Local correlation of delta-DOR signals with low signal-to-noise ratio and severe radio frequency interference

2021 ◽  
Author(s):  
Maoli Ma ◽  
Peijia Li ◽  
Fengxian Tong ◽  
Weimin Zheng ◽  
Kondo Tetsuro ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Signal To Noise Ratio ◽  
Radio Frequency Interference ◽  
Local Correlation ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals Receiver phase alignment using fitted SVD derived sensitivities from routine prescans

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256700
Author(s):  
Olivia W. Stanley ◽  
Ravi S. Menon ◽  
L. Martyn Klassen
Keyword(s):  
Singular Value Decomposition ◽  
Radio Frequency ◽  
Signal To Noise Ratio ◽  
Singular Value ◽  
7 Tesla ◽  
Signal To Noise ◽  
Phase Signal ◽  
Noise Ratio ◽  
Value Decomposition ◽  
Svd Method

Magnetic resonance imaging radio frequency arrays are composed of multiple receive coils that have their signals combined to form an image. Combination requires an estimate of the radio frequency coil sensitivities to align signal phases and prevent destructive interference. At lower fields this can be accomplished using a uniform physical reference coil. However, at higher fields, uniform volume coils are lacking and, when available, suffer from regions of low receive sensitivity that result in poor sensitivity estimation and combination. Several approaches exist that do not require a physical reference coil but require manual intervention, specific prescans, or must be completed post-acquisition. This makes these methods impractical for large multi-volume datasets such as those collected for novel types of functional MRI or quantitative susceptibility mapping, where magnitude and phase are important. This pilot study proposes a fitted SVD method which utilizes existing combination methods to create a phase sensitive combination method targeted at large multi-volume datasets. This method uses any multi-image prescan to calculate the relative receive sensitivities using voxel-wise singular value decomposition. These relative sensitivities are fitted to the solid harmonics using an iterative least squares fitting algorithm. Fits of the relative sensitivities are used to align the phases of the receive coils and improve combination in subsequent acquisitions during the imaging session. This method is compared against existing approaches in the human brain at 7 Tesla by examining the combined data for the presence of singularities and changes in phase signal-to-noise ratio. Two additional applications of the method are also explored, using the fitted SVD method in an asymmetrical coil and in a case with subject motion. The fitted SVD method produces singularity-free images and recovers between 95–100% of the phase signal-to-noise ratio depending on the prescan data resolution. Using solid harmonic fitting to interpolate singular value decomposition derived receive sensitivities from existing prescans allows the fitted SVD method to be used on all acquisitions within a session without increasing exam duration. Our fitted SVD method is able to combine imaging datasets accurately without supervision during online reconstruction.

scholarly journals High Radio Frequency Studies of Equatorial and Southern AGN

1996 ◽  
Vol 175 ◽  
pp. 99-100
Author(s):  
M. Tornikoski ◽  
E. Valtaoja
Keyword(s):  
Radio Frequency ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Data Set ◽  
Noise Ratio

The Swedish-ESO Submillimetre Telescope (SEST) has been used for the high radio frequency observations of our group's AGN monitoring projects since the end of 1987.Our SEST results from October 1987 until June 1994 will be published in A&AS (in press); the data will be available electronically. The data set consists of 155 sources with the signal-to-noise -ratio of at least one observation (at 90 or 230 GHz) ≥ 4.

scholarly journals Weak Signal Extraction from Lunar Penetrating Radar Channel 1 Data Based on Local Correlation

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 573 ◽  
Author(s):  
Zhuo Jia ◽  
Sixin Liu ◽  
Ling Zhang ◽  
Bin Hu ◽  
Jianmin Zhang
Keyword(s):  
Signal To Noise Ratio ◽  
Lunar Regolith ◽  
Signal Extraction ◽  
Weak Signal ◽  
Human Beings ◽  
Subsurface Structure ◽  
Local Correlation ◽  
Signal To Noise ◽  
Near Surface ◽  
Noise Ratio

Knowledge of the subsurface structure not only provides useful information on lunar geology, but it also can quantify the potential lunar resources for human beings. The dual-frequency lunar penetrating radar (LPR) aboard the Yutu rover offers a Special opportunity to understand the subsurface structure to a depth of several hundreds of meters using a low-frequency channel (channel 1), as well as layer near-surface stratigraphic structure of the regolith using high-frequency observations (channel 2). The channel 1 data of the LPR has a very low signal-to-noise ratio. However, the extraction of weak signals from the data represents a problem worth exploring. In this article, we propose a weak signal extraction method in view of local correlation to analyze the LPR CH-1 data, to facilitate a study of the lunar regolith structure. First, we build a pre-processing workflow to increase the signal-to-noise ratio (SNR). Second, we apply the K-L transform to separate the horizontal signal and then use the seislet transform (ST) to reserve the continuous signal. Then, the local correlation map is calculated using the two denoising results and a time–space dependent weighting operator is constructed to suppress the noise residuals. The weak signal after noise suppression may provide a new reference for subsequent data interpretation. Finally, in combination with the regional geology and previous research, we provide some speculative interpretations of the LPR CH-1 data.

