Relevant bibliographies by topics / Signal to noise ratio

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Signal to noise ratio'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Signal to noise ratio"

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J.S., Ashwin, and Manoharan N. "Audio Denoising Based on Short Time Fourier Transform." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 1 (2018): 89–92. https://doi.org/10.11591/ijeecs.v9.i1.pp89-92.

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This paper presents a novel audio de-noising scheme in a given speech signal. The recovery of original from the communication channel without any noise is a difficult task. Many de-noising techniques have been proposed for the removal of noises from a digital signal. In this paper, an audio denoising technique based on Short Time Fourier Transform (STFT) is implemented. The proposed architecture uses a novel approach to estimate environmental noise from speech adaptively. Here original speech signals are given as input signal. Using AWGN, noises are added to the signal. Then noised signals are
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S. Ashwin, J., and N. Manoharan. "Audio Denoising Based on Short Time Fourier Transform." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 1 (2018): 89. http://dx.doi.org/10.11591/ijeecs.v9.i1.pp89-92.

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<p>This paper presents a novel audio de-noising scheme in a given speech signal. The recovery of original from the communication channel without any noise is a difficult task. Many de-noising techniques have been proposed for the removal of noises from a digital signal. In this paper, an audio de-noising technique based on Short Time Fourier Transform (STFT) is implemented. The proposed architecture uses a novel approach to estimate environmental noise from speech adaptively. Here original speech signals are given as input signal. Using AWGN, noises are added to the signal. Then noised s
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Yang, Ren Di, and Yan Li Zhang. "Denoising of ECG Signal Based on Empirical Mode Decomposition and Adaptive Noise Cancellation." Applied Mechanics and Materials 40-41 (November 2010): 140–45. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.140.

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To remove the noises in ECG and to overcome the disadvantage of the denoising method only based on empirical mode decomposition (EMD), a combination of EMD and adaptive noise cancellation is introduced in this paper. The noisy ECG signals are firstly decomposed into intrinsic mode functions (IMFs) by EMD. Then the IMFs corresponding to noises are used to reconstruct signal. The reconstructed signal as the reference input of adaptive noise cancellation and the noisy ECG as the basic input, the de-noised ECG signal is obtained after adaptive filtering. The de-noised ECG has high signal-to-noise
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Selvaraj, Poovarasan, and E. Chandra. "Ideal ratio mask estimation using supervised DNN approach for target speech signal enhancement." Journal of Intelligent & Fuzzy Systems 42, no. 3 (2022): 1869–83. http://dx.doi.org/10.3233/jifs-211236.

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The most challenging process in recent Speech Enhancement (SE) systems is to exclude the non-stationary noises and additive white Gaussian noise in real-time applications. Several SE techniques suggested were not successful in real-time scenarios to eliminate noises in the speech signals due to the high utilization of resources. So, a Sliding Window Empirical Mode Decomposition including a Variant of Variational Model Decomposition and Hurst (SWEMD-VVMDH) technique was developed for minimizing the difficulty in real-time applications. But this is the statistical framework that takes a long tim
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C, Shraddha, Chayadevi M L, Anusuya M A, and Vani H Y. "Enhancing Noise Reduction with Bionic Wavelet and Adaptive Filtering." Inteligencia Artificial 27, no. 74 (2024): 214–26. http://dx.doi.org/10.4114/intartif.vol27iss74pp214-226.

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Speech signals often contain different forms of background and environmental noise. For the development of an efficient speech recognition system, it is essential to preprocess noisy speech signals to reduce the impact of these disturbances. Notably, prior research has paid limited attention to pink and babble noises. This gap in knowledge inspired us to develop and implement hybrid algorithms tailored to handle these specific noise types. We introduce a hybrid method that combines the Bionic Wavelet transform with Adaptive Filtering to enhance signal strength. The performance of this method i
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Memduh; TAŞCIOĞLU, KÖSE. "Signal-to-noise ratio estimation of noisy transient signals." Communications Faculty Of Science University of Ankara 57, no. 1 (2015): 11–19. http://dx.doi.org/10.1501/commua1-2_0000000084.

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Najafipour, Abbas, Abbas Babaee, and S. Mohammad Shahrtash. "Comparing the trustworthiness of signal-to-noise ratio and peak signal-to-noise ratio in processing noisy partial discharge signals." IET Science, Measurement & Technology 7, no. 2 (2013): 112–18. http://dx.doi.org/10.1049/iet-smt.2012.0113.

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Smith, Robert C., and Robert C. Lange. "Signal to Noise Ratio." Critical Reviews in Diagnostic Imaging 42, no. 2 (2001): 135–40. http://dx.doi.org/10.3109/20014091086711.

