DLMO) is the gold standard for measuring human circadian phase (i.e., timing). The collection of DLMO is expensive and time consuming since multiple saliva or blood samples are required overnight in special conditions, and the samples must then be assayed for melatonin. Recently, several computational approaches have been designed for estimating DLMO. These methods collect daily sampled data (e.g., sleep onset/offset times) or frequently sampled data (e.g., light exposure/skin temperature/physical activity collected every minute) to train learning models for estimating DLMO. One limitation of these studies is that they only leverage one time-scale data. We propose a two-step framework for estimating DLMO using data from both time scales. The first step summarizes data from before the current day, whereas the second step combines this summary with frequently sampled data of the current day. We evaluate three moving average models that input sleep timing data as the first step and use recurrent neural network models as the second step. The results using data from 207 undergraduates show that our two-step model with two time-scale features has statistically significantly lower root-mean-square errors than models that use either daily sampled data or frequently sampled data.

Enhanced Information Recovery in 2D On-and Off-Resonance Nutation NQR using the Maximum Entropy Method

1996 ◽  
Vol 51 (5-6) ◽  
pp. 337-347 ◽  
Author(s):  
Mariusz Maćkowiak ◽  
Piotr Kątowski
Keyword(s):  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Theoretical Description ◽  
Entropy Method ◽  
Zero Field ◽  
Resonance Effects ◽  
Information Recovery ◽  
Truncation Errors ◽  
Enhanced Resolution ◽  
2D Data

Abstract Two-dimensional zero-field nutation NQR spectroscopy has been used to determine the full quadrupolar tensor of spin - 3/2 nuclei in serveral molecular crystals containing the 3 5 Cl and 7 5 As nuclei. The problems of reconstructing 2D-nutation NQR spectra using conventional methods and the advantages of using implementation of the maximum entropy method (MEM) are analyzed. It is shown that the replacement of conventional Fourier transform by an alternative data processing by MEM in 2D NQR spectroscopy leads to sensitivity improvement, reduction of instrumental artefacts and truncation errors, shortened data acquisition times and suppression of noise, while at the same time increasing the resolution. The effects of off-resonance irradiation in nutation experiments are demonstrated both experimentally and theoretically. It is shown that off-resonance nutation spectroscopy is a useful extension of the conventional on-resonance experiments, thus facilitating the determination of asymmetry parameters in multiple spectrum. The theoretical description of the off-resonance effects in 2D nutation NQR spectroscopy is given, and general exact formulas for the asymmetry parameter are obtained. In off-resonance conditions, the resolution of the nutation NQR spectrum decreases with the spectrometer offset. However, an enhanced resolution can be achieved by using the maximum entropy method in 2D-data reconstruction.

Estimating the distribution of primary reflection coefficients

Geophysics ◽  
2003 ◽  
Vol 68 (4) ◽  
pp. 1417-1422 ◽  
Author(s):  
Danilo R. Velis
Keyword(s):  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Entropy Method ◽  
Reflection Coefficients ◽  
Log Data ◽  
Neuquen Basin ◽  
Neuquén Basin ◽  
Skewness And Kurtosis

The distribution of primary reflection coefficients can be estimated by means of the maximum entropy method, giving rise to smooth nonparametric functions which are consistent with the data. Instead of using classical moments (e.g. skewness and kurtosis) to constraint the maximization, nonconventional sample statistics help to improve the quality of the estimates. Results using real log data from various wells located in the Neuquen Basin (Argentina) show the effectiveness of the method to estimate both robust and consistent distributions that may be used to simulate realistic sequences.

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