Low-level signals radiomonitoring using periodic spectrum samples analysis

2021 ◽  
Author(s):  
A.A. Potapov
Keyword(s):  
Radio Frequency ◽  
Detection Method ◽  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Spectrum Analyzer ◽  
Signal To Noise ◽  
Recording Time ◽  
Low Level ◽  
Periodic Spectrum ◽  
Noise Ratio

The article presents practical implementation of energy detection method based upon non-parametric statistics computed using periodic spectrum samples provided by measuring equipment. The method enables efficient detection and monitoring for signals with low or negative signal-to-noise ratio. The method's sensitivity is limited by measuring equipment inherent noise fluctuations and can be a priory experimentally established for certain experimental hardware settings and desirable spectrum samples lengths. Sensitivity thresholds (in terms of signal-to-noise ratio) for reliable (with probability > 0,98) signal detection for a typical spectrum analyzer used in experiment varied from –11 dB to + 0,6 dB for spectrum samples lengths ranged between 30 000 and 470 spectrums respectively. The suggested energy detection method can be used for unstable and intermittent signals detection, which are active (or above sensitivity threshold) only for a fraction of spectrum sample recording time. The method is independent of signal's modulation (if any is used), amplitude variability profile and signal's probability distribution features. Experimentally determined sensitivity threshold levels for real radio frequency signals coincided within 1,9 dB tolerances with corresponding levels estimated from spectrum analyzer inherent noise fluctuations for all implemented spectrum samples lengths. The data recording time for abovementioned spectrum samples lengths ranged between 207 and 3,2 seconds respectively and was entirely hardware-dependent parameter. Experiment proved equal efficiency and reliability of the suggested method for reliable detection for both white noise signal (generated by analog generator) and broadcasted LTE signal (generated by cellular base stations), which were affected by multi-path propagation effects and average signal level instability due to subscribers time-varying activity. The experiment showed the proposed energy detection method besides detection of low-level radio frequency signals (down to –11 dB SNR) provides highly reliable assessment of the detected signal's signal-to-noise ratio with 0,6 dB tolerance and 0,95 probability. The energy detection method demonstrated zero level of false detections when there was no signal at the spectrum analyzer input (the input port of the instrument was terminated by a matched load), which is essential for method applicability in tasks of highly reliable detection of low-level signals from various types of sources. Taking into account specifications of available hardware, required sensitivity level and limits for data recording time it is possible to choose optimal length of spectrum sample for the energy detection method, which would be the most reasonable for any task in question. The energy detection method based upon non-parametric statistics computed using periodic spectrum samples can be effectively used in detection and radiomonitoring of low-level signals, in radio frequency electromagnetic compatibility research tasks and radio propagation path properties analysis in high loss environment.

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.

Experiments in Bright-Field Imaging with Hollow-Cone Illumination

1981 ◽  
Vol 39 ◽  
pp. 226-227
Author(s):  
W. Kunath ◽  
K. Weiss ◽  
E. Zeitler
Keyword(s):  
Signal To Noise Ratio ◽  
Carbon Support ◽  
Electronic System ◽  
Optic Axis ◽  
Hollow Cone ◽  
Signal To Noise ◽  
Bright Field ◽  
Noise Ratio ◽  
Single Field ◽  
Field Imaging

Bright-field images taken with axial illumination show spurious high contrast patterns which obscure details smaller than 15 ° Hollow-cone illumination (HCI), however, reduces this disturbing granulation by statistical superposition and thus improves the signal-to-noise ratio. In this presentation we report on experiments aimed at selecting the proper amount of tilt and defocus for improvement of the signal-to-noise ratio by means of direct observation of the electron images on a TV monitor.Hollow-cone illumination is implemented in our microscope (single field condenser objective, Cs = .5 mm) by an electronic system which rotates the tilted beam about the optic axis. At low rates of revolution (one turn per second or so) a circular motion of the usual granulation in the image of a carbon support film can be observed on the TV monitor. The size of the granular structures and the radius of their orbits depend on both the conical tilt and defocus.

Information capacity and bandwidth limitations in the SEM

1989 ◽  
Vol 47 ◽  
pp. 84-85
Author(s):  
D. C. Joy ◽  
R. D. Bunn
Keyword(s):  
Signal To Noise Ratio ◽  
Critical Dimension ◽  
Information Capacity ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Sem Image ◽  
Probe Size ◽  
Minimum Number ◽  
Noise Ratio ◽  
Signal Channel

The information available from an SEM image is limited both by the inherent signal to noise ratio that characterizes the image and as a result of the transformations that it may undergo as it is passed through the amplifying circuits of the instrument. In applications such as Critical Dimension Metrology it is necessary to be able to quantify these limitations in order to be able to assess the likely precision of any measurement made with the microscope.The information capacity of an SEM signal, defined as the minimum number of bits needed to encode the output signal, depends on the signal to noise ratio of the image - which in turn depends on the probe size and source brightness and acquisition time per pixel - and on the efficiency of the specimen in producing the signal that is being observed. A detailed analysis of the secondary electron case shows that the information capacity C (bits/pixel) of the SEM signal channel could be written as :

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