SYNTHESIS OF THE TRANSMITTING ANTENNA PATERN MAXIMIZING THE MEAN SIGNAL-TO-NOISE RATIO IN THE RADAR RESPONSIBILITYANGULAR SECTOR IN CASE OF TRANSMITTIONSOF POINT NOISE SOURCES

We studied the power spectrum of the diaphragm electromyogram (EMG) at frequencies between 31 and 246 Hz in four young normal subjects and five patients with chronic obstructive lung disease (COPD). Diaphragm EMGs were analyzed during spontaneous breathing and maximum inspiratory efforts to determine the effect of signal-to-noise ratio on the power spectrum and if treadmill exercise to dyspnea was associated with diaphragm fatigue. We found that the centroid frequencies of the power spectra (fc) were strongly correlated (r = 0.93) with ratios of power at high frequencies to power at low frequencies (H/L) for all subjects. Of the two indices, H/L had the largest standard deviation expressed as a percentage of the mean. The mean values of both of these decreased significantly after exercise, fc from 100.2 to 97.3 and H/L from 1.07 to 0.97. Signal-to-noise ratios were higher in maximal inspiratory efforts and after exercise in normal subjects and higher in COPD patients. The signal-to-noise ratio was correlated negatively with fc and H/L, indicating that these indices of the shape of the power spectrum are influenced by signal strength and noise levels as well as muscle function. We conclude that the fc and H/L index similar qualities of the power spectrum, that they are partially determined by the signal-to-noise ratio, and that, in some cases, exercise to dyspnea is associated with apparently mild diaphragm fatigue.

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Signal to Noise Ratio as a Cross-Platform Metric for Intraoperative Fluorescence Imaging

Molecular Imaging ◽

10.1177/1536012120913693 ◽

2020 ◽

Vol 19 ◽

pp. 153601212091369

Author(s):

Asmaysinh Gharia ◽

Efthymios P. Papageorgiou ◽

Simeon Giverts ◽

Catherine Park ◽

Mekhail Anwar

Keyword(s):

Tumor Cells ◽

Imaging System ◽

Signal To Noise Ratio ◽

Size Variation ◽

Pixel Size ◽

Signal To Noise ◽

Noise Sources ◽

Noise Measurements ◽

Noise Ratio ◽

The Impact

Real-time molecular imaging to guide curative cancer surgeries is critical to ensure removal of all tumor cells; however, visualization of microscopic tumor foci remains challenging. Wide variation in both imager instrumentation and molecular labeling agents demands a common metric conveying the ability of a system to identify tumor cells. Microscopic disease, comprised of a small number of tumor cells, has a signal on par with the background, making the use of signal (or tumor) to background ratio inapplicable in this critical regime. Therefore, a metric that incorporates the ability to subtract out background, evaluating the signal itself relative to the sources of uncertainty, or noise is required. Here we introduce the signal to noise ratio (SNR) to characterize the ultimate sensitivity of an imaging system and optimize factors such as pixel size. Variation in the background (noise) is due to electronic sources, optical sources, and spatial sources (heterogeneity in tumor marker expression, fluorophore binding, and diffusion). Here, we investigate the impact of these noise sources and ways to limit its effect on SNR. We use empirical tumor and noise measurements to procedurally generate tumor images and run a Monte Carlo simulation of microscopic disease imaging to optimize parameters such as pixel size.

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SIGNAL‐TO‐NOISE RATIO AND RECORD QUALITY

Geophysics ◽

10.1190/1.1439443 ◽

1964 ◽

Vol 29 (6) ◽

pp. 922-925 ◽

Cited By ~ 3

Author(s):

Arne Junger

Keyword(s):

Phase Shift ◽

Signal To Noise Ratio ◽

Random Phase ◽

Large Change ◽

Seismic Record ◽

Signal To Noise ◽

Change In The Mean ◽

The Mean ◽

Noise Ratio

The appearance of a seismic record is a function of the signal‐to‐noise ratio. This ratio is expressed quantitatively, but it can not be measured on the record. The quality of the record is expressed by the lineup of events and constancy of character across the record, but is generally not expressed numerically. The appearance of the record is here expressed numerically by the mean phase shift from perfect lineup of various events. A statistical relationship is established between this mean phase shift and the signal‐to‐noise ratio. A seismic record may be approximated by considering the signal to have a sinusoidal waveform and the noise to be a continuous sine wave with the same frequency as the signal and with random phase shift with respect to the signal on various traces. The resulting record will show a random phase shift, the mean value of which is a function of the signal‐to‐noise ratio. A plot of these two values shows that with increasing signal‐to‐noise ratio there is very little change in the mean phase shift, and thus of the quality of the record, until a value of one‐half for the signal‐to‐noise ratio is reached, showing that the noise dominates the record up to this point. For values of the signal‐to‐noise ratio between one‐half and two, there is a large change in the mean phase shift, indicating a strong visual improvement for this range. For a signal‐to‐noise ratio larger than two, the signal predominates visually, and only a slight improvement in quality can be obtained with additional improvements in the signal‐to‐noise ratio. These conclusions are in agreement with experimental data published elsewhere.

