Imaging obstructed ventilation with NMR using inert fluorinated gases

2000 ◽  
Vol 88 (6) ◽  
pp. 2279-2286 ◽  
Author(s):  
Dean O. Kuethe ◽  
Arvind Caprihan ◽  
H. Michael Gach ◽  
Irving J. Lowe ◽  
Eiichi Fukushima
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Signal To Noise Ratio ◽  
Brightness Variation ◽  
Signal To Noise ◽  
Pixel Value ◽  
Noise Ratio ◽  
Normal Ventilation ◽  
Q Values

We partially obstructed the left bronchi of rats and imaged an inert insoluble gas, SF6, in the lungs with NMR using a technique that clearly differentiates obstructed and normal ventilation. When the inhaled fraction of O2is high, SF6 concentrates dramatically in regions of the lung with low ventilation-to-perfusion ratios (V˙a/Q˙); therefore, these regions are brighter in an image than whereV˙a/Q˙ values are normal or high. A second image, made when the inhaled fraction of O2 is low, serves as a reference because the SF6 fraction is nearly uniform, regardless ofV˙a/Q˙. The quotient of the first and second images displays the low-V˙a/Q˙ regions and is corrected for other causes of brightness variation. The technique may provide sufficient quantification ofV˙a/Q˙ to be a useful research tool. The noise in the quotient image is described by the probability density function for the quotient of two normal random variables. When the signal-to-noise ratio of the denominator image is >10, the signal-to-noise ratio of the quotient image is similar to that of the parent images and decreases with pixel value.

scholarly journals Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation Environment

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 520 ◽  
Author(s):  
Felipe A. P. de Figueiredo ◽  
Claudio F. Dias ◽  
Eduardo R. de Lima ◽  
Gustavo Fraidenraich
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Signal To Noise Ratio ◽  
Base Station ◽  
Line Of Sight ◽  
High Signal ◽  
Signal To Noise ◽  
Sum Capacity ◽  
Base Station Antennas

The use of large-scale antenna arrays grants considerable benefits in energy and spectral efficiency to wireless systems due to spatial resolution and array gain techniques. By assuming a dominant line-of-sight environment in a massive multiple-input multiple-output scenario, we derive analytical expressions for the sum-capacity. Then, we show that convenient simplifications on the sum-capacity expressions are possible when working at low and high signal-to-noise ratio regimes. Furthermore, in the case of low and high signal-to-noise ratio regimes, it is demonstrated that the Gamma probability density function can approximate the probability density function of the instantaneous channel sum-capacity as the number of served devices and base station antennas grows, respectively. A second important demonstration presented in this work is that a Gamma probability density function can also be used to approximate the probability density function of the summation of the channel’s singular values as the number of devices increases. Finally, it is important to highlight that the presented framework is useful for a massive number of Internet of Things devices as we show that the transmit power of each device can be made inversely proportional to the number of base station antennas.

scholarly journals Improved Linearization of the Optimal Compression Function for Laplacian Source

2014 ◽  
Vol 65 (3) ◽  
pp. 179-183 ◽  
Author(s):  
Zoran H. Perić ◽  
Lazar Z–. Velimirović ◽  
Milan R. Dinčić
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Quantization Noise ◽  
Fast Process ◽  
Compression Function ◽  
Hierarchical Coding ◽  
Noise Ratio

Abstract In this paper, linearization of the optimal compression function is done and hierarchical coding (by coding the regions firstly and then the cells inside the region) is applied, achieving simple and fast process of coding and decoding. The signal at the entrance of the scalar quantizer is modeled by Laplacian probability density function. It is shown that the linearization of inner regions very little influences distortion and therefore only the last region should be optimized. Two methods of optimization of the last region are proposed, that improve performances of the scalar quantizer, and obtained SQNR (signal-to-quantization noise ratio) is close to that of the nonlinear optimal compression function.

scholarly journals Analysis of FM demodulator output noise with applications to FM telemetry

2006 ◽  
Vol 2006 ◽  
pp. 1-13 ◽  
Author(s):  
Rajendra Kumar
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Noise Power ◽  
Signal To Noise ◽  
Output Noise ◽  
Simplified Analysis ◽  
Modulation Schemes ◽  
Non Gaussian ◽  
Probability Density Function Pdf

We present an analysis for evaluating the probability density function (pdf) of the noise at the output of the frequency demodulator. It is shown that the noise is non-Gaussian and that for low to medium signal-to-noise power ratios, its pdf differs very significantly from the Gaussian pdf commonly assumed in simplified analysis. These results are very important for analyzing the performance of the PCM/FM type of modulation schemes used in telemetry systems as illustrated in the paper.

