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Bibliografie tematiche / Signal to noise ratio

Letteratura scientifica selezionata sul tema "Signal to noise ratio"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 6 settembre 2023

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Articoli di riviste sul tema "Signal to noise ratio"

1

S. Ashwin, J., e N. Manoharan. "Audio Denoising Based on Short Time Fourier Transform". Indonesian Journal of Electrical Engineering and Computer Science 9, n. 1 (1 gennaio 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 signals are de-noised using STFT techniques. Finally Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR) values for noised and de-noised signals are obtained.</p>

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2

Yang, Ren Di, e Yan Li Zhang. "Denoising of ECG Signal Based on Empirical Mode Decomposition and Adaptive Noise Cancellation". Applied Mechanics and Materials 40-41 (novembre 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 ratio, preferable correlation coefficient and lower mean square error. Through analyzing these performance parameters and testing the denoising method using MIT-BIH Database, the conclusion can be drawn that the combination of EMD and adaptive noise cancellation has considered the frequency distribution of ECG and noises, eliminate the noises effectively and need not to select a proper threshold.

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3

Memduh; TAŞCIOĞLU, KÖSE. "Signal-to-noise ratio estimation of noisy transient signals". Communications Faculty Of Science University of Ankara 57, n. 1 (2015): 11–19. http://dx.doi.org/10.1501/commua1-2_0000000084.

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4

Selvaraj, Poovarasan, e E. Chandra. "Ideal ratio mask estimation using supervised DNN approach for target speech signal enhancement". Journal of Intelligent & Fuzzy Systems 42, n. 3 (2 febbraio 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 time for computations. Hence in this article, this SWEMD-VVMDH technique is extended using Deep Neural Network (DNN) that learns the decomposed speech signals via SWEMD-VVMDH efficiently to achieve SE. At first, the noisy speech signals are decomposed into Intrinsic Mode Functions (IMFs) by the SWEMD Hurst (SWEMDH) technique. Then, the Time-Delay Estimation (TDE)-based VVMD was performed on the IMFs to elect the most relevant IMFs according to the Hurst exponent and lessen the low- as well as high-frequency noise elements in the speech signal. For each signal frame, the target features are chosen and fed to the DNN that learns these features to estimate the Ideal Ratio Mask (IRM) in a supervised manner. The abilities of DNN are enhanced for the categories of background noise, and the Signal-to-Noise Ratio (SNR) of the speech signals. Also, the noise category dimension and the SNR dimension are chosen for training and testing manifold DNNs since these are dimensions often taken into account for the SE systems. Further, the IRM in each frequency channel for all noisy signal samples is concatenated to reconstruct the noiseless speech signal. At last, the experimental outcomes exhibit considerable improvement in SE under different categories of noises.

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5

Smith, Robert C., e Robert C. Lange. "Signal to Noise Ratio". Critical Reviews in Diagnostic Imaging 42, n. 2 (gennaio 2001): 135–40. http://dx.doi.org/10.3109/20014091086711.

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6

Johnson, Don. "Signal-to-noise ratio". Scholarpedia 1, n. 12 (2006): 2088. http://dx.doi.org/10.4249/scholarpedia.2088.

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7

Najafipour, Abbas, Abbas Babaee e 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, n. 2 (1 marzo 2013): 112–18. http://dx.doi.org/10.1049/iet-smt.2012.0113.

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8

Muhammad Basharat, Muhammad Basharat, Ming Ding Ming Ding, Yang Li Yang Li, Hongwei Cai Hongwei Cai e Jiancheng Fang Jiancheng Fang. "Noise reduction and signal to noise ratio improvement in magneto-optical polarization rotation measurement". Chinese Optics Letters 16, n. 8 (2018): 081201. http://dx.doi.org/10.3788/col201816.081201.