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An MMSE Overlay Cognitive Wireless System

International Journal of Interdisciplinary Telecommunications and Networking ◽

10.4018/jitn.2013010101 ◽

2013 ◽

Vol 5 (1) ◽

pp. 1-13

Author(s):

Monirosharieh Vameghestahbanati ◽

Hasan S. Mir ◽

Mohamed El-Tarhuni

Keyword(s):

Mean Squared Error ◽

Signal To Noise Ratio ◽

Signal To Noise ◽

Wireless System ◽

Transmitter Power ◽

Channel Estimate ◽

Squared Error ◽

The Mean ◽

Noise Ratio ◽

New System

In this paper, the authors propose a framework that allows an overlay (new) system to operate simultaneously with a legacy (existing) system. By jointly optimizing the transmitter and the receiver filters of the overlay system, the sum of the mean-squared error (MSE) of the new system plus the excess MSE in the existing system due to the introduction of the overlay system is minimized. The effects of varying key parameters such as the overlay transmitter power and the amount of overlap between the legacy and the overlay systems are investigated. Furthermore, the sensitivity of the system to accuracy of signal-to-noise ratio (SNR) estimate and the channel estimate is also examined.

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Signal-to-noise ratio optimization for discrete noise sources

10.1109/aps.1973.1147142 ◽

2005 ◽

Author(s):

G. Delisie ◽

J. Cummins

Keyword(s):

Signal To Noise Ratio ◽

Signal To Noise ◽

Noise Sources ◽

Noise Ratio ◽

Ratio Optimization

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Signal to Noise Ratio as a Cross-Platform Metric for Intraoperative Fluorescence Imaging

10.1101/847863 ◽

2019 ◽

Author(s):

Asmaysinh Gharia ◽

Efthymios P. Papageorgiou ◽

Simeon Giverts ◽

Catherine Park ◽

Mekhail Anwar

Keyword(s):

Tumor Cells ◽

Imaging System ◽

Signal To Noise Ratio ◽

Size Variation ◽

Pixel Size ◽

Signal To Noise ◽

Noise Sources ◽

Noise Measurements ◽

Noise Ratio ◽

The Impact

AbstractReal-time molecular imaging to guide curative cancer surgeries is critical to ensure removal of all tumor cells, however visualization of microscopic tumor foci remains challenging. Wide variation in both imager instrumentation and molecular labeling agents demands a common metric conveying the ability of a system to identify tumor cells. Microscopic disease, comprised of a small number of tumor cells, has a signal on par with the background, making the use of signal (or tumor) to background ratio inapplicable in this critical regime. Therefore, a metric that incorporates the ability to subtract out background, evaluating the signal itself relative to the sources of uncertainty, or noise is required. Here we introduce the signal-to-noise ratio (SNR) to characterize the ultimate sensitivity of an imaging system, and optimize factors such as pixel size. Variation in the background (noise) are due to electronic sources, optical sources, and spatial sources (heterogeneity in tumor marker expression, fluorophore binding, diffusion). Here we investigate the impact of these noise sources and ways to limit its effect on SNR. We use empirical tumor and noise measurements to procedurally generate tumor images and run a monte carlo simulation of microscopic disease imaging to optimize parameters such as pixel size.

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Interleaved Acoustic Environments: Impact of an Auditory Scene Classification Procedure on Speech Perception in Cochlear Implant Users

Trends in Hearing ◽

10.1177/23312165211014118 ◽

2021 ◽

Vol 25 ◽

pp. 233121652110141

Author(s):

Anja Eichenauer ◽

Uwe Baumann ◽

Timo Stöver ◽

Tobias Weissgerber

Keyword(s):