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Johnson, Don. "Signal-to-noise ratio." Scholarpedia 1, no. 12 (2006): 2088. http://dx.doi.org/10.4249/scholarpedia.2088.

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Kharchenko, O. I., and V. M. Kartashov. "Standing out of Radiosignals on the Background of Noises Based on the Effect of Stochastic Resonance." Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, no. 86 (September 30, 2021): 39–44. https://doi.org/10.20535/radap.2021.86.39-44.

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The main and most difficult problem of signals receiving is the problem of noise immunity to find the best methods of receiving radio signals in the presence of interference. The analysis of radio signals standing out with the help of stochastic resonance effect are given. The linear frequency modulated and phase-code-manipulated signals are considered as an object of research. Linear frequency modulated phase-code-manipulated signals are widely used in radar and telecommunication systems. The phenomenon of stochastic resonance is a type of cooperative effect of noise and weak signal under a c
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Dissertations / Theses on the topic "Signal to noise ratio"

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Pauluzzi, David Renato. "Signal-to-noise ratio and signal-to-impairment ratio estimation in AWGN and wireless channels." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq22377.pdf.

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Hamid, Syamsul Bahrin Abdul. "Enhancing signal to noise ratio for electrostatic transducers." Thesis, University of Strathclyde, 2013. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=24250.

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This Thesis describes the design, manufacture and evaluation of a Fluidically Amplified Ultrasonic Transducer (FLAUT) for an air-coupled application. The transducer utilises a pipe as an amplification mechanism to increase the output pressure; and as a dissipation mechanism to reduce inherent noise within the transducer. The new transducer design introduces the concept of matched thin plate, cavity and pipe, of which the individual geometry enhances one another. Design methodologies, which consist of analytical modelling and Finite Element (FE) Modelling, have been implemented. The analytical
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Armstrong, Juliane. "Random inter stimulus interval increases signal-to-noise ratio." Digital Commons @ East Tennessee State University, 2012. https://dc.etsu.edu/honors/29.

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Incremental improvements are continuously being made to P300-Speller BCI paradigms. Accurate classification depends on a high signal-to-noise ratio (SNR) between the target and nontarget items. Fixed presentation rates produce a large flash-evoked response that persists throughout the recording epoch, which can potentially undermine the classification of P300-responses. By introducing a random interstimulus interval (ISI) to a previously improved P300-Speller paradigm (i.e., Checkerboard Paradigm; CBP) we expect to reduce the deleterious flash-evoked responses and increase the P300 classificat
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Cheng, Lui. "Improvement of signal-to-noise ratio in uterine EMG recordings." Thesis, Texas A&M University, 2003. http://hdl.handle.net/1969.1/1548.

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The objective of this study is to remove or, at least, reduce the noise in uterine EMG recordings, which at their present noise level render the data unusable. Predicting when true labor will start and recognizing when labor actually starts are important for both normal and complex pregnancies. For normal pregnancy, the prognosis of labor is important for reducing unnecessary hospital costs. About 10% of the four million babies born each year in the United States are born prematurely. At $1,500 a day for neonatal intensive care, this comprises national health care expenses of well over $5 bil
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Cheraghi, Parisa. "Fast and accurate spectrum sensing low signal noise ratio environment." Thesis, University of Surrey, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.581799.

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Opportunistic Spectrum Access (OSA) [1] promises tremendous gain in improving spectral efficiency. The main objective of OSA is to offer the ability of identifying and exploiting the under-utilised spectrum in an instantaneous manner in a wireless device, without any user intrusion. Hence, the initial requirement of any OSA device is the ability to perform spectrum sensing. Local narrow-band spectrum sensing has been quite well investigated in the literature. However, it is realised that existing schemes can hardly meet the requirements of a fast and accurate spectrum sensing particulariy in v
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Koul, Ashish 1979. "Use of intermicrophone correlation in estimating signal to noise ratio." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/29672.

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Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.<br>Includes bibliographical references (leaf 42).<br>This thesis presents the design, analysis, and simulation of a system that uses the correlation coefficient of audio inputs gathered at two spatially separate microphones to determine the signal to noise ratio in the environment. This work is motivated by past research in microphone array hearing aids, where accurate estimates of SNR were shown to improve performance. Signal to noise ratio is defined as the ratio of en
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Liu, Janet (Janet Kay) 1976. "Determining signal-to-noise ratio in a burst coherent demodulator." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80142.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.<br>Includes bibliographical references (leaves 59-60).<br>by Janet Liu.<br>S.B.and M.Eng.
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Aldokhail, Abdullah M. "Automated Signal to Noise Ratio Analysis for Magnetic Resonance Imaging Using a Noise Distribution Model." University of Toledo Health Science Campus / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=mco1469557255.