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9

Kropotov, Y. A., A. A. Belov e A. Y. Prockuryakov. "Increasing signal/acoustic interference ratio in telecommunications audio exchange by adaptive filtering methods". Information Technology and Nanotechnology, n. 2416 (2019): 271–76. http://dx.doi.org/10.18287/1613-0073-2019-2416-271-276.

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The paper deals with the issues of increasing signal/noise ratio in telecommunication audio exchange systems. The study of characteristics of speech signals and acoustic noises, such as mathematical expectation, dispersion, relative intensity of acoustic speech signals and various types of acoustic noises and interference is carried out. It is shown that in the design of telecommunications systems, in particular loudspeaker systems operating under the influence of external acoustic noise of high intensity, it is necessary to solve the problem of developing algorithms to effectively suppress the above mentioned interference to ensure the necessary signal/noise ratio in communication systems. A mathematical model of the autocorrelation function of the speech signal by using the Lagrange interpolation polynomial of order 10, considered the creation of adaptive algorithms to suppress acoustic noise by linear filtering methods. Thus suppression of acoustic noises and hindrances is possible at the expense of operated change of area of a cutting in the interval from 0 Hz to 300-1000 Hz, depending on a hindrance conditions.

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10

Jenkin, Robin. "Contrast Signal to Noise Ratio". Electronic Imaging 2021, n. 17 (18 gennaio 2021): 186–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.17.avm-186.

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The detection and recognition of objects is essential for the operation of autonomous vehicles and robots. Designing and predicting the performance of camera systems intended to supply information to neural networks and vision algorithms is nontrivial. Optimization has to occur across many parameters, such as focal length, f-number, pixel and sensor size, exposure regime and transmission schemes. As such numerous metrics are being explored to assist with these design choices. Detectability index (SNRI) is derived from signal detection theory as applied to imaging systems and is used to estimate the ability of a system to statistically distinguish objects [1], most notably in the medical imaging and defense fields [2]. A new metric is proposed, Contrast Signal to Noise Ratio (CSNR), which is calculated simply as mean contrast divided by the standard deviation of the contrast. This is distinct from contrast to noise ratio which uses the noise of the image as the denominator [3,4]. It is shown mathematically that the metric is proportional to the idealized observer for a cobblestone target and a constant may be calculated to estimate SNRI from CSNR, accounting for target size. Results are further compared to Contrast Detection Probability (CDP), which is a relatively new objective image quality metric proposed within IEEE P2020 to rank the performance of camera systems intended for use in autonomous vehicles [5]. CSNR is shown to generate information in illumination and contrast conditions where CDP saturates and further can be modified to provide CDP-like results.

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Tesi sul tema "Signal to noise ratio"

1

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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2

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 modelling identifies the required geometry for the FLAUT based on the desired operating resonant frequency; while FE then verifies the vibrational characteristics of the design. Through the application of FE modelling and practical analysis, FLAUT devices have been designed, developed and compared with experiment. The sensitivity analysis is utilised to realise a design and manufacturing tolerance requirements. The devices were manufactured in the operating range of 25 kHz to 85 kHz. Air-coupled pulse-echo insertion loss was found to be 61.3 dB, an improvement of 9.1 dB over the conventional cavity only design. Results from the proof of concept prototype indicate that the output of the FLAUT is maximised when the pipe radius is designed to be as large as practically possible while maintaining the matched resonant frequencies. This correlates well with theory both in term of sensitivity and noise. Furthermore, the pressure output of a FLAUT array is maximised by arranging the cell spacing to be as close as practically possible. Thus, the cells were spaced at multiples of 2.25 to the cavity radius – to reduce the risk of cell damage. An analytical method to simulate, and a technique to measure the inherent noise using a specially designed hybrid isolation vessel has been developed. From the measurement, the FLAUT noise is found to be 5.8 W, an improvement of 2.7 dB compared to the conventional cavity only design.