scholarly journals Derivation of Probability Density Function of Signal-to-Interference-Plus-Noise Ratio for the MS-to-MS Interference Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Ho-Kyung Son ◽  
Che-Young Kim
Keyword(s):  
Monte Carlo ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Interference Effect ◽  
Numerical Verification ◽  
Analytical Derivation ◽  
Mobile Stations ◽  
Noise Ratio ◽  
Analytical Result

This paper provides an analytical derivation of the probability density function of signal-to-interference-plus-noise ratio in the scenario where mobile stations interfere with each other. This analysis considers cochannel interference and adjacent channel interference. This could also remove the need for Monte Carlo simulations when evaluating the interference effect between mobile stations. Numerical verification shows that the analytical result agrees well with a Monte Carlo simulation. Also, we applied analytical methods for evaluating the interference effect between mobile stations using adjacent frequency bands. The analytical derivation of the probability density function can be used to provide the technical criteria for sharing a frequency band.

scholarly journals Keyframe Selection of Frame Similarity to Generate Scene Segmentation Based on Point Operation

2018 ◽  
Vol 8 (5) ◽  
pp. 2839
Author(s):  
Wisnu Widiarto ◽  
Mochamad Hariadi ◽  
Eko Mulyanto Yuniarno
Keyword(s):  
Correction Factor ◽  
Video Segmentation ◽  
Signal To Noise Ratio ◽  
Gamma Correction ◽  
Signal To Noise ◽  
Distance Difference ◽  
Pixel Value ◽  
Noise Ratio ◽  
The Difference ◽  
Original Frame

<span lang="EN-US">Video segmentation has been done by grouping similar frames according to the threshold. Two-frame similarity calculations have been performed based on several operations on the frame: point operation, spatial operation, geometric operation and arithmatic operation. In this research, similarity calculations have been applied using point operation: frame difference, gamma correction and peak signal to noise ratio. Three-point operation has been performed in accordance with the intensity and pixel frame values. Frame differences have been operated based on the pixel value level. Gamma correction has analyzed pixel values and lighting values. The peak signal to noise ratio (PSNR) has been related to the difference value (noise) between the original frame and the next frame. If the distance difference between the two frames was smaller then the two frames were more similar. If two frames had a higher gamma correction factor, then the correction factor would have an increasingly similar effect on the two frames. If the value of PSNR was greater then the comparison of two frames would be more similar. The combination of the three point operation methods would be able to determine several similar frames incorporated in the same segment</span>

scholarly journals Method for Modeling Residual Variance in Biomedical Signals Applied to Transcranial Doppler Ultrasonography Waveforms

2019 ◽  
Author(s):  
Kian Jalaleddini ◽  
Samuel G. Thorpe ◽  
Nicolas Canac ◽  
Amber Y. Dorn ◽  
Corey M. Thibeault ◽  
...  
Keyword(s):  
Probability Density ◽  
Transcranial Doppler ◽  
Cerebral Blood Flow Velocity ◽  
Signal To Noise Ratio ◽  
Temporal Structure ◽  
Residual Variance ◽  
Pulse Waveform ◽  
Signal To Noise ◽  
Diagnostic Significance ◽  
Noise Ratio

AbstractTranscranial Doppler (TCD) ultrasonography measures pulsatile cerebral blood flow velocity in the arteries and veins of the head and neck. The velocity pulse waveform morphology has been shown to have physiological and diagnostic significance. However, the measured pulses may exhibit a high degree of variability that deteriorates the estimates of clinical parameters. This study characterizes the TCD residual variance that result in pulse variability.We retrospectively utilized the data from 82 subjects. A trained sonographer insonated the middle cerebral arteries using a 2MHz hand-held probe. We implemented a multi-stage algorithm to identify the TCD residuals in each scan: pulses were identified; outlier pulses were flagged and removed; the average pulse waveform was taken as the ensemble average of the accepted pulse waveforms; finally, the resampled average pulse waveforms subtracted from individual pulses were taken as the TCD residuals. For each scan, we reported the signal to noise ratio and parameterized models for residuals: their amplitude structure using probability density function models and their temporal structure using autoregressive models.The signal to noise ratio 90% range was [1.7, 18.2] dB. The estimated probability density functions were best characterized by a generalized normal distribution whose beta parameter was smaller than 2 in 93% of scans. The identified frequency structure showed the dynamics were low-pass in nature.Analysis of the TCD residuals is useful in the assessment of the signal quality. Moreover, our identified models can also be used to generate synthetic TCD signal that enables future realistic simulation studies.

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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