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Hassana, Ramesh Rakesh Kashyap. "Transform Domain Acquisition of Spread Spectrum Signals in a Low Signal to Noise Ratio Environment." Ohio University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1289579500.

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Lie, Chin Cheong Patrick. "Iterative algorithms for fast, signal-to-noise ratio insensitive image restoration." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=63767.

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Books on the topic "Signal to noise ratio"

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Sivathevan, T. Signal to quantization noise ratio. University of East London, 1994.

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Curran, Paul J. Estimating the signal-to-noise ratio of AVIRIS data. National Aeronautics and Space Administration, Ames Research Center, 1989.

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J, Curran Paul. Estimating the signal-to-noise ratio of AVIRIS data. National Aeronautics and Space Administration, Ames Research Center, 1989.

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J, Curran Paul. Estimating the signal-to-noise ratio of AVIRIS data. National Aeronautics and Space Administration, Ames Research Center, 1989.

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United States. National Telecommunications and Information Administration., ed. Estimation of system gain and bias using noisy observations with known noise power ratio. U.S. Dept. of Commerce, National Telecommunications and Information Administration, 2002.

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Robinson, A. P. The relationship between vision carrier-to-noise ratio and picture signal-to-noise ratio ina system 1 television receiver. BBC, 1987.

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Halford, Donald. Transparent metrology of signal to noise ratios of noisy band-limited digital signals. U.S. Dept. of Commerce, National Bureau of Standards, 1985.

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Walsh, Norman J. Bandwidth and signal-to-noise ratio enhancement of the NPS Transient Electromagnetic Scattering Laboratory. Naval Postgraduate School, 1989.

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Jones, Michael G. A comparison of signal enhancement methods for extracting tonal acoustic signals. National Aeronautics and Space Administration, Langley Research Center, 1998.

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Matthew, Sneddon, and Langley Research Center, eds. Laboratory study of the noticeability and annoyance of sounds of low signal-to-noise ratio. National Aeronautics and Space Administration, Langley Research Center, 1996.

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Book chapters on the topic "Signal to noise ratio"

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Weik, Martin H. "signal/noise ratio." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_17386.

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Zhou, Tianshou. "Signal-to-Noise Ratio." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_514.

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Weik, Martin H. "signal-to-noise ratio." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_17409.

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Weik, Martin H. "signal-plus-noise to noise ratio." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_17391.

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Runge, Val M., and Johannes T. Heverhagen. "Signal-to-Noise Ratio Versus Contrast-to-Noise Ratio." In The Physics of Clinical MR Taught Through Images. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85413-3_16.

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Simon, Marvin K., and Samuel Dolinar. "Signal-to-Noise Ratio Estimation." In Autonomous Software-Defined Radio Receivers for Deep Space Applications. John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/9780470087800.ch6.

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Jensen, Lindsay G., Loren K. Mell, Christin A. Knowlton, et al. "Signal-to-Noise Ratio (SNR)." In Encyclopedia of Radiation Oncology. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-540-85516-3_424.

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Freye, Enno. "Optimising signal-to-noise ratio." In Cerebral Monitoring in the Operating Room and the Intensive Care Unit. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-1886-3_11.

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Weik, Martin H. "photodetector signal-to-noise ratio." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_13982.

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Weik, Martin H. "postdetector signal-to-noise ratio." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_14374.

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Conference papers on the topic "Signal to noise ratio"

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Galleani, Lorenzo, Leon Cohen, and Douglas Nelson. "Local signal to noise ratio." In SPIE Optics + Photonics, edited by Franklin T. Luk. SPIE, 2006. http://dx.doi.org/10.1117/12.684026.

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Papic, Veljko, Zeljko Djurovic, Goran Kvascev, and Predrag Tadic. "On signal-to-noise ratio estimation." In Melecon 2010 - 2010 15th IEEE Mediterranean Electrotechnical Conference. IEEE, 2010. http://dx.doi.org/10.1109/melcon.2010.5476314.

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Lu, Ning H. "A Signal-to-Noise Ratio Enhancer." In 2011 IEEE Sensors Applications Symposium (SAS). IEEE, 2011. http://dx.doi.org/10.1109/sas.2011.5739765.

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Papadopoulos, Pavlos, Andreas Tsiartas, James Gibson, and Shrikanth Narayanan. "A supervised signal-to-noise ratio estimation of speech signals." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6855207.