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3

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 classification SNR. Data were recorded from 32 EEG locations (right mastoid referenced) from 13 subjects using the CBP with two conditions. In the Random ISI (RI) condition, ISI varied between 0 ms and 187.5 ms and averaged 93.75 ms. In the Fixed ISI (SI) condition, ISI remained static at 93.75 ms. In both conditions, participants were instructed to spell out 72 characters using an 8x9 matrix of alphanumeric characters by silently counting each target flash. The first 36 characters served as ‘calibration’ data for a stepwise linear discriminant analysis (SWLDA; 0 - 800 ms poststimulus epochs). This SWLDA classifier was then used to provide online feedback for an additional 36 character selections. Absolute amplitude of target and nontarget responses were summed across the recording epoch for each subject and averaged between Pz and Cz (maximum). Target averages were then divided by nontarget averages to create a SNR measure and compared between RI and FI conditions. The RI manipulation produced a significantly (p = .04) larger SNR (M = 5.85) than the FI condition (M =4.07).Further analysis of the averaged waveforms revealed a significantly (p = .05) greater positive peak at Cz (253 ms peak latency) for the RI condition. Classification performance measures for RI and FI conditions were high for accuracy (84 and 85%, respectively; NS) and bitrate (21 and 23 bits/min, respectively; NS). Together these results suggest that while randomizing ISI can yield higher SNR, response classification is not affected. It is possible that SWLDA is a useful classification method, in general; however, these data suggest that it does not capitalize on the additional information gained from the increase in SNR. Alternative classification techniques that can take advantage of specific subcomponents of the response may be able to utilize this additional information to improve BCI speed and accuracy.

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4

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 billion. Spectral analysis, filter design, and 1/3 octave analysis were applied to analyze the uterine EMG recordings. Signal-to-noise ratio was increased with IIR Butterworth bandstop filter. The spectral band between 0.25 and 0.4 Hz shows matching of the Toco belt via spectral analysis. Nevertheless, 1/3 octave analysis gives the highest correct detection percentage compare with frequency analysis and filter design.

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5

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 very low signal-to-noise-ratio (SNR) range without introducing high complexity to the system. Furthermore, increase in the spectrum utilisation calls for spectrum sensing techniques that adopt an architecture to simultaneously search over multiple frequency sub-bands at a time. However, the literature of sub-band spectrum sensing is rather limited at this time. The main contributions of this thesis is two-fold: First a clusterd-based differential energy detection for local sensing of multi- carrier based system is proposed. The proposed approach can form fast and reliable decision of spectrum availability even in very low SNR environment. The underlying initiative of the proposed scheme is applying order statistics on the clustered differential Energy Spectral Density (ESD) in order to exploit the channel frequency diversity inherent in high data-rate communications. Second contribution is three-fold: 1) re-defining the objective of the sub- band level spectrum sensing device to a model estimator, 2) deriving the optimal model selection estimator for sub-band level spectrum sensing for fixed and variable number of users along with a sub-optimal solution based on Bayesian statistical modelling and 3) proposing a practical model selection estimator with relaxed sample size constraint and limited system knowledge for sub-band spectrum sensing applications in Orthogonal Frequency-Division Multiple Access (OFDMA) systems. The result obtained showed that through exploitation of the channel frequency selectivity the performance of the stat-of-the-art spectrum sensing techniques can be significantly improved. Furthermore, by modelling the sub-band level spectrum sensing through model estimation allows for new spectrum sensing approach. It was proved both analytically and through simulations that the proposed approach have significantly extended to state-of-the-art spectrum sensing. Key words: Differential, energy detection, low signal-to- noise ratio (SNR), multi- carrier, opportunistic spectrum access, spectrum sensing.

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6

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.
Includes bibliographical references (leaf 42).
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 energy in the direct component (audio sources originating in front of a broadside array) to energy in the interference component (sources originating from the sides of the array). The design presented is a simple hypothesis testing mechanism for determining whether the SNR exceeds a fixed level. In the analysis, behavior of the system is studied theoretically under varying conditions of reverberation in the environment, and processing parameters are determined to optimize system performance. Finally, simulations test the true performance of the system to verify the validity of the theoretical analysis.
by Ashish Koul.
M.Eng.and S.B.