Speech Perception ◽

Cochlear Implant ◽

Signal To Noise Ratio ◽

Scene Classification ◽

Signal To Noise ◽

Acoustic Environment ◽

The Mean ◽

Noise Ratio ◽

Acoustic Environments ◽

The Impact

Clinical speech perception tests with simple presentation conditions often overestimate the impact of signal preprocessing on speech perception in complex listening environments. A new procedure was developed to assess speech perception in interleaved acoustic environments of different complexity that allows investigation of the impact of an automatic scene classification (ASC) algorithm on speech perception. The procedure was applied in cohorts of normal hearing (NH) controls and uni- and bilateral cochlear implant (CI) users. Speech reception thresholds (SRTs) were measured by means of a matrix sentence test in five acoustic environments that included different noise conditions (amplitude modulated and continuous), two spatial configurations, and reverberation. The acoustic environments were encapsulated in a randomized, mixed order single experimental run. Acoustic room simulation was played back with a loudspeaker auralization setup with 128 loudspeakers. 18 NH, 16 unilateral, and 16 bilateral CI users participated. SRTs were evaluated for each individual acoustic environment and as mean-SRT. Mean-SRTs improved by 2.4 dB signal-to-noise ratio for unilateral and 1.3 dB signal-to-noise ratio for bilateral CI users with activated ASC. Without ASC, the mean-SRT of bilateral CI users was 3.7 dB better than the SRT of unilateral CI users. The mean-SRT indicated significant differences, with NH group performing best and unilateral CI users performing worse with a difference of up to 13 dB compared to NH. The proposed speech test procedure successfully demonstrated that speech perception and benefit with ASC depend on the acoustic environment.

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Comparison between STIR and T2-weighted SPAIR sequences in the evaluation of inflammatory sacroiliitis: diagnostic performance and signal-to-noise ratio

Radiologia Brasileira ◽

10.1590/0100-3984.2019.0077 ◽

2020 ◽

Vol 53 (4) ◽

pp. 223-228

Author(s):

Vitor Faeda Dalto ◽

Rodrigo Luppino Assad ◽

Mario Müller Lorenzato ◽

Michel Daoud Crema ◽

Paulo Louzada-Junior ◽

...

Keyword(s):

Diagnostic Performance ◽

Recent Literature ◽

Signal To Noise Ratio ◽

Sacroiliac Joints ◽

Signal To Noise ◽

Asas Criteria ◽

The Mean ◽

Noise Ratio ◽

Magnetic Resonance Imaging Mri ◽

Active Inflammation

Abstract Objective: To compare two different fat-saturated magnetic resonance imaging (MRI) techniques-STIR and T2 SPAIR-in terms of image quality, as well as in terms of their diagnostic performance in detecting sacroiliac joints (SIJ) active inflammation. Materials and Methods: We included 69 consecutive patients with suspected spondyloarthritis undergoing MRI between 2012 and 2014. The signal-to-noise ratio (SNR) was calculated with the method recommended by the American College of Radiology. Two readers evaluated SIJ MRI following ASAS criteria to assess diagnostic performance regarding the detection of active SIJ inflammation. T1 SPIR Gd+ sequence was used as the reference standard. Results: The mean SNR was 72.8 for the T1 SPIR Gd+ sequence, compared with 14.1 and 37.6 for the STIR and T2 SPAIR sequences, respectively. The sensitivity and specificity of STIR and SPAIR T2 sequences did not show any statistically significant differences, for the diagnosis of sacroiliitis with active inflammation. Conclusion: Our results corroborate those in the recent literature suggesting that STIR sequences are not superior to T2 SPAIR sequences for SIJ evaluation in patients with suspected spondyloarthritis. On 1.5-T MRI, T2-weighted SPAIR sequences provide better SNRs than do STIR sequences, which reinforces that T2 SPAIR sequences may be an advantageous option for the evaluation of sacroiliitis.

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On the optimum antenna pattern for widebeam radar reflectivity estimation

Advances in Radio Science ◽

10.5194/ars-5-119-2007 ◽

2007 ◽

Vol 5 ◽

pp. 119-125

Author(s):

F. Gerbl ◽

E. M. Biebl

Keyword(s):

Signal To Noise Ratio ◽

Estimation Algorithm ◽

Antenna Pattern ◽

Target Range ◽

Limiting Factor ◽

Signal To Noise ◽

Aspect Angle ◽

Radar Systems ◽

The Mean ◽

Noise Ratio

Abstract. Noise is a limiting factor in radar systems. The power received from a target depends on the target reflectivity varying with the aspect angle, the target range and the antenna pattern, amongst others. Inevitably, an estimation algorithm for the mean target reflectivity weights noise contributions from greater aspect angles stronger than such from smaller aspect angles due to the target range increasing with increasing aspect angle. However, as an opposed effect, due to the supposed equidistant rather than equiangular sampling along the linear aperture, noise contributions for greater aspect angles have lower influence than those for smaller aspect angles. In the paper, the signal-to-noise ratio for a linear, equidistantly sampled aperture and an arbitrary antenna pattern is determined. Moreover, an upper limit for the achievable signal-to-noise ratio is given, and the antenna pattern maximizing the signal-to-noise ratio is derived.

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