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Malbet, Fabien, Alain Chelli, and Romain G. Petrov. "AMBER performances: signal-to-noise ratio analysis." In Astronomical Telescopes and Instrumentation, edited by Pierre J. Lena and Andreas Quirrenbach. SPIE, 2000. http://dx.doi.org/10.1117/12.390213.

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Martin, David, Trevor Vent, Chloe J. Choi, Bruno Barufaldi, Raymond J. Acciavatti, and Andrew Maidment. "Signal-to-noise ratio and contrast-to-noise ratio measurements for next generation tomosynthesis." In Physics of Medical Imaging, edited by Hilde Bosmans, Wei Zhao, and Lifeng Yu. SPIE, 2021. http://dx.doi.org/10.1117/12.2582279.

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Vračar, Miodrag, and Stevo Vračar. "Improvement of the signal-to-noise ratio by using a statistical filter." In 11th International Scientific Conference on Defensive Technologies - OTEX 2024. Military Technical Institute, Belgrade, 2024. http://dx.doi.org/10.5937/oteh24051v.

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The need to increase the signal-to-noise ratio is a significant task in almost all sensor applications that are exposed to a significant influence of ambient noise. The most common methods used to reduce ambient and/or measurement noise are based on the use of analog and digital filters or wavelet signal decomposition. The paper describes a new approach to signal filtering based on the application of the so-called a statistical filter that was realized by applying statistics of higher order, i.e. the fourth order cumulants. The statistical filter is successfully tested in analysis various type
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Yongjiang, Dai, Wu Haibin, and Yang Xuedong. "The Study of Streghthening Signal-to-Noise Ratio on CO2 Coherent Laser Radar." In Coherent Laser Radar. Optica Publishing Group, 1991. http://dx.doi.org/10.1364/clr.1991.me5.

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The signal-to-noise ratio of system is a impotant parameter for the CO2 coherent laser radar. In order to improve the signal-to-noise ratio of system the matched filter, interlock processor and frame-average are extensive applied(1). The methods of improving signal-to-noise ratio of the system by computer are digital-filter, accumulated average, interlock operation and so on(2),(3). At first, the backwave signal from target has been analysed, then the coast average method has been used in signal processing. We have developed a computer system for improved signal to noies ratio and it has been
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Urey, Hakan, William T. Rhodes, H. John Caulfield, and Zafer Urey. "High-signal-to-noise-ratio image processing in low-signal-to-bias-ratio environments." In Optical Science, Engineering and Instrumentation '97, edited by Bahram Javidi and Demetri Psaltis. SPIE, 1997. http://dx.doi.org/10.1117/12.284198.

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Pirogov, Yuri A., Valeri V. Gladun, Evgeni N. Terentiev, and Vladimir S. Ivanov. "Superresolution in multiray radio vision systems with small signal/noise ratio." In AeroSense 2000, edited by Roger M. Smith and Roger Appleby. SPIE, 2000. http://dx.doi.org/10.1117/12.391832.

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Reports on the topic "Signal to noise ratio"

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Doerry, Armin Walter, and Brandeis Marquette. Radar antenna pointing for optimized signal to noise ratio. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1088061.

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Rakuljic, George A. Holographic Crosstalk and Signal-to-Noise Ratio in Orthogonal Data Storage. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada250146.

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Halford, Donald. Transparent metrology of signal to noise ratios of noisy band-limited digital signals. National Bureau of Standards, 1985. http://dx.doi.org/10.6028/nbs.tn.1077.

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Hippenstiel, R. Signal to Noise Ratio Improvement Using Wavelet and Frequency Domain Based Processing. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada404025.

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Kirsteins, I. P. On the Probability Density of Signal-to-Noise Ratio in an Improved Detector. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada152529.

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Feng, Y. P., I. McNulty, Z. Xu, and E. Gluskin. Signal-to-noise ratio of intensity interferometry experiments with highly asymmetric x-ray sources. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/510394.

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Feng, Y. P., I. McNulty, Z. Xu, and E. Gluskin. Signal-to-noise ratio of intensity interferometry experiments with highly asymmetric x-ray sources. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/461284.

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Khatri, C. G., C. Radhakrishna Rao, and Y. N. Sun. Tables for Obtaining Confidence Bounds for Realized Signal to Noise Ratio with an Estimated Discriminant Function. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada166059.

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Maidanik, G., and K. J. Becker. Primitive Comparison of the Signal-to-Noise Ratios of Pressure and Velocity Planar Arrays. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada329353.

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Nuttall, Albert H. Required Threshold Settings and Signal-to-Noise Ratios for Combined Normalization and Or-ing. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada235774.

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