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7

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.
Includes bibliographical references (leaves 59-60).
by Janet Liu.
S.B.and M.Eng.

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8

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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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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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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Libri sul tema "Signal to noise ratio"

1

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

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2

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

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5

United States. National Telecommunications and Information Administration., a cura di. Estimation of system gain and bias using noisy observations with known noise power ratio. [Boulder, Colo.]: U.S. Dept. of Commerce, National Telecommunications and Information Administration, 2002.

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6

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

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7

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

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8

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

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

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Matthew, Sneddon, e Langley Research Center, a cura di. Laboratory study of the noticeability and annoyance of sounds of low signal-to-noise ratio. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1996.

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Capitoli di libri sul tema "Signal to noise ratio"

1

Weik, Martin H. "signal/noise ratio". In Computer Science and Communications Dictionary, 1582. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_17386.

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2

Zhou, Tianshou. "Signal-to-Noise Ratio". In Encyclopedia of Systems Biology, 1939–40. New York, NY: 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, 1585–86. Boston, MA: 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, 1583. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_17391.

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5

Runge, Val M., e Johannes T. Heverhagen. "Signal-to-Noise Ratio Versus Contrast-to-Noise Ratio". In The Physics of Clinical MR Taught Through Images, 39. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85413-3_16.

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Simon, Marvin K., e Samuel Dolinar. "Signal-to-Noise Ratio Estimation". In Autonomous Software-Defined Radio Receivers for Deep Space Applications, 121–92. Hoboken, NJ, USA: 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, Michelle Kolton Mackay, Filip T. Troicki, Jaganmohan Poli, Edward J. Gracely et al. "Signal-to-Noise Ratio (SNR)". In Encyclopedia of Radiation Oncology, 789–90. Berlin, Heidelberg: 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, 104–12. Dordrecht: 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, 1268. Boston, MA: 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, 1307. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_14374.

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Atti di convegni sul tema "Signal to noise ratio"

1

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

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2

Papic, Veljko, Zeljko Djurovic, Goran Kvascev e 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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3

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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4

Papadopoulos, Pavlos, Andreas Tsiartas, James Gibson e 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 e Romain G. Petrov. "AMBER performances: signal-to-noise ratio analysis". In Astronomical Telescopes and Instrumentation, a cura di Pierre J. Lena e 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 e Andrew Maidment. "Signal-to-noise ratio and contrast-to-noise ratio measurements for next generation tomosynthesis". In Physics of Medical Imaging, a cura di Hilde Bosmans, Wei Zhao e Lifeng Yu. SPIE, 2021. http://dx.doi.org/10.1117/12.2582279.

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Yongjiang, Dai, Wu Haibin e Yang Xuedong. "The Study of Streghthening Signal-to-Noise Ratio on CO2 Coherent Laser Radar". In Coherent Laser Radar. Washington, D.C.: 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 increased by 60db.

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8

Urey, Hakan, William T. Rhodes, H. John Caulfield e Zafer Urey. "High-signal-to-noise-ratio image processing in low-signal-to-bias-ratio environments". In Optical Science, Engineering and Instrumentation '97, a cura di Bahram Javidi e Demetri Psaltis. SPIE, 1997. http://dx.doi.org/10.1117/12.284198.

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9

Fauss, M., K. G. Nagananda, A. M. Zoubir e H. V. Poor. "Sequential joint signal detection and signal-to-noise ratio estimation". In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7953029.

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10

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

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Rapporti di organizzazioni sul tema "Signal to noise ratio"

1

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

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2

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

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3

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

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4

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

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5

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

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6

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

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7

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

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8

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

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9

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

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10

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

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